ANNA UNIVERSITY, CHENNAI
AFFILIATED INSTITUTIONS
REGULATIONS - 2013
M.E. COMPUTER SCIENCE AND ENGINEERING
I - IV SEMESTERS (FULL TIME) CURRICULUM AND SYLLABUS
405
THEORY
Course Code
MA7155
CP7101
CP7102
CP7103
M.E. Computer Science
and Engineering
SEMESTER I
Course Title
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 4 2
0 0 2 1
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 2 1
0 0 4 2
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
Total L T P C
0 0 12 6
12 1 12 19
Total L T P C
0 0 24 12
0 0 24 12
Applied Probability and Statistics
Design and Management of Computer Networks
Advanced Data Structures and Algorithms
Multicore Architectures
Elective I
Elective II
PRACTICAL
Course Code
Course Title
CP7111
Advanced Data Structures Laboratory
CP7112
Case Study - Network Design (Team Work)
Total
SEMESTER II
THEORY
Course Code
CP7201
CP7202
CP7203
CP7204
Course Title
Theoretical Foundations of Computer Science
Advanced Databases
Principles of Programming Languages
Advanced Operating Systems
Elective III
Elective IV
PRACTICAL
Course Code
Course Title
CP7211
Advanced Databases Laboratory
CP7212
Case Study - Operating Systems Design (Team Work)
Total
SEMESTER III
THEORY
Course Code
Course Title
CP7301
Software Process and Project Management
Elective V
Elective VI
Elective VII
PRACTICAL
Course Code
CP7311
Project Work (Phase I)
Course Title
SEMESTER IV
PRACTICAL
Course Code
CP7411
Project Work (Phase II)
Course Title
TOAL NO OF CREDITS 75ELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER I
ELECTIVE-I Course Title L T P C
Course Code Formal Models of Software Systems 3 0 0 3
SE7103 Performance Evaluation of Computer Systems 3 0 0 3
CP7001 Probabilistic Reasoning Systems 3 0 0 3
CP7002 Data Analysis and Business Intelligence 3 0 0 3
CP7003 Image Processing and Analysis 3 0 0 3
CP7004 Sensing Techniques and Sensors 3 0 0 3
NE7001
ELECTIVE-II Course Title Randomized Algorithms L T P C
Course Code Mobile and Pervasive Computing 3 0 0 3
CP7005 Parallel Programming Paradigms 3 0 0 3
NE7002 Software Requirements Engineering 3 0 0 3
CP7006 Speech Processing and Synthesis 3 0 0 3
CP7007 Machine Learning Techniques 3 0 0 3
CP7008 3 0 0 3
CP7009 36 0 0 36
Total
SEMESTER II
ELECTIVE-III Course Title L T P C
Course Code Concurrency Models 3 0 0 3
CP7010 Real Time Systems 3 0 0 3
CP7011 Computer Vision 3 0 0 3
CP7012 Network and Information Security 3 0 0 3
NE7202 Design and Analysis of Parallel Algorithms 3 0 0 3
CP7013 Software Architectures 3 0 0 3
CP7014
ELECTIVE-IV Course Title L T P C
Course Code Model Checking and Program Verification 3 0 0 3
CP7015 Embedded Software Development 3 0 0 3
CP7016 Cloud Computing 3 0 0 3
IF7202 Data Visualization Techniques 3 0 0 3
CP7017 Protocols and Architecture for Wireless Sensor Networks 3 0 0 3
NE7005 Language Technologies 3 0 0 3
CP7018 36 0 0 36
TotalELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER III
ELECTIVE-V Social Network Analysis
Course Code Managing Big Data
NE7012 Mobile Application Development
CP7019 Bio-inspired Computing
NE7011 Medical Image Processing
CP7020 Software Design
CP7021
CP7022
ELECTIVE-VI Reconfigurable Computing
Course Code Energy Aware Computing
CP7023 Information Retrieval Techniques
IF7013 Data Mining Techniques
CP7024 Bio Informatics
CP7025 Software Quality Assurance
IF7002
CP7026
ELECTIVE-VII
Course Code
CP7027
CP7028
CP7029
CP7030
CP7031
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
51 0 0 51
Multiobjective Optimization Techniques
Enterprise Application Integration
Information Storage Management
Robotics
Compiler Optimization Techniques
Total
TOAL NO OF CREDITS 123PROGRAM EDUCATIONAL OBJECTIVES (PEO):
Graduates of this M. E. Computer Science and Engineering will be able to
1. Apply the necessary mathematical tools and fundamental & advanced knowledge of
computer science & engineering
2. Develop computer/software/network systems understanding the importance of social,
business, technical, environmental, and human context in which the systems would
work
3. Articulate fundamental concepts, design underpinnings of computer/software/network
systems, and research findings to train professionals or to educate engineering
students
4. Contribute effectively as a team member/leader, using common tools and
environment, in computer science and engineering projects, research, or education
5. Pursue life-long learning and research in selected fields of computer science &
engineering and contribute to the growth of those fields and society at large
PROGRAM OUTCOMES:
Apply knowledge of mathematics, science, engineering fundamentals and an
engineering specialization to the conceptualization of engineering models.
Identify, formulate, research literature and solve complex engineering problems
reaching substantiated conclusions using first principles of mathematics and
engineering sciences.
Design solutions for complex engineering problems and design systems,
components or processes that meet specified needs with appropriate consideration
for public health and safety, cultural, societal, and environmental considerations.
Conduct investigations of complex problems including design of experiments,
analysis and interpretation of data, and synthesis of information to provide valid
conclusions.
Create, select and apply appropriate techniques, resources, and modern engineering
tools, including prediction and modeling, to complex engineering activities, with an
understanding of the limitations.
Function effectively as an individual, and as a member or leader in diverse teams
and in multi-disciplinary settings.
Communicate effectively on complex engineering activities with the engineering
community and with society at large, such as being able to comprehend and write
effective reports and design documentation, make effective presentations, and give
and receive clear instructions.
Demonstrate understanding of the societal, health, safety, legal and cultural issues
and the consequent responsibilities relevant to engineering practice.
Understand and commit to professional ethics and responsibilities and norms of
engineering practice.
Understand the impact of engineering solutions in a societal context and demonstrate
knowledge of and need for sustainable development.
Demonstrate a knowledge and understanding of management and business
practices, such as risk and change management, and understand their limitations.
Recognize the need for, and have the ability to engage in independent and life-long
learning.
1MA7155
APPLIED PROBABILITY AND STATISTICS
L T PC
3 1 0 4
OBJECTIVES:
To introduce the basic concepts of one dimensional and two dimensional Random
Variables.
To provide information about Estimation theory, Correlation, Regression and Testing of
hypothesis.
To enable the students to use the concepts of multivariate normal distribution and
principle components analysis.
UNIT I
ONE DIMENSIONAL RANDOM VARIABLES
9+3
Random variables - Probability function – Moments – Moment generating functions and
their properties – Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal
distributions – Functions of a Random Variable.
UNIT II
TWO DIMENSIONAL RANDOM VARIABLES
9+3
Joint distributions – Marginal and Conditional distributions – Functions of two dimensional
random variables – Regression Curve – Correlation.
UNIT III
ESTIMATION THEORY
9+3
Unbiased Estimators – Method of Moments – Maximum Likelihood Estimation - Curve fitting
by Principle of least squares – Regression Lines.
UNIT IV
TESTING OF HYPOTHESES
9+3
Sampling distributions - Type I and Type II errors - Tests based on Normal, t,2 and F
distributions for testing of mean, variance and proportions – Tests for Independence of
attributes and Goodness of fit.
UNIT V
MULTIVARIATE ANALYSIS
9+3
Random Vectors and Matrices - Mean vectors and Covariance matrices - Multivariate
Normal density and its properties - Principal components Population
principal
components - Principal components from standardized variables.
TOTAL 45+15=60 PERIODS
OUTCOME:
The student will able to acquire the basic concepts of Probability and Statistical
techniques for solving mathematical problems which will be useful in solving Engineering
problems
REFERENCES:
1 Jay L. Devore, “Probability and Statistics For Engineering and the Sciences”,Thomson
and Duxbury, 2002.
2.Richard Johnson. ”Miller & Freund’s Probability and Statistics for Engineer”, Prentice
–
Hall , Seventh Edition, 2007.
3.Richard A. Johnson and Dean W. Wichern, “Applied Multivariate Statistical Analysis”,
Pearson Education, Asia, Fifth Edition, 2002.
4.Gupta S.C. and Kapoor V.K.”Fundamentals of Mathematical Statistics”, Sultan an Sons,
2001.
5.Dallas E Johnson , “Applied Multivariate Methods for Data Analysis”, Thomson an Duxbury
press,1998.
2CP7101
DESIGN AND MANAGEMENT OF COMPUTER NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION TO NETWORK MANAGEMENT
9
Overview of Analysis, Architecture and Design Process-System Methodology, Service
methodology, Service Description - Service characteristics - Performance Characteristics -
Network supportability - Requirement analysis – User Requirements – Application
Requirements – Device Requirements – Network Requirements – Other Requirements -
Requirement specification and map.
UNIT II
REQUIREMENTS ANALYSIS
9
Requirement Analysis Process – Gathering and Listing Requirements- Developing service
metrics – Characterizing behavior – Developing RMA requirements – Developing delay
Requirements - Developing capacity Requirements - Developing supplemental performance
Requirements –Requirements mapping – Developing the requirements specification
UNIT III
FLOW ANALYSIS
9
Individual and Composite Flows – Critical Flows - Identifying and developing flows – Data
sources and sinks – Flow models- Flow prioritization – Flow specification algorithms –
Example Applications of Flow Analysis
UNIT IV
NETWORK ARCHITECTURE
9
Architecture and design – Component Architectures – Reference Architecture – Architecture
Models – System and Network Architecture – Addressing and Routing Architecture –
Addressing and Routing Fundamentals – Addressing Mechanisms – Addressing Strategies
– Routing Strategies – Network Management Architecture – Network Management
Mechanisms Performance Architecture – Performance Mechanisms – Security and Privacy
Architecture – Planning security and privacy Mechanisms
UNIT V
NETWORK DESIGN
9
Design Concepts – Design Process - Network Layout – Design Traceability – Design Metrics
– Logical Network Design – Topology Design – Bridging, Switching and Routing Protocols-
Physical Network Design – Selecting Technologies and Devices for Campus and Enterprise
Networks – Optimizing Network Design
TOTAL : 45 PERIODS
REFERENCES:
1. Network Analysis, Architecture, and Design By James D. McCabe, Morgan Kaufmann,
Third Edition, 2007.ISBN-13: 978-0123704801
2. Computer Networks: A Systems Approach by Larry L. Peterson, Bruce S. Davie - 2007,
Elsevier Inc.
3. Top-down Network Design: [a Systems Analysis Approach to Enterprise Network
Design] By Priscilla Oppenheimer, Cisco Press , 3rd Edition, ISBN-13: 978-1-58720-
283-4 ISBN-10: 1-58720-283-2
4. Integrated Management of Networked Systems: Concepts, Architectures, and Their
Operational Application (The Morgan Kaufmann Series in Networking), Heinz-Gerd
Hegering, Sebastian Abeck, and Bernhard Neumair, 1999.
5. “Network Design and Management” – by Steven T.Karris, Orchard publications, Second
edition, Copyright 2009, ISBN 978-1-934404-15-7
6. “Network Design, Management and Technical Perspective”, Teresa C. Mann-Rubinson
and Kornel Terplan, CRC Press, 1999
7. “Ethernet Networks-Design, Implementation, Operation and Management by Gilbert
Held, John Wiley and sons, Fourth Edition
8. James Kurose and Keith Ross, “Computer Networking: A Top-Down Approach Featuring
the Internet”, 1999
3CP7102
ADVANCED DATA STRUCTURES AND ALGORITHMS
LTPC
3003
OBJECTIVES:
To understand the principles of iterative and recursive algorithms.
To learn the graph search algorithms.
To study network flow and linear programming problems.
To learn the hill climbing and dynamic programming design techniques.
To develop recursive backtracking algorithms.
To get an awareness of NP completeness and randomized algorithms.
To learn the principles of shared and concurrent objects.
To learn concurrent data structures.
UNIT I
ITERATIVE AND RECURSIVE ALGORITHMS
9
Iterative Algorithms: Measures of Progress and Loop Invariants-Paradigm Shift: Sequence of
Actions versus Sequence of Assertions- Steps to Develop an Iterative Algorithm-Different
Types of Iterative Algorithms--Typical Errors-Recursion-Forward versus Backward- Towers
of Hanoi-Checklist for Recursive Algorithms-The Stack Frame-Proving Correctness with
Strong Induction- Examples of Recursive Algorithms-Sorting and Selecting Algorithms-
Operations on Integers- Ackermann’s Function- Recursion on Trees-Tree Traversals-
Examples- Generalizing the Problem - Heap Sort and Priority Queues-Representing
Expressions.
UNIT II
OPTIMISATION ALGORITHMS
9
Optimization Problems-Graph Search Algorithms-Generic Search-Breadth-First Search-
Dijkstra’s Shortest-Weighted-Path -Depth-First Search-Recursive Depth-First Search-Linear
Ordering of a Partial Order- Network Flows and Linear Programming-Hill Climbing-Primal
Dual Hill Climbing- Steepest Ascent Hill Climbing-Linear Programming-Recursive
Backtracking-Developing Recursive Backtracking Algorithm- Pruning Branches-Satisfiability
UNIT III
DYNAMIC PROGRAMMING ALGORITHMS
9
Developing a Dynamic Programming Algorithm-Subtle Points- Question for the Little Bird-
Subinstances and Subsolutions-Set of Substances-Decreasing Time and Space-Number of
Solutions-Code. Reductions and NP-Completeness-Satisfiability-Proving NP-Completeness-
3-Coloring- Bipartite Matching. Randomized Algorithms-Randomness to Hide Worst Cases-
Optimization Problems with a Random Structure.
UNIT IV
SHARED OBJECTS AND CONCURRENT OBJECTS
9
Shared Objects and Synchronization -Properties of Mutual Exclusion-The Mora l- The
Producer–Consumer Problem -The Readers–Writers Problem-Realities of Parallelization-
Parallel Programming- Principles- Mutual Exclusion-Time- Critical Sections--Thread
Solutions-The Filter Lock-Fairness-Lamport’s Bakery Algorithm-Bounded Timestamps-Lower
Bounds on the Number of Locations-Concurrent Objects- Concurrency and Correctness-
Sequential Objects-Quiescent Consistency- Sequential Consistency-Linearizability- Formal
Definitions- Progress Conditions- The Java Memory Model
UNIT V
CONCURRENT DATA STRUCTURES
9
Practice-Linked Lists-The Role of Locking-List-Based Sets-Concurrent Reasoning- Coarse-
Grained Synchronization-Fine-Grained Synchronization-Optimistic Synchronization- Lazy
Synchronization-Non-Blocking Synchronization-Concurrent Queues and the ABA Problem-
Queues-A Bounded Partial Queue-An Unbounded Total Queue-An Unbounded Lock-Free
Queue-Memory Reclamation and the ABA Problem- Dual Data Structures- Concurrent
Stacks and Elimination- An Unbounded Lock-Free Stack- Elimination-The Elimination
Backoff Stack
TOTAL : 45 PERIODS
4OUTCOMES:
Upon completion of the course, the students will be able to
Design and apply iterative and recursive algorithms.
Design and implement optimisation algorithms in specific applications.
Design appropriate shared objects and concurrent objects for applications.
Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7103
MULTICORE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
To understand the recent trends in the field of Computer Architecture and identify
performance related parameters
To appreciate the need for parallel processing
To expose the students to the problems related to multiprocessing
To understand the different types of multicore architectures
To expose the students to warehouse-scale and embedded architectures
UNIT I
FUNDAMENTALS OF QUANTITATIVE DESIGN AND ANALYSIS
9
Classes of Computers – Trends in Technology, Power, Energy and Cost – Dependability –
Measuring, Reporting and Summarizing Performance – Quantitative Principles of Computer
Design – Classes of Parallelism - ILP, DLP, TLP and RLP - Multithreading - SMT and CMP
Architectures – Limitations of Single Core Processors - The Multicore era – Case Studies of
Multicore Architectures.
UNIT II
DLP IN VECTOR, SIMD AND GPU ARCHITECTURES
9
Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics Processing
Units - Detecting and Enhancing Loop Level Parallelism - Case Studies.
UNIT III
TLP AND MULTIPROCESSORS
9
Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues -
Performance Issues – Synchronization Issues – Models of Memory Consistency -
Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks.
UNIT IV
RLP AND DLP IN WAREHOUSE-SCALE ARCHITECTURES
9
Programming Models and Workloads for Warehouse-Scale Computers – Architectures for
Warehouse-Scale Computing – Physical Infrastructure and Costs – Cloud Computing –
Case Studies.
5UNIT V
ARCHITECTURES FOR EMBEDDED SYSTEMS
9
Features and Requirements of Embedded Systems – Signal Processing and Embedded
Applications – The Digital Signal Processor – Embedded Multiprocessors - Case Studies.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the limitations of ILP and the need for multicore architectures
Discuss the issues related to multiprocessing and suggest solutions
Point out the salient features of different multicore architectures and how they exploit
parallelism
Critically analyze the different types of inter connection networks
Discuss the architecture of GPUs, warehouse-scale computers and embedded
processors
REFERENCES:
1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative
Approach”, Morgan Kaufmann / Elsevier, 5th edition, 2012.
2. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw-Hill Education, 2003
3. Richard Y. Kain, “Advanced Computer Architecture a Systems Design Approach”,
Prentice Hall, 2011.
4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/
Software Approach” , Morgan Kaufmann / Elsevier, 1997.
CP7111
ADVANCED DATA STRUCTURES LABORATORY
L T PC
0 0 4 2
OBJECTIVES:
To learn to implement iterative and recursive algorithms.
To learn to design and implement algorithms using hill climbing and dynamic
programming techniques.
To learn to implement shared and concurrent objects.
To learn to implement concurrent data structures.
LAB EXERCISES:
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week or two two-hour sessions per
week. There will be about 15 exercises in a semester. It is recommended that all
implementations are carried out in Java. If C or C++ has to be used, then the threads library
will be required for concurrency. Exercises should be designed to cover the following topics:
Implementation of graph search algorithms.
Implementation and application of network flow and linear programming problems.
Implementation of algorithms using the hill climbing and dynamic programming
design techniques.
Implementation of recursive backtracking algorithms.
Implementation of randomized algorithms.
Implementation of various locking and synchronization mechanisms for concurrent
linked lists, concurrent queues, and concurrent stacks.
Developing applications involving concurrency.
TOTAL :60 PERIODS
6OUTCOMES:
Upon completion of the course, the students will be able to
1. Design and apply iterative and recursive algorithms.
2. Design and implement algorithms using the hill climbing and dynamic programming
and recursive backtracking techniques.
3. Design and implement optimisation algorithms for specific applications.
4. Design and implement randomized algorithms.
5. Design appropriate shared objects and concurrent objects for applications.
6. Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C.2 E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7112
CASE STUDY: NETWORK DESIGN (Team Work)
LT PC
0 0 21
CASE STUDY : 1
Analyzing the performance of various configurations and protocols in LAN.
1.1. Establishing a Local Area Network (LAN): The main objective is to set up a Local
Area Network,concepts involved in this network are IP addressing and the Address
Resolution Protocol (ARP). The required equipments are192.168.1.1 ,192.168.1.2,
192.168.1.3,Host A Host B Host C, Switch/HUB, three PC`s equipped with at least one
NIC, one HUB or Switch and the necessary cables. Once the physical LAN is set up the
hosts need to be configured using the ifconfig command. To verify communication
among the machines the ping command is used. Next, to manipulate the routing tables
at the hosts to understand how machines know where to send packets. Since the ifconfig
command places a default route into the routing tables this route must be deleted. to
́blindfoldˇ the machine. The ping command is used again to show that communication is
no longer available. To re-establish communication the routes are put back into the
routing table one host at a time. Communication is once again verified using the ping
command.
1.2. Connecting two LANs using multi-router topology with static routes:
The main objective is to extend routing connection by using multiple routers. The
concepts include IP addressing and basic network routing principles. Connect two LANs
topology. During router configuration attention is paid to the types of interfaces as
additional issues are involved with set-up. For example, the serial interfaces require
clocking mechanisms to be set correctly. Once the interfaces are working the ping
command is used to check for communication between LANs. The failure of
communication illustrates the need for routes to be established inside the routing
infrastructure. Static routes are used to show how packets can be transported through
any reasonable route. It is run trace route on two different configurations to demonstrate
the implementation of different routes.
71.3 Analyzing the performance of various configurations and protocols
Original TCP versus the above modified one: To compare the performance between the
operation of TCP with congestion control and the operation of TCP as implemented . The
main objective is for students to examine how TCP responds to a congested network.
The concepts involved in the lab include network congestion and the host responsibilites
for communicating over a network. This lab requires three PC’s connected to a
switch.One PC is designated as the target host and the other two PC’s will transfer a file
from the targethost using FTP. A load is placed on the network to simulate congestion
and the file is transfered, first by the host using the normal TCP and then by the host
using the modified version. This procedure is performed multiple times to determine
average statistics. The students are then asked to summarize the results and draw
conclusions about the performance differences and the underlying implications for hosts
operating in a network environment.
Case Study 2:
RIP and OSPF Redistribution
This case study addresses the issue of integrating Routing Information Protocol (RIP)
networks with Open Shortest Path First (OSPF) networks. Most OSPF networks also use
RIP to communicate with hosts or to communicate with portions of the internetwork that do
not use OSPF. This case study should provide examples of how to complete the following
phases in redistributing information between RIP and OSPF networks, including the
following topics:
• Configuring a RIP Network
• Adding OSPF to the Center of a RIP Network
• Adding OSPF Areas
• Setting Up Mutual Redistribution
‘Case Study 3:
Dial-on-Demand Routing
This case study should describe the use of DDR to connect a worldwide network that
consists of a central site located in Mumbai and remote sites located in Chennai, Bangalore,
and Hyderabad. The following scenarios should be considered:
• Having the Central Site Dial Out
Describe the central and remote site configurations for three setups: a central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Include examples of the usage of rotary groups and access lists.
• Having the Central and Remote Sites Dial In and Dial Out
Describe the central and remote site configurations for three setups: central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Also describes the usage of Point-to-Point Protocol (PPP)
encapsulation and the Challenge Handshake Authentication Protocol (CHAP).
• Having Remote Sites Dial Out
A common configuration is one in which the remote sites place calls to the central site but
the central site does not dial out. In a “star” topology, it is possible for all of the remote
routers to have their serial interfaces on the same subnet as the central site serial interface.
• Using DDR as a Backup to Leased Lines
Describes the use of DDR as a backup method to leased lines and provides examples of
how to use floating static routes on single and shared interfaces.
• Using Leased Lines and Dial Backup
Describes the use of Data Terminal Ready (DTR) dialing and V.25bis dialing with leased
lines.
Case Study 4:
Network Security
This case study should provide the specific actions you can take to improve the security of
your network. Before going into specifics, however, you should understand the following
basic concepts that are essential to any security system:
8• Know your enemy
This case study refers to attackers or intruders. Consider who might want to circumvent
your security measures and identify their motivations. Determine what they might want to
do and the damage that they could cause to your network. Security measures can never
make it impossible for a user to perform unauthorized tasks with a computer system. They
can only make it harder. The goal is to make sure the network security controls are beyond
the attacker’s ability or motivation.
• Count the cost
Security measures almost always reduce convenience, especially for sophisticated users.
Security can delay work and create expensive administrative and educational overhead. It
can use significant computing resources and require dedicated hardware. When you
design your security measures, understand their costs and weigh those costs against the
potential benefits. To do that, you must understand the costs of the measures themselves
and the costs and likelihoods of security breaches. If you incur security costs out of
proportion to the actual dangers, you have done yourself a disservice.
• Identify your assumptions
Every security system has underlying assumptions. For example, you might assume that
your network is not tapped, or that attackers know less than you do, that they are using
standard software, or that a locked room is safe. Be sure to examine and justify your
assumptions. Any hidden assumption is a potential security hole.
• Control your secrets
Most security is based on secrets. Passwords and encryption keys, for example, are
secrets. Too often, though, the secrets are not really all that secret. The most important
part of keeping secrets is knowing the areas you need to protect. What knowledge would
enable someone to circumvent your system? You should jealously guard that knowledge
and assume that everything else is known to your adversaries. The more secrets you
have, the harder it will be to keep all of them. Security systems should be designed so that
only a limited number of secrets need to be kept.
Know your weaknesses
Every security system has vulnerabilities. You should understand your system’s weak
points and know how they could be exploited. You should also know the areas that present
the largest danger and prevent access to them immediately. Understanding the weak
points is the first step toward turning them into secure areas.
Limit the scope of access
You should create appropriate barriers inside your system so that if intruders access one
part of the system, they do not automatically have access to the rest of the system. The
security of a system is only as good as the weakest security level of any single host in the
system.
• Remember physical security Physical access to a computer (or a router) usually gives a
sufficiently sophisticated user total control over that computer. Physical access to a
network link usually allows a person to tap that link, jam it, or inject traffic into it. It
makes no sense to install complicated software security measures when access to the
hardware is not controlled.
Case Study 5:
Controlling Traffic Flow
In this case study, the firewall router allows incoming new connections to one or more
communication servers or hosts. Having a designated router act as a firewall is desirable
because it clearly identifies the router’s purpose as the external gateway and avoids
encumbering other routers with this task. In the event that the internal network needs to
isolate itself, the firewall router provides the point of isolation so that the rest of the internal
network structure is not affected. Connections to the hosts are restricted to incoming file
transfer protocol (FTP) requests and email services. The incoming Telnet, or modem
connections to the communication server are screened by the communication server running
TACACS username authentication.
9Case Study 6:
Defining Access Lists
Access lists define the actual traffic that will be permitted or denied, whereas an access
group applies an access list definition to an interface. Access lists can be used to deny
connections that are known to be a security risk and then permit all other connections, or to
permit those connections that are considered acceptable and deny all the rest. For firewall
implementation, the latter is the more secure method. In this case study, incoming email and
news are permitted for a few hosts, but FTP, Telnet, and rlogin services are permitted only to
hosts on the firewall subnet. IP extended access lists (range 100 to 199) and transmission
control protocol (TCP) or user datagram protocol (UDP) port numbers are used to filter
traffic. When a connection is to be established for email, Telnet, FTP, and so forth, the
connection will attempt to open a service on a specified port number. You can, therefore,
filter out selected types of connections by denying packets that are attempting to use that
service. An access list is invoked after a routing decision has been made but before the
packet is sent out on an interface. The best place to define an access list is on a preferred
host using your favorite text editor. You can create a file that contains the access-list
commands, place the file (marked readable) in the default TFTP directory, and then network
load the file onto the router.
Case Study 7:
Configuring a fire wall
Consider a Fire wall communication server with single inbound modem.
modem to ensure security for LAN
Configure the
Case Study 8:
Integrating EIGRP (Enhanced Interior Gateway Routing Protocol) into Existing
Networks:
The case study should provide the benefits and considerations involved in integrating
Enhanced IGRP into the following types of internetworks:
• IP—The existing IP network is running IGRP
• Novell IPX—The existing IPX network is running RIP and SAP
• AppleTalk—The existing AppleTalk network is running the Routing Table Maintenance
Protocol (RTMP)
When integrating Enhanced IGRP into existing networks, plan a phased implementation.
Add Enhanced IGRP at the periphery of the network by configuring Enhanced IGRP on a
boundary router on the backbone off the core network. Then integrate Enhanced IGRP into
the core network.
CP7201
THEORETICAL FOUNDATIONS OF COMPUTER SCIENCE
LTPC
310 4
OBJECTIVES:
To review sets, relations, functions, and other foundations
To understand propositional and predicate logics and their applications
To understand lambda calculus and functional programming
To understand graph structures and their applications
To understand formal models of computation, computability, and decidability
UNIT I
FOUNDATIONS
9
Sets – relations – equivalence relations – partial orders – functions – recursive functions –
sequences – induction principle – structural induction – recursive algorithms – counting –
pigeonhole principle – permutations and combinations – recurrence relations
10UNIT II
LOGIC AND LOGIC PROGRAMMING
9
Propositional logic – syntax – interpretations and models – deduction theorems – normal
forms – inference rules – SAT solvers - predicate logic – syntax – proof theory – semantics
of predicate logic – undecidability of predicate logic – inferences in first-order logic – logic
programming – definite programs – SLD resolution – normal programs – SLDNF resolution –
introduction to Prolog
UNIT III
LAMBDA CALCULUS AND FUNCTIONAL PROGRAMMING
9
Lambda notation for functions – syntax – curried functions – parametric polymorphism –
lambda reduction – alpha reduction – beta reduction – beta abstraction – extensionality
theorem – delta reduction – reduction strategies – normal forms – Church-Rosser Theorems
– pure lambda calculus – constants – arithmetic – conditionals – Iteration – recursion –
introduction to functional programming
UNIT IV
GRAPH STRUCTURES
9
Tree Structures – Graph structures – graph representations – regular graph structures –
random graphs – Connectivity – Cycles – Graph Coloring – Cliques, Vertex Covers,
Independent sets – Spanning Trees – network flows – matching
UNIT V
STATE MACHINES
9
Languages and Grammars – Finite State Machines – State machines and languages –
Turing Machines – Computational Complexity – computability – Decidability – Church's
Thesis
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able
To explain sets, relations, functions
To conduct proofs using induction, pigeonhole principle, and logic
To apply counting, permutations, combinations, and recurrence relations
To apply recursive functions and lambda calculus
To explain logic programming and functional programming principles
To apply sequential structures, tree structures, and graph structures
To explain computational models, computability, and complexity
REFERENCES:
1. Uwe Schoning, “Logic for Computer Scientists”, Birkhauser, 2008.
2. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
3. John Harrison, “Handbook of Practical Logic and Automated Reasoning”, Cambridge
University Press, 2009.
4. Greg Michaelson, “An introduction to functional programming through lambda
calculus”, Dover Publications, 2011.
5. Kenneth Slonneger and Barry Kurtz, “Formal syntax and semantics of programming
languages”, Addison Wesley, 1995.
6. Kenneth H. Rosen, “Discrete Mathematics and its applications”, Seventh Edition,
Tata McGraw Hill, 2011.
7. Sriram Pemmaraju and Steven Skiena, “Computational Discrete Mathematics”,
Cambridge University Press, 2003.
8. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
9. Norman L. Biggs, “Discrete Mathematics”, Second Edition, Oxford University Press,
2002.
10. Juraj Hromkovic, “Theoretical Computer Science”, Springer, 1998.
11. J. E. Hopcroft, Rajeev Motwani, and J. D. Ullman, “Introduction to Automata Theory,
Languages, and Computation”, Third Edition, Pearson, 2008.
11CP7202
ADVANCED DATABASES
LTPC
3 003
OBJECTIVES:
To learn the modeling and design of databases.
To acquire knowledge on parallel and distributed databases and its applications.
To study the usage and applications of Object Oriented database
To understand the principles of intelligent databases.
To understand the usage of advanced data models.
To learn emerging databases such as XML, Cloud and Big Data.
To acquire inquisitive attitude towards research topics in databases.
UNIT I
PARALLEL AND DISTRIBUTED DATABASES
9
Database System Architectures: Centralized and Client-Server Architectures – Server
System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O
Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism –
Design of Parallel Systems- Distributed Database Concepts - Distributed Data Storage –
Distributed Transactions – Commit Protocols – Concurrency Control – Distributed Query
Processing – Case Studies
UNIT II
OBJECT AND OBJECT RELATIONAL DATABASES
9
Concepts for Object Databases: Object Identity – Object structure – Type Constructors –
Encapsulation of Operations – Methods – Persistence – Type and Class Hierarchies –
Inheritance – Complex Objects – Object Database Standards, Languages and Design:
ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems: Object
Relational features in SQL/Oracle – Case Studies.
UNIT III
INTELLIGENT DATABASES
9
Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)- Taxonomy- Applications-
Design Principles for Active Rules- Temporal Databases: Overview of Temporal Databases-
TSQL2- Deductive Databases: Logic of Query Languages – Datalog- Recursive Rules-
Syntax and Semantics of Datalog Languages- Implementation of Rules and Recursion-
Recursive Queries in SQL- Spatial Databases- Spatial Data Types- Spatial Relationships-
Spatial Data Structures-Spatial Access Methods- Spatial DB Implementation.
UNIT IV
ADVANCED DATA MODELS
9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data
Management - Location Dependent Data Distribution - Mobile Transaction Models -
Concurrency Control - Transaction Commit Protocols- Multimedia Databases- Information
Retrieval- Data Warehousing- Data Mining- Text Mining.
UNIT V
EMERGING TECHNOLOGIES
9
XML Databases: XML-Related Technologies-XML Schema- XML Query Languages- Storing
XML in Databases-XML and SQL- Native XML Databases- Web Databases- Geographic
Information Systems- Biological Data Management- Cloud Based Databases: Data Storage
Systems on the Cloud- Cloud Storage Architectures-Cloud Data Models- Query Languages-
Introduction to Big Data-Storage-Analysis.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Select the appropriate high performance database like parallel and distributed
database
Model and represent the real world data using object oriented database
Design a semantic based database to meaningful data access
Embed the rule set in the database to implement intelligent databases
Represent the data using XML database for better interoperability
Handle Big data and store in a transparent manner in the cloud
To solve the issues related to the data storage and retrieval
12REFERENCES:
1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition,
Pearson Education/Addison Wesley, 2007.
2. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to
Design, Implementation and Management”, Third Edition, Pearson Education, 2007.
3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”,
Fifth Edition, McGraw Hill, 2006.
4. C.J.Date, A.Kannan and S.Swamynathan, ”An Introduction to Database Systems”,
Eighth Edition, Pearson Education, 2006.
5. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”,
McGraw Hill, Third Edition 2004.
CP7203
PRINCIPLES OF PROGRAMMING LANGUAGES
LTPC
3003
OBJECTIVES:
To understand and describe syntax and semantics of programming languages
To understand data, data types, and basic statements
To understand call-return architecture and ways of implementing them
To understand object-orientation, concurrency, and event handling in programming
languages
To develop programs in non-procedural programming paradigms
UNIT I
SYNTAX AND SEMANTICS
9
Evolution of programming languages – describing syntax – context-free grammars – attribute
grammars – describing semantics – lexical analysis – parsing – recursive-decent – bottom-
up parsing
UNIT II
DATA, DATA TYPES, AND BASIC STATEMENTS
9
Names – variables – binding – type checking – scope – scope rules – lifetime and garbage
collection – primitive data types – strings – array types – associative arrays – record types –
union types – pointers and references – Arithmetic expressions – overloaded operators –
type conversions – relational and boolean expressions – assignment statements – mixed-
mode assignments – control structures – selection – iterations – branching – guarded
statements
UNIT III
SUBPROGRAMS AND IMPLEMENTATIONS
9
Subprograms – design issues – local referencing – parameter passing – overloaded
methods – generic methods – design issues for functions – semantics of call and return –
implementing simple subprograms – stack and dynamic local variables – nested
subprograms – blocks – dynamic scoping
UNIT IV
OBJECT-ORIENTATION, CONCURRENCY, AND EVENT HANDLING
9
Object-orientation – design issues for OOP languages – implementation of object-oriented
constructs – concurrency – semaphores – monitors – message passing – threads –
statement level concurrency – exception handling – even handling
UNIT V
FUNCTIONAL AND LOGIC PROGRAMMING LANGUAGES
9
Introduction to lambda calculus – fundamentals of functional programming languages –
Programming with Scheme – Programming with ML – Introduction to logic and logic
programming – Programming with Prolog – multi-paradigm languages
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe syntax and semantics of programming languages
Explain data, data types, and basic statements of programming languages
Design and implement subprogram constructs
13
Apply object-oriented, concurrency, and event handling programming constructs
Develop programs in Scheme, ML, and Prolog
Understand and adopt new programming languages
REFERENCES:
1. Robert W. Sebesta, “Concepts of Programming Languages”, Tenth Edition, Addison
Wesley, 2012.
2. Michael L. Scott, “Programming Language Pragmatics”, Third Edition, Morgan
Kaufmann, 2009.
3. R. Kent Dybvig, “The Scheme programming language”, Fourth Edition, MIT Press, 2009.
4. Jeffrey D. Ullman, “Elements of ML programming”, Second Edition, Prentice Hall, 1998.
5. Richard A. O'Keefe, “The craft of Prolog”, MIT Press, 2009.
6. W. F. Clocksin and C. S. Mellish, “Programming in Prolog: Using the ISO Standard”, Fifth
Edition, Springer, 2003.
CP7204
ADVANCED OPERATING SYSTEMS
LTPC
3 003
OBJECTIVES:
To learn the fundamentals of Operating Systems
To gain knowledge on Distributed operating system concepts that includes architecture,
Mutual exclusion algorithms, Deadlock detection algorithms and agreement protocols
To gain insight on to the distributed resource management components viz. the
algorithms for implementation of distributed shared memory, recovery and commit
protocols
To know the components and management aspects of Real time, Mobile operating
systems
UNIT I
FUNDAMENTALS OF OPERATING SYSTEMS
9
Overview – Synchronization Mechanisms – Processes and Threads - Process Scheduling –
Deadlocks: Detection, Prevention and Recovery – Models of Resources – Memory
Management Techniques.
UNIT II
DISTRIBUTED OPERATING SYSTEMS
9
Issues in Distributed Operating System – Architecture – Communication Primitives –
Lamport’s Logical clocks – Causal Ordering of Messages – Distributed Mutual Exclusion
Algorithms – Centralized and Distributed Deadlock Detection Algorithms – Agreement
Protocols.
UNIT III
DISTRIBUTED RESOURCE MANAGEMENT
9
Distributed File Systems – Design Issues - Distributed Shared Memory – Algorithms for
Implementing Distributed Shared memory–Issues in Load Distributing – Scheduling
Algorithms – Synchronous and Asynchronous Check Pointing and Recovery – Fault
Tolerance – Two-Phase Commit Protocol – Nonblocking Commit Protocol – Security and
Protection.
UNIT IV
REAL TIME AND MOBILE OPERATING SYSTEMS
9
Basic Model of Real Time Systems - Characteristics- Applications of Real Time Systems –
Real Time Task Scheduling - Handling Resource Sharing - Mobile Operating Systems –
Micro Kernel Design - Client Server Resource Access – Processes and Threads - Memory
Management - File system.
UNIT V
CASE STUDIES
9
Linux System: Design Principles - Kernel Modules - Process Management Scheduling -
Memory Management - Input-Output Management - File System - Interprocess
Communication. iOS and Android: Architecture and SDK Framework - Media Layer -
Services Layer - Core OS Layer - File System.
14OUTCOMES:
Upon Completion of the course, the students should be able to:
Discuss the various synchronization, scheduling and memory management issues
Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols of
Distributed operating system
Discuss the various resource management techniques for distributed systems
Identify the different features of real time and mobile operating systems
Install and use available open source kernel
Modify existing open source kernels in terms of functionality or features used
REFERENCES:
1. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
2. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,
Seventh Edition, John Wiley & Sons, 2004.
3. Daniel P Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
4. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
5. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload
media, 2011.
CP7211
ADVANCED DATABASE LABORATORY
LT P C
0 0 21
OBJECTIVES:
To learn to work on distributed data bases
To understand and work on object oriented databases
To gain knowledge in parallel data base by experimenting it
To learn to work on active database
To study and explore deductive database
To work on the data mining tool weka
To represent and work with the database using XML
DISTRIBUTED DATABASE:
1. Consider a distributed database for a bookstore with 4 sites called S1, S2, S3 and S4.
Consider the following relations:
Books ( ISBN, primary Author, topic, total Stock, price )
Book Store (store No, city, state, zip, inventoryValue )
Stock (store No, ISBN, Qty )
Total Stock is the total number of books in stock and inventory Value is the total
inventory value for the store in dollars.
Consider that Books are fragmented by price amounts into:
F1: Books: price up to $20
F2: Books: price from $20.01 to $50
F3: Books: price from $50.01 to $100
F4: Books: price $100.01 and above
Similarly, Book Stores are divided by ZIP codes into:
15S1: Bookstore: Zip up to 25000
S2: Bookstore: Zip 25001 to 50000
S3: Bookstore: Zip 50001 to 75000
S4: Bookstore: Zip 75001 to 99999
Task: Write SQL query for the following
1. Insert and Display details in each table.
2. Find the total number of books in stock where price is between $15 and $55.
3. Update the book price of book No=1234 from $45 to $55 at site S3.
4. Find total number of book at site S2.
2. Implement deadlock detection algorithm for distributed database using wait-for graph
and test with the following information.
Consider five transactions T1, T2, T3, T4 and T5 with
T1 initiated at site S1 and spawning an agent at site S2
T2 initiated at site S3 and spawning an agent at site S1
T3 initiated at site S1 and spawning an agent at site S3
T4 initiated at site S2 and spawning an agent at site S3
T5 initiated at site S3
The locking information for these transactions is shown in the following table
Transactions
T1
T1
T2
T2
T3
T3
T4
T4
T5
Data items locked
by transactions
X1
X6
X4
X5
X2
-
X7
X8
X3
Data items transaction is
waiting for
X8
X2
X1
-
X7
X3
-
X5
X7
Site involved
in operations
S1
S2
S1
S3
S1
S3
S2
S3
S3
Produce local wait for graph for each of the sites and construct global wait for graph and
check for dead lock.
OBJECT ORIENTED DATABASE:
3. A University wants to track persons associated with them. A person can be an
Employee or Student. Employees are Faculty, Technicians and Project associates.
Students are Full time students, Part time students and Teaching Assistants.
a) Design an Enhanced Entity Relationship (EER) Model for university database.
Write OQL for the following
i.
Insert details in each object.
ii.
Display the Employee details.
iii.
Display Student Details.
iv.
Modify person details.
v.
Delete person details.
b)
Extend the design by incorporating the following information.
Students are registering for courses which are handled by instructor researchers
(graduate students). Faculty are advisors to graduate students. Instructor researchers’ class
16is a category with super class of faculty and graduate students. Faculty are having
sponsored research projects with a grant supporting instruction researchers. Grants are
sanctioned by different agencies. Faculty belongs to different departments. Department is
chaired by a faculty. Implement for the Insertion and Display of details in each class.
PARALLEL DATABASE:
4. Consider the application for University Counselling for Engineering Colleges. The
college, department and vacancy details are maintained in 3 sites. Students are
allocated colleges in these 3 sites simultaneously. Implement this application using
parallel database [State any assumptions you have made].
5. There are 5 processors working in a parallel environment and producing output. The
output record contains college details and students mark information. Implement
parallel join and parallel sort algorithms to get the marks from different colleges of the
university and publish 10 ranks for each discipline.
ACTIVE DATABASE:
6. Create triggers and assertions for Bank database handling deposits and loan and
admission database handling seat allocation and vacancy position. Design the above
relational database schema and implement the following triggers and assertions.
a. When a deposit is made by a customer, create a trigger for updating
customers account and bank account
b. When a loan is issued to the customer, create a trigger for updating
customer’s loan account and bank account.
c. Create assertion for bank database so that the total loan amount does not
exceed the total balance in the bank.
d. When an admission is made, create a trigger for updating the seat allocation
details and vacancy position.
DEDUCTIVE DATABASE:
7. Construct a knowledge database for kinship domain (family relations) with facts.
Extract the following relations using rules.
Parent, Sibling, Brother, Sister, Child, Daughter, Son, Spouse, Wife, husband,
Grandparent, Grandchild, Cousin, Aunt and Uncle.
WEKA TOOL:
8. Work with Weka tool classification and clustering algorithms using the given training
data and test with the unknown sample. Also experiment with different scenarios and
large data set
RID Age Income Student
1 youth high no
2 youth high no
3 middle_aged high no
4 senior medium no
5 senior low yes
6 senior low yes
7 middle_aged low yes
8 youth medium no
9 youth low yes
10 senior medium yes
11 Youth medium yes
12 middle_aged medium no
13 middle_aged high yes
14 senior medium no
17
Credit_
rating
fair
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
Class: buys_
computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
noQUERY PROCESSING
9. Implement Query Optimizer with Relational Algebraic expression construction and
execution plan generation for choosing an efficient execution strategy for processing
the given query.
Also design employee database and test the algorithm with following sample queries.
a) Select empid, empname from employee where experience > 5
b) Find all managers working at London Branch
XML
10. Design XML Schema for the given company database
Department ( deptName, deptNo, deptManagerSSN, deptManagerStartDate,
deptLocation )
Employee ( empName, empSSN, empSex, empSalary, empBirthDate,
empDeptNo, empSupervisorSSN, empAddress, empWorksOn)
Project ( projName, projNo, projLocation, projDeptNo, projWorker )
a. Implement the following queries using XQuery and XPath
i.
ii.
iii.
iv.
b.
Retrieve the department name, manager name, and manager salary for every
department’
Retrieve the employee name, supervisor name and employee salary for each
employee who works in the Research Department.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project with more than
one employee working on it
Implement a storage structure for storing XML database and test with the above
schema.
TOTAL : 30 PERIODS
OUTCOMES:
Work on distributed databases
Create and work on object oriented databases
Create and work with parallel database
Experiment on active database
Explore the features of deductive database
To work on weka tool for clustering and classification
Represent the database using XML and work on it
CP7212
CASE STUDY – OPERATING SYSTEMS DESIGN
(Team Work)
L T PC
0 0 4 2
OBJECTIVES:
1. To develop capabilities to work at systems level
2. To learn about issues in designing and implementing modern operating systems
3. To understand team formation, team issues, and allocating roles and responsibilities
4. To make effective presentations on the work done
5. To develop effective written communication skills
LAB EXERCISES:
A team of three or four students will work on assigned case study / mini-project. Case Study
/ Mini-project can be designed on the following lines:
1. Development of a reasonably sized dynamically loadable kernel module for Linux
kernel
182. Study educational operating systems such as Minix (http://www.minix3.org/), Weenix
(http://weenix.cs.brown.edu/mediawiki/index.php/Weenix) and develop reasonably
sized interesting modules for them
3. Study the Android open source operating system for mobile devices
(http://source.android.com/) and develop / modify some modules.
4. Study any embedded and real-time operating system such as eCos
(http://ecos.sourceware.org/) and develop / modify some modules.
TOTAL : 60 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Develop assigned modules of operating systems design carrying out coding, testing,
and documentation work involved.
Describe team issuses and apply suitable methods to resolve the same.
Demonstrate individual competence in building medium size operating system
components.
Demonstrate ethical and professional attributes of a computer engineer.
Prepare suitable plan with clear statements of deliverables, and track the same.
Make individual presentation of the work carried out.
Prepare well-organized written documents to communicate individual work
accomplished.
REFERENCES:
1. Watts S. Humphrey, “Introduction to Team Software Process”, Addison-Wesley, SEI
Series in Software Engineering, 1999.
2. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
3. T. W. Doeppner, “Operating Systems in Depth: Design and Programming”, Wiley, 2010.
4. S. Tanenbaum and A. S. Woodhull, “Operating Systems Design and Implementation”,
Third Edition, Prentice Hall, 2006.
5. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, “Operating System Concepts”,
Ninth Edition, John Wiley & Sons, 2012.
6. Daniel P. Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
7. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
CP7301
SOFTWARE PROCESS AND PROJECT MANAGEMENT
LTPC
3104
OBJECTIVES:
1.
2.
3.
4.
5.
To
To
To
To
To
understand overall SDLC and adopt suitable processes
elicite, analyze, prioritize, and manage both functional and quality requirements
estimate efforts required, plan, and track the plans
understand and apply configuration and quality management techniques
evaluate, manage, and design processes
(A mini-project can be chosen by the instructor and use it as a context for the
tutorials)
UNIT I
DEVELOPMENT LIFE CYCLE PROCESSES
9
Overview of software development life cycle – introduction to processes – Personal Software
Process (PSP) – Team software process (TSP) – Unified processes – agile processes –
choosing the right process Tutorial: Software development using PSP
19UNIT II
REQUIREMENTS MANAGEMENT
9
Functional requirements and quality attributes – elicitation techniques – Quality Attribute
Workshops (QAW) – analysis, prioritization, and trade-off – Architecture Centric
Development Method (ACDM) – requirements documentation and specification – change
management – traceability of requirements
Tutorial: Conduct QAW, elicit, analyze, prioritize, and document requirements using ACDM
UNIT III
ESTIMATION, PLANNING, AND TRACKING
9
Identifying and prioritizing risks – risk mitigation plans – estimation techniques – use case
points – function points – COCOMO II – top-down estimation – bottom-up estimation – work
breakdown structure – macro and micro plans – planning poker – wideband delphi –
documenting the plan – tracking the plan – earned value method (EVM)
Tutorial: Estimation, planning, and tracking exercises
UNIT IV
CONFIGURATION AND QUALITY MANAGEMENT
9
identifying artifacts to be configured – naming conventions and version control –
configuration control – quality assurance techniques – peer reviews – Fegan inspection –
unit, integration, system, and acceptance testing – test data and test cases – bug tracking –
causal analysis
Tutorial: version control exercises, development of test cases, causal analysis of defects
UNIT V
SOFTWARE PROCESS DEFINITION AND MANAGEMENT
9
Process elements – process architecture – relationship between elements – process
modeling – process definition techniques – ETVX (entry-task-validation-exit) – process
baselining – process assessment and improvement – CMMI – Six Sigma
Tutorial: process measurement exercises, process definition using ETVX
TOTAL 45+15=60 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
1. Explain software development life cycle
2. Adopt a suitable process for software development
3. Elicit functional and quality requirements
4. Analyze, prioritize, and manage requirements
5. Perform trade-off among conflicting requirements
6. Identify and prioritize risks and create mitigation plans
7. Estimate the efforts required for software development
8. Perform planning and tracking activities
9. Control the artifacts during software development
10. Perform various tests to ensure quality
11. Define new processes based on the needs
12. Adopt best practices for process improvement
REFERENCES:
1. Pankaj Jalote, “Software Project Management in Practice”, Pearson, 2002.
2. Chris F. Kemerer, “Software Project Management – Readings and Cases”, McGraw Hill,
1997.
3. Watts S. Humphrey, “PSP: A self-improvement process for software engineers”,
Addison-Wesley, 2005.
4. Watts S. Humphrey, “Introduction to the Team Software Process”, Addison-Wesley,
2000.
5. Orit Hazzan and Yael Dubinsky, “Agile software engineering”, Springer, 2008.
6. James R. Persse, “Process Improvement Essentials”, O’Reilly, 2006.
7. Roger S. Pressman, “Software Engineering – A Practitioner’s Approach”, Seventh
Edition, McGraw Hill, 2010.
20SE7103
FORMAL MODELS OF SOFTWARE SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the basic elements of Z
To understand relations, functions, and logical structures in Z
To understand Z schemas and schema calculus
To learn selected Z case studies
To understand Z schema refinement
UNIT I
FOUNDATIONS OF Z
9
Understanding formal methods – motivation for formal methods – informal requirements to
formal specifications – validating formal specifications – Overview of Z specification – basic
elements of Z – sets and types – declarations – variables – expressions – operators –
predicates and equations
UNIT II
STRUCTURES IN Z
9
Tuples and records – relations, tables, databases – pairs and binary relations – functions –
sequences – propositional logic in Z – predicate logic in Z – Z and boolean types – set
comprehension – lambda calculus in Z – simple formal specifications – modeling systems
and change
UNIT III
Z SCHEMAS AND SCHEMA CALCULUS
9
Z schemas – schema calculus – schema conjunction and disjunction – other schema
calculus operators – schema types and bindings – generic definitions – free types – formal
reasoning – checking specifications – precondition calculation – machine-checked proofs
UNIT IV
Z CASE STUDIES
9
Case Study: Text processing system – Case Study: Eight Queens – Case Study: Graphical
User Interface – Case Study: Safety critical protection system – Case Study: Concurrency
and real time systems
UNIT V
Z REFINEMENT
9
Refinement of Z specification – generalizing refinements – refinement strategies – program
derivation and verification – refinement calculus – data structures – state schemas –
functions and relations – operation schemas – schema expressions – refinement case study
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Apply the basic elements of Z
Develop relational, functional, and logical Z structures
Develop Z schema as models of software systems
Perform verifications and conduct proofs using Z models
Refine Z models towards implementing software systems
REFERENCES:
1. Jonathan Jacky, “The way of Z: Practical programming with formal methods”,
Cambridge University Press, 1996.
2. Antoni Diller, “Z: An introduction to formal methods”, Second Edition, Wiley, 1994.
3. Jim Woodcock and Jim Davies, “Using Z – Specification, Refinement, and Proof”,
Prentice Hall, 1996.
4. J. M. Spivey, “The Z notation: A reference manual”, Second Edition, Prentice Hall,
1992.
5. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
6. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
21CP7001
PERFORMANCE EVALUATION OF COMPUTER SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for performance evaluation of
computer systems
To understand the metrics used for performance evaluation
To understand the analytical modeling of computer systems
To enable the students to develop new queueing analysis for both simple and complex
systems
To appreciate the use of smart scheduling and introduce the students to analytical
techniques for evaluating scheduling policies
UNIT I
OVERVIEW OF PERFORMANCE EVALUATION
9
Need for Performance Evaluation in Computer Systems – Overview of Performance
Evaluation Methods – Introduction to Queueing – Probability Review – Generating Random
Variables for Simulation – Sample Paths, Convergence and Averages – Little’s Law and
other Operational Laws – Modification for Closed Systems.
UNIT II
MARKOV CHAINS AND SIMPLE QUEUES
9
Discrete-Time Markov Chains – Ergodicity Theory – Real World Examples – Google, Aloha
– Transition to Continuous-Time Markov Chain – M/M/1 and PASTA.
UNIT III
MULTI-SERVER AND MULTI-QUEUE SYSTEMS
9
Server Farms: M/M/k and M/M/k/k – Capacity Provisioning for Server Farms – Time
Reversibility and Burke’s Theorem – Networks of Queues and Jackson Product Form –
Classed and Closed Networks of Queues.
UNIT IV
REAL-WORLD WORKLOADS
9
Case Study of Real-world Workloads – Phase-Type Distributions and Matrix-Alalytic
Methods – Networks with Time-Sharing Servers – M/G/1 Queue and the Inspection Paradox
– Task Assignment Policies for Server Farms.
UNIT V
SMART SCHEDULING IN THE M/G/1
9
Performance Metrics – Scheduling Non-Preemptive and Preemptive Non-Size-Based
Policies - . Scheduling Non-Preemptive and Preemptive Size-Based Policies – Scheduling -
SRPT and Fairness.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for performance evaluation and the metrics used for it
Discuss open and closed queueing networks
Define Little’e law and other operational laws
Apply the operational laws to open and closed systems
Use discrete-time and continuous-time Markov chains to model real world systems
Develop analytical techniques for evaluating scheduling policies
REFERENCES:
1. Mor Harchol - Balter, “Performance Modeling and Design of Computer Systems –
Queueing Theory in Action”, Cambridge University Press, 2013.
2. Raj Jain, “The Art of Computer Systems Performance Analysis: Techniques for
Experimental Design, Measurement, Simulation and Modeling”, Wiley-Interscience,
1991.
3. Lieven Eeckhout, “Computer Architecture Performance Evaluation Methods”, Morgan
and Claypool Publishers, 2010.
4. Paul J. Fortier and Howard E. Michel, “Computer Systems Performance Evaluation and
Prediction”, Elsevier, 2003.
5. David J. Lilja, “Measuring Computer Performance: A Practitioner’s Guide”, Cambridge
University Press, 2000.
226. Krishna Kant, “Introduction to Computer System Performance Evaluation”, McGraw-Hill,
1992.
7. K. S. Trivedi, “Probability and Statistics with Reliability, Queueing and Computer Science
Applications”, John Wiley and Sons, 2001.
CP7002
PROBABILISTIC REASONING SYSTEMS
LT P C
3 0 0 3
OBJECTIVES:
To construct and reason with Bayesian networks
To reason with temporal models
To make exact and approximate inferences with graphical models
To understand learning of parameters for probabilistic graphical models
To understand actions and decisions with probabilistic graphical models
UNIT I
REPRESENTATION
9
Probability Theory, Graphs, Bayesian network representation: Bayes networks,
Independence in graphs – Undirected graphical models: Parameterization, Markov Network
independencies – Conditional Bayesian networks.
UNIT II
TEMPLATE BASED REPRESENTATION
9
Temporal models (Dynamic Bayesian networks , Hidden Markov Models) – Directed
probabilistic models for object-relational domains – Inference in temporal models: Kalman
filters.
UNIT III
INFERENCE
9
Exact inference: Variable elimination – Exact inference: Clique trees (Junction trees) –
Approximate inference: Forward sampling, Importance sampling, MCMC – MAP inference:
Variable elimination for MAP, Max-product in clique trees.
UNIT IV
LEARNING
9
Learning graphical models – Parameter estimation: maximum-likelihood estimation, MLE for
Bayesian networks, Bayesian parameter estimation – Structure learning in Bayesian
networks: Constraint based, structure scores, structure search – Partially observed data:
Parameter estimation, Learning models with hidden variables – Learning undirected
models: Maximum likelihood
UNIT V
ACTIONS AND DECISIONS
Causality – Utilities and decisions – Structured decision problems
9
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Construct Bayesian networks
Reason with Bayesian networks
Reason with Dynamic networks and Hidden Markov Models
Conduct inferences with Bayesian networks
Implement algorithms to learn probabilistic graphical models
Explain actions and decisions with probabilistic graphical models
REFERENCES:
1. Daphne Koller and Nir Friedman, “Probabilistic Graphical Models: Principles and
Techniques”, MIT Press, 2009.
2. David Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
3. Adnan Darwiche, “Modeling and Reasoning with Bayesian networks”, Cambridge
University Press, 2009.
4. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
5. Stuart Russel and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third
Edition, Prentice Hall, 2009.
23CP7003
DATA ANALYSIS AND BUSINESS INTELLIGENCE
LTPC
3 003
OBJECTIVES:
To understand linear regression models
To understand logistic regression models
To understand generalized linear models
To understand simulation using regression models
To understand causal inference
To understand multilevel regression
To understand data collection and model understanding
UNIT I
LINEAR REGRESSION
9
Introduction to data analysis – Statistical processes – statistical models – statistical inference
– review of random variables and probability distributions – linear regression – one predictor
– multiple predictors – prediction and validation – linear transformations – centering and
standardizing – correlation – logarithmic transformations – other transformations – building
regression models – fitting a series of regressions
UNIT II
LOGISTIC AND GENERALIZED LINEAR MODELS
9
Logistic regression – logistic regression coefficients – latent-data formulation – building a
logistic regression model – logistic regression with interactions – evaluating, checking, and
comparing fitted logistic regressions – identifiability and separation – Poisson regression –
logistic-binomial model – Probit regression – multinomial regression – robust regression
using t model – building complex generalized linear models – constructive choice models
UNIT III
SIMULATION AND CAUSAL INFERENCE
9
Simulation of probability models – summarizing linear regressions – simulation of non-linear
predictions – predictive simulation for generalized linear models – fake-data simulation –
simulating and comparing to actual data – predictive simulation to check the fit of a time-
series model – causal inference – randomized experiments – observational studies – causal
inference using advanced models – matching – instrumental variables
UNIT IV
MULTILEVEL REGRESSION
9
Multilevel structures – clustered data – multilevel linear models – partial pooling – group-
level predictors – model building and statistical significance – varying intercepts and slopes –
scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression –
multi-level generalized linear models
UNIT V
DATA COLLECTION AND MODEL UNDERSTANDING
9
Design of data collection – classical power calculations – multilevel power calculations –
power calculation using fake-data simulation – understanding and summarizing fitted models
– uncertainty and variability – variances – R2 and explained variance – multiple comparisons
and statistical significance – analysis of variance – ANOVA and multilevel linear and general
linear models – missing data imputation
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Build and apply linear regression models
Build and apply logistic regression models
Build and apply generalized linear models
Perform simulation using regression models
Perform casual inference from data
Build and apply multilevel regression models
Perform data collection and variance analysis
REFERENCES:
1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and
multilevel/Hierarchical Models", Cambridge University Press, 2006.
2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.
3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012.
244. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second
Edition, Oxford University Press, 2006.
5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and
data mining applications", Academic Press, 2009.
6. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An
Example-based Approach", Third Edition, Cambridge University Press, 2010.
8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer,
2011.
CP7004
IMAGE PROCESSING AND ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the basics of digital images
To understand noise models
To understand spatial domain filters
To understand frequency domain filters
To learn basic image analysis --- segmentation, edge detection, and corner detection
To learn morphological operations and texture analysis
To understand processing of color images
To understand image compression techniques
UNIT I
SPATIAL DOMAIN PROCESSING
9
Introduction to image processing – imaging modalities – image file formats – image sensing
and acquisition – image sampling and quantization – noise models – spatial filtering
operations – histograms – smoothing filters – sharpening filters – fuzzy techniques for spatial
filtering – spatial filters for noise removal
UNIT II
FREQUENCY DOMAIN PROCESSING
9
Frequency domain – Review of Fourier Transform (FT), Discrete Fourier Transform (DFT),
and Fast Fourier Transform (FFT) – filtering in frequency domain – image smoothing –
image sharpening – selective filtering – frequency domain noise filters – wavelets – Haar
Transform – multiresolution expansions – wavelet transforms – wavelets based image
processing
UNIT III
SEGMENTATION AND EDGE DETECTION
9
Thresholding techniques – region growing methods – region splitting and merging – adaptive
thresholding – threshold selection – global valley – histogram concavity – edge detection –
template matching – gradient operators – circular operators – differential edge operators –
hysteresis thresholding – Canny operator – Laplacian operator – active contours – object
segmentation
UNIT IV
INTEREST POINTS, MORPHOLOGY, AND TEXTURE
9
Corner and interest point detection – template matching – second order derivatives –
median filter based detection – Harris interest point operator – corner orientation – local
invariant feature detectors and descriptors – morphology – dilation and erosion –
morphological operators – grayscale morphology – noise and morphology – texture – texture
analysis – co-occurrence matrices – Laws' texture energy approach – Ade's eigen filter
approach
25UNIT V
COLOR IMAGES AND IMAGE COMPRESSION
9
Color models – pseudo colors – full-color image processing – color transformations –
smoothing and sharpening of color images – image segmentation based on color – noise in
color images.
Image Compression – redundancy in images – coding redundancy – irrelevant information in
images – image compression models – basic compression methods – digital image
watermarking.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain image modalities, sensing, acquisition, sampling, and quantization
Explain image noise models
Implement spatial filter operations
Explain frequency domain transformations
Implement frequency domain filters
Apply segmentation algorithms
Apply edge detection techniques
Apply corner and interest point detection algorithms
Apply morphological operations
Perform texture analysis
Analyze color images
Implement image compression algorithms
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction
using Java”, Springer, 2008.
3. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011.
4. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson,
2008.
5. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
6. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
7. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
NE7001
SENSING TECHNIQUES AND SENSORS
LT P C
3003
OBJECTIVES:
To study the sensor characteristics and the fundamental principles of sensing
To understand the sensor interface electronics
To study selected motion-related sensors
To study light and radiation detectors
To study selected temperature sensors
To study selected chemical sensors
UNIT I
PRINCIPLES OF SENSING
9
Data Acquisition – sensor characteristics – electric charges, fields, potentials – capacitance
– magnetism – inductance – resistance – piezoelectric – pyroelectric – Hall effect –
thermoelectric effects – sound waves – heat transfer – light – dynamic models of sensors
UNIT II
OPTICAL COMPONENTS AND INTERFACE ELECTRONICS
9
Radiometry – Photometry – mirrors – lenses – fibre optics – concentrators – Interface
circuits – amplifiers – light-to-voltage – excitation circuits – ADC – Digitization –
Capacitance-to-voltage – bridge circuits – data transmission – noise in sensors and circuits –
calibration – low power sensors
26UNIT III
MOTION RELATED SENSORS
9
Occupancy and motion detectors: ultrasonic – microwave – capacitive detectors –
triboelectric – optoelectronic motion sensors – optical presence sensor – Pressure Gradient
sensorsVelocity and acceleration sensors: Accelerometer characteristics – capacitative
accelerometers – piezoelectric accelerometers – piezoresistive accelerometers – thermal
accelerometers – Gyroscopes – piezoelectric cables – gravitational sensors
UNIT IV
LIGHT AND RADIATION DETECTORS
9
Light Detectors: Photo diodes – photo transistor – photo resistor – cooled detectors – CCD
and CMOS image sensors – thermal detectors – optical design – gas flame detectors
Radiation Detectors: scintillating detectors – ionization detectors – cloud and bubble
chambers
UNIT V
TEMPERATURE AND CHEMICAL SENSORS
9
Temperature Sensors: coupling with objects – temperature reference points – thermo
resistive sensors – thermo electric contact sensors – semiconductor sensors – acoustic
sensors – piezoelectric sensors Chemical sensors: characteristics – classes of chemical
sensors – biochemical sensors – multi-sensor arrays – electronic noses and tongues
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain sensor characteristics
Explain the physics of sensors
Explain optical components of sensors
Apply sensor interface electronics
Choose and use appropriate motion-related sensors
Choose and use appropriate light and radiation detectors
Choose and use appropriate temperature sensors
Choose and use appropriate chemical sensors
REFERENCE:
1. Jacob Fraden, “Handbook of Modern Sensors: Physics, Designs, and Applications”,
Fourth Edition, Springer, 2010.
CP7005
RANDOMIZED ALGORITHMS
LTPC
30 03
OBJECTIVES:
To understand the mathematical foundations needed for understanding and designing
randomized algorithms
To appreciate the need for randomized algorithms
To expose the students to probabilistic methods
To understand the concept of random walk
To expose the students to different types of applications of randomized algorithms
UNIT I
INTRODUCTION TO RANDOMIZED ALGORITHMS
9
Introduction to Randomized Algorithms - Min-cut – Elementary Probability Theory – Models
of Randomized Algorithms – Classification of Randomized Algorithms – Paradigms of the
Design of Randomized Algorithms - Game Theoretic Techniques – Game Tree Evaluation –
Minimax Principle – Randomness and Non Uniformity.
UNIT II
PROBABILISTIC METHODS
9
Moments and Deviations – occupancy Problems – Markov and Chebyshev Inequalities –
Randomized Selection – Two Point Sampling – The Stable Marriage Problem – The
Probabilistic Method – Maximum Satisfiability – Expanding Graphs – Method of Conditional
Probabilities – Markov Chains and Random Walks – 2-SAT Example – Random Walks on
Graphs – Random Connectivity.
27UNIT III
ALGEBRAIC TECHNIQUES AND APPLICATIONS
9
Fingerprinting Techniques – Verifying Polynomial Identities – Perfect Matching in Graphs –
Pattern Matching – Verification of Matrix Multiplication - Data Structuring Problems –
Random Treaps – Skip Lists – Hash Tables.
UNIT IV
GEOMETRIC AND GRAPH ALGORITHMS
9
Randomized Incremental Construction – Convex Hulls – Duality – Trapezoidal
Decompositions – Linear Programming – Graph Algorithms – Min-cut – Minimum Spanning
Trees.
UNIT V HASHING AND ONLINE ALGORITHMS
9
Hashing – Universal Hashing - Online Algorithms – Randomized Online Algorithms - Online
Paging – Adversary Models – Relating the Adversaries – The k-server Problem.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for randomized algorithms
Discuss the classification of randomized algorithms
Present the various paradigms for designing randomized algorithms
Discuss the different probabilistic methods used for designing randomized algorithms
Apply the techniques studied to design algorithms for different applications like matrix
multiplication, hashing, linear programming
REFERENCES:
1. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
2. Juraj Hromkovic,”Design and Analysis of Randomized Algorithms”, Springer, 2010.
3. Michael Mitzenmacher and Eli Upfal, “Probabilty and Computing – Randomized
Algorithms and Probabilistic Analysis”, Cambridge University Press, 2005.
NE7002
MOBILE AND PERVASIVE COMPUTING
LT P C
3003
OBJECTIVES :
To understand the basics of Mobile Computing and Personal Computing
To learn the role of cellular networks in Mobile and Pervasive Computing
To expose to the concept of sensor and mesh networks
To expose to the context aware and wearable computing
To learn to develop applications in mobile and pervasive computing environment
UNIT I
INTRODUCTION
9
Differences between Mobile Communication and Mobile Computing – Contexts and Names
– Functions – Applications and Services – New Applications – Making Legacy Applications
Mobile Enabled – Design Considerations – Integration of Wireless and Wired Networks –
Standards Bodies – Pervasive Computing – Basics and Vision – Principles of Pervasive
Computing – Categories of Pervasive Devices
UNIT II
3G AND 4G CELLULAR NETWORKS
9
Migration to 3G Networks – IMT 2000 and UMTS – UMTS Architecture – User Equipment –
Radio Network Subsystem – UTRAN – Node B – RNC functions – USIM – Protocol Stack –
CS and PS Domains – IMS Architecture – Handover – 3.5G and 3.9G a brief discussion –
4G LAN and Cellular Networks – LTE – Control Plane – NAS and RRC – User Plane –
PDCP, RLC and MAC – WiMax IEEE 802.16d/e – WiMax Internetworking with 3GPP
28UNIT III
SENSOR AND MESH NETWORKS
9
Sensor Networks – Role in Pervasive Computing – In Network Processing and Data
Dissemination – Sensor Databases – Data Management in Wireless Mobile Environments –
Wireless Mesh Networks – Architecture – Mesh Routers – Mesh Clients – Routing – Cross
Layer Approach – Security Aspects of Various Layers in WMN – Applications of Sensor and
Mesh networks
UNIT IV
CONTEXT AWARE COMPUTING & WEARABLE COMPUTING
9
Adaptability – Mechanisms for Adaptation - Functionality and Data – Transcoding –
Location Aware Computing – Location Representation – Localization Techniques –
Triangulation and Scene Analysis – Delaunay Triangulation and Voronoi graphs – Types of
Context – Role of Mobile Middleware – Adaptation and Agents – Service Discovery
Middleware
Health BAN- Medical and Technological Requirements-Wearable Sensors-Intra-BAN
communications
UNIT V
APPLICATION DEVELOPMENT
9
Three tier architecture - Model View Controller Architecture - Memory Management –
Information Access Devices – PDAs and Smart Phones – Smart Cards and Embedded
Controls – J2ME – Programming for CLDC – GUI in MIDP – Application Development ON
Android and iPhone
TOTAL:45 PERIODS
OUTCOMES:
At the end of the course the student should be able to
Design a basic architecture for a pervasive computing environment
Design and allocate the resources on the 3G-4G wireless networks
Analyze the role of sensors in Wireless networks
Work out the routing in mesh network
Deploy the location and context information for application development
Develop mobile computing applications based on the paradigm of context aware
computing and wearable computing
REFERENCES:
1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile Computing: Technology,
Applications and Service Creation”, 2nd ed, Tata McGraw Hill, 2010.
2. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
3. .Pei Zheng and Lionel M Li, ‘Smart Phone & Next Generation Mobile Computing’,
Morgan Kaufmann Publishers, 2006.
4. Frank Adelstein, ‘Fundamentals of Mobile and Pervasive Computing’, TMH, 2005
5. Jochen Burthardt et al, ‘Pervasive Computing: Technology and Architecture of Mobile
Internet Applications’, Pearson Education, 2003
6. Feng Zhao and Leonidas Guibas, ‘Wireless Sensor Networks’, Morgan Kaufmann
Publishers, 2004
7. Uwe Hansmaan et al, ‘Principles of Mobile Computing’, Springer, 2003
8. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
9. Mohammad s. Obaidat et al, “Pervasive Computing and Networking”,John wiley
10. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”,
Wiley, 2009
11. Frank Adelstein Sandeep K. S. Gupta Golden G. Richard III Loren Schwiebert
“Fundamentals of Mobile and Pervasive Computing, “, McGraw-Hill, 2005
29CP7006
PARALLEL PROGRAMMING PARADIGMS
LTPC
3 003
OBJECTIVES:
To understand models of and issues in concurrency in computing
To develop message-passing parallel programs using MPI
To develop shared-memory parallel programs using Pthreads
To develop shared-memory parallel programs using OpenMP
To use GPU for parallel programming using OpenCL and CUDA
UNIT I
FOUNDATIONS OF PARALLEL PROGRAMMING
9
Motivation for parallel programming - Concurrency in computing – basics of processes,
multiprocessing, and threads – cache – cache mappings – caches and programs – virtual
memory – instruction level parallelism – hardware multi-threading – SIMD – MIMD –
interconnection networks – cache coherence – shared-memory model – issues in shared-
memory model – distributed-memory model – issues in distributed-memory model – hybrid
model – I/O – performance of parallel programs – parallel program design
UNIT II
MESSAGE PASSING PARADIGM
9
Basic MPI programming – MPI_Init and MPI_Finalize – MPI communicators – SPMD
programs – message passing – MPI_Send and MPI_Recv – message matching – MPI I/O –
parallel I/O – collective communication – MPI_Reduce – MPI_Allreduce – broadcast –
scatter – gather – allgather – derived types – remote memory access – dynamic process
management – MPI for grids – performance evaluation of MPI programs
UNIT III
SHARED MEMORY PARADIGM: PTHREADS
9
Basics of Pthreads – thread synchronization – critical sections – busy-waiting – mutexes –
semaphores – barriers and condition variables – read-write locks – Caches, cache
coherence and false sharing – thread safety – Pthreads case study
UNIT IV
SHARED MEMORY PARADIGM: OPENMP
9
Basic OpenMP constructs – scope of variabls – reduction clause – parallel for directive –
loops in OpenMP – scheduling loops – synchronization in OpenMP – Case Study:
Producer-Consumer problem – cache issues – threads safety in OpenMP – OpenMP best
practices
UNIT V
GRAPHICAL PROCESSING PARADIGMS: OPENCL AND CUDA
9
Introduction to CUDA – CUDA programming examples – CUDA execution model – CUDA
memory hierarchy – CUDA case study - introduction to OpenCL – OpenCL programming
examples – Programs and Kernels – Buffers and Images – Event model – OpenCL case
study
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain models of parallel programming
Explain hardware level support for concurrency
Explain issues in parallel programming
Develop message-passing parallel programs using MPI framework
Develop shared-memory parallel programs using Pthreads
Develop shared-memory parallel programs using OpenMP
Develop CUDA programs
Develop OpenCL programs
REFERENCES:
1. Peter S. Pacheco, “An introduction to parallel programming”, Morgan Kaufmann,
2011.
2. M. J. Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw Hill,
2003.
3. W. Gropp, E. Lusk, and R. Thakur, “Using MPI-2: Advanced features of the message
passing interface”, MIT Press, 1999.
304. W. Gropp, E. Lusk, and A. Skjellum, “Using MPI: Portable parallel programming with
the message passing interface”, Second Edition, MIT Press, 1999.
5. B. Chapman, G. Jost, and Ruud van der Pas, “Using OpenMP”, MIT Press, 2008.
6. D. R. Butenhof, “Programming with POSIX Threads”, Addison Wesley, 1997.
7. B. Lewis and D. J. Berg, “Multithreaded programming with Pthreads”, Sun
Microsystems Press, 1998.
8. A. Munshi, B. Gaster, T. G. Mattson, J. Fung, and D. Ginsburg, “OpenCL
programming guide”, Addison Wesley, 2011.
9. Rob Farber, “CUDA application design and development”, Morgan Haufmann, 2011.
CP7007
SOFTWARE REQUIREMENTS ENGINEERING
LTPC
3 003
OBJECTIVES:
1. Understand system requirements
2. Identify different types of requirement
3. Generate requirements be elicitation
4. Develop requirements documentation
5. Evaluate the requirements
UNIT I
DOMAIN UNDERSTANDING
9
Introduction – Types of requirements – Requirements engineering process – Validating
requirements – Requirements and design – Requirements and test cases – introduction to
business domain – Problem analysis – Fish bone diagram – Business requirements –
Business process modeling – Business use cases – Business modeling notations – UML
Activity diagrams.
UNIT II
REQUIREMENTS ELICITATION
9
Introduction – Understanding stakeholders' needs – Elicitation techniques – interviews,
questionnaire, workshop, brainstorming, prototyping – Documenting stakeholders' needs
UNIT III
FUNCTIONAL REQUIREMENTS
9
Introduction – Features and Use cases – Use case scenarios – Documenting use cases –
Levels of details – SRS documents.
UNIT IV
QUALITY ATTRIBUTES AND USER EXPERIENCE
9
Quality of solution – Quality attributes – Eliciting quality attributes – Quality attribute
workshop (QAW) – Documenting quality attributes – Six part scenarios – Usability
requirements – Eliciting and documenting usability requirements – Modeling user experience
– Specifying UI design
UNIT V
MANAGING REQUIREMENTS
9
Defining scope of the project – Context diagram – Managing requirements – Requirements
properties – Traceability – Managing changes – Requirements metrics – Requirements
management tools.
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Define a process for requirments engineering
Execute a process for gathering requirments through elicitation techniques.
Validate requirements according to criteria such as feasibility, clarity, preciseness
etc.
Develop and document functional requirements for different types of systems.
Develop and document quality attributes of the system to be implemented
Communicate the requirments to stakeholders
Negotiate with stakeholders in order to agree on a set of requirements.
Detect and resolve feature interactions
31REFERENCES:
1. Axel van Lamsweerde, "Requirements Engineering", Wiley, 2009
2. Gerald Kotonya, Ian Sommerville, "Requirements Engineering: Processes and
Techniques", John Wiley and Sons, 1998
3. Dean Leffingwell and Don Widrig, “Managing Software Requirements: A Use Case
Approach (2nd Edition) ”, Addison-wesley, 2003
4. SEI Report, “Quality Attributes Workshop”,
http://www.sei.cmu.edu/library/abstracts/reports/03tr016.cfm , 2003
5. J Nielsen, “Usability Engineering”, Academic Press, 1993
CP7008
SPEECH PROCESSING AND SYNTHESIS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for speech processing
To understand the basic concepts and algorithms of speech processing and synthesis
To familiarize the students with the various speech signal representation, coding and
recognition techniques
To appreciate the use of speech processing in current technologies and to expose the
students to real– world applications of speech processing
UNIT I
FUNDAMENTALS OF SPEECH PROCESSING
9
Introduction – Spoken Language Structure – Phonetics and Phonology – Syllables and
Words – Syntax and Semantics – Probability, Statistics and Information Theory – Probability
Theory – Estimation Theory – Significance Testing – Information Theory.
UNIT II
SPEECH SIGNAL REPRESENTATIONS AND CODING
9
Overview of Digital Signal Processing – Speech Signal Representations – Short time Fourier
Analysis – Acoustic Model of Speech Production – Linear Predictive Coding – Cepstral
Processing – Formant Frequencies – The Role of Pitch – Speech Coding – LPC Coder.
UNITIII
SPEECH RECOGNITION
9
Hidden Markov Models – Definition – Continuous and Discontinuous HMMs – Practical
Issues – Limitations. Acoustic Modeling – Variability in the Speech Signal – Extracting
Features – Phonetic Modeling – Adaptive Techniques – Confidence Measures – Other
Techniques.
UNITIV
TEXT ANALYSIS
9
Lexicon – Document Structure Detection – Text Normalization – Linguistic Analysis –
Homograph Disambiguation – Morphological Analysis – Letter-to-sound Conversion –
Prosody – Generation schematic – Speaking Style – Symbolic Prosody – Duration
Assignment – Pitch Generation
UNIT V
SPEECH SYNTHESIS
9
Attributes – Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic
Modification of Speech – Source-filter Models for Prosody Modification – Evaluation of TTS
Systems.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the various temporal, spectral and cepstral features required for identifying
speech units – phoneme, syllable and word
Determine and apply Mel-frequency cepstral coefficients for processing all types of
signals
Justify the use of formant and concatenative approaches to speech synthesis
32
Identify the apt approach of speech synthesis depending on the language to be
processed
Determine the various encoding techniques for representing speech.
REFERENCES:
1. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing – A
guide to Theory, Algorithm and System Development”, Prentice Hall PTR, 2001.
2. Thomas F.Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education,
2002.
3. Lawrence Rabiner and Biing-Hwang Juang, “Fundamentals of Speech Recognition”,
Prentice Hall Signal Processing Series, 1993.
4. Sadaoki Furui, “Digital Speech Processing: Synthesis, and Recognition, Second Edition,
(Signal Processing and Communications)”, Marcel Dekker, 2000.
5. Joseph Mariani, “Language and Speech Processing”, Wiley, 2009.
CP7009
MACHINE LEARNING TECHNIQUES
LTPC
3 003
OBJECTIVES:
1. To understand the machine learning theory
2. To implement linear and non-linear learning models
3. To implement distance-based clustering techniques
4. To build tree and rule based models
5. To apply reinforcement learning techniques
UNIT I
FOUNDATIONS OF LEARNING
9
Components of learning – learning models – geometric models – probabilistic models – logic
models – grouping and grading – learning versus design – types of learning – supervised –
unsupervised – reinforcement – theory of learning – feasibility of learning – error and noise –
training versus testing – theory of generalization – generalization bound – approximation-
generalization tradeoff – bias and variance – learning curve
[
UNIT II
LINEAR MODELS
9
Linear classification – univariate linear regression – multivariate linear regression –
regularized regression – Logistic regression – perceptrons – multilayer neural networks –
learning neural networks structures – support vector machines – soft margin SVM – going
beyond linearity – generalization and overfitting – regularization – validation
UNIT III
DISTANCE-BASED MODELS
9
Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchical
clustering – k-d trees – locality sensitive hashing – non-parametric regression – ensemble
learning – bagging and random forests – boosting – meta learning
UNIT IV
TREE AND RULE MODELS
9
Decision trees – learning decision trees – ranking and probability estimation trees –
regression trees – clustering trees – learning ordered rule lists – learning unordered rule lists
– descriptive rule learning – association rule mining – first-order rule learning
UNIT V
REINFORCEMENT LEARNING
9
Passive reinforcement learning – direct utility estimation – adaptive dynamic programming –
temporal-difference learning – active reinforcement learning – exploration – learning an
action-utility function – Generalization in reinforcement learning – policy search –
applications in game playing – applications in robot control
TOTAL : 45 PERIODS
33OUTCOMES:
Upon Completion of the course,the students will be able to
To explain theory underlying machine learning
To construct algorithms to learn linear and non-linear models
To implement data clustering algorithms
To construct algorithms to learn tree and rule-based models
To apply reinforcement learning techniques
REFERENCES:
1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook
Publishers, 2012.
2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of
data”, Cambridge University Press, 2012.
3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012.
4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007.
5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning”,
MIT Press, 2012.
7. T. M. Mitchell, “Machine Learning”, McGraw Hill, 1997.
8. S. Russel and P. Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition,
Prentice Hall, 2009.
CP7010
CONCURRENCY MODELS
LTPC
3 003
OBJECTIVES:
To model concurrency in FSP
To specify and check safety and liveness properties
To understand concurrency architectures and design
To apply linear temporal logic to safety and liveness analysis
To apply Petri nets for concurrency modeling and analysis
UNIT I
FSP AND GRAPH MODELS
9
Concurrency and issues in concurrency – models of concurrency – graphical models – FSP
& LTSA – modeling processes with FSP – concurrency models with FSP – shared action –
structure diagrams – issues with shared objects – modeling mutual exclusion – conditional
synchronization – modeling semaphores – nested monitors – monitor invariants
UNIT II
SAFETY AND LIVENESS PROPERTIES
9
Deadlocks – deadlock analysis in models – dining philosophers problem – safety properties
– single-lane bridge problem – liveness properties – liveness of the single-lane bridge –
readers-writers problem – message passing – asynchronous message passing models –
synchronous message passing models – rendezvous
UNIT III
CONCURRENCY ARCHITECTURES AND DESIGN
9
Modeling dynamic systems – modeling timed systems – concurrent architectures – Filter
pipeline – Supervisor-worker model – announcer-listener model – model-based design –
from requirements to models – from models to implementations – implementing concurrency
in Java – program verification
UNIT IV
LINEAR TEMPORAL LOGIC (LTL)
9
Syntax of LTL – semantics of LTL – practical LTL patterns – equivalences between LTL
statements – specification using LTL – LTL and FSP – Fluent proposition – Temporal
propositions – Fluent Linear Temporal Logic (FLTL) – FLTL assertions in FSP – Database
ring problem
34UNIT V
PETRI NETS
9
Introduction to Petri nets – examples – place-transition nets – graphical and linear algebraic
representations – concurrency & conflict – coverability graphs – decision procedures –
liveness – colored Petri nets (CPN) – modeling & verification using CPN – non-hierarchical
CPN – modeling protocols – hierarchical CPN – timed CPN – applications of Petri Nets
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Develop concurrency models and FSP
State safety and liveness properties in FSP
Verify properties using LTSA tool
Explain concurrency architectures
Design concurrent Java programs from models
Apply Linear Temporal Logic to state safety and liveness properties
Assert LTL properties in FSP and check using LTSA tool
Model and analyze concurrency using Petri nets
REFERENCES:
1. Jeff Magee & Jeff Kramer, “Concurrency: State Models and Java Programs”, Second
Edition, John Wiley, 2006.
2. M. Huth & M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
3. B. Goetz, T. Peierls, J. Bloch, J. Bowbeer, D. Holmes, and D. Lea, “Java Concurrency in
Practice”, Addison-Wesley Professional, 2006.
4. Wolfgang Reisig, “Petri Nets: An Introduction”, Springer, 2011.
5. K. Jensen and L. M. Kristensen, “Colored Petri Nets: Modeling and Validation of
Concurrent Systems”, Springer, 2009.
6. Wolfgang Reisig, “Understanding Petri Nets: Modeling Techniques, Analysis Methods,
Case Studies”, Springer, 2013.
CP7011
REAL TIME SYSTEMS
LTPC
3 003
OBJECTIVES:
To provide good understanding of fundamental concepts in real time systems.
To provide understanding of advanced topics in real time systems.
To provide understanding on basic multi-task scheduling algorithms for periodic,
aperiodic, and sporadic tasks as well as understand the impact of the latter two on
scheduling
To expose to understand capabilities of commercial off-the-shelf R-T kernel.
To expose to real time communications and databases.
UNIT I
INTRODUCTION
9
Real-time systems – Applications – Basic Model – Characteristics – Safety and Reliability –
Real-Time tasks – Timing Constraints – Modelling Timing Constraints.
UNIT II
SCHEDULING REAL-TIME TASKS
9
Concepts – Types of RT Tasks and their Characteristics – Task Scheduling – Clock-Driven
Scheduling – Hybrid Schedulers - Event-Driven Scheduling – EDF Scheduling – RMA –
Issues with RMA – Issues in Using RMA in Practical Situations
35UNIT III
RESOURCE SHARING AMONG RT TASKS & SCHEDULING RT TASKS 9
Resource Sharing Among RT Tasks – Priority Inversion – PIP – HLP – PCP – Types of
Priority Inversions Under PCP – Features of PCP – Issues in using Resource Sharing
Protocol – Handling Task Dependencies – Multiprocessor Task Allocation – Dynamic
Allocation of Tasks – Fault-Tolerant Scheduling of Tasks – Clocks in Distributed RT Systems
– Centralized and Distributed Clock Synchronization.
UNIT IV
COMMERCIAL RT OPERATING SYSTEMS
9
Time Services – Features of RT OS – Unix as a RT OS – Unix Based RT OS – Windows as
a RT OS – POSIX – Survey of RTOS: PSOS – VRTX – VxWorks – QNX - μC/OS-II – RT
Linux – Lynx – Windows CE – Benching RT Systems.
UNIT V
RT COMMUNICATION & DATABASES
9
Examples of Applications Requiring RT Communication – Basic Concepts – RT
Communication in a LAN – Soft & Hard RT Communication in a LAN – Bounded Access
Protocols for LANs – Performance Comparison – RT Communication Over Packet Switched
Networks – QoS Framework – Routing – Resource Reservation – Rate Control – QoS
Models - Examples Applications of RT Databases – RT Databases – Characteristics of
Temporal Data – Concurrency Control in RT Databases – Commercial RT Databases.
TOTAL: 45 PERIODS
OUTCOMES:
Understand the basics and importance of real-time systems
Generate a high-level analysis document based on requirements specifications
Generate a high-level design document based on analysis documentation
Generate a test plan based on requirements specification
Generate a validation plan based on all documentation
Understand basic multi-task scheduling algorithms for periodic, aperiodic, and
sporadic tasks as well as understand the impact of the latter two on scheduling
Understand capabilities of at least one commercial off-the-shelf R-T kernel
REFERENCES:
1. Rajib Mall, "Real-Time Systems: Theory and Practice," Pearson, 2008.
2. Jane W. Liu, "Real-Time Systems" Pearson Education, 2001.
3. Krishna and Shin, "Real-Time Systems," Tata McGraw Hill. 1999.
4. Alan C. Shaw, “Real-Time Systems and Software”, Wiley, 2001.
5. Philip Laplante, “Real-Time Systems Design and Analysis”, 2nd Edition, Prentice Hall
of India.
6. Resource Management in Real-time Systems and Networks, C. Siva Ram Murthy
and G. Manimaran, MIT Press, March 2001.
CP7012
COMPUTER VISION
LTPC
3 003
OBJECTIVES:
To review image processing techniques for computer vision
To understand shape and region analysis
To understand Hough Transform and its applications to detect lines, circles, ellipses
To understand three-dimensional image analysis techniques
To understand motion analysis
To study some applications of computer vision algorithms
UNIT I
IMAGE PROCESSING FOUNDATIONS
9
Review of image processing techniques – classical filtering operations – thresholding
techniques – edge detection techniques – corner and interest point detection – mathematical
morphology – texture
36UNIT II
SHAPES AND REGIONS
9
Binary shape analysis – connectedness – object labeling and counting – size filtering –
distance functions – skeletons and thinning – deformable shape analysis – boundary
tracking procedures – active contours – shape models and shape recognition – centroidal
profiles – handling occlusion – boundary length measures – boundary descriptors – chain
codes – Fourier descriptors – region descriptors – moments
UNIT III
HOUGH TRANSFORM
9
Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line
localization – line fitting – RANSAC for straight line detection – HT based circular object
detection – accurate center location – speed problem – ellipse detection – Case study:
Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial
matched filtering – GHT for ellipse detection – object location – GHT for feature collation
UNIT IV
3D VISION AND MOTION
9
Methods for 3D vision – projection schemes – shape from shading – photometric stereo –
shape from texture – shape from focus – active range finding – surface representations –
point-based representation – volumetric representations – 3D object recognition – 3D
reconstruction – introduction to motion – triangulation – bundle adjustment – translational
alignment – parametric motion – spline-based motion – optical flow – layered motion
UNIT V APPLICATIONS
9
Application: Photo album – Face detection – Face recognition – Eigen faces – Active
appearance and 3D shape models of faces
Application: Surveillance – foreground-background separation – particle filters – Chamfer
matching, tracking, and occlusion – combining views from multiple cameras – human gait
analysis
Application: In-vehicle vision system: locating roadway – road markings – identifying road
signs – locating pedestrians
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Implement fundamental image processing techniques required for computer vision
Perform shape analysis
Implement boundary tracking techniques
Apply chain codes and other region descriptors
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques
Implement motion related techniques
Develop applications using computer vision techniques
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011.
3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge
University Press, 2012.
4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
37NE7202
NETWORK AND INFORMATION SECURITY
LTPC
3 003
OBJECTIVES:
To understand the fundamentals of Cryptography
To acquire knowledge on standard algorithms used to provide confidentiality, integrity
and authenticity.
To understand the various key distribution and management schemes.
To understand how to deploy encryption techniques to secure data in transit across
data networks
To design security applications in the field of Information technology
UNIT I
INTRODUCTION
9
An Overview of Computer Security-Security Services-Security Mechanisms-Security
Attacks-Access Control Matrix, Policy-Security policies, Confidentiality policies, Integrity
policies and Hybrid policies.
UNIT II
CRYPTOSYSTEMS & AUTHENTICATION
9
Classical Cryptography-Substitution Ciphers-permutation Ciphers-Block Ciphers-DES-
Modes of Operation- AES-Linear Cryptanalysis, Differential Cryptanalysis- Hash Function -
SHA 512- Message Authentication Codes-HMAC - Authentication Protocols -
UNIT III
PUBLIC KEY CRYPTOSYSTEMS
9
Introduction to Public key Cryptography- Number theory- The RSA Cryptosystem and
Factoring Integer- Attacks on RSA-The ELGamal
Cryptosystem- Digital Signature
Algorithm-Finite Fields-Elliptic Curves Cryptography- Key management – Session and
Interchange keys, Key exchange and generation-PKI
UNIT IV
SYSTEM IMPLEMENTATION
9
Design Principles, Representing Identity, Access Control Mechanisms, Information Flow and
Confinement Problem
Secure Software Development: Secured Coding - OWASP/SANS Top Vulnerabilities -
Buffer Overflows - Incomplete mediation - XSS - Anti Cross Site Scripting Libraries -
Canonical Data Format - Command Injection - Redirection - Inference – Application Controls
UNIT V
NETWORK SECURITY
9
Secret Sharing Schemes-Kerberos- Pretty Good Privacy (PGP)-Secure Socket Layer (SSL)-
Intruders – HIDS- NIDS - Firewalls - Viruses
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Implement basic security algorithms required by any computing system.
Analyze the vulnerabilities in any computing system and hence be able to design a
security solution.
Analyze the possible security attacks in complex real time systems and their effective
countermeasures
Identify the security issues in the network and resolve it.
Evaluate security mechanisms using rigorous approaches, including theoretical
derivation, modeling, and simulations
Formulate research problems in the computer security field
REFERENCES:
1. William Stallings, “Cryptography and Network Security: Principles and Practices”,
Third Edition, Pearson Education, 2006.
2. Matt Bishop ,“Computer Security art and science ”, Second Edition, Pearson
Education, 2002
3. Wade Trappe and Lawrence C. Washington, “Introduction to Cryptography with
Coding Theory” Second Edition, Pearson Education, 2007
4. Jonathan Katz, and Yehuda Lindell, Introduction to Modern Cryptography, CRC
Press, 2007
385. Douglas R. Stinson, “Cryptography Theory and Practice”, Third Edition, Chapman &
Hall/CRC, 2006
6. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education,
First Edition, 2006.
7. Network Security and Cryptography, Menezes Bernard, Cengage Learning, New
Delhi, 2011
8. Man Young Rhee, Internet Security, Wiley, 2003
9. OWASP top ten security vulnerabilities: http://xml.coverpages.org/OWASP-
TopTen.pdf
CP7013
DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS
LTPC
3 003
OBJECTIVES:
To understand the need for parallel algorithms
To expose the students to different models of parallel computation
To expose the students to parallel sorting and searching algorithms
To understand the application of the concepts studied to different types of problems
To analyze parallel algorithms
UNIT I
INTRODUCTION
9
Introduction to Parallel Algorithms – Models of Parallel Computation – Sorting on an EREW-
SIMD PRAM Computer – Relation between PRAM Models – SIMD Algorithms – MIMD
Algorithms – Selection – Desirable Properties for Parallel Algorithms - Parallel Algorithm for
Selection – Analysis of Parallel Algorithms.
UNIT II
SORTING AND SEARCHING
9
Merging on the EREW and CREW Models - Fast Merging on EREW - Sorting Networks –
Sorting on a Linear Array – Sorting on CRCW, CREW, EREW Models – Searching a Sorted
Sequence – Searching a Random Sequence.
UNIT III
ALGEBRAIC PROBLEMS
9
Generating Permutations and Combinations in Parallel – Matrix Transpositions – Matrix by
Matrix Multiplications – Matrix by Vector multiplication.
UNIT IV
GRAPH THEORY AND COMPUTATIONAL GEOMETRY PROBLEMS
9
Connectivity Matrix – Connected Components – All Pairs Shortest Paths – Minimum
Spanning Trees – Point Inclusion – Intersection, Proximity and Construction Problems -
Sequential Tree Traversal - Basic Design Principles – Algorithm – Analysis.
UNITV
DECISION AND OPTIMIZATION PROBLEMS
9
Computing Prefix Sums – Applications - Job Sequencing with Deadlines – Knapsack
Problem- The Bit Complexity of Parallel Computations.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for parallel algorithms
Discuss the classification of parallel architectures and identify suitable programming
models
Perform sorting on Sorting on CRCW, CREW, EREW Models
Search a sorted as well as random sequence
Develop and analyze algorithms for different applications like matrix multiplication,
shortest path, job sequencing and the knapsack problem.
39REFERENCES:
1. Selim G. Akl, “The Design and Analysis of Parallel Algorithms”, Prentice Hall, New
Jersey, 1989.
2. Michael J. Quinn, “Parallel Computing : Theory & Practice”, Tata McGraw Hill Edition,
2003.
3. Justin R. Smith, “The Design and Analysis of Parallel Algorithms”, Oxford University
Press, USA , 1993.
4. Joseph JaJa, “Introduction to Parallel Algorithms”, Addison-Wesley, 1992.
CP7014
SOFTWARE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
Understand architectural requirements
Identify architectural structures
Develop architectural documentation
Generate architectural alternatives
Evaluate the architecture against the drivers
UNIT I
ARCHITECTURAL DRIVERS
9
Introduction – Standard Definitions of Software Architecture– Architectural structures –
Influence of software architecture on organization – Architecture Business Cycle –
Functional requirements – Technical constraints – Quality Attributes – Quality Attribute
Workshop (QAW) – Documenting Quality Attributes – Six part scenarios
UNIT II
ARCHITECTURAL VIEWS AND DOCUMENTATION
9
Introduction – Standard Definitions for views – Structures and views- Perspectives: Static,
dynamic and physical and the accompanying views – Representing views-available
notations – Good practices in documentation– Documenting the Views using UML – Merits
and Demerits of using visual languages – Need for formal languages - Architectural
Description Languages – ACME
UNIT III
ARCHITECTURAL STYLES
9
Introduction – Data flow styles – Call-return styles – Shared Information styles – Event styles
– Case studies for each style
UNIT IV
ARCHITECTURAL DESIGN
9
Approaches for architectural design – System decomposition – Attributes driven design –
Architecting for specific quality attributes – Performance, Availability – Security –
Architectural conformance
UNIT V
ARCHITECTURE EVALUATION AND SOME SPECIAL TOPICS
9
Need for evaluation – Scenario based evaluation against the drivers – ATAM and its
variations – Case studies in architectural evaluations – SOA and Web services – Cloud
Computing – Adaptive structures
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain key architectural drivers
Explain the influence of architecture on business and technical activities
Identify key architectural structures
Adopt good practices for documenting the architecture
Develop alternative architectures for a given problem
Explain how to use formal languages to specify architecture
Evaluate the architecture against the drivers
Describe the recent trends in software architecture
40REFERENCES:
1. Len Bass, Paul Clements, and Rick Kazman, “Software Architectures Principles and
Practices”, 2n Edition, Addison-Wesley, 2003.
2. Anthony J Lattanze, “Architecting Software Intensive System. A Practitioner's Guide”,
Auerbach Publications, 2010.
3. Paul Clements, Felix Bachmann, Len Bass, David Garlan, James Ivers, Reed Little,
Paulo Merson, Robert Nord, and Judith Stafford, “Documenting Software Architectures.
Views and Beyond”, 2nd Edition, Addison-Wesley, 2010.
4. Paul Clements, Rick Kazman, and Mark Klein, “Evaluating software architectures:
Methods and case studies.”, Addison-Wesley, 2001.
5. David Garlan and Mary Shaw, “Software architecture: Perspectives on an emerging
discipline”, Prentice Hall, 1996.
6. Rajkumar Buyya, James Broberg, and Andrzej Goscinski, “Cloud Computing. Principles
and Paradigms”, John Wiley & Sons, 2011
7. Mark Hansen, “SOA Using Java Web Services”, Prentice Hall, 2007
8. David Garlan, Bradley Schmerl, and Shang-Wen Cheng, “Software Architecture-Based
Self-Adaptation,” 31-56. Mieso K Denko, Laurence Tianruo Yang, and Yan Zang (eds.),
“Autonomic Computing and Networking”. Springer Verlag, 2009.
CP7015
MODEL CHECKING AND PROGRAM VERIFICATION
LTPC
3 003
OBJECTIVES:
To understand automata for model checking
To understand LTL, CTL, and CTL*
To understand timed automata, TCTL, and PCTL
To understand verification of deterministic and recursive programs
To understand verification of object-oriented programs
To understand verification of parallel, distributed, and non-deterministic programs
UNIT I
AUTOMATA AND TEMPORAL LOGICS
9
Automata on finite words – model checking regular properties – automata on infinite words –
Buchi automata – Linear Temporal Logic (LTL) – automata based LTL model checking –
Computational Tree Logic (CTL) – CTL model checking – CTL* model checking
UNIT II
TIMED AND PROBABILISTIC TREE LOGICS
9
Timed automata – timed computational tree logic (TCTL) – TCTL model checking –
probabilistic systems – probabilistic computational tree logic (PCTL) – PCTL model checking
– PCTL* - Markov decision processes
UNIT III
VERIFYING DETERMINISTIC AND RECURSIVE PROGRAMS
9
Introduction to program verification – verification of “while” programs – partial and total
correctness – verification of recursive programs – case study: binary search – verifying
recursive programs with parameters
UNIT IV
VERIFYING OBJECT-ORIENTED AND PARALLEL PROGRAMS
9
Partial and total correctness of object-oriented programs – case study: Insertion in linked
lists – verification of disjoint parallel programs – verifying programs with shared variables –
case study: parallel zero search – verification of synchronization – case study: the mutual
exclusion problem
UNIT V
VERIFYING NON-DETERMINISTIC AND DISTRIBUTED PROGRAMS
9
Introduction to non-deterministic programs – partial and total correctness of non-
deterministic programs – case study: The Welfare Crook Problem – syntax and semantics of
distributed programs – verification of distributed programs – case study: A Transmission
Problem – introduction to fairness
TOTAL : 45 PERIODS
41OUTCOMES:
Upon Completion of the course,the students will be able to
Perform model checking using LTL
Perform model checking using CTL
Perform model checking using CTL*
Perform model checking using TCTL and PCTL
Verify deterministic and recursive programs
Verify object-oriented programs
Verify parallel, distributed, and non-deterministic programs
REFERENCES:
1. C. Baier, J.-P. Katoen, and K. G. Larsen, “Principles of Model Checking”, MIT Press,
2008.
2. E. M. Clarke, O. Grumberg, and D. A. Peled, “Model Checking”, MIT Press, 1999.
3. M. Ben-Ari, “Principles of the SPIN Model Checker”, Springer, 2008.
4. K. R. Apt, F. S. de Boer, E.-R. Olderog, and A. Pnueli, “Verification of Sequential and
Concurrent Programs”, Third Edition, Springer, 2010.
5. M. Huth and M. Ryan, “Logic in Computer Science --- Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
6. B. Berard et al., “Systems and Software Verification: Model-checking techniques and
tools”, Springer, 2010.
7. J. B. Almeida, M. J. Frade, J. S. Pinto, and S. M. de Sousa, “Rigorous Software
Development: An Introduction to Program Verification”, Springer, 2011.
CP7016
EMBEDDED SOFTWARE DEVELOPMENT
LTPC
3 003
OBJECTIVES:
To understand processors and their instruction sets for embedded systems
To understand hardware platform for embedded systems
To design and analyze programs for embedded systems
To design multi-tasking embedded systems with RTOS
To understand overall embedded systems development lifecycle
To understand distributed and multi-processor embedded systems
UNIT I
PROCESSORS AND INSTRUCTION SETS
9
Introduction to embedded computing – overview of embedded system design process –
instruction sets of processors: ARM, PIC, TI C55x, TI C64x – programming I/O – modes and
exceptions – co-processors – memory system – CPU performance – CPU power
consumption
UNIT II
EMBEDDED COMPUTING PLATFORM
9
Basic computing platforms – CPU Bus – memory devices and systems – choosing a
platform – development environments – debugging – consumer electronics architecture –
platform-level performance analysis – design example: Audio Player
UNIT III
PROGRAM DESIGN AND ANALYSIS
9
Components for embedded programs – models of programs – Assembly, linking, and
loading – compiler optimizations – program-level performance analysis – performance
optimization – program-level energy optimization – optimizing program size – program
validation and testing – design example: Digital Still Camera
UNIT IV
PROCESSES AND OPERATING SYSTEMS
9
Multiples tasks and multiple processes – multirate systems – pre-emptive RTOS – priority-
based scheduling – inter-process communication – evaluating OS performance – processes
and power optimization – Case study: Real-time and embedded Linux – design example:
Telephone answering machine
42UNIT V
SYSTEM DESIGN, NETWORKS, AND MULTIPROCESSORS
9
System design methodologies – requirements analysis – specifications – architecture design
– quality assurance – distributed embedded systems – shared-memory multiprocessors –
design example: Video accelerator
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Develop assembley code for processors such as ARM, PIC Microcontroller, TI C55x,
TI C64x, etc.
Choose appropriate hardware platform for a given application
Perform platform-level performance analysis
Design, develop, and debug embedded programs optimized for size or performance
Develop embedded applications using an RTOS
Perform OS-level performance analysis
Employ best practices in embedded software engineering
Develop distributed embedded systems and systems with shared-memory
concurrency
REFERENCES:
1. Marilyn Wolf, “Computers as Components: Principles of Embedded Computing
Systems Design”, Third Edition, Morgan Kaufmann, 2012.
2. Christopher Hallinan, “Embedded Linux Primer: A Practical Real-World Approach”,
Second Edition, Prentice Hall, 2010.
3. Karim Yaghmour et al., “Building Embedded Linux Systems”, O’Reilly, 2008.
4. Arnold S. Berger, “Embedded Systems Design: An Introduction to Processes, Tools,
and Techniques”, CMP Books, 2001.
5. David E. Simon, “An embedded Software Primer”, Addison-Wesley, 1999.
IF7202
CLOUD COMPUTING
LTPC
3 003
OBJECTIVES:
To introduce the broad perceptive of cloud architecture and model
To understand the concept of Virtualization
To be familiar with the lead players in cloud.
To understand the features of cloud simulator
To apply different cloud programming model as per need.
To be able to set up a private cloud.
To understand the design of cloud Services.
To learn to design the trusted cloud Computing system
UNIT I
CLOUD ARCHITECTURE AND MODEL
9
Technologies for Network-Based System – System Models for Distributed and Cloud
Computing – NIST Cloud Computing Reference Architecture.
Cloud Models:- Characteristics – Cloud Services – Cloud models (IaaS, PaaS, SaaS) –
Public vs Private Cloud –Cloud Solutions - Cloud ecosystem – Service management –
Computing on demand.
UNIT II
VIRTUALIZATION
9
Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization -
Virtualization Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O
Devices - Virtual Clusters and Resource management – Virtualization for Data-center
Automation.
43UNIT III
CLOUD INFRASTRUCTURE
9
Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture
Development – Design Challenges - Inter Cloud Resource Management – Resource
Provisioning and Platform Deployment – Global Exchange of Cloud Resources.
UNIT IV
PROGRAMMING MODEL
9
Parallel and Distributed Programming Paradigms – MapReduce , Twister and Iterative
MapReduce – Hadoop Library from Apache – Mapping Applications - Programming Support
- Google App Engine, Amazon AWS - Cloud Software Environments -Eucalyptus, Open
Nebula, OpenStack, Aneka, CloudSim
UNIT V
SECURITY IN THE CLOUD
9
Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security
– Security Governance – Risk Management – Security Monitoring – Security Architecture
Design – Data Security – Application Security – Virtual Machine Security - Identity
Management and Access Control – Autonomic Security.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,the students will be able to
Compare the strengths and limitations of cloud computing
Identify the architecture, infrastructure and delivery models of cloud computing
Apply suitable virtualization concept.
Choose the appropriate cloud player
Choose the appropriate Programming Models and approach.
Address the core issues of cloud computing such as security, privacy and interoperability
Design Cloud Services
Set a private cloud
REFERENCES:
1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From
Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.
2. John W.Rittinghouse and James F.Ransome, “Cloud Computing: Implementation,
Management, and Security”, CRC Press, 2010.
3. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical
Approach”, TMH, 2009.
4. Kumar Saurabh, “ Cloud Computing – insights into New-Era Infrastructure”, Wiley
India,2011.
5. George Reese, “Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud” O'Reilly
6. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and
Processes”, Elsevier/Morgan Kaufmann, 2005.
7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, “Grid and Cloud
Computing – A Business Perspective on Technology and Applications”, Springer.
8. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A comprehensive Guide to
Secure Cloud Computing”, Wiley – India, 2010.
9. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi, ‘Mastering Cloud Computing”,
TMGH,2013.
10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011
11. Michael Miller, Cloud Computing, Que Publishing,2008
12. Nick Antonopoulos, Cloud computing, Springer Publications, 2010
44CP7017
DATA VISUALIZATION TECHNIQUES
LT PC
3 003
COURSE OBJECTIVES:
To introduce visual perception and core skills for visual analysis
To understand visualization for time-series analysis
To understand visualization for ranking analysis
To understand visualization for deviation analysis
To understand visualization for distribution analysis
To understand visualization for correlation analysis
To understand visualization for multivariate analysis
To understand issues and best practices in information dashboard design
UNIT I
CORE SKILLS FOR VISUAL ANALYSIS
9
Information visualization – effective data analysis – traits of meaningful data – visual
perception – making abstract data visible – building blocks of information visualization –
analytical interaction – analytical navigation – optimal quantitative scales – reference lines
and regions – trellises and crosstabs – multiple concurrent views – focus and context –
details on demand – over-plotting reduction – analytical patterns – pattern examples
UNIT II
TIME-SERIES, RANKING, AND DEVIATION ANALYSIS
9
Time-series analysis – time-series patterns – time-series displays – time-series best
practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – best
practices – deviation analysis – deviation analysis displays – deviation analysis best
practices
UNIT III
DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS
9
Distribution analysis – describing distributions – distribution patterns – distribution displays –
distribution analysis best practices – correlation analysis – describing correlations –
correlation patterns – correlation displays – correlation analysis techniques and best
practices – multivariate analysis – multivariate patterns – multivariate displays – multivariate
analysis techniques and best practices
UNIT IV
INFORMATION DASHBOARD DESIGN I
9
Information dashboard – categorizing dashboards – typical dashboard data – dashboard
design issues and best practices – visual perception – limits of short-term memory – visually
encoding data – Gestalt principles – principles of visual perception for dashboard design
UNIT V
INFORMATION DASHBOARD DESIGN II
9
Characteristics of dashboards – key goals in visual design process – dashboard display
media – designing dashboards for usability – meaningful organization – maintaining
consistency – aesthetics of dashboards – testing for usability – case studies: sales
dashboard, CIO dashboard, Telesales dashboard, marketing analysis dashboard
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
Explain principles of visual perception
Apply core skills for visual analysis
Apply visualization techniques for various data analysis tasks
Design information dashboard
REFERENCES:
1. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis",
Analytics Press, 2009.
2. Stephen Few, "Information dashboard design: The effective visual communication of
data", O'Reilly, 2006.
3. Edward R. Tufte, "The visual display of quantitative information", Second Edition,
Graphics Press, 2001.
4. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013.
455. Ben Fry, "Visualizing data: Exploring and explaining data with the processing
environment", O'Reilly, 2008.
6. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking
business intelligence beyond reporting", Wiley, 2010.
7. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley,
2011.
NE7005
PROTOCOLS AND ARCHITECTURE FOR WIRELESS
SENSOR NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION AND OVERVIEW OF WIRELESS SENSOR NETWORKS 9
Background of Sensor Network Technology, Application of Sensor Networks, Challenges for
Wireless Sensor Networks, Mobile Adhoc NETworks (MANETs) and Wireless Sensor
Networks, Enabling Technologies For Wireless Sensor Networks.
UNIT II
ARCHITECTURES
9
Single-node Architecture, Hardware Components & Design Constraints, Operating Systems
and Execution Environments, Introduction to TinyOS and nesC, Network Architecture,
Sensor Network Scenarios, Optimization Goals and Figures of Merit, Design Principles for
WSNs, Service Interfaces of WSNs, Gateway Concepts.
UNIT III
DEPLOYMENT AND CONFIGURATION
9
Localization and Positioning, Coverage and Connectivity, Single-hop and Multi-hop
Localization, Self Configuring Localization Systems, Sensor Management
Network Protocols: Issues in Designing MAC Protocol for WSNs, Classification of MAC
Protocols, S-MAC Protocol, B-MAC Protocol, IEEE 802.15.4 Standard and Zig Bee,
Dissemination Protocol for Large Sensor Network.
UNIT IV
ROUTING PROTOCOLS AND DATA MANIPULATION
9
Issues in Designing Routing Protocols, Classification of Routing Protocols, Energy-Efficient
Routing, Unicast, Broadcast and Multicast, Geographic Routing.
Data Centric and Content based Routing, Storage and Retrieval in Network, Compression
Technologies for WSN, Data Aggregation Technique.
UNIT V
SENSOR NETWORK PLATFORMS AND TOOLS
9
Sensor Node Hardware – Berkeley Motes, Programming Challenges, Node-level Software
Platforms, Node-level Simulators, State-centric Programming.
TOTAL : 45 PERIODS
REFERENCES:
1. Holger Karl & Andreas Willig, “Protocols And Architectures for Wireless Sensor
Networks", John Wiley, 2005.
2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information
Processing Approach", Elsevier, 2007.
3. Raghavendra, Cauligi S, Sivalingam, Krishna M., Zanti Taieb, “Wireless Sensor
Network”, Springer 1st Ed. 2004 (ISBN: 978-4020-7883-5).
4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks- Technology,
Protocols, and Applications”, John Wiley, 2007.
5. N. P. Mahalik, “Sensor Networks and Configuration: Fundamentals, Standards,
Platforms, and Applications” Springer Verlag.
6. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.
46CP7018
LANGUAGE TECHNOLOGIES
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for language processing
To understand the representation and processing of Morphology and Part-of Speech
Taggers
To understand different aspects of natural language syntax and the various methods
used for processing syntax
To understand different methods of disambiguating word senses
To know about various applications of natural language processing
To learn the indexing and searching processes of a typical information retrieval
system and to study NLP based retrieval systems
To gain knowledge about typical text categorization and clustering techniques
UNIT I
INTRODUCTION
9
Natural Language Processing – Mathematical Foundations – Elementary Probability Theory
– Essential information Theory - Linguistics Essentials - Parts of Speech and Morphology –
Phrase Structure – Semantics – Corpus Based Work.
UNIT II
WORDS
9
Collocations – Statistical Inference – n-gram Models – Word Sense Disambiguation –
Lexical Acquisition.
UNIT III
GRAMMAR
9
Markov Models – Part-of-Speech Tagging – Probabilistic Context Free Grammars - Parsing.
UNIT IV
INFORMATION RETRIEVAL
9
Information Retrieval Architecture – Indexing - Storage – Compression Techniques –
Retrieval Approaches – Evaluation - Search Engines - Commercial Search Engine Features
– Comparison - Performance Measures – Document Processing - NLP based Information
Retrieval – Information Extraction.
UNIT V
TEXT MINING
9
Categorization – Extraction Based Categorization – Clustering - Hierarchical Clustering -
Document Classification and Routing - Finding and Organizing Answers from Text Search –
Text Categorization and Efficient Summarization using Lexical Chains – Machine Translation
- Transfer Metaphor - Interlingual and Statistical Approaches.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the different linguistic components of given sentences
Design a morphological analyser for a language of your choice using finite state
automata concepts
Implement a parser by providing suitable grammar and words
Discuss algorithms for word sense disambiguation
Build a tagger to semantically tag words using WordNet
Design an application that uses different aspects of language processing.
REFERENCES:
1. Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural
Language Processing “, MIT Press, 1999.
2. Daniel Jurafsky and James H. Martin, “ Speech and Language Processing” , Pearson,
2008.
3. Ron Cole, J.Mariani, et.al “Survey of the State of the Art in Human Language
Technology”, Cambridge University Press, 1997.
Michael W. Berry, “ Survey of Text Mining: Clustering, Classification and Retrieval”,
Springer Verlag, 2003.
4.
47NE7012
SOCIAL NETWORKS ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the components of the social network
To model and visualize the social network
To mine the users in the social network
To understand the evolution of the social network
To mine the interest of the user
UNIT I
INTRODUCTION
9
Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis -
Development of Social Network Analysis - Key concepts and measures in network analysis -
Discussion networks - Blogs and online communities - Web-based networks
UNIT II
MODELING AND VISUALIZATION
9
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-
Based Representations- Node-Link Diagrams - Hybrid Representations - Modelling and
aggregating social network data – Random Walks and their Applications –Use of Hadoop
and Map Reduce - Ontological representation of social individuals and relationships.
UNIT III
MINING COMMUNITIES
9
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in
Social Networks - Evaluating Communities – Core Methods for Community Detection &
Mining - Applications of Community Mining Algorithms - Node Classification in Social
Networks.
UNIT IV
EVOLUTION
9
Evolution in Social Networks – Framework - Tracing Smoothly Evolving Communities -
Models and Algorithms for Social Influence Analysis - Influence Related Statistics - Social
Similarity and Influence - Influence Maximization in Viral Marketing - Algorithms and
Systems for Expert Location in Social Networks - Expert Location without Graph Constraints
- with Score Propagation – Expert Team Formation - Link Prediction in Social Networks -
Feature based Link Prediction - Bayesian Probabilistic Models - Probabilistic Relational
Models
UNIT V
TEXT AND OPINION MINING
9
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis - Product
review mining – Review Classification – Tracking sentiments towards topics over time
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Work on the internals components of the social network
Model and visualize the social network
Mine the behaviour of the users in the social network
Predict the possible next outcome of the social network
Mine the opinion of the user
REFERENCES:
1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011
2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.
3. Borko Furht, “Handbook of Social Network Technologies and Applications”, Springer, 1st
edition, 2010.
484. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking –
Techniques and applications”, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social Network
Analysis: Trends, Tools and Research Advances”, Springer, 2009.
7. Toby Segaran, “Programming Collective Intelligence”, O’Reilly, 2012
CP7019
MANAGING BIG DATA
LTPC
3 003
OBJECTIVES:
Understand big data for business intelligence
Learn business case studies for big data analytics
Understand nosql big data management
Perform map-reduce analytics using Hadoop and related tools
UNIT I
UNDERSTANDING BIG DATA
9
What is big data – why big data – convergence of key trends – unstructured data – industry
examples of big data – web analytics – big data and marketing – fraud and big data – risk
and big data – credit risk management – big data and algorithmic trading – big data and
healthcare – big data in medicine – advertising and big data – big data technologies –
introduction to Hadoop – open source technologies – cloud and big data – mobile business
intelligence – Crowd sourcing analytics – inter and trans firewall analytics
UNIT II
NOSQL DATA MANAGEMENT
9
Introduction to NoSQL – aggregate data models – aggregates – key-value and document
data models – relationships – graph databases – schemaless databases – materialized
views – distribution models – sharding – master-slave replication – peer-peer replication –
sharding and replication – consistency – relaxing consistency – version stamps – map-
reduce – partitioning and combining – composing map-reduce calculations
UNIT III
BASICS OF HADOOP
9
Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop
pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface
– data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based
data structures
UNIT IV
MAPREDUCE APPLICATIONS
9
MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of
MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and
YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input
formats – output formats
UNIT V
HADOOP RELATED TOOLS
9
Hbase – data model and implementations – Hbase clients – Hbase examples –
praxis.Cassandra – cassandra data model – cassandra examples – cassandra clients –
Hadoop integration.
Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts.
Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –
HiveQL queries.
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe big data and use cases from selected business domains
Explain NoSQL big data management
Install, configure, and run Hadoop and HDFS
Perform map-reduce analytics using Hadoop
Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics
49REFERENCES:
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
NE7011
MOBILE APPLICATION DEVELOPMENT
LTPC
3 003
OBJECTIVES:
1. Understand system requirements for mobile applications
2. Generate suitable design using specific mobile development frameworks
3. Generate mobile application design
4. Implement the design using specific mobile development frameworks
5. Deploy the mobile applications in marketplace for distribution
UNIT I
INTRODUCTION
5
Introduction to mobile applications – Embedded systems - Market and business drivers for
mobile applications – Publishing and delivery of mobile applications – Requirements
gathering and validation for mobile applications
UNIT II
BASIC DESIGN
8
Introduction – Basics of embedded systems design – Embedded OS - Design constraints for
mobile applications, both hardware and software related – Architecting mobile applications
– User interfaces for mobile applications – touch events and gestures – Achieving quality
constraints – performance, usability, security, availability and modifiability.
UNIT III
ADVANCED DESIGN
8
Designing applications with multimedia and web access capabilities – Integration with GPS
and social media networking applications – Accessing applications hosted in a cloud
computing environment – Design patterns for mobile applications.
UNIT IV
TECHNOLOGY I - ANDROID
12
Introduction – Establishing the development environment – Android architecture – Activities
and views – Interacting with UI – Persisting data using SQLite – Packaging and deployment
– Interaction with server side applications – Using Google Maps, GPS and Wifi – Integration
with social media applications.
UNIT V
TECHNOLOGY II - IOS
12
Introduction to Objective C – iOS features – UI implementation – Touch frameworks – Data
persistence using Core Data and SQLite – Location aware applications using Core Location
and Map Kit – Integrating calendar and address book with social media application – Using
Wifi - iPhone marketplace.
TOTAL: 45 PERIODS
OUTCOMES:
Upon the students will be able toCompletion of the course,
1. Describe the requirements for mobile applications
2. Explain the challenges in mobile application design and development
3. Develop design for mobile applications for specific requirements
4. Implement the design using Android SDK
5. Implement the design using Objective C and iOS
6. Deploy mobile applications in Android and iPone marketplace for distribution
50REFERENCES:
1. http://developer.android.com/develop/index.html
2. Jeff McWherter and Scott Gowell, "Professional Mobile Application Development",
Wrox, 2012
3. Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”,
DreamTech, 2012
4. James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2012
5. David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 6
Development: Exploring the iOS SDK”, Apress, 2013.
CP7020
BIO-INSPIRED COMPUTING
LT PC
3 0 03
OBJECTIVES:
Learn evolutionary theory and algorithms
Understand Cellular Automata and artificial life
Learn artificial neural systems and related learning algorithms
Learn developmental and artificial immune systems
Understand behavioral systems especially in the context of Robotics
Understand collective systems such as ACO, PSO, and swarm robotics
UNIT I
EVOLUTIONARY AND CELLULAR SYSTEMS
9
Foundations of evolutionary theory – Genotype – artificial evolution – genetic
representations – initial population – fitness functions – selection and reproduction – genetic
operators – evolutionary measures – evolutionary algorithms – evolutionary electronics –
evolutionary algorithm case study
Cellular systems – cellular automata – modeling with cellular systems – other cellular
systems – computation with cellular systems – artificial life – analysis and synthesis of
cellular systems
UNIT II
NEURAL SYSTEMS
9
Biological nervous systems – artificial neural networks – neuron models – architecture –
signal encoding – synaptic plasticity – unsupervised learning – supervised learning –
reinforcement learning – evolution of neural networks – hybrid neural systems – case study
UNIT III
DEVELOPMENTAL AND IMMUNE SYSTEMS
9
Rewriting systems – synthesis of developmental systems – evolutionary rewriting systems –
evolutionary developmental programs
Biological immune systems – lessons for artificial immune systems – algorithms and
applications – shape space – negative selection algorithm – clonal selection algorithm -
examples
UNIT IV
BEHAVIORAL SYSTEMS
9
Behavior is cognitive science – behavior in AI – behavior based robotics – biological
inspiration for robots – robots as biological models – robot learning – evolution of behavioral
systems – learning in behavioral systems – co-evolution of body and control – towards self
reproduction – simulation and reality
UNIT V
COLLECTIVE SYSTEMS
9
Biological self-organization – Particle Swarm Optimization (PSO) – ant colony optimization
(ACO) – swarm robotics – co-evolutionary dynamics – artificial evolution of competing
systems – artificial evolution of cooperation – case study
TOTAL: 45 PERIODS
51OUTCOMES:
Upon completion of the course, the students will be able to
Implement and apply evolutionary algorithms
Explain cellular automata and artificial life
Implement and apply neural systems
Explain developmental and artificial immune systems
Explain behavioral systems
Implement and apply collective intelligence systems
REFERENCES:
1. D. Floreano and C. Mattiussi, "Bio-Inspired Artificial Intelligence", MIT Press, 2008.
2. F. Neumann and C. Witt, “Bioinspired Computation in combinatorial optimization:
Algorithms and their computational complexity”, Springer, 2010.
3. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010.
4. D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”,
Addison-Wesley, 1989.
5. Simon O. Haykin, “Neural Networks and Learning Machines”, Third Edition, Prentice
Hall, 2008.
6. M. Dorigo and T. Stutzle, “Ant Colony Optimization”, A Bradford Book, 2004.
7. R. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.
CP7021
MEDICAL IMAGE PROCESSING
LT PC
3 0 0 3
COURSE DESCRIPTION:
An advanced graduate level course on medical imaging and medical image analysis. The
course includes topics in medical image formation, medical imaging techniques, such as X-
Ray, Computed Tomography, Magnetic Resonance Imaging, and Nuclear Imaging, image
segmentation, registration, statistical modeling, visualization, and applications of
computational tools for medicine.
OBJECTIVES:
The course will provide the participants with an up-to-date background in current state-of-
the-art in medical imaging and medical image analysis. The aim of the course is to show
how to extract, model, and analyze information from medical data and applications in order
to help diagnosis, treatment and monitoring of diseases through computer science.
UNIT I
INTRODUCTION
9
Introduction to medical imaging technology, systems, and modalities. Brief history;
importance; applications; trends; challenges. Medical Image Formation Principles: X-Ray
physics; X-Ray generation, attenuation, scattering; dose Basic principles of CT;
reconstruction methods; artifacts; CT hardware.
UNIT II
STORAGE AND PROCESSING
9
Medical Image Storage, Archiving and Communication Systems and Formats Picture
archiving and communication system (PACS); Formats: DICOM Radiology Information
Systems (RIS) and Hospital Information Systems (HIS). Medical Image Processing,
Enhancement, Filtering Basic image processing algorithms Thresholding; contrast
enhancement; SNR characteristics; filtering; histogram modeling.
UNIT III
VISUALIZATION
9
Medical Image Visualization Fundamentals of visualization; surface and volume
rendering/visualization; animation; interaction. Magnetic Resonance Imaging (MRI)
Mathematics of MR; spin physics; NMR spectroscopy; imaging principles and hardware;
image artifacts.
52UNIT IV
SEGMENTATION AND CLASSIFICATION
9
Medical Image Segmentation - Histogram-based methods; Region growing and watersheds;
Markov Random Field models; active contours; model-based segmentation. Multi-scale
segmentation; semi-automated methods; clustering-based methods; classification-based
methods; atlas-guided approaches; multi-model segmentation. Medical Image Registration
Intensity-based methods; cost functions; optimization techniques.
UNIT V
NUCLEAR IMAGING
9
PET and SPECT Ultrasound Imaging methods; mathematical principles; resolution; noise
effect; 3D imaging; positron emission tomography; single photon emission tomography;
ultrasound imaging; applications. Medical Image Search and Retrieval Current technology in
medical image search, content-based image retrieval, new trends: ontologies. Applications.
Other Applications of Medical Imaging Validation, Image Guided Surgery, Image Guided
Therapy, Computer Aided Diagnosis/Diagnostic Support Systems.
TOTAL: 45 PERIODS
REFERENCES:
1. Paul Suetens, "Fundamentals of Medical Imaging", Second Edition, Cambridge
University Press, 2009.
2. J. Michael Fitzpatrick and Milan Sonka, "Handbook of Medical Imaging, Volume 2.
Medical Image Processing and Analysis", SPIE Publications, 2009.
3. Kayvan Najarian and Robert Splinter, "Biomedical Signal and Image Processing",
Second Edition, CRC Press, 2005.
4. Geoff Dougherty, "Digital Image Processing for Medical Applications", First Edition,
Cambridge University Press, 2009.
5. Jerry L. Prince and Jonathan Links, "Medical Imaging Signals and Systems", First
Edition, Prentice Hall, 2005.
6. John L. Semmlow, "Biosignal and Medical Image Processing", Second Edition, CRC
Press, 2008.
CP7022
SOFTWARE DESIGN
LTPC
30 03
OBJECTIVES:
Analyze specifications
Describe approaches to design
Develop design documentation
Evaluate the design
UNIT I
SOFTWARE DESIGN PRINCIPLES
9
Introduction – Design process – Managing complexity – Software modeling and notations –
Abstraction – Modularity – Hierarchy – Coupling - Cohesion – Design guidelines and
checklists – Refactoring
UNIT II
OO DESIGN
9
Object model – Classes and objects – Object oriented analysis – Key abstractions and
mechanisms – Object oriented design – Identifying design elements – Detailed design –
Case studies.
UNIT III
DESIGN PATTERNS
9
Introduction to patterns – Design context – Reusable solutions – Documenting reusable
solutions – Standard patterns from GOF book.
UNIT IV
FUNCTION AND SERVICE ORIENTED DESIGNS
9
Structural decomposition – Detailed Design – Function oriented design Case study –
Services – Service identification – Service design – Service composition – choreography and
orchestration – Service oriented design Case study
53UNIT V
USER CENTERED DESIGN AND DESIGN REVIEW
9
Introduction to user centered design – Use in context – Interface and interaction – User
centered design principles – Task analysis – Evaluation – Introduction to design review–
Testing the design – Walk throughs – Review against check lists.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Describe different approaches to desigining a software application
Analyze specifications and identify appropriate design strategies.
Develop an appropriate design for a given set of requirements
Identify applicatble design patterns for the solution
Abstract and document reusable design patterns
Evaluate a given design against the specifications
REFERENCES:
1. Grady Booch et al., "Object Oriented Analysis and Design with Applications", 3rd
Edition, Pearson, 2010.
2. Carlos Otero, "Software Engineering Design: Theory and Practice", CRC Press, 2012
3. David Budgen, "Software Design", 2nd Edtion, Addison Wesley, 2003
4. Alan Shalloway and James R Trott, "Design Patterns Explained: A New Perspective
on Object-Oriented Design", 2nd Edition, Addison-Wesley Professional, 2004
5. Hassan Gomaa, "Software Modeling and Design", Cambridge University Press, 2011
6. Eric Gamma et al., "Design Patterns: Elements of Reusable Object-Oriented
Software", Addison-Wesley Professional, 1994
7. Ian Sommerville, "Software Engineering", 9th Edition, Addison-Wesley, 2010
8. M B Rosson and J M Carroll, "Usability Engineering: Scenario-Based Development
of Human-Computer Interaction", Morgan Kaufmann, 2002
CP7023
RECONFIGURABLE COMPUTING
LTPC
3 003
OBJECTIVES:
To understand the need for reconfigurable computing
To expose the students to various device architectures
To examine the various reconfigurable computing systems
To understand the different types of compute models for programming reconfigurable
architectures
To expose the students to HDL programming and familiarize with the development
environment
To expose the students to the various placement and routing protocols
To develop applications with FPGAs
UNIT I
DEVICE ARCHITECTURE
9
General Purpose Computing Vs Reconfigurable Computing – Simple Programmable Logic
Devices – Complex Programmable Logic Devices – FPGAs – Device Architecture - Case
Studies.
UNIT II
RECONFIGURABLE COMPUTING ARCHITECTURES AND SYSTEMS
9
Reconfigurable Processing Fabric Architectures – RPF Integration into Traditional
Computing Systems – Reconfigurable Computing Systems – Case Studies –
Reconfiguration Management.
UNIT III
PROGRAMMING RECONFIGURABLE SYSTEMS
9
Compute Models - Programming FPGA Applications in HDL – Compiling C for Spatial
Computing – Operating System Support for Reconfigurable Computing.
54UNIT IV
MAPPING DESIGNS TO RECONFIGURABLE PLATFORMS
9
The Design Flow - Technology Mapping – FPGA Placement and Routing – Configuration
Bitstream Generation – Case Studies with Appropriate Tools.
UNIT V
APPLICATION DEVELOPMENT WITH FPGAS
9
Case Studies of FPGA Applications – System on a Programmable Chip (SoPC) Designs.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for reconfigurable architectures
Discuss the architecture of FPGAs
Point out the salient features of different reconfigurable architectures
Build basic modules using any HDL
Develop applications using any HDL and appropriate tools
Design and build an SoPC for a particular application
REFERENCES:
1. Maya B. Gokhale and Paul S. Graham, “Reconfigurable Computing: Accelerating
Computation with Field-Programmable Gate Arrays”, Springer, 2005.
2. Scott Hauck and Andre Dehon (Eds.), “Reconfigurable Computing – The Theory and
Practice of FPGA-Based Computation”, Elsevier / Morgan Kaufmann, 2008.
3. Christophe Bobda, “Introduction to Reconfigurable Computing – Architectures,
Algorithms and Applications”, Springer, 2010.
IF7013
ENERGY AWARE COMPUTING
LTPC
30 03
OBJECTIVES:
This course examines the design of power efficient architecture, power and performance
tradeoffs, restructuring of software and applications and standards for energy aware
Hardware and Software. The objective of this course is:
To know the fundamental principles energy efficient devices
To study the concepts of Energy efficient storage
To introduce energy efficient algorithms
To enable the students to know energy efficient techniques involved to support real-
time systems.
To study Energy aware applications
UNIT I
INTRODUCTION
9
Energy efficient network on chip architecture for multi core system-Energy efficient MIPS
CPU core with fine grained run time power gating – Low power design of Emerging memory
technologies.
UNIT II
ENERGY EFFICIENT STORAGE
9
Disk Energy Management-Power efficient strategies for storage system-Dynamic thermal
management for high performance storage systems-Energy saving technique for Disk
storage systems
UNIT III
ENERGY EFFICIENT ALGORITHMS
9
Scheduling of Parallel Tasks – Task level Dynamic voltage scaling – Speed Scaling –
Processor optimization- Memetic Algorithms – Online job scheduling Algorithms.
55UNIT IV
REAL TIME SYSTEMS
9
Multi processor system – Real Time tasks- Energy Minimization – Energy aware scheduling-
Dynamic Reconfiguration- Adaptive power management-Energy Harvesting Embedded
system
UNIT V
ENERGY AWARE APPLICATIONS
9
On chip network – Video codec Design – Surveillance camera- Low power mobile storage.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Design Power efficient architecture Hardware and Software.
Analyze power and performance trade off between various energy aware storage
devices.
Implement various energy aware algorithms.
Restructure the software and Hardware for Energy aware applications.
Explore the Energy aware applications
REFERENCES:
1. Ishfaq Ah mad, Sanjay Ranka, Handbook of Energy Aware and Green Computing,
Chapman and Hall/CRC, 2012
2. Chong-Min Kyung, Sungioo yoo, Energy Aware system design Algorithms and
Architecture, Springer, 2011.
3. Bob steiger wald ,Chris:Luero, Energy Aware computing, Intel Press,2012.
CP7024
INFORMATION RETRIEVAL TECHNIQUES
LTPC
30 03
OBJECTIVES:
To understand the basics of Information Retrieval with pertinence to modeling, query
operations and indexing
To get an understanding of machine learning techniques for text classification and
clustering
To understand the various applications of Information Retrieval giving emphasis to
Multimedia IR, Web Search
To understand the concepts of digital libraries
UNIT I
INTRODUCTION
8
Motivation – Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean
Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web
Characteristics–The impact of the web on IR ––IR Versus Web Search–Components of a
Search engine
UNIT II
MODELING
10
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term
Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic
Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing
UNIT III
INDEXING
9
Static and Dynamic Inverted Indices – Index Construction and Index Compression Searching
- Sequential Searching and Pattern Matching. Query Operations -Query Languages–Query
Processing - Relevance Feedback and Query Expansion - Automatic Local and Global
Analysis – Measuring Effectiveness and Efficiency.
UNIT IV
CLASSIFICATION AND CLUSTERING
8
Text Classification and Naïve Bayes – Vector Space Classification – Support vector
machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –
Matrix decompositions and latent semantic indexing – Fusion and Meta learning
56UNIT V
SEARCHING AND RANKING
10
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic
Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR:
Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital
Libraries
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Build an Information Retrieval system using the available tools
Identify and design the various components of an Information Retrieval system
Apply machine learning techniques to text classification and clustering which is used
for efficient Information Retrieval
Analyze the Web content structure
Design an efficient search engine
REFERENCES:
1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The
concepts and Technology behind Search (ACM Press Books), Second Edition 2011
2. Christopher D. Manning, PrabhakarRaghavan, HinrichSchutze, Introduction to
Information Retrieval, Cambridge University Press, First South Asian Edition 2012
3. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval
Implementing and Evaluating Search Engines, The MIT Press, Cambridge,
Massachusetts London, England, 2010
CP7025
DATA MINING TECHNIQUES
LTPC
300 3
UNIT I
INTRODUCTION TO DATA MINING
9
Introduction to Data Mining – Data Mining Tasks – Components of Data Mining Algorithms –
Data Mining supporting Techniques – Major Issues in Data Mining – Measurement and Data
– Data Preprocessing – Data sets
UNIT II
OVERVIEW OF DATA MINING ALGORITHMS
9
Overview of Data Mining Algorithms – Models and Patterns – Introduction – The
Reductionist viewpoint on Data Mining Algorithms – Score function for Data Mining
Algorithms- Introduction – Fundamentals of Modeling – Model Structures for Prediction –
Models for probability Distributions and Density functions – The Curve of Dimensionality –
Models for Structured Data – Scoring Patterns – Predictive versus Descriptive score
functions – Scoring Models with Different Complexities – Evaluation of Models and Patterns
– Robust Methods.
UNIT III
CLASSIFICATIONS
9
Classifications – Basic Concepts – Decision Tree induction – Bayes Classification Methods –
Rule Based Classification – Model Evaluation and Selection – Techniques to Improve
Classification Accuracy – Classification: Advanced concepts – Bayesian Belief Networks-
Classification by Back Propagation – Support Vector Machine – Classification using frequent
patterns.
UNIT IV
CLUSTER ANALYSIS
9
Cluster Analysis: Basic concepts and Methods – Cluster Analysis – Partitioning methods –
Hierarchical methods – Density Based Methods – Grid Based Methods – Evaluation of
Clustering – Advanced Cluster Analysis: Probabilistic model based clustering – Clustering
High – Dimensional Data – Clustering Graph and Network Data – Clustering with
Constraints.
57UNIT V
ASSOCIATION RULE MINING AND VISUALIZATION
9
Association Rule Mining – Introduction – Large Item sets – Basic Algorithms – Parallel and
Distributed Algorithms – Comparing Approaches – Incremental Rules – Advanced
Association Rule Techniques – Measuring the Quality of Rules – Visualization of
Multidimensional Data – Diagrams for Multidimensional visualization – Visual Data Mining –
Data Mining Applications – Case Study: WEKA.
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. Jiawei Han, Micheline Kamber , Jian Pei, “Data Mining: Concepts and Techniques”,
Third Edition (The Morgan Kaufmann Series in Data Management Systems), 2012.
2. David J. Hand, Heikki Mannila and Padhraic Smyth “Principles of Data Mining”
(Adaptive Computation and Machine Learning), 2005
3. Margaret H Dunham, “Data Mining: Introductory and Advanced Topics”, 2003
4. Soman, K. P., Diwakar Shyam and Ajay V. “Insight Into Data Mining: Theory And
Practice”, PHI, 2009.
IF7002
BIO INFORMATICS
LTPC
3 003
p
OBJECTIVES:
To get exposed to the domain of bioinformatics
To understand the role of data warehousing and data mining for bioinformatics
To learn to model bioinformatics based applications
To understand how to deploy the pattern matching and visualization techniques in
bioinformatics
To study the Microarray technologies for genome expression
UNIT I
INTRODUCTION
9
Need for Bioinformatics technologies – Overview of Bioinformatics technologies – Structural
bioinformatics – Data format and processing – secondary resources- Applications – Role of
Structural bioinformatics - Biological Data Integration System.
UNIT II
DATAWAREHOUSING AND DATAMINING IN BIOINFORMATICS
9
Bioinformatics data – Data ware housing architecture – data quality – Biomedical data
analysis – DNA data analysis – Protein data analysis – Machine learning – Neural network
architecture- Applications in bioinformatics
UNIT III
MODELING FOR BIOINFORMATICS
9
Hidden markov modeling for biological data analysis – Sequence identification – Sequence
classification – multiple alignment generation – Comparative modeling – Protein modeling –
genomic modeling – Probabilistic modeling – Bayesian networks – Boolean networks -
Molecular modeling – Computer programs for molecular modeling
UNIT IV
PATTERN MATCHING AND VISUALIZATION
9
Gene regulation – motif recognition and motif detection – strategies for motif detection –
Visualization – Fractal analysis – DNA walk models – one dimension – two dimension –
higher dimension – Game representation of Biological sequences – DNA, Protein, Amino
acid sequences
UNIT V
MICROARRAY ANALYSIS
9
Microarray technology for genome expression study – image analysis for data extraction –
preprocessing – segmentation – gridding , spot extraction , normalization, filtering – cluster
analysis – gene network analysis – Compared Evaluation of Scientific Data Management
Systems – Cost Matrix – Evaluation model ,Benchmark , Tradeoffs
TOTAL: 45 PERIODS
58OUTCOMES:
Upon Completion of the course, the students will be able to
Deploy the data warehousing and data mining techniques in Bioinformatics
Model bioinformatics based applications
Deploy the pattern matching and visualization techniques in bioinformatics
Work on the protein sequences
Use the Microarray technologies for genome expression
REFERENCES:
1. Yi-Ping Phoebe Chen (Ed), “Bio Informatics Technologies”, First Indian Reprint, Springer
Verlag, 2007.
2. N.J. Chikhale and Virendra Gomase, "Bioinformatics- Theory and Practice", Himalaya
Publication House, India, 2007
3. Zoe lacroix and Terence Critchlow, “Bio Informatics – Managing Scientific data”, First
Indian Reprint, Elsevier, 2004
4. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003.
5. Arthur M Lesk, “Introduction to Bioinformatics”, Second Edition, Oxford University Press,
2005
6. Burton. E. Tropp, “Molecular Biology: Genes to Proteins “, 4th edition, Jones and Bartlett
Publishers, 2011
7. Dan Gusfield, “Algorithms on Strings Trees and Sequences”, Cambridge University
Press, 1997.
8. P. Baldi, S Brunak , Bioinformatics, “A Machine Learning Approach “, MIT Press, 1998.
CP7026
SOFTWARE QUALITY ASSURANCE
LTPC
3 003
OBJECTIVES:
Describe approaches to quality assurance
Understand quality models
Evaluate the system based on the chosen quality model
UNIT I
INTRODUCTION
9
Introduction – Views on quality – Cost of quality - Quality models – Quality frameworks –
Verification and Validation – Defect taxonomy – Defect management – Statistics and
measurements – IEEE standards – Quality assurance and control processes
UNIT II
VERIFICATION
6
Introduction – Verification techniques – Inspections, reviews, walk-throughs – Case studies
UNIT III
TEST GENERATION
12
Software testing- Validation – Test plan – Test cases - Test Generation – Equivalence
partitioning – Boundary value analysis – Category partition method – Combinatorial
generation - Decision tables – Examples and Case studies
UNIT IV
STRUCTURAL TESTING
12
Introduction – Test adequacy criteria – Control flow graph – Coverages: block, conditions,
multiple conditions, MC/DC, path – Data flow graph – Definition and use coverages – C-use,
P-use, Def-clear, Def-use – Finite state machines – Transition coverage – Fault based
testing – Mutation analysis – Case studies
UNIT V
FUNCTIONAL TESTING
6
Introduction – Test adequacy criteria - Test cases from use cases – Exploratory testing -
Integration, system, acceptance, regression testing – Testing for specific attributes:
Performance, load and stress testing – Usability testing – Security testing - Test automation
– Test oracles
TOTAL : 45 PERIODS
59OUTCOMES:
Upon Completion of the course, the students will be able to
Describe different approaches to testing software applications
Analyze specifications and identify appropriate test generation strategies
Develop an appropriate test design for a given test object
Identify applicatble measurements for the verification and validation effort
Execute the test design
Evaluate the testing effort based on adequate measures
REFERENCES:
1. Boriz Beizer, "Software Testing Techniques", 2nd Edition, DreamTech, 2009.
2. Aditya P. Mathur, "Foundations of Software Testing", Pearson, 2008
3. Mauro Pezze and Michal Young, "Software Testing and Analysis. Process, Principles,
and Techniques", John Wiley 2008
4. Stephen H. Kan, "Metrics and Models in Software Quality Engineering", 2nd Edition,
Pearson, 2003
5. Kshirasagar Naik and Priyadarshi Tripathy (Eds), "Software Testing and Quality
Assurance: Theory and Practice", John Wiley, 2008
6. "Combinatorial Methods in Software Testing", ttp://csrc.nist.gov/groups/SNS/acts/index.html
CP7027
MULTI OBJECTIVE OPTIMIZATION TECHNIQUES
LTPC
3 003
OBJECTIVES:
Learn fundamental principles of Multiobjective Optimization (MOP)
Survey different Multiobjective Optimization algorithms
Introduce various design issues of MOP
Develop and Evaluate MOP Algorithms
Learn Parallel and hybrid MOP Algorithms
Learn other Metaheuristics
UNIT I
INTRODUCTION AND CLASSICAL APPROACHES
9
Multiobjective optimization: Introduction - Multiobjective optimization problem-principles –
Difference between single and multiobjective optimization – Dominance and Pareto
Optimality , Classical Methods – Weighted Sum -
Constraint method – Weighted Metric
methods – Benson’s method - Value Function - Goal Programming methods – Interactive
Methods
UNIT II
MOP EVOLUTIONARY ALGORITHMS
9
Generic MOEA - Various MOEAs: MOGA, NSGA-II, NPGA, PAES, SPEA2, MOMGA, micro
GA - Constrained MOEAs: Penalty Function approach - Constrained Tournament – Ray –
Tai –Seow’s Method.
UNIT III
THEORETICAL ISSUES
9
Fitness Landscapes - Fitness Functions - Pareto Ranking - Pareto Niching and Fitness
Sharing - Recombination Operators - Mating Restriction - Solution Stability and Robustness
- MOEA Complexity - MOEA Scalability - Running Time Analysis - MOEA Computational
Cost - No Free Lunch Theorem.
UNIT IV
MOEA TESTING, ANALYSIS, AND PARALLELIZATION
9
MOEA Experimental Measurements – MOEA Statistical Testing Approaches – MOEA Test
Suites - MOEA Parallelization: Background – Paradigms – Issues - MOEA Local Search
Techniques.
60UNIT V
APPLICATIONS AND ALTERNATIVE METAHEURISTICS
9
Scientific Applications: Computer Science and Computer Engineering - Alternative
Metaheuristics: Simulated Annealing – Tabu Search and Scatter Search – Ant System –
Distributed Reinforcement Learning – Particle Swarm Optimization – Differential Evolution –
Artificial Immune Systems - Other Heuristics.
TOTAL:45 PERIODS
OUTCOMES:
Upon Completion of the course,students will be able to
Explain MOP principles
Explain classical methods to solve MOP problems
Be familiar with and explain structures of different MOP algorithms
Solve constrained MOP problems
Explain various design issues of MOP algorithms
Perform a evaluation and analysis of MOP algorithm results
Explain parallelization of MOP algorithms
Develop parallel and hybrid MOP algorithms
Identify various real time MOP applications
Explain other search algorithms
REFERENCES:
1. Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen, “Evolutionary
Algorithms for Solving Multi-objective Problems”, Second Edition, Springer, 2007.
2. Kalyanmoy Deb, “ Multi-Objective Optimization Using Evolutionary Algorithms”, John
Wiley, 2002.
3. Aimin Zhoua, Bo-Yang Qub, Hui Li c, Shi-Zheng Zhaob, Ponnuthurai Nagaratnam
Suganthan b, Qingfu Zhangd, “Multiobjective evolutionary algorithms: A survey of the
state of the art”, Swarm and Evolutionary Computation (2011) 32–49.
4. E Alba, M Tomassini, “Parallel and evolutionary algorithms”, Evolutionary Computation,
IEEE Transactions on 6 (5), 443-462.
5. Crina Grosan, Ajith Abraham, “Hybrid Evolutionary Algorithms: Methodologies,
Architectures, and Reviews”, Studies in Computational Intelligence, Vol. 75, Springer,
2007.
6. Christian Blum and Andrea Roli. 2003. Metaheuristics in combinatorial optimization:
Overview and conceptual comparison. ACM Comput. Surv. 35, 3 (September 2003),
268-308.
CP7028
ENTERPRISE APPLICATION INTEGRATION
LTPC
30 03
OBJECTIVES:
Describe approaches to enterprise application integration
Understand the integration middleware
Evaluate the integration approaches suitable for a given problem
UNIT I
INTRODUCTION
6
Requirements for EAI - Challenges in EAI – Integration with legacy systems – Integration
with partners - Heterogeneous environment – Implementation approaches – Web services,
messaging, ETL, direct data integration – Middleware requirements – Approaches to
integration – services oriented and messaging.
UNIT II
INTEGRATION PATTERNS
6
Introduction to integration patterns – Architecture for application integration – Integration
patterns – Point to point, broker, message bus, publish/subscribe, Challenges in
performance, security, reliability - Case studies
61UNIT III
SERVICE ORIENTED INTEGRATION
12
Business process integration - Composite applications-services – Web services – Service
choreography and orchestration - Business process modeling - BPMN, Business process
execution - BPEL – Middleware infrastructure - Case studies
UNIT IV
MESSAGING BASED INTEGRATION
9
Messaging – Synchronous and asynchronous – Message structure – Message oriented
middleware – Reliability mechanisms – Challenges – Messaging infrastructure – Java
Messaging Services – Case studies
UNIT V
ENTERPRISE SERVICE BUS
12
Enterprise Service Bus – routing, scalable connectivity, protocol and message
transformations, data enrichment, distribution, correlation, monitoring – Deployment
configurations – Global ESB, Directly connected, Federated, brokered ESBs – Application
server based – Messaging system based – Hardware based ESBs – Support to SOA,
message based and event based integrations - Case studies.
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe different approaches to integration enterprise applications
Analyze specifications and identify appropriate integration approaches
Develop a suitable integration design for a given problem
Identify appropriate integration middleware for a given problem
Evaluate the integration approaches against specified requirements
REFERENCES:
1. George Mentzas and Andreas Frezen (Eds), "Semantic Enterprise Application
Integration for Business Processes: Service-oriented Frameworks", Business Science
Reference, 2009
2. Waseem Roshen, "SOA Based Enterprise Integration", Tata McGrawHill, 2009.
3. G
Hohpe
and
B
Woolf,
"Enterprise
Integration Patterns: Designing,
Building, and Deploying Messaging Solutions", Addison Wesley Professional,
2003
4. D
Linthicum, "Next
Generation
Application
Integration:
From
Simple
Information to Web Services", Addison Wesley, 2003
5. Martin Fowler, "Patterns of Enterprise Application Architecture", Addison- Wesley, 2003
6. Kapil Pant and Matiaz Juric, "Business Process Driven SOA using BPMN and BPEL:
From Business Process Modeling to Orchestration and Service Oriented Architecture",
Packt Publishing, 2008
CP7029
INFORMATION STORAGE MANAGEMENT
LTPC
3 003
UNIT I
INTRODUCTION TO STORAGE TECHNOLOGY
9
Review data creation and the amount of data being created and understand the value of
data to a business, challenges in data storage and data management, Solutions available for
data storage, Core elements of a data center infrastructure, role of each element in
supporting business activities
62UNIT II
STORAGE SYSTEMS ARCHITECTURE
9
Hardware and software components of the host environment, Key protocols and concepts
used by each component ,Physical and logical components of a connectivity environment
,Major physical components of a disk drive and their function, logical constructs of a physical
disk, access characteristics, and performance Implications, Concept of RAID and its
components, Different RAID levels and their suitability for different application environments:
RAID 0, RAID 1, RAID 3, RAID 4, RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and
contrast integrated and modular storage systems ,Iligh-level architecture and working of an
intelligent storage system
UNIT III
INTRODUCTION TO NETWORKED STORAGE
9
Evolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS,
and IP-SAN, Benefits of the different networked storage options, understand the need for
long-term archiving solutions and describe how CAS full fill the need, understand the
appropriateness of the different networked storage options for different application
environments
UNIT IV
INFORMATION AVAILABILITY, MONITORING & MANAGING
DATACENTER
9
List reasons for planned/unplanned outages and the impact of downtime, Impact of
downtime - Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO
and RPO, Identify single points of failure in a storage infrastructure and list solutions to
mitigate these failures, Architecture of backup/recovery and the different backup/ recovery
topologies, replication technologies and their role in ensuring information availability and
business continuity, Remote replication technologies and their role in providing disaster
recovery and business continuity capabilities. Identify key areas to monitor in a data center,
Industry standards for data center monitoring and management, Key metrics to monitor for
different components in a storage infrastructure, Key management tasks in a data center
UNIT V
SECURING STORAGE AND STORAGE VIRTUALIZATION
9
Information security, Critical security attributes for information systems, Storage security
domains, List and analyzes the common threats in each domain, Virtualization technologies,
block-level and file-level virtualization technologies and processes
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. EMC Corporation, Information Storage and Management, Wiley, India.
2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill ,
Osborne, 2003.
3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001.
4. Additional resource material on www.emc.com/resource-library/resource-library.esp
CP7030
ROBOTICS
OBJECTIVES:
To understand robot locomotion and mobile robot kinematics
To understand perception in robotics
To understand mobile robot localization
To understand mobile robot mapping
To understand simultaneous localization and mapping (SLAM)
To understand robot planning and navigation
63
L T P C
3 0 0 3UNIT I
LOCOMOTION AND KINEMATICS
9
Introduction to Robotics – key issues in robot locomotion – legged robots – wheeled mobile
robots – aerial mobile robots – introduction to kinematics – kinematics models and
constraints – robot maneuverability
UNIT II
ROBOT PERCEPTION
9
Sensors for mobile robots – vision for robotics – cameras – image formation – structure from
stereo – structure from motion – optical flow – color tracking – place recognition – range data
UNIT III
MOBILE ROBOT LOCALIZATION
9
Introduction to localization – challenges in localization – localization and navigation – belief
representation – map representation – probabilistic map-based localization – Markov
localization – EKF localization – UKF localization – Grid localization – Monte Carlo
localization – localization in dynamic environments
UNIT IV
MOBILE ROBOT MAPPING
9
Autonomous map building – occupancy grip mapping – MAP occupancy mapping – SLAM –
extended Kalman Filter SLAM – graph-based SLAM – particle filter SLAM – sparse extended
information filter – fastSLAM algorithm
UNIT V
PLANNING AND NAVIGATION
9
Introduction to planning and navigation – planning and reacting – path planning – obstacle
avoidance techniques – navigation architectures – basic exploration algorithms
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain robot locomotion
Apply kinematics models and constraints
Implement vision algorithms for robotics
Implement robot localization techniques
Implement robot mapping techniques
Implement SLAM algorithms
Explain planning and navigation in robotics
REFERENCES:
1. Roland Seigwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, “Introduction to
autonomous mobile robots”, Second Edition, MIT Press, 2011.
2. Sebastian Thrun, Wolfram Burgard, and Dieter Fox, “Probabilistic Robotics”, MIT Press,
2005.
3. Howie Choset et al., “Principles of Robot Motion: Theory, Algorithms, and
Implementations”, A Bradford Book, 2005.
4. Gregory Dudek and Michael Jenkin, “Computational Principles of Mobile Robotics”,
Second Edition, Cambridge University Press, 2010.
5. Maja J. Mataric, “The Robotics Primer”, MIT Press, 2007.
CP7031
COMPILER OPTIMIZATION TECHNIQUES
LTPC
3003
OBJECTIVES:
To understand the optimization techniques used in compiler design.
To be aware of the various computer architectures that support parallelism.
To become familiar with the theoretical background needed for code optimization.
To understand the techniques used for identifying parallelism in a sequential program.
To learn the various optimization algorithms.
64UNIT I
INTRODUCTION
9
Language Processors - The Structure of a Compiler – The Evolution of Programming
Languages- The Science of Building a Compiler – Applications of Compiler Technology
Programming Language Basics - The Lexical Analyzer Generator -Parser Generator -
Overview of Basic Blocks and Flow Graphs - Optimization of Basic Blocks - Principle
Sources of Optimization.
UNIT II
INSTRUCTION-LEVEL PARALLELISM
9
Processor Architectures – Code-Scheduling Constraints – Basic-Block Scheduling –Global
Code Scheduling – Software Pipelining.
UNIT III
OPTIMIZING FOR PARALLELISM AND LOCALITY-THEORY
9
Basic Concepts – Matrix-Multiply: An Example - Iteration Spaces - Affine Array Indexes –
Data Reuse Array data dependence Analysis.
UNITIV
OPTIMIZING FOR PARALLELISM AND LOCALITY – APPLICATION
9
Finding Synchronization - Free Parallelism – Synchronization Between Parallel Loops –
Pipelining – Locality Optimizations – Other Uses of Affine Transforms.
UNIT V
INTERPROCEDURAL ANALYSIS
9
Basic Concepts – Need for Interprocedural Analysis – A Logical Representation of Data
Flow – A Simple Pointer-Analysis Algorithm – Context Insensitive Interprocedural Analysis -
Context-Sensitive Pointer-Analysis - Datalog Implementation by Binary Decision Diagrams.
TOTAL: 45 PERIODS
REFERENCES:
1. Alfred V. Aho, Monica S.Lam, Ravi Sethi, Jeffrey D.Ullman, “Compilers:Principles,
Techniques and Tools”, Second Edition, Pearson Education,2008.
2. Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern Architectures: A
Dependence-based Approach”, Morgan Kaufmann Publishers, 2002.
3. Steven S. Muchnick, “Advanced Compiler Design and Implementation”,Morgan
Kaufmann Publishers - Elsevier Science, India, Indian Reprint 2003.
65ANNA UNIVERSITY, CHENNAI
AFFILIATED INSTITUTIONS
REGULATIONS - 2013
M.E. COMPUTER SCIENCE AND ENGINEERING
I - IV SEMESTERS (FULL TIME) CURRICULUM AND SYLLABUS
405
THEORY
Course Code
MA7155
CP7101
CP7102
CP7103
M.E. Computer Science
and Engineering
SEMESTER I
Course Title
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 4 2
0 0 2 1
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 2 1
0 0 4 2
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
Total L T P C
0 0 12 6
12 1 12 19
Total L T P C
0 0 24 12
0 0 24 12
Applied Probability and Statistics
Design and Management of Computer Networks
Advanced Data Structures and Algorithms
Multicore Architectures
Elective I
Elective II
PRACTICAL
Course Code
Course Title
CP7111
Advanced Data Structures Laboratory
CP7112
Case Study - Network Design (Team Work)
Total
SEMESTER II
THEORY
Course Code
CP7201
CP7202
CP7203
CP7204
Course Title
Theoretical Foundations of Computer Science
Advanced Databases
Principles of Programming Languages
Advanced Operating Systems
Elective III
Elective IV
PRACTICAL
Course Code
Course Title
CP7211
Advanced Databases Laboratory
CP7212
Case Study - Operating Systems Design (Team Work)
Total
SEMESTER III
THEORY
Course Code
Course Title
CP7301
Software Process and Project Management
Elective V
Elective VI
Elective VII
PRACTICAL
Course Code
CP7311
Project Work (Phase I)
Course Title
SEMESTER IV
PRACTICAL
Course Code
CP7411
Project Work (Phase II)
Course Title
TOAL NO OF CREDITS 75ELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER I
ELECTIVE-I Course Title L T P C
Course Code Formal Models of Software Systems 3 0 0 3
SE7103 Performance Evaluation of Computer Systems 3 0 0 3
CP7001 Probabilistic Reasoning Systems 3 0 0 3
CP7002 Data Analysis and Business Intelligence 3 0 0 3
CP7003 Image Processing and Analysis 3 0 0 3
CP7004 Sensing Techniques and Sensors 3 0 0 3
NE7001
ELECTIVE-II Course Title Randomized Algorithms L T P C
Course Code Mobile and Pervasive Computing 3 0 0 3
CP7005 Parallel Programming Paradigms 3 0 0 3
NE7002 Software Requirements Engineering 3 0 0 3
CP7006 Speech Processing and Synthesis 3 0 0 3
CP7007 Machine Learning Techniques 3 0 0 3
CP7008 3 0 0 3
CP7009 36 0 0 36
Total
SEMESTER II
ELECTIVE-III Course Title L T P C
Course Code Concurrency Models 3 0 0 3
CP7010 Real Time Systems 3 0 0 3
CP7011 Computer Vision 3 0 0 3
CP7012 Network and Information Security 3 0 0 3
NE7202 Design and Analysis of Parallel Algorithms 3 0 0 3
CP7013 Software Architectures 3 0 0 3
CP7014
ELECTIVE-IV Course Title L T P C
Course Code Model Checking and Program Verification 3 0 0 3
CP7015 Embedded Software Development 3 0 0 3
CP7016 Cloud Computing 3 0 0 3
IF7202 Data Visualization Techniques 3 0 0 3
CP7017 Protocols and Architecture for Wireless Sensor Networks 3 0 0 3
NE7005 Language Technologies 3 0 0 3
CP7018 36 0 0 36
TotalELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER III
ELECTIVE-V Social Network Analysis
Course Code Managing Big Data
NE7012 Mobile Application Development
CP7019 Bio-inspired Computing
NE7011 Medical Image Processing
CP7020 Software Design
CP7021
CP7022
ELECTIVE-VI Reconfigurable Computing
Course Code Energy Aware Computing
CP7023 Information Retrieval Techniques
IF7013 Data Mining Techniques
CP7024 Bio Informatics
CP7025 Software Quality Assurance
IF7002
CP7026
ELECTIVE-VII
Course Code
CP7027
CP7028
CP7029
CP7030
CP7031
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
51 0 0 51
Multiobjective Optimization Techniques
Enterprise Application Integration
Information Storage Management
Robotics
Compiler Optimization Techniques
Total
TOAL NO OF CREDITS 123PROGRAM EDUCATIONAL OBJECTIVES (PEO):
Graduates of this M. E. Computer Science and Engineering will be able to
1. Apply the necessary mathematical tools and fundamental & advanced knowledge of
computer science & engineering
2. Develop computer/software/network systems understanding the importance of social,
business, technical, environmental, and human context in which the systems would
work
3. Articulate fundamental concepts, design underpinnings of computer/software/network
systems, and research findings to train professionals or to educate engineering
students
4. Contribute effectively as a team member/leader, using common tools and
environment, in computer science and engineering projects, research, or education
5. Pursue life-long learning and research in selected fields of computer science &
engineering and contribute to the growth of those fields and society at large
PROGRAM OUTCOMES:
Apply knowledge of mathematics, science, engineering fundamentals and an
engineering specialization to the conceptualization of engineering models.
Identify, formulate, research literature and solve complex engineering problems
reaching substantiated conclusions using first principles of mathematics and
engineering sciences.
Design solutions for complex engineering problems and design systems,
components or processes that meet specified needs with appropriate consideration
for public health and safety, cultural, societal, and environmental considerations.
Conduct investigations of complex problems including design of experiments,
analysis and interpretation of data, and synthesis of information to provide valid
conclusions.
Create, select and apply appropriate techniques, resources, and modern engineering
tools, including prediction and modeling, to complex engineering activities, with an
understanding of the limitations.
Function effectively as an individual, and as a member or leader in diverse teams
and in multi-disciplinary settings.
Communicate effectively on complex engineering activities with the engineering
community and with society at large, such as being able to comprehend and write
effective reports and design documentation, make effective presentations, and give
and receive clear instructions.
Demonstrate understanding of the societal, health, safety, legal and cultural issues
and the consequent responsibilities relevant to engineering practice.
Understand and commit to professional ethics and responsibilities and norms of
engineering practice.
Understand the impact of engineering solutions in a societal context and demonstrate
knowledge of and need for sustainable development.
Demonstrate a knowledge and understanding of management and business
practices, such as risk and change management, and understand their limitations.
Recognize the need for, and have the ability to engage in independent and life-long
learning.
1MA7155
APPLIED PROBABILITY AND STATISTICS
L T PC
3 1 0 4
OBJECTIVES:
To introduce the basic concepts of one dimensional and two dimensional Random
Variables.
To provide information about Estimation theory, Correlation, Regression and Testing of
hypothesis.
To enable the students to use the concepts of multivariate normal distribution and
principle components analysis.
UNIT I
ONE DIMENSIONAL RANDOM VARIABLES
9+3
Random variables - Probability function – Moments – Moment generating functions and
their properties – Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal
distributions – Functions of a Random Variable.
UNIT II
TWO DIMENSIONAL RANDOM VARIABLES
9+3
Joint distributions – Marginal and Conditional distributions – Functions of two dimensional
random variables – Regression Curve – Correlation.
UNIT III
ESTIMATION THEORY
9+3
Unbiased Estimators – Method of Moments – Maximum Likelihood Estimation - Curve fitting
by Principle of least squares – Regression Lines.
UNIT IV
TESTING OF HYPOTHESES
9+3
Sampling distributions - Type I and Type II errors - Tests based on Normal, t,2 and F
distributions for testing of mean, variance and proportions – Tests for Independence of
attributes and Goodness of fit.
UNIT V
MULTIVARIATE ANALYSIS
9+3
Random Vectors and Matrices - Mean vectors and Covariance matrices - Multivariate
Normal density and its properties - Principal components Population
principal
components - Principal components from standardized variables.
TOTAL 45+15=60 PERIODS
OUTCOME:
The student will able to acquire the basic concepts of Probability and Statistical
techniques for solving mathematical problems which will be useful in solving Engineering
problems
REFERENCES:
1 Jay L. Devore, “Probability and Statistics For Engineering and the Sciences”,Thomson
and Duxbury, 2002.
2.Richard Johnson. ”Miller & Freund’s Probability and Statistics for Engineer”, Prentice
–
Hall , Seventh Edition, 2007.
3.Richard A. Johnson and Dean W. Wichern, “Applied Multivariate Statistical Analysis”,
Pearson Education, Asia, Fifth Edition, 2002.
4.Gupta S.C. and Kapoor V.K.”Fundamentals of Mathematical Statistics”, Sultan an Sons,
2001.
5.Dallas E Johnson , “Applied Multivariate Methods for Data Analysis”, Thomson an Duxbury
press,1998.
2CP7101
DESIGN AND MANAGEMENT OF COMPUTER NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION TO NETWORK MANAGEMENT
9
Overview of Analysis, Architecture and Design Process-System Methodology, Service
methodology, Service Description - Service characteristics - Performance Characteristics -
Network supportability - Requirement analysis – User Requirements – Application
Requirements – Device Requirements – Network Requirements – Other Requirements -
Requirement specification and map.
UNIT II
REQUIREMENTS ANALYSIS
9
Requirement Analysis Process – Gathering and Listing Requirements- Developing service
metrics – Characterizing behavior – Developing RMA requirements – Developing delay
Requirements - Developing capacity Requirements - Developing supplemental performance
Requirements –Requirements mapping – Developing the requirements specification
UNIT III
FLOW ANALYSIS
9
Individual and Composite Flows – Critical Flows - Identifying and developing flows – Data
sources and sinks – Flow models- Flow prioritization – Flow specification algorithms –
Example Applications of Flow Analysis
UNIT IV
NETWORK ARCHITECTURE
9
Architecture and design – Component Architectures – Reference Architecture – Architecture
Models – System and Network Architecture – Addressing and Routing Architecture –
Addressing and Routing Fundamentals – Addressing Mechanisms – Addressing Strategies
– Routing Strategies – Network Management Architecture – Network Management
Mechanisms Performance Architecture – Performance Mechanisms – Security and Privacy
Architecture – Planning security and privacy Mechanisms
UNIT V
NETWORK DESIGN
9
Design Concepts – Design Process - Network Layout – Design Traceability – Design Metrics
– Logical Network Design – Topology Design – Bridging, Switching and Routing Protocols-
Physical Network Design – Selecting Technologies and Devices for Campus and Enterprise
Networks – Optimizing Network Design
TOTAL : 45 PERIODS
REFERENCES:
1. Network Analysis, Architecture, and Design By James D. McCabe, Morgan Kaufmann,
Third Edition, 2007.ISBN-13: 978-0123704801
2. Computer Networks: A Systems Approach by Larry L. Peterson, Bruce S. Davie - 2007,
Elsevier Inc.
3. Top-down Network Design: [a Systems Analysis Approach to Enterprise Network
Design] By Priscilla Oppenheimer, Cisco Press , 3rd Edition, ISBN-13: 978-1-58720-
283-4 ISBN-10: 1-58720-283-2
4. Integrated Management of Networked Systems: Concepts, Architectures, and Their
Operational Application (The Morgan Kaufmann Series in Networking), Heinz-Gerd
Hegering, Sebastian Abeck, and Bernhard Neumair, 1999.
5. “Network Design and Management” – by Steven T.Karris, Orchard publications, Second
edition, Copyright 2009, ISBN 978-1-934404-15-7
6. “Network Design, Management and Technical Perspective”, Teresa C. Mann-Rubinson
and Kornel Terplan, CRC Press, 1999
7. “Ethernet Networks-Design, Implementation, Operation and Management by Gilbert
Held, John Wiley and sons, Fourth Edition
8. James Kurose and Keith Ross, “Computer Networking: A Top-Down Approach Featuring
the Internet”, 1999
3CP7102
ADVANCED DATA STRUCTURES AND ALGORITHMS
LTPC
3003
OBJECTIVES:
To understand the principles of iterative and recursive algorithms.
To learn the graph search algorithms.
To study network flow and linear programming problems.
To learn the hill climbing and dynamic programming design techniques.
To develop recursive backtracking algorithms.
To get an awareness of NP completeness and randomized algorithms.
To learn the principles of shared and concurrent objects.
To learn concurrent data structures.
UNIT I
ITERATIVE AND RECURSIVE ALGORITHMS
9
Iterative Algorithms: Measures of Progress and Loop Invariants-Paradigm Shift: Sequence of
Actions versus Sequence of Assertions- Steps to Develop an Iterative Algorithm-Different
Types of Iterative Algorithms--Typical Errors-Recursion-Forward versus Backward- Towers
of Hanoi-Checklist for Recursive Algorithms-The Stack Frame-Proving Correctness with
Strong Induction- Examples of Recursive Algorithms-Sorting and Selecting Algorithms-
Operations on Integers- Ackermann’s Function- Recursion on Trees-Tree Traversals-
Examples- Generalizing the Problem - Heap Sort and Priority Queues-Representing
Expressions.
UNIT II
OPTIMISATION ALGORITHMS
9
Optimization Problems-Graph Search Algorithms-Generic Search-Breadth-First Search-
Dijkstra’s Shortest-Weighted-Path -Depth-First Search-Recursive Depth-First Search-Linear
Ordering of a Partial Order- Network Flows and Linear Programming-Hill Climbing-Primal
Dual Hill Climbing- Steepest Ascent Hill Climbing-Linear Programming-Recursive
Backtracking-Developing Recursive Backtracking Algorithm- Pruning Branches-Satisfiability
UNIT III
DYNAMIC PROGRAMMING ALGORITHMS
9
Developing a Dynamic Programming Algorithm-Subtle Points- Question for the Little Bird-
Subinstances and Subsolutions-Set of Substances-Decreasing Time and Space-Number of
Solutions-Code. Reductions and NP-Completeness-Satisfiability-Proving NP-Completeness-
3-Coloring- Bipartite Matching. Randomized Algorithms-Randomness to Hide Worst Cases-
Optimization Problems with a Random Structure.
UNIT IV
SHARED OBJECTS AND CONCURRENT OBJECTS
9
Shared Objects and Synchronization -Properties of Mutual Exclusion-The Mora l- The
Producer–Consumer Problem -The Readers–Writers Problem-Realities of Parallelization-
Parallel Programming- Principles- Mutual Exclusion-Time- Critical Sections--Thread
Solutions-The Filter Lock-Fairness-Lamport’s Bakery Algorithm-Bounded Timestamps-Lower
Bounds on the Number of Locations-Concurrent Objects- Concurrency and Correctness-
Sequential Objects-Quiescent Consistency- Sequential Consistency-Linearizability- Formal
Definitions- Progress Conditions- The Java Memory Model
UNIT V
CONCURRENT DATA STRUCTURES
9
Practice-Linked Lists-The Role of Locking-List-Based Sets-Concurrent Reasoning- Coarse-
Grained Synchronization-Fine-Grained Synchronization-Optimistic Synchronization- Lazy
Synchronization-Non-Blocking Synchronization-Concurrent Queues and the ABA Problem-
Queues-A Bounded Partial Queue-An Unbounded Total Queue-An Unbounded Lock-Free
Queue-Memory Reclamation and the ABA Problem- Dual Data Structures- Concurrent
Stacks and Elimination- An Unbounded Lock-Free Stack- Elimination-The Elimination
Backoff Stack
TOTAL : 45 PERIODS
4OUTCOMES:
Upon completion of the course, the students will be able to
Design and apply iterative and recursive algorithms.
Design and implement optimisation algorithms in specific applications.
Design appropriate shared objects and concurrent objects for applications.
Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7103
MULTICORE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
To understand the recent trends in the field of Computer Architecture and identify
performance related parameters
To appreciate the need for parallel processing
To expose the students to the problems related to multiprocessing
To understand the different types of multicore architectures
To expose the students to warehouse-scale and embedded architectures
UNIT I
FUNDAMENTALS OF QUANTITATIVE DESIGN AND ANALYSIS
9
Classes of Computers – Trends in Technology, Power, Energy and Cost – Dependability –
Measuring, Reporting and Summarizing Performance – Quantitative Principles of Computer
Design – Classes of Parallelism - ILP, DLP, TLP and RLP - Multithreading - SMT and CMP
Architectures – Limitations of Single Core Processors - The Multicore era – Case Studies of
Multicore Architectures.
UNIT II
DLP IN VECTOR, SIMD AND GPU ARCHITECTURES
9
Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics Processing
Units - Detecting and Enhancing Loop Level Parallelism - Case Studies.
UNIT III
TLP AND MULTIPROCESSORS
9
Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues -
Performance Issues – Synchronization Issues – Models of Memory Consistency -
Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks.
UNIT IV
RLP AND DLP IN WAREHOUSE-SCALE ARCHITECTURES
9
Programming Models and Workloads for Warehouse-Scale Computers – Architectures for
Warehouse-Scale Computing – Physical Infrastructure and Costs – Cloud Computing –
Case Studies.
5UNIT V
ARCHITECTURES FOR EMBEDDED SYSTEMS
9
Features and Requirements of Embedded Systems – Signal Processing and Embedded
Applications – The Digital Signal Processor – Embedded Multiprocessors - Case Studies.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the limitations of ILP and the need for multicore architectures
Discuss the issues related to multiprocessing and suggest solutions
Point out the salient features of different multicore architectures and how they exploit
parallelism
Critically analyze the different types of inter connection networks
Discuss the architecture of GPUs, warehouse-scale computers and embedded
processors
REFERENCES:
1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative
Approach”, Morgan Kaufmann / Elsevier, 5th edition, 2012.
2. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw-Hill Education, 2003
3. Richard Y. Kain, “Advanced Computer Architecture a Systems Design Approach”,
Prentice Hall, 2011.
4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/
Software Approach” , Morgan Kaufmann / Elsevier, 1997.
CP7111
ADVANCED DATA STRUCTURES LABORATORY
L T PC
0 0 4 2
OBJECTIVES:
To learn to implement iterative and recursive algorithms.
To learn to design and implement algorithms using hill climbing and dynamic
programming techniques.
To learn to implement shared and concurrent objects.
To learn to implement concurrent data structures.
LAB EXERCISES:
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week or two two-hour sessions per
week. There will be about 15 exercises in a semester. It is recommended that all
implementations are carried out in Java. If C or C++ has to be used, then the threads library
will be required for concurrency. Exercises should be designed to cover the following topics:
Implementation of graph search algorithms.
Implementation and application of network flow and linear programming problems.
Implementation of algorithms using the hill climbing and dynamic programming
design techniques.
Implementation of recursive backtracking algorithms.
Implementation of randomized algorithms.
Implementation of various locking and synchronization mechanisms for concurrent
linked lists, concurrent queues, and concurrent stacks.
Developing applications involving concurrency.
TOTAL :60 PERIODS
6OUTCOMES:
Upon completion of the course, the students will be able to
1. Design and apply iterative and recursive algorithms.
2. Design and implement algorithms using the hill climbing and dynamic programming
and recursive backtracking techniques.
3. Design and implement optimisation algorithms for specific applications.
4. Design and implement randomized algorithms.
5. Design appropriate shared objects and concurrent objects for applications.
6. Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C.2 E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7112
CASE STUDY: NETWORK DESIGN (Team Work)
LT PC
0 0 21
CASE STUDY : 1
Analyzing the performance of various configurations and protocols in LAN.
1.1. Establishing a Local Area Network (LAN): The main objective is to set up a Local
Area Network,concepts involved in this network are IP addressing and the Address
Resolution Protocol (ARP). The required equipments are192.168.1.1 ,192.168.1.2,
192.168.1.3,Host A Host B Host C, Switch/HUB, three PC`s equipped with at least one
NIC, one HUB or Switch and the necessary cables. Once the physical LAN is set up the
hosts need to be configured using the ifconfig command. To verify communication
among the machines the ping command is used. Next, to manipulate the routing tables
at the hosts to understand how machines know where to send packets. Since the ifconfig
command places a default route into the routing tables this route must be deleted. to
́blindfoldˇ the machine. The ping command is used again to show that communication is
no longer available. To re-establish communication the routes are put back into the
routing table one host at a time. Communication is once again verified using the ping
command.
1.2. Connecting two LANs using multi-router topology with static routes:
The main objective is to extend routing connection by using multiple routers. The
concepts include IP addressing and basic network routing principles. Connect two LANs
topology. During router configuration attention is paid to the types of interfaces as
additional issues are involved with set-up. For example, the serial interfaces require
clocking mechanisms to be set correctly. Once the interfaces are working the ping
command is used to check for communication between LANs. The failure of
communication illustrates the need for routes to be established inside the routing
infrastructure. Static routes are used to show how packets can be transported through
any reasonable route. It is run trace route on two different configurations to demonstrate
the implementation of different routes.
71.3 Analyzing the performance of various configurations and protocols
Original TCP versus the above modified one: To compare the performance between the
operation of TCP with congestion control and the operation of TCP as implemented . The
main objective is for students to examine how TCP responds to a congested network.
The concepts involved in the lab include network congestion and the host responsibilites
for communicating over a network. This lab requires three PC’s connected to a
switch.One PC is designated as the target host and the other two PC’s will transfer a file
from the targethost using FTP. A load is placed on the network to simulate congestion
and the file is transfered, first by the host using the normal TCP and then by the host
using the modified version. This procedure is performed multiple times to determine
average statistics. The students are then asked to summarize the results and draw
conclusions about the performance differences and the underlying implications for hosts
operating in a network environment.
Case Study 2:
RIP and OSPF Redistribution
This case study addresses the issue of integrating Routing Information Protocol (RIP)
networks with Open Shortest Path First (OSPF) networks. Most OSPF networks also use
RIP to communicate with hosts or to communicate with portions of the internetwork that do
not use OSPF. This case study should provide examples of how to complete the following
phases in redistributing information between RIP and OSPF networks, including the
following topics:
• Configuring a RIP Network
• Adding OSPF to the Center of a RIP Network
• Adding OSPF Areas
• Setting Up Mutual Redistribution
‘Case Study 3:
Dial-on-Demand Routing
This case study should describe the use of DDR to connect a worldwide network that
consists of a central site located in Mumbai and remote sites located in Chennai, Bangalore,
and Hyderabad. The following scenarios should be considered:
• Having the Central Site Dial Out
Describe the central and remote site configurations for three setups: a central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Include examples of the usage of rotary groups and access lists.
• Having the Central and Remote Sites Dial In and Dial Out
Describe the central and remote site configurations for three setups: central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Also describes the usage of Point-to-Point Protocol (PPP)
encapsulation and the Challenge Handshake Authentication Protocol (CHAP).
• Having Remote Sites Dial Out
A common configuration is one in which the remote sites place calls to the central site but
the central site does not dial out. In a “star” topology, it is possible for all of the remote
routers to have their serial interfaces on the same subnet as the central site serial interface.
• Using DDR as a Backup to Leased Lines
Describes the use of DDR as a backup method to leased lines and provides examples of
how to use floating static routes on single and shared interfaces.
• Using Leased Lines and Dial Backup
Describes the use of Data Terminal Ready (DTR) dialing and V.25bis dialing with leased
lines.
Case Study 4:
Network Security
This case study should provide the specific actions you can take to improve the security of
your network. Before going into specifics, however, you should understand the following
basic concepts that are essential to any security system:
8• Know your enemy
This case study refers to attackers or intruders. Consider who might want to circumvent
your security measures and identify their motivations. Determine what they might want to
do and the damage that they could cause to your network. Security measures can never
make it impossible for a user to perform unauthorized tasks with a computer system. They
can only make it harder. The goal is to make sure the network security controls are beyond
the attacker’s ability or motivation.
• Count the cost
Security measures almost always reduce convenience, especially for sophisticated users.
Security can delay work and create expensive administrative and educational overhead. It
can use significant computing resources and require dedicated hardware. When you
design your security measures, understand their costs and weigh those costs against the
potential benefits. To do that, you must understand the costs of the measures themselves
and the costs and likelihoods of security breaches. If you incur security costs out of
proportion to the actual dangers, you have done yourself a disservice.
• Identify your assumptions
Every security system has underlying assumptions. For example, you might assume that
your network is not tapped, or that attackers know less than you do, that they are using
standard software, or that a locked room is safe. Be sure to examine and justify your
assumptions. Any hidden assumption is a potential security hole.
• Control your secrets
Most security is based on secrets. Passwords and encryption keys, for example, are
secrets. Too often, though, the secrets are not really all that secret. The most important
part of keeping secrets is knowing the areas you need to protect. What knowledge would
enable someone to circumvent your system? You should jealously guard that knowledge
and assume that everything else is known to your adversaries. The more secrets you
have, the harder it will be to keep all of them. Security systems should be designed so that
only a limited number of secrets need to be kept.
Know your weaknesses
Every security system has vulnerabilities. You should understand your system’s weak
points and know how they could be exploited. You should also know the areas that present
the largest danger and prevent access to them immediately. Understanding the weak
points is the first step toward turning them into secure areas.
Limit the scope of access
You should create appropriate barriers inside your system so that if intruders access one
part of the system, they do not automatically have access to the rest of the system. The
security of a system is only as good as the weakest security level of any single host in the
system.
• Remember physical security Physical access to a computer (or a router) usually gives a
sufficiently sophisticated user total control over that computer. Physical access to a
network link usually allows a person to tap that link, jam it, or inject traffic into it. It
makes no sense to install complicated software security measures when access to the
hardware is not controlled.
Case Study 5:
Controlling Traffic Flow
In this case study, the firewall router allows incoming new connections to one or more
communication servers or hosts. Having a designated router act as a firewall is desirable
because it clearly identifies the router’s purpose as the external gateway and avoids
encumbering other routers with this task. In the event that the internal network needs to
isolate itself, the firewall router provides the point of isolation so that the rest of the internal
network structure is not affected. Connections to the hosts are restricted to incoming file
transfer protocol (FTP) requests and email services. The incoming Telnet, or modem
connections to the communication server are screened by the communication server running
TACACS username authentication.
9Case Study 6:
Defining Access Lists
Access lists define the actual traffic that will be permitted or denied, whereas an access
group applies an access list definition to an interface. Access lists can be used to deny
connections that are known to be a security risk and then permit all other connections, or to
permit those connections that are considered acceptable and deny all the rest. For firewall
implementation, the latter is the more secure method. In this case study, incoming email and
news are permitted for a few hosts, but FTP, Telnet, and rlogin services are permitted only to
hosts on the firewall subnet. IP extended access lists (range 100 to 199) and transmission
control protocol (TCP) or user datagram protocol (UDP) port numbers are used to filter
traffic. When a connection is to be established for email, Telnet, FTP, and so forth, the
connection will attempt to open a service on a specified port number. You can, therefore,
filter out selected types of connections by denying packets that are attempting to use that
service. An access list is invoked after a routing decision has been made but before the
packet is sent out on an interface. The best place to define an access list is on a preferred
host using your favorite text editor. You can create a file that contains the access-list
commands, place the file (marked readable) in the default TFTP directory, and then network
load the file onto the router.
Case Study 7:
Configuring a fire wall
Consider a Fire wall communication server with single inbound modem.
modem to ensure security for LAN
Configure the
Case Study 8:
Integrating EIGRP (Enhanced Interior Gateway Routing Protocol) into Existing
Networks:
The case study should provide the benefits and considerations involved in integrating
Enhanced IGRP into the following types of internetworks:
• IP—The existing IP network is running IGRP
• Novell IPX—The existing IPX network is running RIP and SAP
• AppleTalk—The existing AppleTalk network is running the Routing Table Maintenance
Protocol (RTMP)
When integrating Enhanced IGRP into existing networks, plan a phased implementation.
Add Enhanced IGRP at the periphery of the network by configuring Enhanced IGRP on a
boundary router on the backbone off the core network. Then integrate Enhanced IGRP into
the core network.
CP7201
THEORETICAL FOUNDATIONS OF COMPUTER SCIENCE
LTPC
310 4
OBJECTIVES:
To review sets, relations, functions, and other foundations
To understand propositional and predicate logics and their applications
To understand lambda calculus and functional programming
To understand graph structures and their applications
To understand formal models of computation, computability, and decidability
UNIT I
FOUNDATIONS
9
Sets – relations – equivalence relations – partial orders – functions – recursive functions –
sequences – induction principle – structural induction – recursive algorithms – counting –
pigeonhole principle – permutations and combinations – recurrence relations
10UNIT II
LOGIC AND LOGIC PROGRAMMING
9
Propositional logic – syntax – interpretations and models – deduction theorems – normal
forms – inference rules – SAT solvers - predicate logic – syntax – proof theory – semantics
of predicate logic – undecidability of predicate logic – inferences in first-order logic – logic
programming – definite programs – SLD resolution – normal programs – SLDNF resolution –
introduction to Prolog
UNIT III
LAMBDA CALCULUS AND FUNCTIONAL PROGRAMMING
9
Lambda notation for functions – syntax – curried functions – parametric polymorphism –
lambda reduction – alpha reduction – beta reduction – beta abstraction – extensionality
theorem – delta reduction – reduction strategies – normal forms – Church-Rosser Theorems
– pure lambda calculus – constants – arithmetic – conditionals – Iteration – recursion –
introduction to functional programming
UNIT IV
GRAPH STRUCTURES
9
Tree Structures – Graph structures – graph representations – regular graph structures –
random graphs – Connectivity – Cycles – Graph Coloring – Cliques, Vertex Covers,
Independent sets – Spanning Trees – network flows – matching
UNIT V
STATE MACHINES
9
Languages and Grammars – Finite State Machines – State machines and languages –
Turing Machines – Computational Complexity – computability – Decidability – Church's
Thesis
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able
To explain sets, relations, functions
To conduct proofs using induction, pigeonhole principle, and logic
To apply counting, permutations, combinations, and recurrence relations
To apply recursive functions and lambda calculus
To explain logic programming and functional programming principles
To apply sequential structures, tree structures, and graph structures
To explain computational models, computability, and complexity
REFERENCES:
1. Uwe Schoning, “Logic for Computer Scientists”, Birkhauser, 2008.
2. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
3. John Harrison, “Handbook of Practical Logic and Automated Reasoning”, Cambridge
University Press, 2009.
4. Greg Michaelson, “An introduction to functional programming through lambda
calculus”, Dover Publications, 2011.
5. Kenneth Slonneger and Barry Kurtz, “Formal syntax and semantics of programming
languages”, Addison Wesley, 1995.
6. Kenneth H. Rosen, “Discrete Mathematics and its applications”, Seventh Edition,
Tata McGraw Hill, 2011.
7. Sriram Pemmaraju and Steven Skiena, “Computational Discrete Mathematics”,
Cambridge University Press, 2003.
8. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
9. Norman L. Biggs, “Discrete Mathematics”, Second Edition, Oxford University Press,
2002.
10. Juraj Hromkovic, “Theoretical Computer Science”, Springer, 1998.
11. J. E. Hopcroft, Rajeev Motwani, and J. D. Ullman, “Introduction to Automata Theory,
Languages, and Computation”, Third Edition, Pearson, 2008.
11CP7202
ADVANCED DATABASES
LTPC
3 003
OBJECTIVES:
To learn the modeling and design of databases.
To acquire knowledge on parallel and distributed databases and its applications.
To study the usage and applications of Object Oriented database
To understand the principles of intelligent databases.
To understand the usage of advanced data models.
To learn emerging databases such as XML, Cloud and Big Data.
To acquire inquisitive attitude towards research topics in databases.
UNIT I
PARALLEL AND DISTRIBUTED DATABASES
9
Database System Architectures: Centralized and Client-Server Architectures – Server
System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O
Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism –
Design of Parallel Systems- Distributed Database Concepts - Distributed Data Storage –
Distributed Transactions – Commit Protocols – Concurrency Control – Distributed Query
Processing – Case Studies
UNIT II
OBJECT AND OBJECT RELATIONAL DATABASES
9
Concepts for Object Databases: Object Identity – Object structure – Type Constructors –
Encapsulation of Operations – Methods – Persistence – Type and Class Hierarchies –
Inheritance – Complex Objects – Object Database Standards, Languages and Design:
ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems: Object
Relational features in SQL/Oracle – Case Studies.
UNIT III
INTELLIGENT DATABASES
9
Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)- Taxonomy- Applications-
Design Principles for Active Rules- Temporal Databases: Overview of Temporal Databases-
TSQL2- Deductive Databases: Logic of Query Languages – Datalog- Recursive Rules-
Syntax and Semantics of Datalog Languages- Implementation of Rules and Recursion-
Recursive Queries in SQL- Spatial Databases- Spatial Data Types- Spatial Relationships-
Spatial Data Structures-Spatial Access Methods- Spatial DB Implementation.
UNIT IV
ADVANCED DATA MODELS
9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data
Management - Location Dependent Data Distribution - Mobile Transaction Models -
Concurrency Control - Transaction Commit Protocols- Multimedia Databases- Information
Retrieval- Data Warehousing- Data Mining- Text Mining.
UNIT V
EMERGING TECHNOLOGIES
9
XML Databases: XML-Related Technologies-XML Schema- XML Query Languages- Storing
XML in Databases-XML and SQL- Native XML Databases- Web Databases- Geographic
Information Systems- Biological Data Management- Cloud Based Databases: Data Storage
Systems on the Cloud- Cloud Storage Architectures-Cloud Data Models- Query Languages-
Introduction to Big Data-Storage-Analysis.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Select the appropriate high performance database like parallel and distributed
database
Model and represent the real world data using object oriented database
Design a semantic based database to meaningful data access
Embed the rule set in the database to implement intelligent databases
Represent the data using XML database for better interoperability
Handle Big data and store in a transparent manner in the cloud
To solve the issues related to the data storage and retrieval
12REFERENCES:
1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition,
Pearson Education/Addison Wesley, 2007.
2. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to
Design, Implementation and Management”, Third Edition, Pearson Education, 2007.
3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”,
Fifth Edition, McGraw Hill, 2006.
4. C.J.Date, A.Kannan and S.Swamynathan, ”An Introduction to Database Systems”,
Eighth Edition, Pearson Education, 2006.
5. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”,
McGraw Hill, Third Edition 2004.
CP7203
PRINCIPLES OF PROGRAMMING LANGUAGES
LTPC
3003
OBJECTIVES:
To understand and describe syntax and semantics of programming languages
To understand data, data types, and basic statements
To understand call-return architecture and ways of implementing them
To understand object-orientation, concurrency, and event handling in programming
languages
To develop programs in non-procedural programming paradigms
UNIT I
SYNTAX AND SEMANTICS
9
Evolution of programming languages – describing syntax – context-free grammars – attribute
grammars – describing semantics – lexical analysis – parsing – recursive-decent – bottom-
up parsing
UNIT II
DATA, DATA TYPES, AND BASIC STATEMENTS
9
Names – variables – binding – type checking – scope – scope rules – lifetime and garbage
collection – primitive data types – strings – array types – associative arrays – record types –
union types – pointers and references – Arithmetic expressions – overloaded operators –
type conversions – relational and boolean expressions – assignment statements – mixed-
mode assignments – control structures – selection – iterations – branching – guarded
statements
UNIT III
SUBPROGRAMS AND IMPLEMENTATIONS
9
Subprograms – design issues – local referencing – parameter passing – overloaded
methods – generic methods – design issues for functions – semantics of call and return –
implementing simple subprograms – stack and dynamic local variables – nested
subprograms – blocks – dynamic scoping
UNIT IV
OBJECT-ORIENTATION, CONCURRENCY, AND EVENT HANDLING
9
Object-orientation – design issues for OOP languages – implementation of object-oriented
constructs – concurrency – semaphores – monitors – message passing – threads –
statement level concurrency – exception handling – even handling
UNIT V
FUNCTIONAL AND LOGIC PROGRAMMING LANGUAGES
9
Introduction to lambda calculus – fundamentals of functional programming languages –
Programming with Scheme – Programming with ML – Introduction to logic and logic
programming – Programming with Prolog – multi-paradigm languages
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe syntax and semantics of programming languages
Explain data, data types, and basic statements of programming languages
Design and implement subprogram constructs
13
Apply object-oriented, concurrency, and event handling programming constructs
Develop programs in Scheme, ML, and Prolog
Understand and adopt new programming languages
REFERENCES:
1. Robert W. Sebesta, “Concepts of Programming Languages”, Tenth Edition, Addison
Wesley, 2012.
2. Michael L. Scott, “Programming Language Pragmatics”, Third Edition, Morgan
Kaufmann, 2009.
3. R. Kent Dybvig, “The Scheme programming language”, Fourth Edition, MIT Press, 2009.
4. Jeffrey D. Ullman, “Elements of ML programming”, Second Edition, Prentice Hall, 1998.
5. Richard A. O'Keefe, “The craft of Prolog”, MIT Press, 2009.
6. W. F. Clocksin and C. S. Mellish, “Programming in Prolog: Using the ISO Standard”, Fifth
Edition, Springer, 2003.
CP7204
ADVANCED OPERATING SYSTEMS
LTPC
3 003
OBJECTIVES:
To learn the fundamentals of Operating Systems
To gain knowledge on Distributed operating system concepts that includes architecture,
Mutual exclusion algorithms, Deadlock detection algorithms and agreement protocols
To gain insight on to the distributed resource management components viz. the
algorithms for implementation of distributed shared memory, recovery and commit
protocols
To know the components and management aspects of Real time, Mobile operating
systems
UNIT I
FUNDAMENTALS OF OPERATING SYSTEMS
9
Overview – Synchronization Mechanisms – Processes and Threads - Process Scheduling –
Deadlocks: Detection, Prevention and Recovery – Models of Resources – Memory
Management Techniques.
UNIT II
DISTRIBUTED OPERATING SYSTEMS
9
Issues in Distributed Operating System – Architecture – Communication Primitives –
Lamport’s Logical clocks – Causal Ordering of Messages – Distributed Mutual Exclusion
Algorithms – Centralized and Distributed Deadlock Detection Algorithms – Agreement
Protocols.
UNIT III
DISTRIBUTED RESOURCE MANAGEMENT
9
Distributed File Systems – Design Issues - Distributed Shared Memory – Algorithms for
Implementing Distributed Shared memory–Issues in Load Distributing – Scheduling
Algorithms – Synchronous and Asynchronous Check Pointing and Recovery – Fault
Tolerance – Two-Phase Commit Protocol – Nonblocking Commit Protocol – Security and
Protection.
UNIT IV
REAL TIME AND MOBILE OPERATING SYSTEMS
9
Basic Model of Real Time Systems - Characteristics- Applications of Real Time Systems –
Real Time Task Scheduling - Handling Resource Sharing - Mobile Operating Systems –
Micro Kernel Design - Client Server Resource Access – Processes and Threads - Memory
Management - File system.
UNIT V
CASE STUDIES
9
Linux System: Design Principles - Kernel Modules - Process Management Scheduling -
Memory Management - Input-Output Management - File System - Interprocess
Communication. iOS and Android: Architecture and SDK Framework - Media Layer -
Services Layer - Core OS Layer - File System.
14OUTCOMES:
Upon Completion of the course, the students should be able to:
Discuss the various synchronization, scheduling and memory management issues
Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols of
Distributed operating system
Discuss the various resource management techniques for distributed systems
Identify the different features of real time and mobile operating systems
Install and use available open source kernel
Modify existing open source kernels in terms of functionality or features used
REFERENCES:
1. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
2. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,
Seventh Edition, John Wiley & Sons, 2004.
3. Daniel P Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
4. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
5. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload
media, 2011.
CP7211
ADVANCED DATABASE LABORATORY
LT P C
0 0 21
OBJECTIVES:
To learn to work on distributed data bases
To understand and work on object oriented databases
To gain knowledge in parallel data base by experimenting it
To learn to work on active database
To study and explore deductive database
To work on the data mining tool weka
To represent and work with the database using XML
DISTRIBUTED DATABASE:
1. Consider a distributed database for a bookstore with 4 sites called S1, S2, S3 and S4.
Consider the following relations:
Books ( ISBN, primary Author, topic, total Stock, price )
Book Store (store No, city, state, zip, inventoryValue )
Stock (store No, ISBN, Qty )
Total Stock is the total number of books in stock and inventory Value is the total
inventory value for the store in dollars.
Consider that Books are fragmented by price amounts into:
F1: Books: price up to $20
F2: Books: price from $20.01 to $50
F3: Books: price from $50.01 to $100
F4: Books: price $100.01 and above
Similarly, Book Stores are divided by ZIP codes into:
15S1: Bookstore: Zip up to 25000
S2: Bookstore: Zip 25001 to 50000
S3: Bookstore: Zip 50001 to 75000
S4: Bookstore: Zip 75001 to 99999
Task: Write SQL query for the following
1. Insert and Display details in each table.
2. Find the total number of books in stock where price is between $15 and $55.
3. Update the book price of book No=1234 from $45 to $55 at site S3.
4. Find total number of book at site S2.
2. Implement deadlock detection algorithm for distributed database using wait-for graph
and test with the following information.
Consider five transactions T1, T2, T3, T4 and T5 with
T1 initiated at site S1 and spawning an agent at site S2
T2 initiated at site S3 and spawning an agent at site S1
T3 initiated at site S1 and spawning an agent at site S3
T4 initiated at site S2 and spawning an agent at site S3
T5 initiated at site S3
The locking information for these transactions is shown in the following table
Transactions
T1
T1
T2
T2
T3
T3
T4
T4
T5
Data items locked
by transactions
X1
X6
X4
X5
X2
-
X7
X8
X3
Data items transaction is
waiting for
X8
X2
X1
-
X7
X3
-
X5
X7
Site involved
in operations
S1
S2
S1
S3
S1
S3
S2
S3
S3
Produce local wait for graph for each of the sites and construct global wait for graph and
check for dead lock.
OBJECT ORIENTED DATABASE:
3. A University wants to track persons associated with them. A person can be an
Employee or Student. Employees are Faculty, Technicians and Project associates.
Students are Full time students, Part time students and Teaching Assistants.
a) Design an Enhanced Entity Relationship (EER) Model for university database.
Write OQL for the following
i.
Insert details in each object.
ii.
Display the Employee details.
iii.
Display Student Details.
iv.
Modify person details.
v.
Delete person details.
b)
Extend the design by incorporating the following information.
Students are registering for courses which are handled by instructor researchers
(graduate students). Faculty are advisors to graduate students. Instructor researchers’ class
16is a category with super class of faculty and graduate students. Faculty are having
sponsored research projects with a grant supporting instruction researchers. Grants are
sanctioned by different agencies. Faculty belongs to different departments. Department is
chaired by a faculty. Implement for the Insertion and Display of details in each class.
PARALLEL DATABASE:
4. Consider the application for University Counselling for Engineering Colleges. The
college, department and vacancy details are maintained in 3 sites. Students are
allocated colleges in these 3 sites simultaneously. Implement this application using
parallel database [State any assumptions you have made].
5. There are 5 processors working in a parallel environment and producing output. The
output record contains college details and students mark information. Implement
parallel join and parallel sort algorithms to get the marks from different colleges of the
university and publish 10 ranks for each discipline.
ACTIVE DATABASE:
6. Create triggers and assertions for Bank database handling deposits and loan and
admission database handling seat allocation and vacancy position. Design the above
relational database schema and implement the following triggers and assertions.
a. When a deposit is made by a customer, create a trigger for updating
customers account and bank account
b. When a loan is issued to the customer, create a trigger for updating
customer’s loan account and bank account.
c. Create assertion for bank database so that the total loan amount does not
exceed the total balance in the bank.
d. When an admission is made, create a trigger for updating the seat allocation
details and vacancy position.
DEDUCTIVE DATABASE:
7. Construct a knowledge database for kinship domain (family relations) with facts.
Extract the following relations using rules.
Parent, Sibling, Brother, Sister, Child, Daughter, Son, Spouse, Wife, husband,
Grandparent, Grandchild, Cousin, Aunt and Uncle.
WEKA TOOL:
8. Work with Weka tool classification and clustering algorithms using the given training
data and test with the unknown sample. Also experiment with different scenarios and
large data set
RID Age Income Student
1 youth high no
2 youth high no
3 middle_aged high no
4 senior medium no
5 senior low yes
6 senior low yes
7 middle_aged low yes
8 youth medium no
9 youth low yes
10 senior medium yes
11 Youth medium yes
12 middle_aged medium no
13 middle_aged high yes
14 senior medium no
17
Credit_
rating
fair
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
Class: buys_
computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
noQUERY PROCESSING
9. Implement Query Optimizer with Relational Algebraic expression construction and
execution plan generation for choosing an efficient execution strategy for processing
the given query.
Also design employee database and test the algorithm with following sample queries.
a) Select empid, empname from employee where experience > 5
b) Find all managers working at London Branch
XML
10. Design XML Schema for the given company database
Department ( deptName, deptNo, deptManagerSSN, deptManagerStartDate,
deptLocation )
Employee ( empName, empSSN, empSex, empSalary, empBirthDate,
empDeptNo, empSupervisorSSN, empAddress, empWorksOn)
Project ( projName, projNo, projLocation, projDeptNo, projWorker )
a. Implement the following queries using XQuery and XPath
i.
ii.
iii.
iv.
b.
Retrieve the department name, manager name, and manager salary for every
department’
Retrieve the employee name, supervisor name and employee salary for each
employee who works in the Research Department.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project with more than
one employee working on it
Implement a storage structure for storing XML database and test with the above
schema.
TOTAL : 30 PERIODS
OUTCOMES:
Work on distributed databases
Create and work on object oriented databases
Create and work with parallel database
Experiment on active database
Explore the features of deductive database
To work on weka tool for clustering and classification
Represent the database using XML and work on it
CP7212
CASE STUDY – OPERATING SYSTEMS DESIGN
(Team Work)
L T PC
0 0 4 2
OBJECTIVES:
1. To develop capabilities to work at systems level
2. To learn about issues in designing and implementing modern operating systems
3. To understand team formation, team issues, and allocating roles and responsibilities
4. To make effective presentations on the work done
5. To develop effective written communication skills
LAB EXERCISES:
A team of three or four students will work on assigned case study / mini-project. Case Study
/ Mini-project can be designed on the following lines:
1. Development of a reasonably sized dynamically loadable kernel module for Linux
kernel
182. Study educational operating systems such as Minix (http://www.minix3.org/), Weenix
(http://weenix.cs.brown.edu/mediawiki/index.php/Weenix) and develop reasonably
sized interesting modules for them
3. Study the Android open source operating system for mobile devices
(http://source.android.com/) and develop / modify some modules.
4. Study any embedded and real-time operating system such as eCos
(http://ecos.sourceware.org/) and develop / modify some modules.
TOTAL : 60 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Develop assigned modules of operating systems design carrying out coding, testing,
and documentation work involved.
Describe team issuses and apply suitable methods to resolve the same.
Demonstrate individual competence in building medium size operating system
components.
Demonstrate ethical and professional attributes of a computer engineer.
Prepare suitable plan with clear statements of deliverables, and track the same.
Make individual presentation of the work carried out.
Prepare well-organized written documents to communicate individual work
accomplished.
REFERENCES:
1. Watts S. Humphrey, “Introduction to Team Software Process”, Addison-Wesley, SEI
Series in Software Engineering, 1999.
2. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
3. T. W. Doeppner, “Operating Systems in Depth: Design and Programming”, Wiley, 2010.
4. S. Tanenbaum and A. S. Woodhull, “Operating Systems Design and Implementation”,
Third Edition, Prentice Hall, 2006.
5. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, “Operating System Concepts”,
Ninth Edition, John Wiley & Sons, 2012.
6. Daniel P. Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
7. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
CP7301
SOFTWARE PROCESS AND PROJECT MANAGEMENT
LTPC
3104
OBJECTIVES:
1.
2.
3.
4.
5.
To
To
To
To
To
understand overall SDLC and adopt suitable processes
elicite, analyze, prioritize, and manage both functional and quality requirements
estimate efforts required, plan, and track the plans
understand and apply configuration and quality management techniques
evaluate, manage, and design processes
(A mini-project can be chosen by the instructor and use it as a context for the
tutorials)
UNIT I
DEVELOPMENT LIFE CYCLE PROCESSES
9
Overview of software development life cycle – introduction to processes – Personal Software
Process (PSP) – Team software process (TSP) – Unified processes – agile processes –
choosing the right process Tutorial: Software development using PSP
19UNIT II
REQUIREMENTS MANAGEMENT
9
Functional requirements and quality attributes – elicitation techniques – Quality Attribute
Workshops (QAW) – analysis, prioritization, and trade-off – Architecture Centric
Development Method (ACDM) – requirements documentation and specification – change
management – traceability of requirements
Tutorial: Conduct QAW, elicit, analyze, prioritize, and document requirements using ACDM
UNIT III
ESTIMATION, PLANNING, AND TRACKING
9
Identifying and prioritizing risks – risk mitigation plans – estimation techniques – use case
points – function points – COCOMO II – top-down estimation – bottom-up estimation – work
breakdown structure – macro and micro plans – planning poker – wideband delphi –
documenting the plan – tracking the plan – earned value method (EVM)
Tutorial: Estimation, planning, and tracking exercises
UNIT IV
CONFIGURATION AND QUALITY MANAGEMENT
9
identifying artifacts to be configured – naming conventions and version control –
configuration control – quality assurance techniques – peer reviews – Fegan inspection –
unit, integration, system, and acceptance testing – test data and test cases – bug tracking –
causal analysis
Tutorial: version control exercises, development of test cases, causal analysis of defects
UNIT V
SOFTWARE PROCESS DEFINITION AND MANAGEMENT
9
Process elements – process architecture – relationship between elements – process
modeling – process definition techniques – ETVX (entry-task-validation-exit) – process
baselining – process assessment and improvement – CMMI – Six Sigma
Tutorial: process measurement exercises, process definition using ETVX
TOTAL 45+15=60 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
1. Explain software development life cycle
2. Adopt a suitable process for software development
3. Elicit functional and quality requirements
4. Analyze, prioritize, and manage requirements
5. Perform trade-off among conflicting requirements
6. Identify and prioritize risks and create mitigation plans
7. Estimate the efforts required for software development
8. Perform planning and tracking activities
9. Control the artifacts during software development
10. Perform various tests to ensure quality
11. Define new processes based on the needs
12. Adopt best practices for process improvement
REFERENCES:
1. Pankaj Jalote, “Software Project Management in Practice”, Pearson, 2002.
2. Chris F. Kemerer, “Software Project Management – Readings and Cases”, McGraw Hill,
1997.
3. Watts S. Humphrey, “PSP: A self-improvement process for software engineers”,
Addison-Wesley, 2005.
4. Watts S. Humphrey, “Introduction to the Team Software Process”, Addison-Wesley,
2000.
5. Orit Hazzan and Yael Dubinsky, “Agile software engineering”, Springer, 2008.
6. James R. Persse, “Process Improvement Essentials”, O’Reilly, 2006.
7. Roger S. Pressman, “Software Engineering – A Practitioner’s Approach”, Seventh
Edition, McGraw Hill, 2010.
20SE7103
FORMAL MODELS OF SOFTWARE SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the basic elements of Z
To understand relations, functions, and logical structures in Z
To understand Z schemas and schema calculus
To learn selected Z case studies
To understand Z schema refinement
UNIT I
FOUNDATIONS OF Z
9
Understanding formal methods – motivation for formal methods – informal requirements to
formal specifications – validating formal specifications – Overview of Z specification – basic
elements of Z – sets and types – declarations – variables – expressions – operators –
predicates and equations
UNIT II
STRUCTURES IN Z
9
Tuples and records – relations, tables, databases – pairs and binary relations – functions –
sequences – propositional logic in Z – predicate logic in Z – Z and boolean types – set
comprehension – lambda calculus in Z – simple formal specifications – modeling systems
and change
UNIT III
Z SCHEMAS AND SCHEMA CALCULUS
9
Z schemas – schema calculus – schema conjunction and disjunction – other schema
calculus operators – schema types and bindings – generic definitions – free types – formal
reasoning – checking specifications – precondition calculation – machine-checked proofs
UNIT IV
Z CASE STUDIES
9
Case Study: Text processing system – Case Study: Eight Queens – Case Study: Graphical
User Interface – Case Study: Safety critical protection system – Case Study: Concurrency
and real time systems
UNIT V
Z REFINEMENT
9
Refinement of Z specification – generalizing refinements – refinement strategies – program
derivation and verification – refinement calculus – data structures – state schemas –
functions and relations – operation schemas – schema expressions – refinement case study
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Apply the basic elements of Z
Develop relational, functional, and logical Z structures
Develop Z schema as models of software systems
Perform verifications and conduct proofs using Z models
Refine Z models towards implementing software systems
REFERENCES:
1. Jonathan Jacky, “The way of Z: Practical programming with formal methods”,
Cambridge University Press, 1996.
2. Antoni Diller, “Z: An introduction to formal methods”, Second Edition, Wiley, 1994.
3. Jim Woodcock and Jim Davies, “Using Z – Specification, Refinement, and Proof”,
Prentice Hall, 1996.
4. J. M. Spivey, “The Z notation: A reference manual”, Second Edition, Prentice Hall,
1992.
5. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
6. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
21CP7001
PERFORMANCE EVALUATION OF COMPUTER SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for performance evaluation of
computer systems
To understand the metrics used for performance evaluation
To understand the analytical modeling of computer systems
To enable the students to develop new queueing analysis for both simple and complex
systems
To appreciate the use of smart scheduling and introduce the students to analytical
techniques for evaluating scheduling policies
UNIT I
OVERVIEW OF PERFORMANCE EVALUATION
9
Need for Performance Evaluation in Computer Systems – Overview of Performance
Evaluation Methods – Introduction to Queueing – Probability Review – Generating Random
Variables for Simulation – Sample Paths, Convergence and Averages – Little’s Law and
other Operational Laws – Modification for Closed Systems.
UNIT II
MARKOV CHAINS AND SIMPLE QUEUES
9
Discrete-Time Markov Chains – Ergodicity Theory – Real World Examples – Google, Aloha
– Transition to Continuous-Time Markov Chain – M/M/1 and PASTA.
UNIT III
MULTI-SERVER AND MULTI-QUEUE SYSTEMS
9
Server Farms: M/M/k and M/M/k/k – Capacity Provisioning for Server Farms – Time
Reversibility and Burke’s Theorem – Networks of Queues and Jackson Product Form –
Classed and Closed Networks of Queues.
UNIT IV
REAL-WORLD WORKLOADS
9
Case Study of Real-world Workloads – Phase-Type Distributions and Matrix-Alalytic
Methods – Networks with Time-Sharing Servers – M/G/1 Queue and the Inspection Paradox
– Task Assignment Policies for Server Farms.
UNIT V
SMART SCHEDULING IN THE M/G/1
9
Performance Metrics – Scheduling Non-Preemptive and Preemptive Non-Size-Based
Policies - . Scheduling Non-Preemptive and Preemptive Size-Based Policies – Scheduling -
SRPT and Fairness.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for performance evaluation and the metrics used for it
Discuss open and closed queueing networks
Define Little’e law and other operational laws
Apply the operational laws to open and closed systems
Use discrete-time and continuous-time Markov chains to model real world systems
Develop analytical techniques for evaluating scheduling policies
REFERENCES:
1. Mor Harchol - Balter, “Performance Modeling and Design of Computer Systems –
Queueing Theory in Action”, Cambridge University Press, 2013.
2. Raj Jain, “The Art of Computer Systems Performance Analysis: Techniques for
Experimental Design, Measurement, Simulation and Modeling”, Wiley-Interscience,
1991.
3. Lieven Eeckhout, “Computer Architecture Performance Evaluation Methods”, Morgan
and Claypool Publishers, 2010.
4. Paul J. Fortier and Howard E. Michel, “Computer Systems Performance Evaluation and
Prediction”, Elsevier, 2003.
5. David J. Lilja, “Measuring Computer Performance: A Practitioner’s Guide”, Cambridge
University Press, 2000.
226. Krishna Kant, “Introduction to Computer System Performance Evaluation”, McGraw-Hill,
1992.
7. K. S. Trivedi, “Probability and Statistics with Reliability, Queueing and Computer Science
Applications”, John Wiley and Sons, 2001.
CP7002
PROBABILISTIC REASONING SYSTEMS
LT P C
3 0 0 3
OBJECTIVES:
To construct and reason with Bayesian networks
To reason with temporal models
To make exact and approximate inferences with graphical models
To understand learning of parameters for probabilistic graphical models
To understand actions and decisions with probabilistic graphical models
UNIT I
REPRESENTATION
9
Probability Theory, Graphs, Bayesian network representation: Bayes networks,
Independence in graphs – Undirected graphical models: Parameterization, Markov Network
independencies – Conditional Bayesian networks.
UNIT II
TEMPLATE BASED REPRESENTATION
9
Temporal models (Dynamic Bayesian networks , Hidden Markov Models) – Directed
probabilistic models for object-relational domains – Inference in temporal models: Kalman
filters.
UNIT III
INFERENCE
9
Exact inference: Variable elimination – Exact inference: Clique trees (Junction trees) –
Approximate inference: Forward sampling, Importance sampling, MCMC – MAP inference:
Variable elimination for MAP, Max-product in clique trees.
UNIT IV
LEARNING
9
Learning graphical models – Parameter estimation: maximum-likelihood estimation, MLE for
Bayesian networks, Bayesian parameter estimation – Structure learning in Bayesian
networks: Constraint based, structure scores, structure search – Partially observed data:
Parameter estimation, Learning models with hidden variables – Learning undirected
models: Maximum likelihood
UNIT V
ACTIONS AND DECISIONS
Causality – Utilities and decisions – Structured decision problems
9
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Construct Bayesian networks
Reason with Bayesian networks
Reason with Dynamic networks and Hidden Markov Models
Conduct inferences with Bayesian networks
Implement algorithms to learn probabilistic graphical models
Explain actions and decisions with probabilistic graphical models
REFERENCES:
1. Daphne Koller and Nir Friedman, “Probabilistic Graphical Models: Principles and
Techniques”, MIT Press, 2009.
2. David Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
3. Adnan Darwiche, “Modeling and Reasoning with Bayesian networks”, Cambridge
University Press, 2009.
4. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
5. Stuart Russel and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third
Edition, Prentice Hall, 2009.
23CP7003
DATA ANALYSIS AND BUSINESS INTELLIGENCE
LTPC
3 003
OBJECTIVES:
To understand linear regression models
To understand logistic regression models
To understand generalized linear models
To understand simulation using regression models
To understand causal inference
To understand multilevel regression
To understand data collection and model understanding
UNIT I
LINEAR REGRESSION
9
Introduction to data analysis – Statistical processes – statistical models – statistical inference
– review of random variables and probability distributions – linear regression – one predictor
– multiple predictors – prediction and validation – linear transformations – centering and
standardizing – correlation – logarithmic transformations – other transformations – building
regression models – fitting a series of regressions
UNIT II
LOGISTIC AND GENERALIZED LINEAR MODELS
9
Logistic regression – logistic regression coefficients – latent-data formulation – building a
logistic regression model – logistic regression with interactions – evaluating, checking, and
comparing fitted logistic regressions – identifiability and separation – Poisson regression –
logistic-binomial model – Probit regression – multinomial regression – robust regression
using t model – building complex generalized linear models – constructive choice models
UNIT III
SIMULATION AND CAUSAL INFERENCE
9
Simulation of probability models – summarizing linear regressions – simulation of non-linear
predictions – predictive simulation for generalized linear models – fake-data simulation –
simulating and comparing to actual data – predictive simulation to check the fit of a time-
series model – causal inference – randomized experiments – observational studies – causal
inference using advanced models – matching – instrumental variables
UNIT IV
MULTILEVEL REGRESSION
9
Multilevel structures – clustered data – multilevel linear models – partial pooling – group-
level predictors – model building and statistical significance – varying intercepts and slopes –
scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression –
multi-level generalized linear models
UNIT V
DATA COLLECTION AND MODEL UNDERSTANDING
9
Design of data collection – classical power calculations – multilevel power calculations –
power calculation using fake-data simulation – understanding and summarizing fitted models
– uncertainty and variability – variances – R2 and explained variance – multiple comparisons
and statistical significance – analysis of variance – ANOVA and multilevel linear and general
linear models – missing data imputation
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Build and apply linear regression models
Build and apply logistic regression models
Build and apply generalized linear models
Perform simulation using regression models
Perform casual inference from data
Build and apply multilevel regression models
Perform data collection and variance analysis
REFERENCES:
1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and
multilevel/Hierarchical Models", Cambridge University Press, 2006.
2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.
3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012.
244. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second
Edition, Oxford University Press, 2006.
5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and
data mining applications", Academic Press, 2009.
6. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An
Example-based Approach", Third Edition, Cambridge University Press, 2010.
8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer,
2011.
CP7004
IMAGE PROCESSING AND ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the basics of digital images
To understand noise models
To understand spatial domain filters
To understand frequency domain filters
To learn basic image analysis --- segmentation, edge detection, and corner detection
To learn morphological operations and texture analysis
To understand processing of color images
To understand image compression techniques
UNIT I
SPATIAL DOMAIN PROCESSING
9
Introduction to image processing – imaging modalities – image file formats – image sensing
and acquisition – image sampling and quantization – noise models – spatial filtering
operations – histograms – smoothing filters – sharpening filters – fuzzy techniques for spatial
filtering – spatial filters for noise removal
UNIT II
FREQUENCY DOMAIN PROCESSING
9
Frequency domain – Review of Fourier Transform (FT), Discrete Fourier Transform (DFT),
and Fast Fourier Transform (FFT) – filtering in frequency domain – image smoothing –
image sharpening – selective filtering – frequency domain noise filters – wavelets – Haar
Transform – multiresolution expansions – wavelet transforms – wavelets based image
processing
UNIT III
SEGMENTATION AND EDGE DETECTION
9
Thresholding techniques – region growing methods – region splitting and merging – adaptive
thresholding – threshold selection – global valley – histogram concavity – edge detection –
template matching – gradient operators – circular operators – differential edge operators –
hysteresis thresholding – Canny operator – Laplacian operator – active contours – object
segmentation
UNIT IV
INTEREST POINTS, MORPHOLOGY, AND TEXTURE
9
Corner and interest point detection – template matching – second order derivatives –
median filter based detection – Harris interest point operator – corner orientation – local
invariant feature detectors and descriptors – morphology – dilation and erosion –
morphological operators – grayscale morphology – noise and morphology – texture – texture
analysis – co-occurrence matrices – Laws' texture energy approach – Ade's eigen filter
approach
25UNIT V
COLOR IMAGES AND IMAGE COMPRESSION
9
Color models – pseudo colors – full-color image processing – color transformations –
smoothing and sharpening of color images – image segmentation based on color – noise in
color images.
Image Compression – redundancy in images – coding redundancy – irrelevant information in
images – image compression models – basic compression methods – digital image
watermarking.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain image modalities, sensing, acquisition, sampling, and quantization
Explain image noise models
Implement spatial filter operations
Explain frequency domain transformations
Implement frequency domain filters
Apply segmentation algorithms
Apply edge detection techniques
Apply corner and interest point detection algorithms
Apply morphological operations
Perform texture analysis
Analyze color images
Implement image compression algorithms
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction
using Java”, Springer, 2008.
3. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011.
4. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson,
2008.
5. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
6. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
7. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
NE7001
SENSING TECHNIQUES AND SENSORS
LT P C
3003
OBJECTIVES:
To study the sensor characteristics and the fundamental principles of sensing
To understand the sensor interface electronics
To study selected motion-related sensors
To study light and radiation detectors
To study selected temperature sensors
To study selected chemical sensors
UNIT I
PRINCIPLES OF SENSING
9
Data Acquisition – sensor characteristics – electric charges, fields, potentials – capacitance
– magnetism – inductance – resistance – piezoelectric – pyroelectric – Hall effect –
thermoelectric effects – sound waves – heat transfer – light – dynamic models of sensors
UNIT II
OPTICAL COMPONENTS AND INTERFACE ELECTRONICS
9
Radiometry – Photometry – mirrors – lenses – fibre optics – concentrators – Interface
circuits – amplifiers – light-to-voltage – excitation circuits – ADC – Digitization –
Capacitance-to-voltage – bridge circuits – data transmission – noise in sensors and circuits –
calibration – low power sensors
26UNIT III
MOTION RELATED SENSORS
9
Occupancy and motion detectors: ultrasonic – microwave – capacitive detectors –
triboelectric – optoelectronic motion sensors – optical presence sensor – Pressure Gradient
sensorsVelocity and acceleration sensors: Accelerometer characteristics – capacitative
accelerometers – piezoelectric accelerometers – piezoresistive accelerometers – thermal
accelerometers – Gyroscopes – piezoelectric cables – gravitational sensors
UNIT IV
LIGHT AND RADIATION DETECTORS
9
Light Detectors: Photo diodes – photo transistor – photo resistor – cooled detectors – CCD
and CMOS image sensors – thermal detectors – optical design – gas flame detectors
Radiation Detectors: scintillating detectors – ionization detectors – cloud and bubble
chambers
UNIT V
TEMPERATURE AND CHEMICAL SENSORS
9
Temperature Sensors: coupling with objects – temperature reference points – thermo
resistive sensors – thermo electric contact sensors – semiconductor sensors – acoustic
sensors – piezoelectric sensors Chemical sensors: characteristics – classes of chemical
sensors – biochemical sensors – multi-sensor arrays – electronic noses and tongues
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain sensor characteristics
Explain the physics of sensors
Explain optical components of sensors
Apply sensor interface electronics
Choose and use appropriate motion-related sensors
Choose and use appropriate light and radiation detectors
Choose and use appropriate temperature sensors
Choose and use appropriate chemical sensors
REFERENCE:
1. Jacob Fraden, “Handbook of Modern Sensors: Physics, Designs, and Applications”,
Fourth Edition, Springer, 2010.
CP7005
RANDOMIZED ALGORITHMS
LTPC
30 03
OBJECTIVES:
To understand the mathematical foundations needed for understanding and designing
randomized algorithms
To appreciate the need for randomized algorithms
To expose the students to probabilistic methods
To understand the concept of random walk
To expose the students to different types of applications of randomized algorithms
UNIT I
INTRODUCTION TO RANDOMIZED ALGORITHMS
9
Introduction to Randomized Algorithms - Min-cut – Elementary Probability Theory – Models
of Randomized Algorithms – Classification of Randomized Algorithms – Paradigms of the
Design of Randomized Algorithms - Game Theoretic Techniques – Game Tree Evaluation –
Minimax Principle – Randomness and Non Uniformity.
UNIT II
PROBABILISTIC METHODS
9
Moments and Deviations – occupancy Problems – Markov and Chebyshev Inequalities –
Randomized Selection – Two Point Sampling – The Stable Marriage Problem – The
Probabilistic Method – Maximum Satisfiability – Expanding Graphs – Method of Conditional
Probabilities – Markov Chains and Random Walks – 2-SAT Example – Random Walks on
Graphs – Random Connectivity.
27UNIT III
ALGEBRAIC TECHNIQUES AND APPLICATIONS
9
Fingerprinting Techniques – Verifying Polynomial Identities – Perfect Matching in Graphs –
Pattern Matching – Verification of Matrix Multiplication - Data Structuring Problems –
Random Treaps – Skip Lists – Hash Tables.
UNIT IV
GEOMETRIC AND GRAPH ALGORITHMS
9
Randomized Incremental Construction – Convex Hulls – Duality – Trapezoidal
Decompositions – Linear Programming – Graph Algorithms – Min-cut – Minimum Spanning
Trees.
UNIT V HASHING AND ONLINE ALGORITHMS
9
Hashing – Universal Hashing - Online Algorithms – Randomized Online Algorithms - Online
Paging – Adversary Models – Relating the Adversaries – The k-server Problem.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for randomized algorithms
Discuss the classification of randomized algorithms
Present the various paradigms for designing randomized algorithms
Discuss the different probabilistic methods used for designing randomized algorithms
Apply the techniques studied to design algorithms for different applications like matrix
multiplication, hashing, linear programming
REFERENCES:
1. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
2. Juraj Hromkovic,”Design and Analysis of Randomized Algorithms”, Springer, 2010.
3. Michael Mitzenmacher and Eli Upfal, “Probabilty and Computing – Randomized
Algorithms and Probabilistic Analysis”, Cambridge University Press, 2005.
NE7002
MOBILE AND PERVASIVE COMPUTING
LT P C
3003
OBJECTIVES :
To understand the basics of Mobile Computing and Personal Computing
To learn the role of cellular networks in Mobile and Pervasive Computing
To expose to the concept of sensor and mesh networks
To expose to the context aware and wearable computing
To learn to develop applications in mobile and pervasive computing environment
UNIT I
INTRODUCTION
9
Differences between Mobile Communication and Mobile Computing – Contexts and Names
– Functions – Applications and Services – New Applications – Making Legacy Applications
Mobile Enabled – Design Considerations – Integration of Wireless and Wired Networks –
Standards Bodies – Pervasive Computing – Basics and Vision – Principles of Pervasive
Computing – Categories of Pervasive Devices
UNIT II
3G AND 4G CELLULAR NETWORKS
9
Migration to 3G Networks – IMT 2000 and UMTS – UMTS Architecture – User Equipment –
Radio Network Subsystem – UTRAN – Node B – RNC functions – USIM – Protocol Stack –
CS and PS Domains – IMS Architecture – Handover – 3.5G and 3.9G a brief discussion –
4G LAN and Cellular Networks – LTE – Control Plane – NAS and RRC – User Plane –
PDCP, RLC and MAC – WiMax IEEE 802.16d/e – WiMax Internetworking with 3GPP
28UNIT III
SENSOR AND MESH NETWORKS
9
Sensor Networks – Role in Pervasive Computing – In Network Processing and Data
Dissemination – Sensor Databases – Data Management in Wireless Mobile Environments –
Wireless Mesh Networks – Architecture – Mesh Routers – Mesh Clients – Routing – Cross
Layer Approach – Security Aspects of Various Layers in WMN – Applications of Sensor and
Mesh networks
UNIT IV
CONTEXT AWARE COMPUTING & WEARABLE COMPUTING
9
Adaptability – Mechanisms for Adaptation - Functionality and Data – Transcoding –
Location Aware Computing – Location Representation – Localization Techniques –
Triangulation and Scene Analysis – Delaunay Triangulation and Voronoi graphs – Types of
Context – Role of Mobile Middleware – Adaptation and Agents – Service Discovery
Middleware
Health BAN- Medical and Technological Requirements-Wearable Sensors-Intra-BAN
communications
UNIT V
APPLICATION DEVELOPMENT
9
Three tier architecture - Model View Controller Architecture - Memory Management –
Information Access Devices – PDAs and Smart Phones – Smart Cards and Embedded
Controls – J2ME – Programming for CLDC – GUI in MIDP – Application Development ON
Android and iPhone
TOTAL:45 PERIODS
OUTCOMES:
At the end of the course the student should be able to
Design a basic architecture for a pervasive computing environment
Design and allocate the resources on the 3G-4G wireless networks
Analyze the role of sensors in Wireless networks
Work out the routing in mesh network
Deploy the location and context information for application development
Develop mobile computing applications based on the paradigm of context aware
computing and wearable computing
REFERENCES:
1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile Computing: Technology,
Applications and Service Creation”, 2nd ed, Tata McGraw Hill, 2010.
2. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
3. .Pei Zheng and Lionel M Li, ‘Smart Phone & Next Generation Mobile Computing’,
Morgan Kaufmann Publishers, 2006.
4. Frank Adelstein, ‘Fundamentals of Mobile and Pervasive Computing’, TMH, 2005
5. Jochen Burthardt et al, ‘Pervasive Computing: Technology and Architecture of Mobile
Internet Applications’, Pearson Education, 2003
6. Feng Zhao and Leonidas Guibas, ‘Wireless Sensor Networks’, Morgan Kaufmann
Publishers, 2004
7. Uwe Hansmaan et al, ‘Principles of Mobile Computing’, Springer, 2003
8. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
9. Mohammad s. Obaidat et al, “Pervasive Computing and Networking”,John wiley
10. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”,
Wiley, 2009
11. Frank Adelstein Sandeep K. S. Gupta Golden G. Richard III Loren Schwiebert
“Fundamentals of Mobile and Pervasive Computing, “, McGraw-Hill, 2005
29CP7006
PARALLEL PROGRAMMING PARADIGMS
LTPC
3 003
OBJECTIVES:
To understand models of and issues in concurrency in computing
To develop message-passing parallel programs using MPI
To develop shared-memory parallel programs using Pthreads
To develop shared-memory parallel programs using OpenMP
To use GPU for parallel programming using OpenCL and CUDA
UNIT I
FOUNDATIONS OF PARALLEL PROGRAMMING
9
Motivation for parallel programming - Concurrency in computing – basics of processes,
multiprocessing, and threads – cache – cache mappings – caches and programs – virtual
memory – instruction level parallelism – hardware multi-threading – SIMD – MIMD –
interconnection networks – cache coherence – shared-memory model – issues in shared-
memory model – distributed-memory model – issues in distributed-memory model – hybrid
model – I/O – performance of parallel programs – parallel program design
UNIT II
MESSAGE PASSING PARADIGM
9
Basic MPI programming – MPI_Init and MPI_Finalize – MPI communicators – SPMD
programs – message passing – MPI_Send and MPI_Recv – message matching – MPI I/O –
parallel I/O – collective communication – MPI_Reduce – MPI_Allreduce – broadcast –
scatter – gather – allgather – derived types – remote memory access – dynamic process
management – MPI for grids – performance evaluation of MPI programs
UNIT III
SHARED MEMORY PARADIGM: PTHREADS
9
Basics of Pthreads – thread synchronization – critical sections – busy-waiting – mutexes –
semaphores – barriers and condition variables – read-write locks – Caches, cache
coherence and false sharing – thread safety – Pthreads case study
UNIT IV
SHARED MEMORY PARADIGM: OPENMP
9
Basic OpenMP constructs – scope of variabls – reduction clause – parallel for directive –
loops in OpenMP – scheduling loops – synchronization in OpenMP – Case Study:
Producer-Consumer problem – cache issues – threads safety in OpenMP – OpenMP best
practices
UNIT V
GRAPHICAL PROCESSING PARADIGMS: OPENCL AND CUDA
9
Introduction to CUDA – CUDA programming examples – CUDA execution model – CUDA
memory hierarchy – CUDA case study - introduction to OpenCL – OpenCL programming
examples – Programs and Kernels – Buffers and Images – Event model – OpenCL case
study
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain models of parallel programming
Explain hardware level support for concurrency
Explain issues in parallel programming
Develop message-passing parallel programs using MPI framework
Develop shared-memory parallel programs using Pthreads
Develop shared-memory parallel programs using OpenMP
Develop CUDA programs
Develop OpenCL programs
REFERENCES:
1. Peter S. Pacheco, “An introduction to parallel programming”, Morgan Kaufmann,
2011.
2. M. J. Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw Hill,
2003.
3. W. Gropp, E. Lusk, and R. Thakur, “Using MPI-2: Advanced features of the message
passing interface”, MIT Press, 1999.
304. W. Gropp, E. Lusk, and A. Skjellum, “Using MPI: Portable parallel programming with
the message passing interface”, Second Edition, MIT Press, 1999.
5. B. Chapman, G. Jost, and Ruud van der Pas, “Using OpenMP”, MIT Press, 2008.
6. D. R. Butenhof, “Programming with POSIX Threads”, Addison Wesley, 1997.
7. B. Lewis and D. J. Berg, “Multithreaded programming with Pthreads”, Sun
Microsystems Press, 1998.
8. A. Munshi, B. Gaster, T. G. Mattson, J. Fung, and D. Ginsburg, “OpenCL
programming guide”, Addison Wesley, 2011.
9. Rob Farber, “CUDA application design and development”, Morgan Haufmann, 2011.
CP7007
SOFTWARE REQUIREMENTS ENGINEERING
LTPC
3 003
OBJECTIVES:
1. Understand system requirements
2. Identify different types of requirement
3. Generate requirements be elicitation
4. Develop requirements documentation
5. Evaluate the requirements
UNIT I
DOMAIN UNDERSTANDING
9
Introduction – Types of requirements – Requirements engineering process – Validating
requirements – Requirements and design – Requirements and test cases – introduction to
business domain – Problem analysis – Fish bone diagram – Business requirements –
Business process modeling – Business use cases – Business modeling notations – UML
Activity diagrams.
UNIT II
REQUIREMENTS ELICITATION
9
Introduction – Understanding stakeholders' needs – Elicitation techniques – interviews,
questionnaire, workshop, brainstorming, prototyping – Documenting stakeholders' needs
UNIT III
FUNCTIONAL REQUIREMENTS
9
Introduction – Features and Use cases – Use case scenarios – Documenting use cases –
Levels of details – SRS documents.
UNIT IV
QUALITY ATTRIBUTES AND USER EXPERIENCE
9
Quality of solution – Quality attributes – Eliciting quality attributes – Quality attribute
workshop (QAW) – Documenting quality attributes – Six part scenarios – Usability
requirements – Eliciting and documenting usability requirements – Modeling user experience
– Specifying UI design
UNIT V
MANAGING REQUIREMENTS
9
Defining scope of the project – Context diagram – Managing requirements – Requirements
properties – Traceability – Managing changes – Requirements metrics – Requirements
management tools.
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Define a process for requirments engineering
Execute a process for gathering requirments through elicitation techniques.
Validate requirements according to criteria such as feasibility, clarity, preciseness
etc.
Develop and document functional requirements for different types of systems.
Develop and document quality attributes of the system to be implemented
Communicate the requirments to stakeholders
Negotiate with stakeholders in order to agree on a set of requirements.
Detect and resolve feature interactions
31REFERENCES:
1. Axel van Lamsweerde, "Requirements Engineering", Wiley, 2009
2. Gerald Kotonya, Ian Sommerville, "Requirements Engineering: Processes and
Techniques", John Wiley and Sons, 1998
3. Dean Leffingwell and Don Widrig, “Managing Software Requirements: A Use Case
Approach (2nd Edition) ”, Addison-wesley, 2003
4. SEI Report, “Quality Attributes Workshop”,
http://www.sei.cmu.edu/library/abstracts/reports/03tr016.cfm , 2003
5. J Nielsen, “Usability Engineering”, Academic Press, 1993
CP7008
SPEECH PROCESSING AND SYNTHESIS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for speech processing
To understand the basic concepts and algorithms of speech processing and synthesis
To familiarize the students with the various speech signal representation, coding and
recognition techniques
To appreciate the use of speech processing in current technologies and to expose the
students to real– world applications of speech processing
UNIT I
FUNDAMENTALS OF SPEECH PROCESSING
9
Introduction – Spoken Language Structure – Phonetics and Phonology – Syllables and
Words – Syntax and Semantics – Probability, Statistics and Information Theory – Probability
Theory – Estimation Theory – Significance Testing – Information Theory.
UNIT II
SPEECH SIGNAL REPRESENTATIONS AND CODING
9
Overview of Digital Signal Processing – Speech Signal Representations – Short time Fourier
Analysis – Acoustic Model of Speech Production – Linear Predictive Coding – Cepstral
Processing – Formant Frequencies – The Role of Pitch – Speech Coding – LPC Coder.
UNITIII
SPEECH RECOGNITION
9
Hidden Markov Models – Definition – Continuous and Discontinuous HMMs – Practical
Issues – Limitations. Acoustic Modeling – Variability in the Speech Signal – Extracting
Features – Phonetic Modeling – Adaptive Techniques – Confidence Measures – Other
Techniques.
UNITIV
TEXT ANALYSIS
9
Lexicon – Document Structure Detection – Text Normalization – Linguistic Analysis –
Homograph Disambiguation – Morphological Analysis – Letter-to-sound Conversion –
Prosody – Generation schematic – Speaking Style – Symbolic Prosody – Duration
Assignment – Pitch Generation
UNIT V
SPEECH SYNTHESIS
9
Attributes – Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic
Modification of Speech – Source-filter Models for Prosody Modification – Evaluation of TTS
Systems.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the various temporal, spectral and cepstral features required for identifying
speech units – phoneme, syllable and word
Determine and apply Mel-frequency cepstral coefficients for processing all types of
signals
Justify the use of formant and concatenative approaches to speech synthesis
32
Identify the apt approach of speech synthesis depending on the language to be
processed
Determine the various encoding techniques for representing speech.
REFERENCES:
1. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing – A
guide to Theory, Algorithm and System Development”, Prentice Hall PTR, 2001.
2. Thomas F.Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education,
2002.
3. Lawrence Rabiner and Biing-Hwang Juang, “Fundamentals of Speech Recognition”,
Prentice Hall Signal Processing Series, 1993.
4. Sadaoki Furui, “Digital Speech Processing: Synthesis, and Recognition, Second Edition,
(Signal Processing and Communications)”, Marcel Dekker, 2000.
5. Joseph Mariani, “Language and Speech Processing”, Wiley, 2009.
CP7009
MACHINE LEARNING TECHNIQUES
LTPC
3 003
OBJECTIVES:
1. To understand the machine learning theory
2. To implement linear and non-linear learning models
3. To implement distance-based clustering techniques
4. To build tree and rule based models
5. To apply reinforcement learning techniques
UNIT I
FOUNDATIONS OF LEARNING
9
Components of learning – learning models – geometric models – probabilistic models – logic
models – grouping and grading – learning versus design – types of learning – supervised –
unsupervised – reinforcement – theory of learning – feasibility of learning – error and noise –
training versus testing – theory of generalization – generalization bound – approximation-
generalization tradeoff – bias and variance – learning curve
[
UNIT II
LINEAR MODELS
9
Linear classification – univariate linear regression – multivariate linear regression –
regularized regression – Logistic regression – perceptrons – multilayer neural networks –
learning neural networks structures – support vector machines – soft margin SVM – going
beyond linearity – generalization and overfitting – regularization – validation
UNIT III
DISTANCE-BASED MODELS
9
Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchical
clustering – k-d trees – locality sensitive hashing – non-parametric regression – ensemble
learning – bagging and random forests – boosting – meta learning
UNIT IV
TREE AND RULE MODELS
9
Decision trees – learning decision trees – ranking and probability estimation trees –
regression trees – clustering trees – learning ordered rule lists – learning unordered rule lists
– descriptive rule learning – association rule mining – first-order rule learning
UNIT V
REINFORCEMENT LEARNING
9
Passive reinforcement learning – direct utility estimation – adaptive dynamic programming –
temporal-difference learning – active reinforcement learning – exploration – learning an
action-utility function – Generalization in reinforcement learning – policy search –
applications in game playing – applications in robot control
TOTAL : 45 PERIODS
33OUTCOMES:
Upon Completion of the course,the students will be able to
To explain theory underlying machine learning
To construct algorithms to learn linear and non-linear models
To implement data clustering algorithms
To construct algorithms to learn tree and rule-based models
To apply reinforcement learning techniques
REFERENCES:
1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook
Publishers, 2012.
2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of
data”, Cambridge University Press, 2012.
3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012.
4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007.
5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning”,
MIT Press, 2012.
7. T. M. Mitchell, “Machine Learning”, McGraw Hill, 1997.
8. S. Russel and P. Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition,
Prentice Hall, 2009.
CP7010
CONCURRENCY MODELS
LTPC
3 003
OBJECTIVES:
To model concurrency in FSP
To specify and check safety and liveness properties
To understand concurrency architectures and design
To apply linear temporal logic to safety and liveness analysis
To apply Petri nets for concurrency modeling and analysis
UNIT I
FSP AND GRAPH MODELS
9
Concurrency and issues in concurrency – models of concurrency – graphical models – FSP
& LTSA – modeling processes with FSP – concurrency models with FSP – shared action –
structure diagrams – issues with shared objects – modeling mutual exclusion – conditional
synchronization – modeling semaphores – nested monitors – monitor invariants
UNIT II
SAFETY AND LIVENESS PROPERTIES
9
Deadlocks – deadlock analysis in models – dining philosophers problem – safety properties
– single-lane bridge problem – liveness properties – liveness of the single-lane bridge –
readers-writers problem – message passing – asynchronous message passing models –
synchronous message passing models – rendezvous
UNIT III
CONCURRENCY ARCHITECTURES AND DESIGN
9
Modeling dynamic systems – modeling timed systems – concurrent architectures – Filter
pipeline – Supervisor-worker model – announcer-listener model – model-based design –
from requirements to models – from models to implementations – implementing concurrency
in Java – program verification
UNIT IV
LINEAR TEMPORAL LOGIC (LTL)
9
Syntax of LTL – semantics of LTL – practical LTL patterns – equivalences between LTL
statements – specification using LTL – LTL and FSP – Fluent proposition – Temporal
propositions – Fluent Linear Temporal Logic (FLTL) – FLTL assertions in FSP – Database
ring problem
34UNIT V
PETRI NETS
9
Introduction to Petri nets – examples – place-transition nets – graphical and linear algebraic
representations – concurrency & conflict – coverability graphs – decision procedures –
liveness – colored Petri nets (CPN) – modeling & verification using CPN – non-hierarchical
CPN – modeling protocols – hierarchical CPN – timed CPN – applications of Petri Nets
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Develop concurrency models and FSP
State safety and liveness properties in FSP
Verify properties using LTSA tool
Explain concurrency architectures
Design concurrent Java programs from models
Apply Linear Temporal Logic to state safety and liveness properties
Assert LTL properties in FSP and check using LTSA tool
Model and analyze concurrency using Petri nets
REFERENCES:
1. Jeff Magee & Jeff Kramer, “Concurrency: State Models and Java Programs”, Second
Edition, John Wiley, 2006.
2. M. Huth & M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
3. B. Goetz, T. Peierls, J. Bloch, J. Bowbeer, D. Holmes, and D. Lea, “Java Concurrency in
Practice”, Addison-Wesley Professional, 2006.
4. Wolfgang Reisig, “Petri Nets: An Introduction”, Springer, 2011.
5. K. Jensen and L. M. Kristensen, “Colored Petri Nets: Modeling and Validation of
Concurrent Systems”, Springer, 2009.
6. Wolfgang Reisig, “Understanding Petri Nets: Modeling Techniques, Analysis Methods,
Case Studies”, Springer, 2013.
CP7011
REAL TIME SYSTEMS
LTPC
3 003
OBJECTIVES:
To provide good understanding of fundamental concepts in real time systems.
To provide understanding of advanced topics in real time systems.
To provide understanding on basic multi-task scheduling algorithms for periodic,
aperiodic, and sporadic tasks as well as understand the impact of the latter two on
scheduling
To expose to understand capabilities of commercial off-the-shelf R-T kernel.
To expose to real time communications and databases.
UNIT I
INTRODUCTION
9
Real-time systems – Applications – Basic Model – Characteristics – Safety and Reliability –
Real-Time tasks – Timing Constraints – Modelling Timing Constraints.
UNIT II
SCHEDULING REAL-TIME TASKS
9
Concepts – Types of RT Tasks and their Characteristics – Task Scheduling – Clock-Driven
Scheduling – Hybrid Schedulers - Event-Driven Scheduling – EDF Scheduling – RMA –
Issues with RMA – Issues in Using RMA in Practical Situations
35UNIT III
RESOURCE SHARING AMONG RT TASKS & SCHEDULING RT TASKS 9
Resource Sharing Among RT Tasks – Priority Inversion – PIP – HLP – PCP – Types of
Priority Inversions Under PCP – Features of PCP – Issues in using Resource Sharing
Protocol – Handling Task Dependencies – Multiprocessor Task Allocation – Dynamic
Allocation of Tasks – Fault-Tolerant Scheduling of Tasks – Clocks in Distributed RT Systems
– Centralized and Distributed Clock Synchronization.
UNIT IV
COMMERCIAL RT OPERATING SYSTEMS
9
Time Services – Features of RT OS – Unix as a RT OS – Unix Based RT OS – Windows as
a RT OS – POSIX – Survey of RTOS: PSOS – VRTX – VxWorks – QNX - μC/OS-II – RT
Linux – Lynx – Windows CE – Benching RT Systems.
UNIT V
RT COMMUNICATION & DATABASES
9
Examples of Applications Requiring RT Communication – Basic Concepts – RT
Communication in a LAN – Soft & Hard RT Communication in a LAN – Bounded Access
Protocols for LANs – Performance Comparison – RT Communication Over Packet Switched
Networks – QoS Framework – Routing – Resource Reservation – Rate Control – QoS
Models - Examples Applications of RT Databases – RT Databases – Characteristics of
Temporal Data – Concurrency Control in RT Databases – Commercial RT Databases.
TOTAL: 45 PERIODS
OUTCOMES:
Understand the basics and importance of real-time systems
Generate a high-level analysis document based on requirements specifications
Generate a high-level design document based on analysis documentation
Generate a test plan based on requirements specification
Generate a validation plan based on all documentation
Understand basic multi-task scheduling algorithms for periodic, aperiodic, and
sporadic tasks as well as understand the impact of the latter two on scheduling
Understand capabilities of at least one commercial off-the-shelf R-T kernel
REFERENCES:
1. Rajib Mall, "Real-Time Systems: Theory and Practice," Pearson, 2008.
2. Jane W. Liu, "Real-Time Systems" Pearson Education, 2001.
3. Krishna and Shin, "Real-Time Systems," Tata McGraw Hill. 1999.
4. Alan C. Shaw, “Real-Time Systems and Software”, Wiley, 2001.
5. Philip Laplante, “Real-Time Systems Design and Analysis”, 2nd Edition, Prentice Hall
of India.
6. Resource Management in Real-time Systems and Networks, C. Siva Ram Murthy
and G. Manimaran, MIT Press, March 2001.
CP7012
COMPUTER VISION
LTPC
3 003
OBJECTIVES:
To review image processing techniques for computer vision
To understand shape and region analysis
To understand Hough Transform and its applications to detect lines, circles, ellipses
To understand three-dimensional image analysis techniques
To understand motion analysis
To study some applications of computer vision algorithms
UNIT I
IMAGE PROCESSING FOUNDATIONS
9
Review of image processing techniques – classical filtering operations – thresholding
techniques – edge detection techniques – corner and interest point detection – mathematical
morphology – texture
36UNIT II
SHAPES AND REGIONS
9
Binary shape analysis – connectedness – object labeling and counting – size filtering –
distance functions – skeletons and thinning – deformable shape analysis – boundary
tracking procedures – active contours – shape models and shape recognition – centroidal
profiles – handling occlusion – boundary length measures – boundary descriptors – chain
codes – Fourier descriptors – region descriptors – moments
UNIT III
HOUGH TRANSFORM
9
Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line
localization – line fitting – RANSAC for straight line detection – HT based circular object
detection – accurate center location – speed problem – ellipse detection – Case study:
Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial
matched filtering – GHT for ellipse detection – object location – GHT for feature collation
UNIT IV
3D VISION AND MOTION
9
Methods for 3D vision – projection schemes – shape from shading – photometric stereo –
shape from texture – shape from focus – active range finding – surface representations –
point-based representation – volumetric representations – 3D object recognition – 3D
reconstruction – introduction to motion – triangulation – bundle adjustment – translational
alignment – parametric motion – spline-based motion – optical flow – layered motion
UNIT V APPLICATIONS
9
Application: Photo album – Face detection – Face recognition – Eigen faces – Active
appearance and 3D shape models of faces
Application: Surveillance – foreground-background separation – particle filters – Chamfer
matching, tracking, and occlusion – combining views from multiple cameras – human gait
analysis
Application: In-vehicle vision system: locating roadway – road markings – identifying road
signs – locating pedestrians
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Implement fundamental image processing techniques required for computer vision
Perform shape analysis
Implement boundary tracking techniques
Apply chain codes and other region descriptors
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques
Implement motion related techniques
Develop applications using computer vision techniques
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011.
3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge
University Press, 2012.
4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
37NE7202
NETWORK AND INFORMATION SECURITY
LTPC
3 003
OBJECTIVES:
To understand the fundamentals of Cryptography
To acquire knowledge on standard algorithms used to provide confidentiality, integrity
and authenticity.
To understand the various key distribution and management schemes.
To understand how to deploy encryption techniques to secure data in transit across
data networks
To design security applications in the field of Information technology
UNIT I
INTRODUCTION
9
An Overview of Computer Security-Security Services-Security Mechanisms-Security
Attacks-Access Control Matrix, Policy-Security policies, Confidentiality policies, Integrity
policies and Hybrid policies.
UNIT II
CRYPTOSYSTEMS & AUTHENTICATION
9
Classical Cryptography-Substitution Ciphers-permutation Ciphers-Block Ciphers-DES-
Modes of Operation- AES-Linear Cryptanalysis, Differential Cryptanalysis- Hash Function -
SHA 512- Message Authentication Codes-HMAC - Authentication Protocols -
UNIT III
PUBLIC KEY CRYPTOSYSTEMS
9
Introduction to Public key Cryptography- Number theory- The RSA Cryptosystem and
Factoring Integer- Attacks on RSA-The ELGamal
Cryptosystem- Digital Signature
Algorithm-Finite Fields-Elliptic Curves Cryptography- Key management – Session and
Interchange keys, Key exchange and generation-PKI
UNIT IV
SYSTEM IMPLEMENTATION
9
Design Principles, Representing Identity, Access Control Mechanisms, Information Flow and
Confinement Problem
Secure Software Development: Secured Coding - OWASP/SANS Top Vulnerabilities -
Buffer Overflows - Incomplete mediation - XSS - Anti Cross Site Scripting Libraries -
Canonical Data Format - Command Injection - Redirection - Inference – Application Controls
UNIT V
NETWORK SECURITY
9
Secret Sharing Schemes-Kerberos- Pretty Good Privacy (PGP)-Secure Socket Layer (SSL)-
Intruders – HIDS- NIDS - Firewalls - Viruses
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Implement basic security algorithms required by any computing system.
Analyze the vulnerabilities in any computing system and hence be able to design a
security solution.
Analyze the possible security attacks in complex real time systems and their effective
countermeasures
Identify the security issues in the network and resolve it.
Evaluate security mechanisms using rigorous approaches, including theoretical
derivation, modeling, and simulations
Formulate research problems in the computer security field
REFERENCES:
1. William Stallings, “Cryptography and Network Security: Principles and Practices”,
Third Edition, Pearson Education, 2006.
2. Matt Bishop ,“Computer Security art and science ”, Second Edition, Pearson
Education, 2002
3. Wade Trappe and Lawrence C. Washington, “Introduction to Cryptography with
Coding Theory” Second Edition, Pearson Education, 2007
4. Jonathan Katz, and Yehuda Lindell, Introduction to Modern Cryptography, CRC
Press, 2007
385. Douglas R. Stinson, “Cryptography Theory and Practice”, Third Edition, Chapman &
Hall/CRC, 2006
6. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education,
First Edition, 2006.
7. Network Security and Cryptography, Menezes Bernard, Cengage Learning, New
Delhi, 2011
8. Man Young Rhee, Internet Security, Wiley, 2003
9. OWASP top ten security vulnerabilities: http://xml.coverpages.org/OWASP-
TopTen.pdf
CP7013
DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS
LTPC
3 003
OBJECTIVES:
To understand the need for parallel algorithms
To expose the students to different models of parallel computation
To expose the students to parallel sorting and searching algorithms
To understand the application of the concepts studied to different types of problems
To analyze parallel algorithms
UNIT I
INTRODUCTION
9
Introduction to Parallel Algorithms – Models of Parallel Computation – Sorting on an EREW-
SIMD PRAM Computer – Relation between PRAM Models – SIMD Algorithms – MIMD
Algorithms – Selection – Desirable Properties for Parallel Algorithms - Parallel Algorithm for
Selection – Analysis of Parallel Algorithms.
UNIT II
SORTING AND SEARCHING
9
Merging on the EREW and CREW Models - Fast Merging on EREW - Sorting Networks –
Sorting on a Linear Array – Sorting on CRCW, CREW, EREW Models – Searching a Sorted
Sequence – Searching a Random Sequence.
UNIT III
ALGEBRAIC PROBLEMS
9
Generating Permutations and Combinations in Parallel – Matrix Transpositions – Matrix by
Matrix Multiplications – Matrix by Vector multiplication.
UNIT IV
GRAPH THEORY AND COMPUTATIONAL GEOMETRY PROBLEMS
9
Connectivity Matrix – Connected Components – All Pairs Shortest Paths – Minimum
Spanning Trees – Point Inclusion – Intersection, Proximity and Construction Problems -
Sequential Tree Traversal - Basic Design Principles – Algorithm – Analysis.
UNITV
DECISION AND OPTIMIZATION PROBLEMS
9
Computing Prefix Sums – Applications - Job Sequencing with Deadlines – Knapsack
Problem- The Bit Complexity of Parallel Computations.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for parallel algorithms
Discuss the classification of parallel architectures and identify suitable programming
models
Perform sorting on Sorting on CRCW, CREW, EREW Models
Search a sorted as well as random sequence
Develop and analyze algorithms for different applications like matrix multiplication,
shortest path, job sequencing and the knapsack problem.
39REFERENCES:
1. Selim G. Akl, “The Design and Analysis of Parallel Algorithms”, Prentice Hall, New
Jersey, 1989.
2. Michael J. Quinn, “Parallel Computing : Theory & Practice”, Tata McGraw Hill Edition,
2003.
3. Justin R. Smith, “The Design and Analysis of Parallel Algorithms”, Oxford University
Press, USA , 1993.
4. Joseph JaJa, “Introduction to Parallel Algorithms”, Addison-Wesley, 1992.
CP7014
SOFTWARE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
Understand architectural requirements
Identify architectural structures
Develop architectural documentation
Generate architectural alternatives
Evaluate the architecture against the drivers
UNIT I
ARCHITECTURAL DRIVERS
9
Introduction – Standard Definitions of Software Architecture– Architectural structures –
Influence of software architecture on organization – Architecture Business Cycle –
Functional requirements – Technical constraints – Quality Attributes – Quality Attribute
Workshop (QAW) – Documenting Quality Attributes – Six part scenarios
UNIT II
ARCHITECTURAL VIEWS AND DOCUMENTATION
9
Introduction – Standard Definitions for views – Structures and views- Perspectives: Static,
dynamic and physical and the accompanying views – Representing views-available
notations – Good practices in documentation– Documenting the Views using UML – Merits
and Demerits of using visual languages – Need for formal languages - Architectural
Description Languages – ACME
UNIT III
ARCHITECTURAL STYLES
9
Introduction – Data flow styles – Call-return styles – Shared Information styles – Event styles
– Case studies for each style
UNIT IV
ARCHITECTURAL DESIGN
9
Approaches for architectural design – System decomposition – Attributes driven design –
Architecting for specific quality attributes – Performance, Availability – Security –
Architectural conformance
UNIT V
ARCHITECTURE EVALUATION AND SOME SPECIAL TOPICS
9
Need for evaluation – Scenario based evaluation against the drivers – ATAM and its
variations – Case studies in architectural evaluations – SOA and Web services – Cloud
Computing – Adaptive structures
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain key architectural drivers
Explain the influence of architecture on business and technical activities
Identify key architectural structures
Adopt good practices for documenting the architecture
Develop alternative architectures for a given problem
Explain how to use formal languages to specify architecture
Evaluate the architecture against the drivers
Describe the recent trends in software architecture
40REFERENCES:
1. Len Bass, Paul Clements, and Rick Kazman, “Software Architectures Principles and
Practices”, 2n Edition, Addison-Wesley, 2003.
2. Anthony J Lattanze, “Architecting Software Intensive System. A Practitioner's Guide”,
Auerbach Publications, 2010.
3. Paul Clements, Felix Bachmann, Len Bass, David Garlan, James Ivers, Reed Little,
Paulo Merson, Robert Nord, and Judith Stafford, “Documenting Software Architectures.
Views and Beyond”, 2nd Edition, Addison-Wesley, 2010.
4. Paul Clements, Rick Kazman, and Mark Klein, “Evaluating software architectures:
Methods and case studies.”, Addison-Wesley, 2001.
5. David Garlan and Mary Shaw, “Software architecture: Perspectives on an emerging
discipline”, Prentice Hall, 1996.
6. Rajkumar Buyya, James Broberg, and Andrzej Goscinski, “Cloud Computing. Principles
and Paradigms”, John Wiley & Sons, 2011
7. Mark Hansen, “SOA Using Java Web Services”, Prentice Hall, 2007
8. David Garlan, Bradley Schmerl, and Shang-Wen Cheng, “Software Architecture-Based
Self-Adaptation,” 31-56. Mieso K Denko, Laurence Tianruo Yang, and Yan Zang (eds.),
“Autonomic Computing and Networking”. Springer Verlag, 2009.
CP7015
MODEL CHECKING AND PROGRAM VERIFICATION
LTPC
3 003
OBJECTIVES:
To understand automata for model checking
To understand LTL, CTL, and CTL*
To understand timed automata, TCTL, and PCTL
To understand verification of deterministic and recursive programs
To understand verification of object-oriented programs
To understand verification of parallel, distributed, and non-deterministic programs
UNIT I
AUTOMATA AND TEMPORAL LOGICS
9
Automata on finite words – model checking regular properties – automata on infinite words –
Buchi automata – Linear Temporal Logic (LTL) – automata based LTL model checking –
Computational Tree Logic (CTL) – CTL model checking – CTL* model checking
UNIT II
TIMED AND PROBABILISTIC TREE LOGICS
9
Timed automata – timed computational tree logic (TCTL) – TCTL model checking –
probabilistic systems – probabilistic computational tree logic (PCTL) – PCTL model checking
– PCTL* - Markov decision processes
UNIT III
VERIFYING DETERMINISTIC AND RECURSIVE PROGRAMS
9
Introduction to program verification – verification of “while” programs – partial and total
correctness – verification of recursive programs – case study: binary search – verifying
recursive programs with parameters
UNIT IV
VERIFYING OBJECT-ORIENTED AND PARALLEL PROGRAMS
9
Partial and total correctness of object-oriented programs – case study: Insertion in linked
lists – verification of disjoint parallel programs – verifying programs with shared variables –
case study: parallel zero search – verification of synchronization – case study: the mutual
exclusion problem
UNIT V
VERIFYING NON-DETERMINISTIC AND DISTRIBUTED PROGRAMS
9
Introduction to non-deterministic programs – partial and total correctness of non-
deterministic programs – case study: The Welfare Crook Problem – syntax and semantics of
distributed programs – verification of distributed programs – case study: A Transmission
Problem – introduction to fairness
TOTAL : 45 PERIODS
41OUTCOMES:
Upon Completion of the course,the students will be able to
Perform model checking using LTL
Perform model checking using CTL
Perform model checking using CTL*
Perform model checking using TCTL and PCTL
Verify deterministic and recursive programs
Verify object-oriented programs
Verify parallel, distributed, and non-deterministic programs
REFERENCES:
1. C. Baier, J.-P. Katoen, and K. G. Larsen, “Principles of Model Checking”, MIT Press,
2008.
2. E. M. Clarke, O. Grumberg, and D. A. Peled, “Model Checking”, MIT Press, 1999.
3. M. Ben-Ari, “Principles of the SPIN Model Checker”, Springer, 2008.
4. K. R. Apt, F. S. de Boer, E.-R. Olderog, and A. Pnueli, “Verification of Sequential and
Concurrent Programs”, Third Edition, Springer, 2010.
5. M. Huth and M. Ryan, “Logic in Computer Science --- Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
6. B. Berard et al., “Systems and Software Verification: Model-checking techniques and
tools”, Springer, 2010.
7. J. B. Almeida, M. J. Frade, J. S. Pinto, and S. M. de Sousa, “Rigorous Software
Development: An Introduction to Program Verification”, Springer, 2011.
CP7016
EMBEDDED SOFTWARE DEVELOPMENT
LTPC
3 003
OBJECTIVES:
To understand processors and their instruction sets for embedded systems
To understand hardware platform for embedded systems
To design and analyze programs for embedded systems
To design multi-tasking embedded systems with RTOS
To understand overall embedded systems development lifecycle
To understand distributed and multi-processor embedded systems
UNIT I
PROCESSORS AND INSTRUCTION SETS
9
Introduction to embedded computing – overview of embedded system design process –
instruction sets of processors: ARM, PIC, TI C55x, TI C64x – programming I/O – modes and
exceptions – co-processors – memory system – CPU performance – CPU power
consumption
UNIT II
EMBEDDED COMPUTING PLATFORM
9
Basic computing platforms – CPU Bus – memory devices and systems – choosing a
platform – development environments – debugging – consumer electronics architecture –
platform-level performance analysis – design example: Audio Player
UNIT III
PROGRAM DESIGN AND ANALYSIS
9
Components for embedded programs – models of programs – Assembly, linking, and
loading – compiler optimizations – program-level performance analysis – performance
optimization – program-level energy optimization – optimizing program size – program
validation and testing – design example: Digital Still Camera
UNIT IV
PROCESSES AND OPERATING SYSTEMS
9
Multiples tasks and multiple processes – multirate systems – pre-emptive RTOS – priority-
based scheduling – inter-process communication – evaluating OS performance – processes
and power optimization – Case study: Real-time and embedded Linux – design example:
Telephone answering machine
42UNIT V
SYSTEM DESIGN, NETWORKS, AND MULTIPROCESSORS
9
System design methodologies – requirements analysis – specifications – architecture design
– quality assurance – distributed embedded systems – shared-memory multiprocessors –
design example: Video accelerator
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Develop assembley code for processors such as ARM, PIC Microcontroller, TI C55x,
TI C64x, etc.
Choose appropriate hardware platform for a given application
Perform platform-level performance analysis
Design, develop, and debug embedded programs optimized for size or performance
Develop embedded applications using an RTOS
Perform OS-level performance analysis
Employ best practices in embedded software engineering
Develop distributed embedded systems and systems with shared-memory
concurrency
REFERENCES:
1. Marilyn Wolf, “Computers as Components: Principles of Embedded Computing
Systems Design”, Third Edition, Morgan Kaufmann, 2012.
2. Christopher Hallinan, “Embedded Linux Primer: A Practical Real-World Approach”,
Second Edition, Prentice Hall, 2010.
3. Karim Yaghmour et al., “Building Embedded Linux Systems”, O’Reilly, 2008.
4. Arnold S. Berger, “Embedded Systems Design: An Introduction to Processes, Tools,
and Techniques”, CMP Books, 2001.
5. David E. Simon, “An embedded Software Primer”, Addison-Wesley, 1999.
IF7202
CLOUD COMPUTING
LTPC
3 003
OBJECTIVES:
To introduce the broad perceptive of cloud architecture and model
To understand the concept of Virtualization
To be familiar with the lead players in cloud.
To understand the features of cloud simulator
To apply different cloud programming model as per need.
To be able to set up a private cloud.
To understand the design of cloud Services.
To learn to design the trusted cloud Computing system
UNIT I
CLOUD ARCHITECTURE AND MODEL
9
Technologies for Network-Based System – System Models for Distributed and Cloud
Computing – NIST Cloud Computing Reference Architecture.
Cloud Models:- Characteristics – Cloud Services – Cloud models (IaaS, PaaS, SaaS) –
Public vs Private Cloud –Cloud Solutions - Cloud ecosystem – Service management –
Computing on demand.
UNIT II
VIRTUALIZATION
9
Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization -
Virtualization Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O
Devices - Virtual Clusters and Resource management – Virtualization for Data-center
Automation.
43UNIT III
CLOUD INFRASTRUCTURE
9
Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture
Development – Design Challenges - Inter Cloud Resource Management – Resource
Provisioning and Platform Deployment – Global Exchange of Cloud Resources.
UNIT IV
PROGRAMMING MODEL
9
Parallel and Distributed Programming Paradigms – MapReduce , Twister and Iterative
MapReduce – Hadoop Library from Apache – Mapping Applications - Programming Support
- Google App Engine, Amazon AWS - Cloud Software Environments -Eucalyptus, Open
Nebula, OpenStack, Aneka, CloudSim
UNIT V
SECURITY IN THE CLOUD
9
Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security
– Security Governance – Risk Management – Security Monitoring – Security Architecture
Design – Data Security – Application Security – Virtual Machine Security - Identity
Management and Access Control – Autonomic Security.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,the students will be able to
Compare the strengths and limitations of cloud computing
Identify the architecture, infrastructure and delivery models of cloud computing
Apply suitable virtualization concept.
Choose the appropriate cloud player
Choose the appropriate Programming Models and approach.
Address the core issues of cloud computing such as security, privacy and interoperability
Design Cloud Services
Set a private cloud
REFERENCES:
1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From
Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.
2. John W.Rittinghouse and James F.Ransome, “Cloud Computing: Implementation,
Management, and Security”, CRC Press, 2010.
3. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical
Approach”, TMH, 2009.
4. Kumar Saurabh, “ Cloud Computing – insights into New-Era Infrastructure”, Wiley
India,2011.
5. George Reese, “Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud” O'Reilly
6. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and
Processes”, Elsevier/Morgan Kaufmann, 2005.
7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, “Grid and Cloud
Computing – A Business Perspective on Technology and Applications”, Springer.
8. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A comprehensive Guide to
Secure Cloud Computing”, Wiley – India, 2010.
9. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi, ‘Mastering Cloud Computing”,
TMGH,2013.
10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011
11. Michael Miller, Cloud Computing, Que Publishing,2008
12. Nick Antonopoulos, Cloud computing, Springer Publications, 2010
44CP7017
DATA VISUALIZATION TECHNIQUES
LT PC
3 003
COURSE OBJECTIVES:
To introduce visual perception and core skills for visual analysis
To understand visualization for time-series analysis
To understand visualization for ranking analysis
To understand visualization for deviation analysis
To understand visualization for distribution analysis
To understand visualization for correlation analysis
To understand visualization for multivariate analysis
To understand issues and best practices in information dashboard design
UNIT I
CORE SKILLS FOR VISUAL ANALYSIS
9
Information visualization – effective data analysis – traits of meaningful data – visual
perception – making abstract data visible – building blocks of information visualization –
analytical interaction – analytical navigation – optimal quantitative scales – reference lines
and regions – trellises and crosstabs – multiple concurrent views – focus and context –
details on demand – over-plotting reduction – analytical patterns – pattern examples
UNIT II
TIME-SERIES, RANKING, AND DEVIATION ANALYSIS
9
Time-series analysis – time-series patterns – time-series displays – time-series best
practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – best
practices – deviation analysis – deviation analysis displays – deviation analysis best
practices
UNIT III
DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS
9
Distribution analysis – describing distributions – distribution patterns – distribution displays –
distribution analysis best practices – correlation analysis – describing correlations –
correlation patterns – correlation displays – correlation analysis techniques and best
practices – multivariate analysis – multivariate patterns – multivariate displays – multivariate
analysis techniques and best practices
UNIT IV
INFORMATION DASHBOARD DESIGN I
9
Information dashboard – categorizing dashboards – typical dashboard data – dashboard
design issues and best practices – visual perception – limits of short-term memory – visually
encoding data – Gestalt principles – principles of visual perception for dashboard design
UNIT V
INFORMATION DASHBOARD DESIGN II
9
Characteristics of dashboards – key goals in visual design process – dashboard display
media – designing dashboards for usability – meaningful organization – maintaining
consistency – aesthetics of dashboards – testing for usability – case studies: sales
dashboard, CIO dashboard, Telesales dashboard, marketing analysis dashboard
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
Explain principles of visual perception
Apply core skills for visual analysis
Apply visualization techniques for various data analysis tasks
Design information dashboard
REFERENCES:
1. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis",
Analytics Press, 2009.
2. Stephen Few, "Information dashboard design: The effective visual communication of
data", O'Reilly, 2006.
3. Edward R. Tufte, "The visual display of quantitative information", Second Edition,
Graphics Press, 2001.
4. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013.
455. Ben Fry, "Visualizing data: Exploring and explaining data with the processing
environment", O'Reilly, 2008.
6. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking
business intelligence beyond reporting", Wiley, 2010.
7. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley,
2011.
NE7005
PROTOCOLS AND ARCHITECTURE FOR WIRELESS
SENSOR NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION AND OVERVIEW OF WIRELESS SENSOR NETWORKS 9
Background of Sensor Network Technology, Application of Sensor Networks, Challenges for
Wireless Sensor Networks, Mobile Adhoc NETworks (MANETs) and Wireless Sensor
Networks, Enabling Technologies For Wireless Sensor Networks.
UNIT II
ARCHITECTURES
9
Single-node Architecture, Hardware Components & Design Constraints, Operating Systems
and Execution Environments, Introduction to TinyOS and nesC, Network Architecture,
Sensor Network Scenarios, Optimization Goals and Figures of Merit, Design Principles for
WSNs, Service Interfaces of WSNs, Gateway Concepts.
UNIT III
DEPLOYMENT AND CONFIGURATION
9
Localization and Positioning, Coverage and Connectivity, Single-hop and Multi-hop
Localization, Self Configuring Localization Systems, Sensor Management
Network Protocols: Issues in Designing MAC Protocol for WSNs, Classification of MAC
Protocols, S-MAC Protocol, B-MAC Protocol, IEEE 802.15.4 Standard and Zig Bee,
Dissemination Protocol for Large Sensor Network.
UNIT IV
ROUTING PROTOCOLS AND DATA MANIPULATION
9
Issues in Designing Routing Protocols, Classification of Routing Protocols, Energy-Efficient
Routing, Unicast, Broadcast and Multicast, Geographic Routing.
Data Centric and Content based Routing, Storage and Retrieval in Network, Compression
Technologies for WSN, Data Aggregation Technique.
UNIT V
SENSOR NETWORK PLATFORMS AND TOOLS
9
Sensor Node Hardware – Berkeley Motes, Programming Challenges, Node-level Software
Platforms, Node-level Simulators, State-centric Programming.
TOTAL : 45 PERIODS
REFERENCES:
1. Holger Karl & Andreas Willig, “Protocols And Architectures for Wireless Sensor
Networks", John Wiley, 2005.
2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information
Processing Approach", Elsevier, 2007.
3. Raghavendra, Cauligi S, Sivalingam, Krishna M., Zanti Taieb, “Wireless Sensor
Network”, Springer 1st Ed. 2004 (ISBN: 978-4020-7883-5).
4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks- Technology,
Protocols, and Applications”, John Wiley, 2007.
5. N. P. Mahalik, “Sensor Networks and Configuration: Fundamentals, Standards,
Platforms, and Applications” Springer Verlag.
6. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.
46CP7018
LANGUAGE TECHNOLOGIES
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for language processing
To understand the representation and processing of Morphology and Part-of Speech
Taggers
To understand different aspects of natural language syntax and the various methods
used for processing syntax
To understand different methods of disambiguating word senses
To know about various applications of natural language processing
To learn the indexing and searching processes of a typical information retrieval
system and to study NLP based retrieval systems
To gain knowledge about typical text categorization and clustering techniques
UNIT I
INTRODUCTION
9
Natural Language Processing – Mathematical Foundations – Elementary Probability Theory
– Essential information Theory - Linguistics Essentials - Parts of Speech and Morphology –
Phrase Structure – Semantics – Corpus Based Work.
UNIT II
WORDS
9
Collocations – Statistical Inference – n-gram Models – Word Sense Disambiguation –
Lexical Acquisition.
UNIT III
GRAMMAR
9
Markov Models – Part-of-Speech Tagging – Probabilistic Context Free Grammars - Parsing.
UNIT IV
INFORMATION RETRIEVAL
9
Information Retrieval Architecture – Indexing - Storage – Compression Techniques –
Retrieval Approaches – Evaluation - Search Engines - Commercial Search Engine Features
– Comparison - Performance Measures – Document Processing - NLP based Information
Retrieval – Information Extraction.
UNIT V
TEXT MINING
9
Categorization – Extraction Based Categorization – Clustering - Hierarchical Clustering -
Document Classification and Routing - Finding and Organizing Answers from Text Search –
Text Categorization and Efficient Summarization using Lexical Chains – Machine Translation
- Transfer Metaphor - Interlingual and Statistical Approaches.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the different linguistic components of given sentences
Design a morphological analyser for a language of your choice using finite state
automata concepts
Implement a parser by providing suitable grammar and words
Discuss algorithms for word sense disambiguation
Build a tagger to semantically tag words using WordNet
Design an application that uses different aspects of language processing.
REFERENCES:
1. Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural
Language Processing “, MIT Press, 1999.
2. Daniel Jurafsky and James H. Martin, “ Speech and Language Processing” , Pearson,
2008.
3. Ron Cole, J.Mariani, et.al “Survey of the State of the Art in Human Language
Technology”, Cambridge University Press, 1997.
Michael W. Berry, “ Survey of Text Mining: Clustering, Classification and Retrieval”,
Springer Verlag, 2003.
4.
47NE7012
SOCIAL NETWORKS ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the components of the social network
To model and visualize the social network
To mine the users in the social network
To understand the evolution of the social network
To mine the interest of the user
UNIT I
INTRODUCTION
9
Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis -
Development of Social Network Analysis - Key concepts and measures in network analysis -
Discussion networks - Blogs and online communities - Web-based networks
UNIT II
MODELING AND VISUALIZATION
9
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-
Based Representations- Node-Link Diagrams - Hybrid Representations - Modelling and
aggregating social network data – Random Walks and their Applications –Use of Hadoop
and Map Reduce - Ontological representation of social individuals and relationships.
UNIT III
MINING COMMUNITIES
9
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in
Social Networks - Evaluating Communities – Core Methods for Community Detection &
Mining - Applications of Community Mining Algorithms - Node Classification in Social
Networks.
UNIT IV
EVOLUTION
9
Evolution in Social Networks – Framework - Tracing Smoothly Evolving Communities -
Models and Algorithms for Social Influence Analysis - Influence Related Statistics - Social
Similarity and Influence - Influence Maximization in Viral Marketing - Algorithms and
Systems for Expert Location in Social Networks - Expert Location without Graph Constraints
- with Score Propagation – Expert Team Formation - Link Prediction in Social Networks -
Feature based Link Prediction - Bayesian Probabilistic Models - Probabilistic Relational
Models
UNIT V
TEXT AND OPINION MINING
9
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis - Product
review mining – Review Classification – Tracking sentiments towards topics over time
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Work on the internals components of the social network
Model and visualize the social network
Mine the behaviour of the users in the social network
Predict the possible next outcome of the social network
Mine the opinion of the user
REFERENCES:
1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011
2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.
3. Borko Furht, “Handbook of Social Network Technologies and Applications”, Springer, 1st
edition, 2010.
484. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking –
Techniques and applications”, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social Network
Analysis: Trends, Tools and Research Advances”, Springer, 2009.
7. Toby Segaran, “Programming Collective Intelligence”, O’Reilly, 2012
CP7019
MANAGING BIG DATA
LTPC
3 003
OBJECTIVES:
Understand big data for business intelligence
Learn business case studies for big data analytics
Understand nosql big data management
Perform map-reduce analytics using Hadoop and related tools
UNIT I
UNDERSTANDING BIG DATA
9
What is big data – why big data – convergence of key trends – unstructured data – industry
examples of big data – web analytics – big data and marketing – fraud and big data – risk
and big data – credit risk management – big data and algorithmic trading – big data and
healthcare – big data in medicine – advertising and big data – big data technologies –
introduction to Hadoop – open source technologies – cloud and big data – mobile business
intelligence – Crowd sourcing analytics – inter and trans firewall analytics
UNIT II
NOSQL DATA MANAGEMENT
9
Introduction to NoSQL – aggregate data models – aggregates – key-value and document
data models – relationships – graph databases – schemaless databases – materialized
views – distribution models – sharding – master-slave replication – peer-peer replication –
sharding and replication – consistency – relaxing consistency – version stamps – map-
reduce – partitioning and combining – composing map-reduce calculations
UNIT III
BASICS OF HADOOP
9
Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop
pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface
– data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based
data structures
UNIT IV
MAPREDUCE APPLICATIONS
9
MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of
MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and
YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input
formats – output formats
UNIT V
HADOOP RELATED TOOLS
9
Hbase – data model and implementations – Hbase clients – Hbase examples –
praxis.Cassandra – cassandra data model – cassandra examples – cassandra clients –
Hadoop integration.
Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts.
Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –
HiveQL queries.
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe big data and use cases from selected business domains
Explain NoSQL big data management
Install, configure, and run Hadoop and HDFS
Perform map-reduce analytics using Hadoop
Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics
49REFERENCES:
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
NE7011
MOBILE APPLICATION DEVELOPMENT
LTPC
3 003
OBJECTIVES:
1. Understand system requirements for mobile applications
2. Generate suitable design using specific mobile development frameworks
3. Generate mobile application design
4. Implement the design using specific mobile development frameworks
5. Deploy the mobile applications in marketplace for distribution
UNIT I
INTRODUCTION
5
Introduction to mobile applications – Embedded systems - Market and business drivers for
mobile applications – Publishing and delivery of mobile applications – Requirements
gathering and validation for mobile applications
UNIT II
BASIC DESIGN
8
Introduction – Basics of embedded systems design – Embedded OS - Design constraints for
mobile applications, both hardware and software related – Architecting mobile applications
– User interfaces for mobile applications – touch events and gestures – Achieving quality
constraints – performance, usability, security, availability and modifiability.
UNIT III
ADVANCED DESIGN
8
Designing applications with multimedia and web access capabilities – Integration with GPS
and social media networking applications – Accessing applications hosted in a cloud
computing environment – Design patterns for mobile applications.
UNIT IV
TECHNOLOGY I - ANDROID
12
Introduction – Establishing the development environment – Android architecture – Activities
and views – Interacting with UI – Persisting data using SQLite – Packaging and deployment
– Interaction with server side applications – Using Google Maps, GPS and Wifi – Integration
with social media applications.
UNIT V
TECHNOLOGY II - IOS
12
Introduction to Objective C – iOS features – UI implementation – Touch frameworks – Data
persistence using Core Data and SQLite – Location aware applications using Core Location
and Map Kit – Integrating calendar and address book with social media application – Using
Wifi - iPhone marketplace.
TOTAL: 45 PERIODS
OUTCOMES:
Upon the students will be able toCompletion of the course,
1. Describe the requirements for mobile applications
2. Explain the challenges in mobile application design and development
3. Develop design for mobile applications for specific requirements
4. Implement the design using Android SDK
5. Implement the design using Objective C and iOS
6. Deploy mobile applications in Android and iPone marketplace for distribution
50REFERENCES:
1. http://developer.android.com/develop/index.html
2. Jeff McWherter and Scott Gowell, "Professional Mobile Application Development",
Wrox, 2012
3. Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”,
DreamTech, 2012
4. James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2012
5. David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 6
Development: Exploring the iOS SDK”, Apress, 2013.
CP7020
BIO-INSPIRED COMPUTING
LT PC
3 0 03
OBJECTIVES:
Learn evolutionary theory and algorithms
Understand Cellular Automata and artificial life
Learn artificial neural systems and related learning algorithms
Learn developmental and artificial immune systems
Understand behavioral systems especially in the context of Robotics
Understand collective systems such as ACO, PSO, and swarm robotics
UNIT I
EVOLUTIONARY AND CELLULAR SYSTEMS
9
Foundations of evolutionary theory – Genotype – artificial evolution – genetic
representations – initial population – fitness functions – selection and reproduction – genetic
operators – evolutionary measures – evolutionary algorithms – evolutionary electronics –
evolutionary algorithm case study
Cellular systems – cellular automata – modeling with cellular systems – other cellular
systems – computation with cellular systems – artificial life – analysis and synthesis of
cellular systems
UNIT II
NEURAL SYSTEMS
9
Biological nervous systems – artificial neural networks – neuron models – architecture –
signal encoding – synaptic plasticity – unsupervised learning – supervised learning –
reinforcement learning – evolution of neural networks – hybrid neural systems – case study
UNIT III
DEVELOPMENTAL AND IMMUNE SYSTEMS
9
Rewriting systems – synthesis of developmental systems – evolutionary rewriting systems –
evolutionary developmental programs
Biological immune systems – lessons for artificial immune systems – algorithms and
applications – shape space – negative selection algorithm – clonal selection algorithm -
examples
UNIT IV
BEHAVIORAL SYSTEMS
9
Behavior is cognitive science – behavior in AI – behavior based robotics – biological
inspiration for robots – robots as biological models – robot learning – evolution of behavioral
systems – learning in behavioral systems – co-evolution of body and control – towards self
reproduction – simulation and reality
UNIT V
COLLECTIVE SYSTEMS
9
Biological self-organization – Particle Swarm Optimization (PSO) – ant colony optimization
(ACO) – swarm robotics – co-evolutionary dynamics – artificial evolution of competing
systems – artificial evolution of cooperation – case study
TOTAL: 45 PERIODS
51OUTCOMES:
Upon completion of the course, the students will be able to
Implement and apply evolutionary algorithms
Explain cellular automata and artificial life
Implement and apply neural systems
Explain developmental and artificial immune systems
Explain behavioral systems
Implement and apply collective intelligence systems
REFERENCES:
1. D. Floreano and C. Mattiussi, "Bio-Inspired Artificial Intelligence", MIT Press, 2008.
2. F. Neumann and C. Witt, “Bioinspired Computation in combinatorial optimization:
Algorithms and their computational complexity”, Springer, 2010.
3. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010.
4. D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”,
Addison-Wesley, 1989.
5. Simon O. Haykin, “Neural Networks and Learning Machines”, Third Edition, Prentice
Hall, 2008.
6. M. Dorigo and T. Stutzle, “Ant Colony Optimization”, A Bradford Book, 2004.
7. R. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.
CP7021
MEDICAL IMAGE PROCESSING
LT PC
3 0 0 3
COURSE DESCRIPTION:
An advanced graduate level course on medical imaging and medical image analysis. The
course includes topics in medical image formation, medical imaging techniques, such as X-
Ray, Computed Tomography, Magnetic Resonance Imaging, and Nuclear Imaging, image
segmentation, registration, statistical modeling, visualization, and applications of
computational tools for medicine.
OBJECTIVES:
The course will provide the participants with an up-to-date background in current state-of-
the-art in medical imaging and medical image analysis. The aim of the course is to show
how to extract, model, and analyze information from medical data and applications in order
to help diagnosis, treatment and monitoring of diseases through computer science.
UNIT I
INTRODUCTION
9
Introduction to medical imaging technology, systems, and modalities. Brief history;
importance; applications; trends; challenges. Medical Image Formation Principles: X-Ray
physics; X-Ray generation, attenuation, scattering; dose Basic principles of CT;
reconstruction methods; artifacts; CT hardware.
UNIT II
STORAGE AND PROCESSING
9
Medical Image Storage, Archiving and Communication Systems and Formats Picture
archiving and communication system (PACS); Formats: DICOM Radiology Information
Systems (RIS) and Hospital Information Systems (HIS). Medical Image Processing,
Enhancement, Filtering Basic image processing algorithms Thresholding; contrast
enhancement; SNR characteristics; filtering; histogram modeling.
UNIT III
VISUALIZATION
9
Medical Image Visualization Fundamentals of visualization; surface and volume
rendering/visualization; animation; interaction. Magnetic Resonance Imaging (MRI)
Mathematics of MR; spin physics; NMR spectroscopy; imaging principles and hardware;
image artifacts.
52UNIT IV
SEGMENTATION AND CLASSIFICATION
9
Medical Image Segmentation - Histogram-based methods; Region growing and watersheds;
Markov Random Field models; active contours; model-based segmentation. Multi-scale
segmentation; semi-automated methods; clustering-based methods; classification-based
methods; atlas-guided approaches; multi-model segmentation. Medical Image Registration
Intensity-based methods; cost functions; optimization techniques.
UNIT V
NUCLEAR IMAGING
9
PET and SPECT Ultrasound Imaging methods; mathematical principles; resolution; noise
effect; 3D imaging; positron emission tomography; single photon emission tomography;
ultrasound imaging; applications. Medical Image Search and Retrieval Current technology in
medical image search, content-based image retrieval, new trends: ontologies. Applications.
Other Applications of Medical Imaging Validation, Image Guided Surgery, Image Guided
Therapy, Computer Aided Diagnosis/Diagnostic Support Systems.
TOTAL: 45 PERIODS
REFERENCES:
1. Paul Suetens, "Fundamentals of Medical Imaging", Second Edition, Cambridge
University Press, 2009.
2. J. Michael Fitzpatrick and Milan Sonka, "Handbook of Medical Imaging, Volume 2.
Medical Image Processing and Analysis", SPIE Publications, 2009.
3. Kayvan Najarian and Robert Splinter, "Biomedical Signal and Image Processing",
Second Edition, CRC Press, 2005.
4. Geoff Dougherty, "Digital Image Processing for Medical Applications", First Edition,
Cambridge University Press, 2009.
5. Jerry L. Prince and Jonathan Links, "Medical Imaging Signals and Systems", First
Edition, Prentice Hall, 2005.
6. John L. Semmlow, "Biosignal and Medical Image Processing", Second Edition, CRC
Press, 2008.
CP7022
SOFTWARE DESIGN
LTPC
30 03
OBJECTIVES:
Analyze specifications
Describe approaches to design
Develop design documentation
Evaluate the design
UNIT I
SOFTWARE DESIGN PRINCIPLES
9
Introduction – Design process – Managing complexity – Software modeling and notations –
Abstraction – Modularity – Hierarchy – Coupling - Cohesion – Design guidelines and
checklists – Refactoring
UNIT II
OO DESIGN
9
Object model – Classes and objects – Object oriented analysis – Key abstractions and
mechanisms – Object oriented design – Identifying design elements – Detailed design –
Case studies.
UNIT III
DESIGN PATTERNS
9
Introduction to patterns – Design context – Reusable solutions – Documenting reusable
solutions – Standard patterns from GOF book.
UNIT IV
FUNCTION AND SERVICE ORIENTED DESIGNS
9
Structural decomposition – Detailed Design – Function oriented design Case study –
Services – Service identification – Service design – Service composition – choreography and
orchestration – Service oriented design Case study
53UNIT V
USER CENTERED DESIGN AND DESIGN REVIEW
9
Introduction to user centered design – Use in context – Interface and interaction – User
centered design principles – Task analysis – Evaluation – Introduction to design review–
Testing the design – Walk throughs – Review against check lists.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Describe different approaches to desigining a software application
Analyze specifications and identify appropriate design strategies.
Develop an appropriate design for a given set of requirements
Identify applicatble design patterns for the solution
Abstract and document reusable design patterns
Evaluate a given design against the specifications
REFERENCES:
1. Grady Booch et al., "Object Oriented Analysis and Design with Applications", 3rd
Edition, Pearson, 2010.
2. Carlos Otero, "Software Engineering Design: Theory and Practice", CRC Press, 2012
3. David Budgen, "Software Design", 2nd Edtion, Addison Wesley, 2003
4. Alan Shalloway and James R Trott, "Design Patterns Explained: A New Perspective
on Object-Oriented Design", 2nd Edition, Addison-Wesley Professional, 2004
5. Hassan Gomaa, "Software Modeling and Design", Cambridge University Press, 2011
6. Eric Gamma et al., "Design Patterns: Elements of Reusable Object-Oriented
Software", Addison-Wesley Professional, 1994
7. Ian Sommerville, "Software Engineering", 9th Edition, Addison-Wesley, 2010
8. M B Rosson and J M Carroll, "Usability Engineering: Scenario-Based Development
of Human-Computer Interaction", Morgan Kaufmann, 2002
CP7023
RECONFIGURABLE COMPUTING
LTPC
3 003
OBJECTIVES:
To understand the need for reconfigurable computing
To expose the students to various device architectures
To examine the various reconfigurable computing systems
To understand the different types of compute models for programming reconfigurable
architectures
To expose the students to HDL programming and familiarize with the development
environment
To expose the students to the various placement and routing protocols
To develop applications with FPGAs
UNIT I
DEVICE ARCHITECTURE
9
General Purpose Computing Vs Reconfigurable Computing – Simple Programmable Logic
Devices – Complex Programmable Logic Devices – FPGAs – Device Architecture - Case
Studies.
UNIT II
RECONFIGURABLE COMPUTING ARCHITECTURES AND SYSTEMS
9
Reconfigurable Processing Fabric Architectures – RPF Integration into Traditional
Computing Systems – Reconfigurable Computing Systems – Case Studies –
Reconfiguration Management.
UNIT III
PROGRAMMING RECONFIGURABLE SYSTEMS
9
Compute Models - Programming FPGA Applications in HDL – Compiling C for Spatial
Computing – Operating System Support for Reconfigurable Computing.
54UNIT IV
MAPPING DESIGNS TO RECONFIGURABLE PLATFORMS
9
The Design Flow - Technology Mapping – FPGA Placement and Routing – Configuration
Bitstream Generation – Case Studies with Appropriate Tools.
UNIT V
APPLICATION DEVELOPMENT WITH FPGAS
9
Case Studies of FPGA Applications – System on a Programmable Chip (SoPC) Designs.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for reconfigurable architectures
Discuss the architecture of FPGAs
Point out the salient features of different reconfigurable architectures
Build basic modules using any HDL
Develop applications using any HDL and appropriate tools
Design and build an SoPC for a particular application
REFERENCES:
1. Maya B. Gokhale and Paul S. Graham, “Reconfigurable Computing: Accelerating
Computation with Field-Programmable Gate Arrays”, Springer, 2005.
2. Scott Hauck and Andre Dehon (Eds.), “Reconfigurable Computing – The Theory and
Practice of FPGA-Based Computation”, Elsevier / Morgan Kaufmann, 2008.
3. Christophe Bobda, “Introduction to Reconfigurable Computing – Architectures,
Algorithms and Applications”, Springer, 2010.
IF7013
ENERGY AWARE COMPUTING
LTPC
30 03
OBJECTIVES:
This course examines the design of power efficient architecture, power and performance
tradeoffs, restructuring of software and applications and standards for energy aware
Hardware and Software. The objective of this course is:
To know the fundamental principles energy efficient devices
To study the concepts of Energy efficient storage
To introduce energy efficient algorithms
To enable the students to know energy efficient techniques involved to support real-
time systems.
To study Energy aware applications
UNIT I
INTRODUCTION
9
Energy efficient network on chip architecture for multi core system-Energy efficient MIPS
CPU core with fine grained run time power gating – Low power design of Emerging memory
technologies.
UNIT II
ENERGY EFFICIENT STORAGE
9
Disk Energy Management-Power efficient strategies for storage system-Dynamic thermal
management for high performance storage systems-Energy saving technique for Disk
storage systems
UNIT III
ENERGY EFFICIENT ALGORITHMS
9
Scheduling of Parallel Tasks – Task level Dynamic voltage scaling – Speed Scaling –
Processor optimization- Memetic Algorithms – Online job scheduling Algorithms.
55UNIT IV
REAL TIME SYSTEMS
9
Multi processor system – Real Time tasks- Energy Minimization – Energy aware scheduling-
Dynamic Reconfiguration- Adaptive power management-Energy Harvesting Embedded
system
UNIT V
ENERGY AWARE APPLICATIONS
9
On chip network – Video codec Design – Surveillance camera- Low power mobile storage.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Design Power efficient architecture Hardware and Software.
Analyze power and performance trade off between various energy aware storage
devices.
Implement various energy aware algorithms.
Restructure the software and Hardware for Energy aware applications.
Explore the Energy aware applications
REFERENCES:
1. Ishfaq Ah mad, Sanjay Ranka, Handbook of Energy Aware and Green Computing,
Chapman and Hall/CRC, 2012
2. Chong-Min Kyung, Sungioo yoo, Energy Aware system design Algorithms and
Architecture, Springer, 2011.
3. Bob steiger wald ,Chris:Luero, Energy Aware computing, Intel Press,2012.
CP7024
INFORMATION RETRIEVAL TECHNIQUES
LTPC
30 03
OBJECTIVES:
To understand the basics of Information Retrieval with pertinence to modeling, query
operations and indexing
To get an understanding of machine learning techniques for text classification and
clustering
To understand the various applications of Information Retrieval giving emphasis to
Multimedia IR, Web Search
To understand the concepts of digital libraries
UNIT I
INTRODUCTION
8
Motivation – Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean
Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web
Characteristics–The impact of the web on IR ––IR Versus Web Search–Components of a
Search engine
UNIT II
MODELING
10
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term
Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic
Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing
UNIT III
INDEXING
9
Static and Dynamic Inverted Indices – Index Construction and Index Compression Searching
- Sequential Searching and Pattern Matching. Query Operations -Query Languages–Query
Processing - Relevance Feedback and Query Expansion - Automatic Local and Global
Analysis – Measuring Effectiveness and Efficiency.
UNIT IV
CLASSIFICATION AND CLUSTERING
8
Text Classification and Naïve Bayes – Vector Space Classification – Support vector
machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –
Matrix decompositions and latent semantic indexing – Fusion and Meta learning
56UNIT V
SEARCHING AND RANKING
10
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic
Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR:
Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital
Libraries
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Build an Information Retrieval system using the available tools
Identify and design the various components of an Information Retrieval system
Apply machine learning techniques to text classification and clustering which is used
for efficient Information Retrieval
Analyze the Web content structure
Design an efficient search engine
REFERENCES:
1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The
concepts and Technology behind Search (ACM Press Books), Second Edition 2011
2. Christopher D. Manning, PrabhakarRaghavan, HinrichSchutze, Introduction to
Information Retrieval, Cambridge University Press, First South Asian Edition 2012
3. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval
Implementing and Evaluating Search Engines, The MIT Press, Cambridge,
Massachusetts London, England, 2010
CP7025
DATA MINING TECHNIQUES
LTPC
300 3
UNIT I
INTRODUCTION TO DATA MINING
9
Introduction to Data Mining – Data Mining Tasks – Components of Data Mining Algorithms –
Data Mining supporting Techniques – Major Issues in Data Mining – Measurement and Data
– Data Preprocessing – Data sets
UNIT II
OVERVIEW OF DATA MINING ALGORITHMS
9
Overview of Data Mining Algorithms – Models and Patterns – Introduction – The
Reductionist viewpoint on Data Mining Algorithms – Score function for Data Mining
Algorithms- Introduction – Fundamentals of Modeling – Model Structures for Prediction –
Models for probability Distributions and Density functions – The Curve of Dimensionality –
Models for Structured Data – Scoring Patterns – Predictive versus Descriptive score
functions – Scoring Models with Different Complexities – Evaluation of Models and Patterns
– Robust Methods.
UNIT III
CLASSIFICATIONS
9
Classifications – Basic Concepts – Decision Tree induction – Bayes Classification Methods –
Rule Based Classification – Model Evaluation and Selection – Techniques to Improve
Classification Accuracy – Classification: Advanced concepts – Bayesian Belief Networks-
Classification by Back Propagation – Support Vector Machine – Classification using frequent
patterns.
UNIT IV
CLUSTER ANALYSIS
9
Cluster Analysis: Basic concepts and Methods – Cluster Analysis – Partitioning methods –
Hierarchical methods – Density Based Methods – Grid Based Methods – Evaluation of
Clustering – Advanced Cluster Analysis: Probabilistic model based clustering – Clustering
High – Dimensional Data – Clustering Graph and Network Data – Clustering with
Constraints.
57UNIT V
ASSOCIATION RULE MINING AND VISUALIZATION
9
Association Rule Mining – Introduction – Large Item sets – Basic Algorithms – Parallel and
Distributed Algorithms – Comparing Approaches – Incremental Rules – Advanced
Association Rule Techniques – Measuring the Quality of Rules – Visualization of
Multidimensional Data – Diagrams for Multidimensional visualization – Visual Data Mining –
Data Mining Applications – Case Study: WEKA.
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. Jiawei Han, Micheline Kamber , Jian Pei, “Data Mining: Concepts and Techniques”,
Third Edition (The Morgan Kaufmann Series in Data Management Systems), 2012.
2. David J. Hand, Heikki Mannila and Padhraic Smyth “Principles of Data Mining”
(Adaptive Computation and Machine Learning), 2005
3. Margaret H Dunham, “Data Mining: Introductory and Advanced Topics”, 2003
4. Soman, K. P., Diwakar Shyam and Ajay V. “Insight Into Data Mining: Theory And
Practice”, PHI, 2009.
IF7002
BIO INFORMATICS
LTPC
3 003
p
OBJECTIVES:
To get exposed to the domain of bioinformatics
To understand the role of data warehousing and data mining for bioinformatics
To learn to model bioinformatics based applications
To understand how to deploy the pattern matching and visualization techniques in
bioinformatics
To study the Microarray technologies for genome expression
UNIT I
INTRODUCTION
9
Need for Bioinformatics technologies – Overview of Bioinformatics technologies – Structural
bioinformatics – Data format and processing – secondary resources- Applications – Role of
Structural bioinformatics - Biological Data Integration System.
UNIT II
DATAWAREHOUSING AND DATAMINING IN BIOINFORMATICS
9
Bioinformatics data – Data ware housing architecture – data quality – Biomedical data
analysis – DNA data analysis – Protein data analysis – Machine learning – Neural network
architecture- Applications in bioinformatics
UNIT III
MODELING FOR BIOINFORMATICS
9
Hidden markov modeling for biological data analysis – Sequence identification – Sequence
classification – multiple alignment generation – Comparative modeling – Protein modeling –
genomic modeling – Probabilistic modeling – Bayesian networks – Boolean networks -
Molecular modeling – Computer programs for molecular modeling
UNIT IV
PATTERN MATCHING AND VISUALIZATION
9
Gene regulation – motif recognition and motif detection – strategies for motif detection –
Visualization – Fractal analysis – DNA walk models – one dimension – two dimension –
higher dimension – Game representation of Biological sequences – DNA, Protein, Amino
acid sequences
UNIT V
MICROARRAY ANALYSIS
9
Microarray technology for genome expression study – image analysis for data extraction –
preprocessing – segmentation – gridding , spot extraction , normalization, filtering – cluster
analysis – gene network analysis – Compared Evaluation of Scientific Data Management
Systems – Cost Matrix – Evaluation model ,Benchmark , Tradeoffs
TOTAL: 45 PERIODS
58OUTCOMES:
Upon Completion of the course, the students will be able to
Deploy the data warehousing and data mining techniques in Bioinformatics
Model bioinformatics based applications
Deploy the pattern matching and visualization techniques in bioinformatics
Work on the protein sequences
Use the Microarray technologies for genome expression
REFERENCES:
1. Yi-Ping Phoebe Chen (Ed), “Bio Informatics Technologies”, First Indian Reprint, Springer
Verlag, 2007.
2. N.J. Chikhale and Virendra Gomase, "Bioinformatics- Theory and Practice", Himalaya
Publication House, India, 2007
3. Zoe lacroix and Terence Critchlow, “Bio Informatics – Managing Scientific data”, First
Indian Reprint, Elsevier, 2004
4. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003.
5. Arthur M Lesk, “Introduction to Bioinformatics”, Second Edition, Oxford University Press,
2005
6. Burton. E. Tropp, “Molecular Biology: Genes to Proteins “, 4th edition, Jones and Bartlett
Publishers, 2011
7. Dan Gusfield, “Algorithms on Strings Trees and Sequences”, Cambridge University
Press, 1997.
8. P. Baldi, S Brunak , Bioinformatics, “A Machine Learning Approach “, MIT Press, 1998.
CP7026
SOFTWARE QUALITY ASSURANCE
LTPC
3 003
OBJECTIVES:
Describe approaches to quality assurance
Understand quality models
Evaluate the system based on the chosen quality model
UNIT I
INTRODUCTION
9
Introduction – Views on quality – Cost of quality - Quality models – Quality frameworks –
Verification and Validation – Defect taxonomy – Defect management – Statistics and
measurements – IEEE standards – Quality assurance and control processes
UNIT II
VERIFICATION
6
Introduction – Verification techniques – Inspections, reviews, walk-throughs – Case studies
UNIT III
TEST GENERATION
12
Software testing- Validation – Test plan – Test cases - Test Generation – Equivalence
partitioning – Boundary value analysis – Category partition method – Combinatorial
generation - Decision tables – Examples and Case studies
UNIT IV
STRUCTURAL TESTING
12
Introduction – Test adequacy criteria – Control flow graph – Coverages: block, conditions,
multiple conditions, MC/DC, path – Data flow graph – Definition and use coverages – C-use,
P-use, Def-clear, Def-use – Finite state machines – Transition coverage – Fault based
testing – Mutation analysis – Case studies
UNIT V
FUNCTIONAL TESTING
6
Introduction – Test adequacy criteria - Test cases from use cases – Exploratory testing -
Integration, system, acceptance, regression testing – Testing for specific attributes:
Performance, load and stress testing – Usability testing – Security testing - Test automation
– Test oracles
TOTAL : 45 PERIODS
59OUTCOMES:
Upon Completion of the course, the students will be able to
Describe different approaches to testing software applications
Analyze specifications and identify appropriate test generation strategies
Develop an appropriate test design for a given test object
Identify applicatble measurements for the verification and validation effort
Execute the test design
Evaluate the testing effort based on adequate measures
REFERENCES:
1. Boriz Beizer, "Software Testing Techniques", 2nd Edition, DreamTech, 2009.
2. Aditya P. Mathur, "Foundations of Software Testing", Pearson, 2008
3. Mauro Pezze and Michal Young, "Software Testing and Analysis. Process, Principles,
and Techniques", John Wiley 2008
4. Stephen H. Kan, "Metrics and Models in Software Quality Engineering", 2nd Edition,
Pearson, 2003
5. Kshirasagar Naik and Priyadarshi Tripathy (Eds), "Software Testing and Quality
Assurance: Theory and Practice", John Wiley, 2008
6. "Combinatorial Methods in Software Testing", ttp://csrc.nist.gov/groups/SNS/acts/index.html
CP7027
MULTI OBJECTIVE OPTIMIZATION TECHNIQUES
LTPC
3 003
OBJECTIVES:
Learn fundamental principles of Multiobjective Optimization (MOP)
Survey different Multiobjective Optimization algorithms
Introduce various design issues of MOP
Develop and Evaluate MOP Algorithms
Learn Parallel and hybrid MOP Algorithms
Learn other Metaheuristics
UNIT I
INTRODUCTION AND CLASSICAL APPROACHES
9
Multiobjective optimization: Introduction - Multiobjective optimization problem-principles –
Difference between single and multiobjective optimization – Dominance and Pareto
Optimality , Classical Methods – Weighted Sum -
Constraint method – Weighted Metric
methods – Benson’s method - Value Function - Goal Programming methods – Interactive
Methods
UNIT II
MOP EVOLUTIONARY ALGORITHMS
9
Generic MOEA - Various MOEAs: MOGA, NSGA-II, NPGA, PAES, SPEA2, MOMGA, micro
GA - Constrained MOEAs: Penalty Function approach - Constrained Tournament – Ray –
Tai –Seow’s Method.
UNIT III
THEORETICAL ISSUES
9
Fitness Landscapes - Fitness Functions - Pareto Ranking - Pareto Niching and Fitness
Sharing - Recombination Operators - Mating Restriction - Solution Stability and Robustness
- MOEA Complexity - MOEA Scalability - Running Time Analysis - MOEA Computational
Cost - No Free Lunch Theorem.
UNIT IV
MOEA TESTING, ANALYSIS, AND PARALLELIZATION
9
MOEA Experimental Measurements – MOEA Statistical Testing Approaches – MOEA Test
Suites - MOEA Parallelization: Background – Paradigms – Issues - MOEA Local Search
Techniques.
60UNIT V
APPLICATIONS AND ALTERNATIVE METAHEURISTICS
9
Scientific Applications: Computer Science and Computer Engineering - Alternative
Metaheuristics: Simulated Annealing – Tabu Search and Scatter Search – Ant System –
Distributed Reinforcement Learning – Particle Swarm Optimization – Differential Evolution –
Artificial Immune Systems - Other Heuristics.
TOTAL:45 PERIODS
OUTCOMES:
Upon Completion of the course,students will be able to
Explain MOP principles
Explain classical methods to solve MOP problems
Be familiar with and explain structures of different MOP algorithms
Solve constrained MOP problems
Explain various design issues of MOP algorithms
Perform a evaluation and analysis of MOP algorithm results
Explain parallelization of MOP algorithms
Develop parallel and hybrid MOP algorithms
Identify various real time MOP applications
Explain other search algorithms
REFERENCES:
1. Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen, “Evolutionary
Algorithms for Solving Multi-objective Problems”, Second Edition, Springer, 2007.
2. Kalyanmoy Deb, “ Multi-Objective Optimization Using Evolutionary Algorithms”, John
Wiley, 2002.
3. Aimin Zhoua, Bo-Yang Qub, Hui Li c, Shi-Zheng Zhaob, Ponnuthurai Nagaratnam
Suganthan b, Qingfu Zhangd, “Multiobjective evolutionary algorithms: A survey of the
state of the art”, Swarm and Evolutionary Computation (2011) 32–49.
4. E Alba, M Tomassini, “Parallel and evolutionary algorithms”, Evolutionary Computation,
IEEE Transactions on 6 (5), 443-462.
5. Crina Grosan, Ajith Abraham, “Hybrid Evolutionary Algorithms: Methodologies,
Architectures, and Reviews”, Studies in Computational Intelligence, Vol. 75, Springer,
2007.
6. Christian Blum and Andrea Roli. 2003. Metaheuristics in combinatorial optimization:
Overview and conceptual comparison. ACM Comput. Surv. 35, 3 (September 2003),
268-308.
CP7028
ENTERPRISE APPLICATION INTEGRATION
LTPC
30 03
OBJECTIVES:
Describe approaches to enterprise application integration
Understand the integration middleware
Evaluate the integration approaches suitable for a given problem
UNIT I
INTRODUCTION
6
Requirements for EAI - Challenges in EAI – Integration with legacy systems – Integration
with partners - Heterogeneous environment – Implementation approaches – Web services,
messaging, ETL, direct data integration – Middleware requirements – Approaches to
integration – services oriented and messaging.
UNIT II
INTEGRATION PATTERNS
6
Introduction to integration patterns – Architecture for application integration – Integration
patterns – Point to point, broker, message bus, publish/subscribe, Challenges in
performance, security, reliability - Case studies
61UNIT III
SERVICE ORIENTED INTEGRATION
12
Business process integration - Composite applications-services – Web services – Service
choreography and orchestration - Business process modeling - BPMN, Business process
execution - BPEL – Middleware infrastructure - Case studies
UNIT IV
MESSAGING BASED INTEGRATION
9
Messaging – Synchronous and asynchronous – Message structure – Message oriented
middleware – Reliability mechanisms – Challenges – Messaging infrastructure – Java
Messaging Services – Case studies
UNIT V
ENTERPRISE SERVICE BUS
12
Enterprise Service Bus – routing, scalable connectivity, protocol and message
transformations, data enrichment, distribution, correlation, monitoring – Deployment
configurations – Global ESB, Directly connected, Federated, brokered ESBs – Application
server based – Messaging system based – Hardware based ESBs – Support to SOA,
message based and event based integrations - Case studies.
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe different approaches to integration enterprise applications
Analyze specifications and identify appropriate integration approaches
Develop a suitable integration design for a given problem
Identify appropriate integration middleware for a given problem
Evaluate the integration approaches against specified requirements
REFERENCES:
1. George Mentzas and Andreas Frezen (Eds), "Semantic Enterprise Application
Integration for Business Processes: Service-oriented Frameworks", Business Science
Reference, 2009
2. Waseem Roshen, "SOA Based Enterprise Integration", Tata McGrawHill, 2009.
3. G
Hohpe
and
B
Woolf,
"Enterprise
Integration Patterns: Designing,
Building, and Deploying Messaging Solutions", Addison Wesley Professional,
2003
4. D
Linthicum, "Next
Generation
Application
Integration:
From
Simple
Information to Web Services", Addison Wesley, 2003
5. Martin Fowler, "Patterns of Enterprise Application Architecture", Addison- Wesley, 2003
6. Kapil Pant and Matiaz Juric, "Business Process Driven SOA using BPMN and BPEL:
From Business Process Modeling to Orchestration and Service Oriented Architecture",
Packt Publishing, 2008
CP7029
INFORMATION STORAGE MANAGEMENT
LTPC
3 003
UNIT I
INTRODUCTION TO STORAGE TECHNOLOGY
9
Review data creation and the amount of data being created and understand the value of
data to a business, challenges in data storage and data management, Solutions available for
data storage, Core elements of a data center infrastructure, role of each element in
supporting business activities
62UNIT II
STORAGE SYSTEMS ARCHITECTURE
9
Hardware and software components of the host environment, Key protocols and concepts
used by each component ,Physical and logical components of a connectivity environment
,Major physical components of a disk drive and their function, logical constructs of a physical
disk, access characteristics, and performance Implications, Concept of RAID and its
components, Different RAID levels and their suitability for different application environments:
RAID 0, RAID 1, RAID 3, RAID 4, RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and
contrast integrated and modular storage systems ,Iligh-level architecture and working of an
intelligent storage system
UNIT III
INTRODUCTION TO NETWORKED STORAGE
9
Evolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS,
and IP-SAN, Benefits of the different networked storage options, understand the need for
long-term archiving solutions and describe how CAS full fill the need, understand the
appropriateness of the different networked storage options for different application
environments
UNIT IV
INFORMATION AVAILABILITY, MONITORING & MANAGING
DATACENTER
9
List reasons for planned/unplanned outages and the impact of downtime, Impact of
downtime - Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO
and RPO, Identify single points of failure in a storage infrastructure and list solutions to
mitigate these failures, Architecture of backup/recovery and the different backup/ recovery
topologies, replication technologies and their role in ensuring information availability and
business continuity, Remote replication technologies and their role in providing disaster
recovery and business continuity capabilities. Identify key areas to monitor in a data center,
Industry standards for data center monitoring and management, Key metrics to monitor for
different components in a storage infrastructure, Key management tasks in a data center
UNIT V
SECURING STORAGE AND STORAGE VIRTUALIZATION
9
Information security, Critical security attributes for information systems, Storage security
domains, List and analyzes the common threats in each domain, Virtualization technologies,
block-level and file-level virtualization technologies and processes
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. EMC Corporation, Information Storage and Management, Wiley, India.
2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill ,
Osborne, 2003.
3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001.
4. Additional resource material on www.emc.com/resource-library/resource-library.esp
CP7030
ROBOTICS
OBJECTIVES:
To understand robot locomotion and mobile robot kinematics
To understand perception in robotics
To understand mobile robot localization
To understand mobile robot mapping
To understand simultaneous localization and mapping (SLAM)
To understand robot planning and navigation
63
L T P C
3 0 0 3UNIT I
LOCOMOTION AND KINEMATICS
9
Introduction to Robotics – key issues in robot locomotion – legged robots – wheeled mobile
robots – aerial mobile robots – introduction to kinematics – kinematics models and
constraints – robot maneuverability
UNIT II
ROBOT PERCEPTION
9
Sensors for mobile robots – vision for robotics – cameras – image formation – structure from
stereo – structure from motion – optical flow – color tracking – place recognition – range data
UNIT III
MOBILE ROBOT LOCALIZATION
9
Introduction to localization – challenges in localization – localization and navigation – belief
representation – map representation – probabilistic map-based localization – Markov
localization – EKF localization – UKF localization – Grid localization – Monte Carlo
localization – localization in dynamic environments
UNIT IV
MOBILE ROBOT MAPPING
9
Autonomous map building – occupancy grip mapping – MAP occupancy mapping – SLAM –
extended Kalman Filter SLAM – graph-based SLAM – particle filter SLAM – sparse extended
information filter – fastSLAM algorithm
UNIT V
PLANNING AND NAVIGATION
9
Introduction to planning and navigation – planning and reacting – path planning – obstacle
avoidance techniques – navigation architectures – basic exploration algorithms
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain robot locomotion
Apply kinematics models and constraints
Implement vision algorithms for robotics
Implement robot localization techniques
Implement robot mapping techniques
Implement SLAM algorithms
Explain planning and navigation in robotics
REFERENCES:
1. Roland Seigwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, “Introduction to
autonomous mobile robots”, Second Edition, MIT Press, 2011.
2. Sebastian Thrun, Wolfram Burgard, and Dieter Fox, “Probabilistic Robotics”, MIT Press,
2005.
3. Howie Choset et al., “Principles of Robot Motion: Theory, Algorithms, and
Implementations”, A Bradford Book, 2005.
4. Gregory Dudek and Michael Jenkin, “Computational Principles of Mobile Robotics”,
Second Edition, Cambridge University Press, 2010.
5. Maja J. Mataric, “The Robotics Primer”, MIT Press, 2007.
CP7031
COMPILER OPTIMIZATION TECHNIQUES
LTPC
3003
OBJECTIVES:
To understand the optimization techniques used in compiler design.
To be aware of the various computer architectures that support parallelism.
To become familiar with the theoretical background needed for code optimization.
To understand the techniques used for identifying parallelism in a sequential program.
To learn the various optimization algorithms.
64UNIT I
INTRODUCTION
9
Language Processors - The Structure of a Compiler – The Evolution of Programming
Languages- The Science of Building a Compiler – Applications of Compiler Technology
Programming Language Basics - The Lexical Analyzer Generator -Parser Generator -
Overview of Basic Blocks and Flow Graphs - Optimization of Basic Blocks - Principle
Sources of Optimization.
UNIT II
INSTRUCTION-LEVEL PARALLELISM
9
Processor Architectures – Code-Scheduling Constraints – Basic-Block Scheduling –Global
Code Scheduling – Software Pipelining.
UNIT III
OPTIMIZING FOR PARALLELISM AND LOCALITY-THEORY
9
Basic Concepts – Matrix-Multiply: An Example - Iteration Spaces - Affine Array Indexes –
Data Reuse Array data dependence Analysis.
UNITIV
OPTIMIZING FOR PARALLELISM AND LOCALITY – APPLICATION
9
Finding Synchronization - Free Parallelism – Synchronization Between Parallel Loops –
Pipelining – Locality Optimizations – Other Uses of Affine Transforms.
UNIT V
INTERPROCEDURAL ANALYSIS
9
Basic Concepts – Need for Interprocedural Analysis – A Logical Representation of Data
Flow – A Simple Pointer-Analysis Algorithm – Context Insensitive Interprocedural Analysis -
Context-Sensitive Pointer-Analysis - Datalog Implementation by Binary Decision Diagrams.
TOTAL: 45 PERIODS
REFERENCES:
1. Alfred V. Aho, Monica S.Lam, Ravi Sethi, Jeffrey D.Ullman, “Compilers:Principles,
Techniques and Tools”, Second Edition, Pearson Education,2008.
2. Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern Architectures: A
Dependence-based Approach”, Morgan Kaufmann Publishers, 2002.
3. Steven S. Muchnick, “Advanced Compiler Design and Implementation”,Morgan
Kaufmann Publishers - Elsevier Science, India, Indian Reprint 2003.
65
AFFILIATED INSTITUTIONS
REGULATIONS - 2013
M.E. COMPUTER SCIENCE AND ENGINEERING
I - IV SEMESTERS (FULL TIME) CURRICULUM AND SYLLABUS
405
THEORY
Course Code
MA7155
CP7101
CP7102
CP7103
M.E. Computer Science
and Engineering
SEMESTER I
Course Title
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 4 2
0 0 2 1
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 2 1
0 0 4 2
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
Total L T P C
0 0 12 6
12 1 12 19
Total L T P C
0 0 24 12
0 0 24 12
Applied Probability and Statistics
Design and Management of Computer Networks
Advanced Data Structures and Algorithms
Multicore Architectures
Elective I
Elective II
PRACTICAL
Course Code
Course Title
CP7111
Advanced Data Structures Laboratory
CP7112
Case Study - Network Design (Team Work)
Total
SEMESTER II
THEORY
Course Code
CP7201
CP7202
CP7203
CP7204
Course Title
Theoretical Foundations of Computer Science
Advanced Databases
Principles of Programming Languages
Advanced Operating Systems
Elective III
Elective IV
PRACTICAL
Course Code
Course Title
CP7211
Advanced Databases Laboratory
CP7212
Case Study - Operating Systems Design (Team Work)
Total
SEMESTER III
THEORY
Course Code
Course Title
CP7301
Software Process and Project Management
Elective V
Elective VI
Elective VII
PRACTICAL
Course Code
CP7311
Project Work (Phase I)
Course Title
SEMESTER IV
PRACTICAL
Course Code
CP7411
Project Work (Phase II)
Course Title
TOAL NO OF CREDITS 75ELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER I
ELECTIVE-I Course Title L T P C
Course Code Formal Models of Software Systems 3 0 0 3
SE7103 Performance Evaluation of Computer Systems 3 0 0 3
CP7001 Probabilistic Reasoning Systems 3 0 0 3
CP7002 Data Analysis and Business Intelligence 3 0 0 3
CP7003 Image Processing and Analysis 3 0 0 3
CP7004 Sensing Techniques and Sensors 3 0 0 3
NE7001
ELECTIVE-II Course Title Randomized Algorithms L T P C
Course Code Mobile and Pervasive Computing 3 0 0 3
CP7005 Parallel Programming Paradigms 3 0 0 3
NE7002 Software Requirements Engineering 3 0 0 3
CP7006 Speech Processing and Synthesis 3 0 0 3
CP7007 Machine Learning Techniques 3 0 0 3
CP7008 3 0 0 3
CP7009 36 0 0 36
Total
SEMESTER II
ELECTIVE-III Course Title L T P C
Course Code Concurrency Models 3 0 0 3
CP7010 Real Time Systems 3 0 0 3
CP7011 Computer Vision 3 0 0 3
CP7012 Network and Information Security 3 0 0 3
NE7202 Design and Analysis of Parallel Algorithms 3 0 0 3
CP7013 Software Architectures 3 0 0 3
CP7014
ELECTIVE-IV Course Title L T P C
Course Code Model Checking and Program Verification 3 0 0 3
CP7015 Embedded Software Development 3 0 0 3
CP7016 Cloud Computing 3 0 0 3
IF7202 Data Visualization Techniques 3 0 0 3
CP7017 Protocols and Architecture for Wireless Sensor Networks 3 0 0 3
NE7005 Language Technologies 3 0 0 3
CP7018 36 0 0 36
TotalELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER III
ELECTIVE-V Social Network Analysis
Course Code Managing Big Data
NE7012 Mobile Application Development
CP7019 Bio-inspired Computing
NE7011 Medical Image Processing
CP7020 Software Design
CP7021
CP7022
ELECTIVE-VI Reconfigurable Computing
Course Code Energy Aware Computing
CP7023 Information Retrieval Techniques
IF7013 Data Mining Techniques
CP7024 Bio Informatics
CP7025 Software Quality Assurance
IF7002
CP7026
ELECTIVE-VII
Course Code
CP7027
CP7028
CP7029
CP7030
CP7031
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
51 0 0 51
Multiobjective Optimization Techniques
Enterprise Application Integration
Information Storage Management
Robotics
Compiler Optimization Techniques
Total
TOAL NO OF CREDITS 123PROGRAM EDUCATIONAL OBJECTIVES (PEO):
Graduates of this M. E. Computer Science and Engineering will be able to
1. Apply the necessary mathematical tools and fundamental & advanced knowledge of
computer science & engineering
2. Develop computer/software/network systems understanding the importance of social,
business, technical, environmental, and human context in which the systems would
work
3. Articulate fundamental concepts, design underpinnings of computer/software/network
systems, and research findings to train professionals or to educate engineering
students
4. Contribute effectively as a team member/leader, using common tools and
environment, in computer science and engineering projects, research, or education
5. Pursue life-long learning and research in selected fields of computer science &
engineering and contribute to the growth of those fields and society at large
PROGRAM OUTCOMES:
Apply knowledge of mathematics, science, engineering fundamentals and an
engineering specialization to the conceptualization of engineering models.
Identify, formulate, research literature and solve complex engineering problems
reaching substantiated conclusions using first principles of mathematics and
engineering sciences.
Design solutions for complex engineering problems and design systems,
components or processes that meet specified needs with appropriate consideration
for public health and safety, cultural, societal, and environmental considerations.
Conduct investigations of complex problems including design of experiments,
analysis and interpretation of data, and synthesis of information to provide valid
conclusions.
Create, select and apply appropriate techniques, resources, and modern engineering
tools, including prediction and modeling, to complex engineering activities, with an
understanding of the limitations.
Function effectively as an individual, and as a member or leader in diverse teams
and in multi-disciplinary settings.
Communicate effectively on complex engineering activities with the engineering
community and with society at large, such as being able to comprehend and write
effective reports and design documentation, make effective presentations, and give
and receive clear instructions.
Demonstrate understanding of the societal, health, safety, legal and cultural issues
and the consequent responsibilities relevant to engineering practice.
Understand and commit to professional ethics and responsibilities and norms of
engineering practice.
Understand the impact of engineering solutions in a societal context and demonstrate
knowledge of and need for sustainable development.
Demonstrate a knowledge and understanding of management and business
practices, such as risk and change management, and understand their limitations.
Recognize the need for, and have the ability to engage in independent and life-long
learning.
1MA7155
APPLIED PROBABILITY AND STATISTICS
L T PC
3 1 0 4
OBJECTIVES:
To introduce the basic concepts of one dimensional and two dimensional Random
Variables.
To provide information about Estimation theory, Correlation, Regression and Testing of
hypothesis.
To enable the students to use the concepts of multivariate normal distribution and
principle components analysis.
UNIT I
ONE DIMENSIONAL RANDOM VARIABLES
9+3
Random variables - Probability function – Moments – Moment generating functions and
their properties – Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal
distributions – Functions of a Random Variable.
UNIT II
TWO DIMENSIONAL RANDOM VARIABLES
9+3
Joint distributions – Marginal and Conditional distributions – Functions of two dimensional
random variables – Regression Curve – Correlation.
UNIT III
ESTIMATION THEORY
9+3
Unbiased Estimators – Method of Moments – Maximum Likelihood Estimation - Curve fitting
by Principle of least squares – Regression Lines.
UNIT IV
TESTING OF HYPOTHESES
9+3
Sampling distributions - Type I and Type II errors - Tests based on Normal, t,2 and F
distributions for testing of mean, variance and proportions – Tests for Independence of
attributes and Goodness of fit.
UNIT V
MULTIVARIATE ANALYSIS
9+3
Random Vectors and Matrices - Mean vectors and Covariance matrices - Multivariate
Normal density and its properties - Principal components Population
principal
components - Principal components from standardized variables.
TOTAL 45+15=60 PERIODS
OUTCOME:
The student will able to acquire the basic concepts of Probability and Statistical
techniques for solving mathematical problems which will be useful in solving Engineering
problems
REFERENCES:
1 Jay L. Devore, “Probability and Statistics For Engineering and the Sciences”,Thomson
and Duxbury, 2002.
2.Richard Johnson. ”Miller & Freund’s Probability and Statistics for Engineer”, Prentice
–
Hall , Seventh Edition, 2007.
3.Richard A. Johnson and Dean W. Wichern, “Applied Multivariate Statistical Analysis”,
Pearson Education, Asia, Fifth Edition, 2002.
4.Gupta S.C. and Kapoor V.K.”Fundamentals of Mathematical Statistics”, Sultan an Sons,
2001.
5.Dallas E Johnson , “Applied Multivariate Methods for Data Analysis”, Thomson an Duxbury
press,1998.
2CP7101
DESIGN AND MANAGEMENT OF COMPUTER NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION TO NETWORK MANAGEMENT
9
Overview of Analysis, Architecture and Design Process-System Methodology, Service
methodology, Service Description - Service characteristics - Performance Characteristics -
Network supportability - Requirement analysis – User Requirements – Application
Requirements – Device Requirements – Network Requirements – Other Requirements -
Requirement specification and map.
UNIT II
REQUIREMENTS ANALYSIS
9
Requirement Analysis Process – Gathering and Listing Requirements- Developing service
metrics – Characterizing behavior – Developing RMA requirements – Developing delay
Requirements - Developing capacity Requirements - Developing supplemental performance
Requirements –Requirements mapping – Developing the requirements specification
UNIT III
FLOW ANALYSIS
9
Individual and Composite Flows – Critical Flows - Identifying and developing flows – Data
sources and sinks – Flow models- Flow prioritization – Flow specification algorithms –
Example Applications of Flow Analysis
UNIT IV
NETWORK ARCHITECTURE
9
Architecture and design – Component Architectures – Reference Architecture – Architecture
Models – System and Network Architecture – Addressing and Routing Architecture –
Addressing and Routing Fundamentals – Addressing Mechanisms – Addressing Strategies
– Routing Strategies – Network Management Architecture – Network Management
Mechanisms Performance Architecture – Performance Mechanisms – Security and Privacy
Architecture – Planning security and privacy Mechanisms
UNIT V
NETWORK DESIGN
9
Design Concepts – Design Process - Network Layout – Design Traceability – Design Metrics
– Logical Network Design – Topology Design – Bridging, Switching and Routing Protocols-
Physical Network Design – Selecting Technologies and Devices for Campus and Enterprise
Networks – Optimizing Network Design
TOTAL : 45 PERIODS
REFERENCES:
1. Network Analysis, Architecture, and Design By James D. McCabe, Morgan Kaufmann,
Third Edition, 2007.ISBN-13: 978-0123704801
2. Computer Networks: A Systems Approach by Larry L. Peterson, Bruce S. Davie - 2007,
Elsevier Inc.
3. Top-down Network Design: [a Systems Analysis Approach to Enterprise Network
Design] By Priscilla Oppenheimer, Cisco Press , 3rd Edition, ISBN-13: 978-1-58720-
283-4 ISBN-10: 1-58720-283-2
4. Integrated Management of Networked Systems: Concepts, Architectures, and Their
Operational Application (The Morgan Kaufmann Series in Networking), Heinz-Gerd
Hegering, Sebastian Abeck, and Bernhard Neumair, 1999.
5. “Network Design and Management” – by Steven T.Karris, Orchard publications, Second
edition, Copyright 2009, ISBN 978-1-934404-15-7
6. “Network Design, Management and Technical Perspective”, Teresa C. Mann-Rubinson
and Kornel Terplan, CRC Press, 1999
7. “Ethernet Networks-Design, Implementation, Operation and Management by Gilbert
Held, John Wiley and sons, Fourth Edition
8. James Kurose and Keith Ross, “Computer Networking: A Top-Down Approach Featuring
the Internet”, 1999
3CP7102
ADVANCED DATA STRUCTURES AND ALGORITHMS
LTPC
3003
OBJECTIVES:
To understand the principles of iterative and recursive algorithms.
To learn the graph search algorithms.
To study network flow and linear programming problems.
To learn the hill climbing and dynamic programming design techniques.
To develop recursive backtracking algorithms.
To get an awareness of NP completeness and randomized algorithms.
To learn the principles of shared and concurrent objects.
To learn concurrent data structures.
UNIT I
ITERATIVE AND RECURSIVE ALGORITHMS
9
Iterative Algorithms: Measures of Progress and Loop Invariants-Paradigm Shift: Sequence of
Actions versus Sequence of Assertions- Steps to Develop an Iterative Algorithm-Different
Types of Iterative Algorithms--Typical Errors-Recursion-Forward versus Backward- Towers
of Hanoi-Checklist for Recursive Algorithms-The Stack Frame-Proving Correctness with
Strong Induction- Examples of Recursive Algorithms-Sorting and Selecting Algorithms-
Operations on Integers- Ackermann’s Function- Recursion on Trees-Tree Traversals-
Examples- Generalizing the Problem - Heap Sort and Priority Queues-Representing
Expressions.
UNIT II
OPTIMISATION ALGORITHMS
9
Optimization Problems-Graph Search Algorithms-Generic Search-Breadth-First Search-
Dijkstra’s Shortest-Weighted-Path -Depth-First Search-Recursive Depth-First Search-Linear
Ordering of a Partial Order- Network Flows and Linear Programming-Hill Climbing-Primal
Dual Hill Climbing- Steepest Ascent Hill Climbing-Linear Programming-Recursive
Backtracking-Developing Recursive Backtracking Algorithm- Pruning Branches-Satisfiability
UNIT III
DYNAMIC PROGRAMMING ALGORITHMS
9
Developing a Dynamic Programming Algorithm-Subtle Points- Question for the Little Bird-
Subinstances and Subsolutions-Set of Substances-Decreasing Time and Space-Number of
Solutions-Code. Reductions and NP-Completeness-Satisfiability-Proving NP-Completeness-
3-Coloring- Bipartite Matching. Randomized Algorithms-Randomness to Hide Worst Cases-
Optimization Problems with a Random Structure.
UNIT IV
SHARED OBJECTS AND CONCURRENT OBJECTS
9
Shared Objects and Synchronization -Properties of Mutual Exclusion-The Mora l- The
Producer–Consumer Problem -The Readers–Writers Problem-Realities of Parallelization-
Parallel Programming- Principles- Mutual Exclusion-Time- Critical Sections--Thread
Solutions-The Filter Lock-Fairness-Lamport’s Bakery Algorithm-Bounded Timestamps-Lower
Bounds on the Number of Locations-Concurrent Objects- Concurrency and Correctness-
Sequential Objects-Quiescent Consistency- Sequential Consistency-Linearizability- Formal
Definitions- Progress Conditions- The Java Memory Model
UNIT V
CONCURRENT DATA STRUCTURES
9
Practice-Linked Lists-The Role of Locking-List-Based Sets-Concurrent Reasoning- Coarse-
Grained Synchronization-Fine-Grained Synchronization-Optimistic Synchronization- Lazy
Synchronization-Non-Blocking Synchronization-Concurrent Queues and the ABA Problem-
Queues-A Bounded Partial Queue-An Unbounded Total Queue-An Unbounded Lock-Free
Queue-Memory Reclamation and the ABA Problem- Dual Data Structures- Concurrent
Stacks and Elimination- An Unbounded Lock-Free Stack- Elimination-The Elimination
Backoff Stack
TOTAL : 45 PERIODS
4OUTCOMES:
Upon completion of the course, the students will be able to
Design and apply iterative and recursive algorithms.
Design and implement optimisation algorithms in specific applications.
Design appropriate shared objects and concurrent objects for applications.
Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7103
MULTICORE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
To understand the recent trends in the field of Computer Architecture and identify
performance related parameters
To appreciate the need for parallel processing
To expose the students to the problems related to multiprocessing
To understand the different types of multicore architectures
To expose the students to warehouse-scale and embedded architectures
UNIT I
FUNDAMENTALS OF QUANTITATIVE DESIGN AND ANALYSIS
9
Classes of Computers – Trends in Technology, Power, Energy and Cost – Dependability –
Measuring, Reporting and Summarizing Performance – Quantitative Principles of Computer
Design – Classes of Parallelism - ILP, DLP, TLP and RLP - Multithreading - SMT and CMP
Architectures – Limitations of Single Core Processors - The Multicore era – Case Studies of
Multicore Architectures.
UNIT II
DLP IN VECTOR, SIMD AND GPU ARCHITECTURES
9
Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics Processing
Units - Detecting and Enhancing Loop Level Parallelism - Case Studies.
UNIT III
TLP AND MULTIPROCESSORS
9
Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues -
Performance Issues – Synchronization Issues – Models of Memory Consistency -
Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks.
UNIT IV
RLP AND DLP IN WAREHOUSE-SCALE ARCHITECTURES
9
Programming Models and Workloads for Warehouse-Scale Computers – Architectures for
Warehouse-Scale Computing – Physical Infrastructure and Costs – Cloud Computing –
Case Studies.
5UNIT V
ARCHITECTURES FOR EMBEDDED SYSTEMS
9
Features and Requirements of Embedded Systems – Signal Processing and Embedded
Applications – The Digital Signal Processor – Embedded Multiprocessors - Case Studies.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the limitations of ILP and the need for multicore architectures
Discuss the issues related to multiprocessing and suggest solutions
Point out the salient features of different multicore architectures and how they exploit
parallelism
Critically analyze the different types of inter connection networks
Discuss the architecture of GPUs, warehouse-scale computers and embedded
processors
REFERENCES:
1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative
Approach”, Morgan Kaufmann / Elsevier, 5th edition, 2012.
2. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw-Hill Education, 2003
3. Richard Y. Kain, “Advanced Computer Architecture a Systems Design Approach”,
Prentice Hall, 2011.
4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/
Software Approach” , Morgan Kaufmann / Elsevier, 1997.
CP7111
ADVANCED DATA STRUCTURES LABORATORY
L T PC
0 0 4 2
OBJECTIVES:
To learn to implement iterative and recursive algorithms.
To learn to design and implement algorithms using hill climbing and dynamic
programming techniques.
To learn to implement shared and concurrent objects.
To learn to implement concurrent data structures.
LAB EXERCISES:
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week or two two-hour sessions per
week. There will be about 15 exercises in a semester. It is recommended that all
implementations are carried out in Java. If C or C++ has to be used, then the threads library
will be required for concurrency. Exercises should be designed to cover the following topics:
Implementation of graph search algorithms.
Implementation and application of network flow and linear programming problems.
Implementation of algorithms using the hill climbing and dynamic programming
design techniques.
Implementation of recursive backtracking algorithms.
Implementation of randomized algorithms.
Implementation of various locking and synchronization mechanisms for concurrent
linked lists, concurrent queues, and concurrent stacks.
Developing applications involving concurrency.
TOTAL :60 PERIODS
6OUTCOMES:
Upon completion of the course, the students will be able to
1. Design and apply iterative and recursive algorithms.
2. Design and implement algorithms using the hill climbing and dynamic programming
and recursive backtracking techniques.
3. Design and implement optimisation algorithms for specific applications.
4. Design and implement randomized algorithms.
5. Design appropriate shared objects and concurrent objects for applications.
6. Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C.2 E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7112
CASE STUDY: NETWORK DESIGN (Team Work)
LT PC
0 0 21
CASE STUDY : 1
Analyzing the performance of various configurations and protocols in LAN.
1.1. Establishing a Local Area Network (LAN): The main objective is to set up a Local
Area Network,concepts involved in this network are IP addressing and the Address
Resolution Protocol (ARP). The required equipments are192.168.1.1 ,192.168.1.2,
192.168.1.3,Host A Host B Host C, Switch/HUB, three PC`s equipped with at least one
NIC, one HUB or Switch and the necessary cables. Once the physical LAN is set up the
hosts need to be configured using the ifconfig command. To verify communication
among the machines the ping command is used. Next, to manipulate the routing tables
at the hosts to understand how machines know where to send packets. Since the ifconfig
command places a default route into the routing tables this route must be deleted. to
́blindfoldˇ the machine. The ping command is used again to show that communication is
no longer available. To re-establish communication the routes are put back into the
routing table one host at a time. Communication is once again verified using the ping
command.
1.2. Connecting two LANs using multi-router topology with static routes:
The main objective is to extend routing connection by using multiple routers. The
concepts include IP addressing and basic network routing principles. Connect two LANs
topology. During router configuration attention is paid to the types of interfaces as
additional issues are involved with set-up. For example, the serial interfaces require
clocking mechanisms to be set correctly. Once the interfaces are working the ping
command is used to check for communication between LANs. The failure of
communication illustrates the need for routes to be established inside the routing
infrastructure. Static routes are used to show how packets can be transported through
any reasonable route. It is run trace route on two different configurations to demonstrate
the implementation of different routes.
71.3 Analyzing the performance of various configurations and protocols
Original TCP versus the above modified one: To compare the performance between the
operation of TCP with congestion control and the operation of TCP as implemented . The
main objective is for students to examine how TCP responds to a congested network.
The concepts involved in the lab include network congestion and the host responsibilites
for communicating over a network. This lab requires three PC’s connected to a
switch.One PC is designated as the target host and the other two PC’s will transfer a file
from the targethost using FTP. A load is placed on the network to simulate congestion
and the file is transfered, first by the host using the normal TCP and then by the host
using the modified version. This procedure is performed multiple times to determine
average statistics. The students are then asked to summarize the results and draw
conclusions about the performance differences and the underlying implications for hosts
operating in a network environment.
Case Study 2:
RIP and OSPF Redistribution
This case study addresses the issue of integrating Routing Information Protocol (RIP)
networks with Open Shortest Path First (OSPF) networks. Most OSPF networks also use
RIP to communicate with hosts or to communicate with portions of the internetwork that do
not use OSPF. This case study should provide examples of how to complete the following
phases in redistributing information between RIP and OSPF networks, including the
following topics:
• Configuring a RIP Network
• Adding OSPF to the Center of a RIP Network
• Adding OSPF Areas
• Setting Up Mutual Redistribution
‘Case Study 3:
Dial-on-Demand Routing
This case study should describe the use of DDR to connect a worldwide network that
consists of a central site located in Mumbai and remote sites located in Chennai, Bangalore,
and Hyderabad. The following scenarios should be considered:
• Having the Central Site Dial Out
Describe the central and remote site configurations for three setups: a central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Include examples of the usage of rotary groups and access lists.
• Having the Central and Remote Sites Dial In and Dial Out
Describe the central and remote site configurations for three setups: central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Also describes the usage of Point-to-Point Protocol (PPP)
encapsulation and the Challenge Handshake Authentication Protocol (CHAP).
• Having Remote Sites Dial Out
A common configuration is one in which the remote sites place calls to the central site but
the central site does not dial out. In a “star” topology, it is possible for all of the remote
routers to have their serial interfaces on the same subnet as the central site serial interface.
• Using DDR as a Backup to Leased Lines
Describes the use of DDR as a backup method to leased lines and provides examples of
how to use floating static routes on single and shared interfaces.
• Using Leased Lines and Dial Backup
Describes the use of Data Terminal Ready (DTR) dialing and V.25bis dialing with leased
lines.
Case Study 4:
Network Security
This case study should provide the specific actions you can take to improve the security of
your network. Before going into specifics, however, you should understand the following
basic concepts that are essential to any security system:
8• Know your enemy
This case study refers to attackers or intruders. Consider who might want to circumvent
your security measures and identify their motivations. Determine what they might want to
do and the damage that they could cause to your network. Security measures can never
make it impossible for a user to perform unauthorized tasks with a computer system. They
can only make it harder. The goal is to make sure the network security controls are beyond
the attacker’s ability or motivation.
• Count the cost
Security measures almost always reduce convenience, especially for sophisticated users.
Security can delay work and create expensive administrative and educational overhead. It
can use significant computing resources and require dedicated hardware. When you
design your security measures, understand their costs and weigh those costs against the
potential benefits. To do that, you must understand the costs of the measures themselves
and the costs and likelihoods of security breaches. If you incur security costs out of
proportion to the actual dangers, you have done yourself a disservice.
• Identify your assumptions
Every security system has underlying assumptions. For example, you might assume that
your network is not tapped, or that attackers know less than you do, that they are using
standard software, or that a locked room is safe. Be sure to examine and justify your
assumptions. Any hidden assumption is a potential security hole.
• Control your secrets
Most security is based on secrets. Passwords and encryption keys, for example, are
secrets. Too often, though, the secrets are not really all that secret. The most important
part of keeping secrets is knowing the areas you need to protect. What knowledge would
enable someone to circumvent your system? You should jealously guard that knowledge
and assume that everything else is known to your adversaries. The more secrets you
have, the harder it will be to keep all of them. Security systems should be designed so that
only a limited number of secrets need to be kept.
Know your weaknesses
Every security system has vulnerabilities. You should understand your system’s weak
points and know how they could be exploited. You should also know the areas that present
the largest danger and prevent access to them immediately. Understanding the weak
points is the first step toward turning them into secure areas.
Limit the scope of access
You should create appropriate barriers inside your system so that if intruders access one
part of the system, they do not automatically have access to the rest of the system. The
security of a system is only as good as the weakest security level of any single host in the
system.
• Remember physical security Physical access to a computer (or a router) usually gives a
sufficiently sophisticated user total control over that computer. Physical access to a
network link usually allows a person to tap that link, jam it, or inject traffic into it. It
makes no sense to install complicated software security measures when access to the
hardware is not controlled.
Case Study 5:
Controlling Traffic Flow
In this case study, the firewall router allows incoming new connections to one or more
communication servers or hosts. Having a designated router act as a firewall is desirable
because it clearly identifies the router’s purpose as the external gateway and avoids
encumbering other routers with this task. In the event that the internal network needs to
isolate itself, the firewall router provides the point of isolation so that the rest of the internal
network structure is not affected. Connections to the hosts are restricted to incoming file
transfer protocol (FTP) requests and email services. The incoming Telnet, or modem
connections to the communication server are screened by the communication server running
TACACS username authentication.
9Case Study 6:
Defining Access Lists
Access lists define the actual traffic that will be permitted or denied, whereas an access
group applies an access list definition to an interface. Access lists can be used to deny
connections that are known to be a security risk and then permit all other connections, or to
permit those connections that are considered acceptable and deny all the rest. For firewall
implementation, the latter is the more secure method. In this case study, incoming email and
news are permitted for a few hosts, but FTP, Telnet, and rlogin services are permitted only to
hosts on the firewall subnet. IP extended access lists (range 100 to 199) and transmission
control protocol (TCP) or user datagram protocol (UDP) port numbers are used to filter
traffic. When a connection is to be established for email, Telnet, FTP, and so forth, the
connection will attempt to open a service on a specified port number. You can, therefore,
filter out selected types of connections by denying packets that are attempting to use that
service. An access list is invoked after a routing decision has been made but before the
packet is sent out on an interface. The best place to define an access list is on a preferred
host using your favorite text editor. You can create a file that contains the access-list
commands, place the file (marked readable) in the default TFTP directory, and then network
load the file onto the router.
Case Study 7:
Configuring a fire wall
Consider a Fire wall communication server with single inbound modem.
modem to ensure security for LAN
Configure the
Case Study 8:
Integrating EIGRP (Enhanced Interior Gateway Routing Protocol) into Existing
Networks:
The case study should provide the benefits and considerations involved in integrating
Enhanced IGRP into the following types of internetworks:
• IP—The existing IP network is running IGRP
• Novell IPX—The existing IPX network is running RIP and SAP
• AppleTalk—The existing AppleTalk network is running the Routing Table Maintenance
Protocol (RTMP)
When integrating Enhanced IGRP into existing networks, plan a phased implementation.
Add Enhanced IGRP at the periphery of the network by configuring Enhanced IGRP on a
boundary router on the backbone off the core network. Then integrate Enhanced IGRP into
the core network.
CP7201
THEORETICAL FOUNDATIONS OF COMPUTER SCIENCE
LTPC
310 4
OBJECTIVES:
To review sets, relations, functions, and other foundations
To understand propositional and predicate logics and their applications
To understand lambda calculus and functional programming
To understand graph structures and their applications
To understand formal models of computation, computability, and decidability
UNIT I
FOUNDATIONS
9
Sets – relations – equivalence relations – partial orders – functions – recursive functions –
sequences – induction principle – structural induction – recursive algorithms – counting –
pigeonhole principle – permutations and combinations – recurrence relations
10UNIT II
LOGIC AND LOGIC PROGRAMMING
9
Propositional logic – syntax – interpretations and models – deduction theorems – normal
forms – inference rules – SAT solvers - predicate logic – syntax – proof theory – semantics
of predicate logic – undecidability of predicate logic – inferences in first-order logic – logic
programming – definite programs – SLD resolution – normal programs – SLDNF resolution –
introduction to Prolog
UNIT III
LAMBDA CALCULUS AND FUNCTIONAL PROGRAMMING
9
Lambda notation for functions – syntax – curried functions – parametric polymorphism –
lambda reduction – alpha reduction – beta reduction – beta abstraction – extensionality
theorem – delta reduction – reduction strategies – normal forms – Church-Rosser Theorems
– pure lambda calculus – constants – arithmetic – conditionals – Iteration – recursion –
introduction to functional programming
UNIT IV
GRAPH STRUCTURES
9
Tree Structures – Graph structures – graph representations – regular graph structures –
random graphs – Connectivity – Cycles – Graph Coloring – Cliques, Vertex Covers,
Independent sets – Spanning Trees – network flows – matching
UNIT V
STATE MACHINES
9
Languages and Grammars – Finite State Machines – State machines and languages –
Turing Machines – Computational Complexity – computability – Decidability – Church's
Thesis
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able
To explain sets, relations, functions
To conduct proofs using induction, pigeonhole principle, and logic
To apply counting, permutations, combinations, and recurrence relations
To apply recursive functions and lambda calculus
To explain logic programming and functional programming principles
To apply sequential structures, tree structures, and graph structures
To explain computational models, computability, and complexity
REFERENCES:
1. Uwe Schoning, “Logic for Computer Scientists”, Birkhauser, 2008.
2. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
3. John Harrison, “Handbook of Practical Logic and Automated Reasoning”, Cambridge
University Press, 2009.
4. Greg Michaelson, “An introduction to functional programming through lambda
calculus”, Dover Publications, 2011.
5. Kenneth Slonneger and Barry Kurtz, “Formal syntax and semantics of programming
languages”, Addison Wesley, 1995.
6. Kenneth H. Rosen, “Discrete Mathematics and its applications”, Seventh Edition,
Tata McGraw Hill, 2011.
7. Sriram Pemmaraju and Steven Skiena, “Computational Discrete Mathematics”,
Cambridge University Press, 2003.
8. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
9. Norman L. Biggs, “Discrete Mathematics”, Second Edition, Oxford University Press,
2002.
10. Juraj Hromkovic, “Theoretical Computer Science”, Springer, 1998.
11. J. E. Hopcroft, Rajeev Motwani, and J. D. Ullman, “Introduction to Automata Theory,
Languages, and Computation”, Third Edition, Pearson, 2008.
11CP7202
ADVANCED DATABASES
LTPC
3 003
OBJECTIVES:
To learn the modeling and design of databases.
To acquire knowledge on parallel and distributed databases and its applications.
To study the usage and applications of Object Oriented database
To understand the principles of intelligent databases.
To understand the usage of advanced data models.
To learn emerging databases such as XML, Cloud and Big Data.
To acquire inquisitive attitude towards research topics in databases.
UNIT I
PARALLEL AND DISTRIBUTED DATABASES
9
Database System Architectures: Centralized and Client-Server Architectures – Server
System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O
Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism –
Design of Parallel Systems- Distributed Database Concepts - Distributed Data Storage –
Distributed Transactions – Commit Protocols – Concurrency Control – Distributed Query
Processing – Case Studies
UNIT II
OBJECT AND OBJECT RELATIONAL DATABASES
9
Concepts for Object Databases: Object Identity – Object structure – Type Constructors –
Encapsulation of Operations – Methods – Persistence – Type and Class Hierarchies –
Inheritance – Complex Objects – Object Database Standards, Languages and Design:
ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems: Object
Relational features in SQL/Oracle – Case Studies.
UNIT III
INTELLIGENT DATABASES
9
Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)- Taxonomy- Applications-
Design Principles for Active Rules- Temporal Databases: Overview of Temporal Databases-
TSQL2- Deductive Databases: Logic of Query Languages – Datalog- Recursive Rules-
Syntax and Semantics of Datalog Languages- Implementation of Rules and Recursion-
Recursive Queries in SQL- Spatial Databases- Spatial Data Types- Spatial Relationships-
Spatial Data Structures-Spatial Access Methods- Spatial DB Implementation.
UNIT IV
ADVANCED DATA MODELS
9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data
Management - Location Dependent Data Distribution - Mobile Transaction Models -
Concurrency Control - Transaction Commit Protocols- Multimedia Databases- Information
Retrieval- Data Warehousing- Data Mining- Text Mining.
UNIT V
EMERGING TECHNOLOGIES
9
XML Databases: XML-Related Technologies-XML Schema- XML Query Languages- Storing
XML in Databases-XML and SQL- Native XML Databases- Web Databases- Geographic
Information Systems- Biological Data Management- Cloud Based Databases: Data Storage
Systems on the Cloud- Cloud Storage Architectures-Cloud Data Models- Query Languages-
Introduction to Big Data-Storage-Analysis.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Select the appropriate high performance database like parallel and distributed
database
Model and represent the real world data using object oriented database
Design a semantic based database to meaningful data access
Embed the rule set in the database to implement intelligent databases
Represent the data using XML database for better interoperability
Handle Big data and store in a transparent manner in the cloud
To solve the issues related to the data storage and retrieval
12REFERENCES:
1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition,
Pearson Education/Addison Wesley, 2007.
2. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to
Design, Implementation and Management”, Third Edition, Pearson Education, 2007.
3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”,
Fifth Edition, McGraw Hill, 2006.
4. C.J.Date, A.Kannan and S.Swamynathan, ”An Introduction to Database Systems”,
Eighth Edition, Pearson Education, 2006.
5. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”,
McGraw Hill, Third Edition 2004.
CP7203
PRINCIPLES OF PROGRAMMING LANGUAGES
LTPC
3003
OBJECTIVES:
To understand and describe syntax and semantics of programming languages
To understand data, data types, and basic statements
To understand call-return architecture and ways of implementing them
To understand object-orientation, concurrency, and event handling in programming
languages
To develop programs in non-procedural programming paradigms
UNIT I
SYNTAX AND SEMANTICS
9
Evolution of programming languages – describing syntax – context-free grammars – attribute
grammars – describing semantics – lexical analysis – parsing – recursive-decent – bottom-
up parsing
UNIT II
DATA, DATA TYPES, AND BASIC STATEMENTS
9
Names – variables – binding – type checking – scope – scope rules – lifetime and garbage
collection – primitive data types – strings – array types – associative arrays – record types –
union types – pointers and references – Arithmetic expressions – overloaded operators –
type conversions – relational and boolean expressions – assignment statements – mixed-
mode assignments – control structures – selection – iterations – branching – guarded
statements
UNIT III
SUBPROGRAMS AND IMPLEMENTATIONS
9
Subprograms – design issues – local referencing – parameter passing – overloaded
methods – generic methods – design issues for functions – semantics of call and return –
implementing simple subprograms – stack and dynamic local variables – nested
subprograms – blocks – dynamic scoping
UNIT IV
OBJECT-ORIENTATION, CONCURRENCY, AND EVENT HANDLING
9
Object-orientation – design issues for OOP languages – implementation of object-oriented
constructs – concurrency – semaphores – monitors – message passing – threads –
statement level concurrency – exception handling – even handling
UNIT V
FUNCTIONAL AND LOGIC PROGRAMMING LANGUAGES
9
Introduction to lambda calculus – fundamentals of functional programming languages –
Programming with Scheme – Programming with ML – Introduction to logic and logic
programming – Programming with Prolog – multi-paradigm languages
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe syntax and semantics of programming languages
Explain data, data types, and basic statements of programming languages
Design and implement subprogram constructs
13
Apply object-oriented, concurrency, and event handling programming constructs
Develop programs in Scheme, ML, and Prolog
Understand and adopt new programming languages
REFERENCES:
1. Robert W. Sebesta, “Concepts of Programming Languages”, Tenth Edition, Addison
Wesley, 2012.
2. Michael L. Scott, “Programming Language Pragmatics”, Third Edition, Morgan
Kaufmann, 2009.
3. R. Kent Dybvig, “The Scheme programming language”, Fourth Edition, MIT Press, 2009.
4. Jeffrey D. Ullman, “Elements of ML programming”, Second Edition, Prentice Hall, 1998.
5. Richard A. O'Keefe, “The craft of Prolog”, MIT Press, 2009.
6. W. F. Clocksin and C. S. Mellish, “Programming in Prolog: Using the ISO Standard”, Fifth
Edition, Springer, 2003.
CP7204
ADVANCED OPERATING SYSTEMS
LTPC
3 003
OBJECTIVES:
To learn the fundamentals of Operating Systems
To gain knowledge on Distributed operating system concepts that includes architecture,
Mutual exclusion algorithms, Deadlock detection algorithms and agreement protocols
To gain insight on to the distributed resource management components viz. the
algorithms for implementation of distributed shared memory, recovery and commit
protocols
To know the components and management aspects of Real time, Mobile operating
systems
UNIT I
FUNDAMENTALS OF OPERATING SYSTEMS
9
Overview – Synchronization Mechanisms – Processes and Threads - Process Scheduling –
Deadlocks: Detection, Prevention and Recovery – Models of Resources – Memory
Management Techniques.
UNIT II
DISTRIBUTED OPERATING SYSTEMS
9
Issues in Distributed Operating System – Architecture – Communication Primitives –
Lamport’s Logical clocks – Causal Ordering of Messages – Distributed Mutual Exclusion
Algorithms – Centralized and Distributed Deadlock Detection Algorithms – Agreement
Protocols.
UNIT III
DISTRIBUTED RESOURCE MANAGEMENT
9
Distributed File Systems – Design Issues - Distributed Shared Memory – Algorithms for
Implementing Distributed Shared memory–Issues in Load Distributing – Scheduling
Algorithms – Synchronous and Asynchronous Check Pointing and Recovery – Fault
Tolerance – Two-Phase Commit Protocol – Nonblocking Commit Protocol – Security and
Protection.
UNIT IV
REAL TIME AND MOBILE OPERATING SYSTEMS
9
Basic Model of Real Time Systems - Characteristics- Applications of Real Time Systems –
Real Time Task Scheduling - Handling Resource Sharing - Mobile Operating Systems –
Micro Kernel Design - Client Server Resource Access – Processes and Threads - Memory
Management - File system.
UNIT V
CASE STUDIES
9
Linux System: Design Principles - Kernel Modules - Process Management Scheduling -
Memory Management - Input-Output Management - File System - Interprocess
Communication. iOS and Android: Architecture and SDK Framework - Media Layer -
Services Layer - Core OS Layer - File System.
14OUTCOMES:
Upon Completion of the course, the students should be able to:
Discuss the various synchronization, scheduling and memory management issues
Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols of
Distributed operating system
Discuss the various resource management techniques for distributed systems
Identify the different features of real time and mobile operating systems
Install and use available open source kernel
Modify existing open source kernels in terms of functionality or features used
REFERENCES:
1. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
2. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,
Seventh Edition, John Wiley & Sons, 2004.
3. Daniel P Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
4. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
5. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload
media, 2011.
CP7211
ADVANCED DATABASE LABORATORY
LT P C
0 0 21
OBJECTIVES:
To learn to work on distributed data bases
To understand and work on object oriented databases
To gain knowledge in parallel data base by experimenting it
To learn to work on active database
To study and explore deductive database
To work on the data mining tool weka
To represent and work with the database using XML
DISTRIBUTED DATABASE:
1. Consider a distributed database for a bookstore with 4 sites called S1, S2, S3 and S4.
Consider the following relations:
Books ( ISBN, primary Author, topic, total Stock, price )
Book Store (store No, city, state, zip, inventoryValue )
Stock (store No, ISBN, Qty )
Total Stock is the total number of books in stock and inventory Value is the total
inventory value for the store in dollars.
Consider that Books are fragmented by price amounts into:
F1: Books: price up to $20
F2: Books: price from $20.01 to $50
F3: Books: price from $50.01 to $100
F4: Books: price $100.01 and above
Similarly, Book Stores are divided by ZIP codes into:
15S1: Bookstore: Zip up to 25000
S2: Bookstore: Zip 25001 to 50000
S3: Bookstore: Zip 50001 to 75000
S4: Bookstore: Zip 75001 to 99999
Task: Write SQL query for the following
1. Insert and Display details in each table.
2. Find the total number of books in stock where price is between $15 and $55.
3. Update the book price of book No=1234 from $45 to $55 at site S3.
4. Find total number of book at site S2.
2. Implement deadlock detection algorithm for distributed database using wait-for graph
and test with the following information.
Consider five transactions T1, T2, T3, T4 and T5 with
T1 initiated at site S1 and spawning an agent at site S2
T2 initiated at site S3 and spawning an agent at site S1
T3 initiated at site S1 and spawning an agent at site S3
T4 initiated at site S2 and spawning an agent at site S3
T5 initiated at site S3
The locking information for these transactions is shown in the following table
Transactions
T1
T1
T2
T2
T3
T3
T4
T4
T5
Data items locked
by transactions
X1
X6
X4
X5
X2
-
X7
X8
X3
Data items transaction is
waiting for
X8
X2
X1
-
X7
X3
-
X5
X7
Site involved
in operations
S1
S2
S1
S3
S1
S3
S2
S3
S3
Produce local wait for graph for each of the sites and construct global wait for graph and
check for dead lock.
OBJECT ORIENTED DATABASE:
3. A University wants to track persons associated with them. A person can be an
Employee or Student. Employees are Faculty, Technicians and Project associates.
Students are Full time students, Part time students and Teaching Assistants.
a) Design an Enhanced Entity Relationship (EER) Model for university database.
Write OQL for the following
i.
Insert details in each object.
ii.
Display the Employee details.
iii.
Display Student Details.
iv.
Modify person details.
v.
Delete person details.
b)
Extend the design by incorporating the following information.
Students are registering for courses which are handled by instructor researchers
(graduate students). Faculty are advisors to graduate students. Instructor researchers’ class
16is a category with super class of faculty and graduate students. Faculty are having
sponsored research projects with a grant supporting instruction researchers. Grants are
sanctioned by different agencies. Faculty belongs to different departments. Department is
chaired by a faculty. Implement for the Insertion and Display of details in each class.
PARALLEL DATABASE:
4. Consider the application for University Counselling for Engineering Colleges. The
college, department and vacancy details are maintained in 3 sites. Students are
allocated colleges in these 3 sites simultaneously. Implement this application using
parallel database [State any assumptions you have made].
5. There are 5 processors working in a parallel environment and producing output. The
output record contains college details and students mark information. Implement
parallel join and parallel sort algorithms to get the marks from different colleges of the
university and publish 10 ranks for each discipline.
ACTIVE DATABASE:
6. Create triggers and assertions for Bank database handling deposits and loan and
admission database handling seat allocation and vacancy position. Design the above
relational database schema and implement the following triggers and assertions.
a. When a deposit is made by a customer, create a trigger for updating
customers account and bank account
b. When a loan is issued to the customer, create a trigger for updating
customer’s loan account and bank account.
c. Create assertion for bank database so that the total loan amount does not
exceed the total balance in the bank.
d. When an admission is made, create a trigger for updating the seat allocation
details and vacancy position.
DEDUCTIVE DATABASE:
7. Construct a knowledge database for kinship domain (family relations) with facts.
Extract the following relations using rules.
Parent, Sibling, Brother, Sister, Child, Daughter, Son, Spouse, Wife, husband,
Grandparent, Grandchild, Cousin, Aunt and Uncle.
WEKA TOOL:
8. Work with Weka tool classification and clustering algorithms using the given training
data and test with the unknown sample. Also experiment with different scenarios and
large data set
RID Age Income Student
1 youth high no
2 youth high no
3 middle_aged high no
4 senior medium no
5 senior low yes
6 senior low yes
7 middle_aged low yes
8 youth medium no
9 youth low yes
10 senior medium yes
11 Youth medium yes
12 middle_aged medium no
13 middle_aged high yes
14 senior medium no
17
Credit_
rating
fair
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
Class: buys_
computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
noQUERY PROCESSING
9. Implement Query Optimizer with Relational Algebraic expression construction and
execution plan generation for choosing an efficient execution strategy for processing
the given query.
Also design employee database and test the algorithm with following sample queries.
a) Select empid, empname from employee where experience > 5
b) Find all managers working at London Branch
XML
10. Design XML Schema for the given company database
Department ( deptName, deptNo, deptManagerSSN, deptManagerStartDate,
deptLocation )
Employee ( empName, empSSN, empSex, empSalary, empBirthDate,
empDeptNo, empSupervisorSSN, empAddress, empWorksOn)
Project ( projName, projNo, projLocation, projDeptNo, projWorker )
a. Implement the following queries using XQuery and XPath
i.
ii.
iii.
iv.
b.
Retrieve the department name, manager name, and manager salary for every
department’
Retrieve the employee name, supervisor name and employee salary for each
employee who works in the Research Department.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project with more than
one employee working on it
Implement a storage structure for storing XML database and test with the above
schema.
TOTAL : 30 PERIODS
OUTCOMES:
Work on distributed databases
Create and work on object oriented databases
Create and work with parallel database
Experiment on active database
Explore the features of deductive database
To work on weka tool for clustering and classification
Represent the database using XML and work on it
CP7212
CASE STUDY – OPERATING SYSTEMS DESIGN
(Team Work)
L T PC
0 0 4 2
OBJECTIVES:
1. To develop capabilities to work at systems level
2. To learn about issues in designing and implementing modern operating systems
3. To understand team formation, team issues, and allocating roles and responsibilities
4. To make effective presentations on the work done
5. To develop effective written communication skills
LAB EXERCISES:
A team of three or four students will work on assigned case study / mini-project. Case Study
/ Mini-project can be designed on the following lines:
1. Development of a reasonably sized dynamically loadable kernel module for Linux
kernel
182. Study educational operating systems such as Minix (http://www.minix3.org/), Weenix
(http://weenix.cs.brown.edu/mediawiki/index.php/Weenix) and develop reasonably
sized interesting modules for them
3. Study the Android open source operating system for mobile devices
(http://source.android.com/) and develop / modify some modules.
4. Study any embedded and real-time operating system such as eCos
(http://ecos.sourceware.org/) and develop / modify some modules.
TOTAL : 60 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Develop assigned modules of operating systems design carrying out coding, testing,
and documentation work involved.
Describe team issuses and apply suitable methods to resolve the same.
Demonstrate individual competence in building medium size operating system
components.
Demonstrate ethical and professional attributes of a computer engineer.
Prepare suitable plan with clear statements of deliverables, and track the same.
Make individual presentation of the work carried out.
Prepare well-organized written documents to communicate individual work
accomplished.
REFERENCES:
1. Watts S. Humphrey, “Introduction to Team Software Process”, Addison-Wesley, SEI
Series in Software Engineering, 1999.
2. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
3. T. W. Doeppner, “Operating Systems in Depth: Design and Programming”, Wiley, 2010.
4. S. Tanenbaum and A. S. Woodhull, “Operating Systems Design and Implementation”,
Third Edition, Prentice Hall, 2006.
5. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, “Operating System Concepts”,
Ninth Edition, John Wiley & Sons, 2012.
6. Daniel P. Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
7. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
CP7301
SOFTWARE PROCESS AND PROJECT MANAGEMENT
LTPC
3104
OBJECTIVES:
1.
2.
3.
4.
5.
To
To
To
To
To
understand overall SDLC and adopt suitable processes
elicite, analyze, prioritize, and manage both functional and quality requirements
estimate efforts required, plan, and track the plans
understand and apply configuration and quality management techniques
evaluate, manage, and design processes
(A mini-project can be chosen by the instructor and use it as a context for the
tutorials)
UNIT I
DEVELOPMENT LIFE CYCLE PROCESSES
9
Overview of software development life cycle – introduction to processes – Personal Software
Process (PSP) – Team software process (TSP) – Unified processes – agile processes –
choosing the right process Tutorial: Software development using PSP
19UNIT II
REQUIREMENTS MANAGEMENT
9
Functional requirements and quality attributes – elicitation techniques – Quality Attribute
Workshops (QAW) – analysis, prioritization, and trade-off – Architecture Centric
Development Method (ACDM) – requirements documentation and specification – change
management – traceability of requirements
Tutorial: Conduct QAW, elicit, analyze, prioritize, and document requirements using ACDM
UNIT III
ESTIMATION, PLANNING, AND TRACKING
9
Identifying and prioritizing risks – risk mitigation plans – estimation techniques – use case
points – function points – COCOMO II – top-down estimation – bottom-up estimation – work
breakdown structure – macro and micro plans – planning poker – wideband delphi –
documenting the plan – tracking the plan – earned value method (EVM)
Tutorial: Estimation, planning, and tracking exercises
UNIT IV
CONFIGURATION AND QUALITY MANAGEMENT
9
identifying artifacts to be configured – naming conventions and version control –
configuration control – quality assurance techniques – peer reviews – Fegan inspection –
unit, integration, system, and acceptance testing – test data and test cases – bug tracking –
causal analysis
Tutorial: version control exercises, development of test cases, causal analysis of defects
UNIT V
SOFTWARE PROCESS DEFINITION AND MANAGEMENT
9
Process elements – process architecture – relationship between elements – process
modeling – process definition techniques – ETVX (entry-task-validation-exit) – process
baselining – process assessment and improvement – CMMI – Six Sigma
Tutorial: process measurement exercises, process definition using ETVX
TOTAL 45+15=60 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
1. Explain software development life cycle
2. Adopt a suitable process for software development
3. Elicit functional and quality requirements
4. Analyze, prioritize, and manage requirements
5. Perform trade-off among conflicting requirements
6. Identify and prioritize risks and create mitigation plans
7. Estimate the efforts required for software development
8. Perform planning and tracking activities
9. Control the artifacts during software development
10. Perform various tests to ensure quality
11. Define new processes based on the needs
12. Adopt best practices for process improvement
REFERENCES:
1. Pankaj Jalote, “Software Project Management in Practice”, Pearson, 2002.
2. Chris F. Kemerer, “Software Project Management – Readings and Cases”, McGraw Hill,
1997.
3. Watts S. Humphrey, “PSP: A self-improvement process for software engineers”,
Addison-Wesley, 2005.
4. Watts S. Humphrey, “Introduction to the Team Software Process”, Addison-Wesley,
2000.
5. Orit Hazzan and Yael Dubinsky, “Agile software engineering”, Springer, 2008.
6. James R. Persse, “Process Improvement Essentials”, O’Reilly, 2006.
7. Roger S. Pressman, “Software Engineering – A Practitioner’s Approach”, Seventh
Edition, McGraw Hill, 2010.
20SE7103
FORMAL MODELS OF SOFTWARE SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the basic elements of Z
To understand relations, functions, and logical structures in Z
To understand Z schemas and schema calculus
To learn selected Z case studies
To understand Z schema refinement
UNIT I
FOUNDATIONS OF Z
9
Understanding formal methods – motivation for formal methods – informal requirements to
formal specifications – validating formal specifications – Overview of Z specification – basic
elements of Z – sets and types – declarations – variables – expressions – operators –
predicates and equations
UNIT II
STRUCTURES IN Z
9
Tuples and records – relations, tables, databases – pairs and binary relations – functions –
sequences – propositional logic in Z – predicate logic in Z – Z and boolean types – set
comprehension – lambda calculus in Z – simple formal specifications – modeling systems
and change
UNIT III
Z SCHEMAS AND SCHEMA CALCULUS
9
Z schemas – schema calculus – schema conjunction and disjunction – other schema
calculus operators – schema types and bindings – generic definitions – free types – formal
reasoning – checking specifications – precondition calculation – machine-checked proofs
UNIT IV
Z CASE STUDIES
9
Case Study: Text processing system – Case Study: Eight Queens – Case Study: Graphical
User Interface – Case Study: Safety critical protection system – Case Study: Concurrency
and real time systems
UNIT V
Z REFINEMENT
9
Refinement of Z specification – generalizing refinements – refinement strategies – program
derivation and verification – refinement calculus – data structures – state schemas –
functions and relations – operation schemas – schema expressions – refinement case study
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Apply the basic elements of Z
Develop relational, functional, and logical Z structures
Develop Z schema as models of software systems
Perform verifications and conduct proofs using Z models
Refine Z models towards implementing software systems
REFERENCES:
1. Jonathan Jacky, “The way of Z: Practical programming with formal methods”,
Cambridge University Press, 1996.
2. Antoni Diller, “Z: An introduction to formal methods”, Second Edition, Wiley, 1994.
3. Jim Woodcock and Jim Davies, “Using Z – Specification, Refinement, and Proof”,
Prentice Hall, 1996.
4. J. M. Spivey, “The Z notation: A reference manual”, Second Edition, Prentice Hall,
1992.
5. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
6. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
21CP7001
PERFORMANCE EVALUATION OF COMPUTER SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for performance evaluation of
computer systems
To understand the metrics used for performance evaluation
To understand the analytical modeling of computer systems
To enable the students to develop new queueing analysis for both simple and complex
systems
To appreciate the use of smart scheduling and introduce the students to analytical
techniques for evaluating scheduling policies
UNIT I
OVERVIEW OF PERFORMANCE EVALUATION
9
Need for Performance Evaluation in Computer Systems – Overview of Performance
Evaluation Methods – Introduction to Queueing – Probability Review – Generating Random
Variables for Simulation – Sample Paths, Convergence and Averages – Little’s Law and
other Operational Laws – Modification for Closed Systems.
UNIT II
MARKOV CHAINS AND SIMPLE QUEUES
9
Discrete-Time Markov Chains – Ergodicity Theory – Real World Examples – Google, Aloha
– Transition to Continuous-Time Markov Chain – M/M/1 and PASTA.
UNIT III
MULTI-SERVER AND MULTI-QUEUE SYSTEMS
9
Server Farms: M/M/k and M/M/k/k – Capacity Provisioning for Server Farms – Time
Reversibility and Burke’s Theorem – Networks of Queues and Jackson Product Form –
Classed and Closed Networks of Queues.
UNIT IV
REAL-WORLD WORKLOADS
9
Case Study of Real-world Workloads – Phase-Type Distributions and Matrix-Alalytic
Methods – Networks with Time-Sharing Servers – M/G/1 Queue and the Inspection Paradox
– Task Assignment Policies for Server Farms.
UNIT V
SMART SCHEDULING IN THE M/G/1
9
Performance Metrics – Scheduling Non-Preemptive and Preemptive Non-Size-Based
Policies - . Scheduling Non-Preemptive and Preemptive Size-Based Policies – Scheduling -
SRPT and Fairness.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for performance evaluation and the metrics used for it
Discuss open and closed queueing networks
Define Little’e law and other operational laws
Apply the operational laws to open and closed systems
Use discrete-time and continuous-time Markov chains to model real world systems
Develop analytical techniques for evaluating scheduling policies
REFERENCES:
1. Mor Harchol - Balter, “Performance Modeling and Design of Computer Systems –
Queueing Theory in Action”, Cambridge University Press, 2013.
2. Raj Jain, “The Art of Computer Systems Performance Analysis: Techniques for
Experimental Design, Measurement, Simulation and Modeling”, Wiley-Interscience,
1991.
3. Lieven Eeckhout, “Computer Architecture Performance Evaluation Methods”, Morgan
and Claypool Publishers, 2010.
4. Paul J. Fortier and Howard E. Michel, “Computer Systems Performance Evaluation and
Prediction”, Elsevier, 2003.
5. David J. Lilja, “Measuring Computer Performance: A Practitioner’s Guide”, Cambridge
University Press, 2000.
226. Krishna Kant, “Introduction to Computer System Performance Evaluation”, McGraw-Hill,
1992.
7. K. S. Trivedi, “Probability and Statistics with Reliability, Queueing and Computer Science
Applications”, John Wiley and Sons, 2001.
CP7002
PROBABILISTIC REASONING SYSTEMS
LT P C
3 0 0 3
OBJECTIVES:
To construct and reason with Bayesian networks
To reason with temporal models
To make exact and approximate inferences with graphical models
To understand learning of parameters for probabilistic graphical models
To understand actions and decisions with probabilistic graphical models
UNIT I
REPRESENTATION
9
Probability Theory, Graphs, Bayesian network representation: Bayes networks,
Independence in graphs – Undirected graphical models: Parameterization, Markov Network
independencies – Conditional Bayesian networks.
UNIT II
TEMPLATE BASED REPRESENTATION
9
Temporal models (Dynamic Bayesian networks , Hidden Markov Models) – Directed
probabilistic models for object-relational domains – Inference in temporal models: Kalman
filters.
UNIT III
INFERENCE
9
Exact inference: Variable elimination – Exact inference: Clique trees (Junction trees) –
Approximate inference: Forward sampling, Importance sampling, MCMC – MAP inference:
Variable elimination for MAP, Max-product in clique trees.
UNIT IV
LEARNING
9
Learning graphical models – Parameter estimation: maximum-likelihood estimation, MLE for
Bayesian networks, Bayesian parameter estimation – Structure learning in Bayesian
networks: Constraint based, structure scores, structure search – Partially observed data:
Parameter estimation, Learning models with hidden variables – Learning undirected
models: Maximum likelihood
UNIT V
ACTIONS AND DECISIONS
Causality – Utilities and decisions – Structured decision problems
9
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Construct Bayesian networks
Reason with Bayesian networks
Reason with Dynamic networks and Hidden Markov Models
Conduct inferences with Bayesian networks
Implement algorithms to learn probabilistic graphical models
Explain actions and decisions with probabilistic graphical models
REFERENCES:
1. Daphne Koller and Nir Friedman, “Probabilistic Graphical Models: Principles and
Techniques”, MIT Press, 2009.
2. David Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
3. Adnan Darwiche, “Modeling and Reasoning with Bayesian networks”, Cambridge
University Press, 2009.
4. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
5. Stuart Russel and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third
Edition, Prentice Hall, 2009.
23CP7003
DATA ANALYSIS AND BUSINESS INTELLIGENCE
LTPC
3 003
OBJECTIVES:
To understand linear regression models
To understand logistic regression models
To understand generalized linear models
To understand simulation using regression models
To understand causal inference
To understand multilevel regression
To understand data collection and model understanding
UNIT I
LINEAR REGRESSION
9
Introduction to data analysis – Statistical processes – statistical models – statistical inference
– review of random variables and probability distributions – linear regression – one predictor
– multiple predictors – prediction and validation – linear transformations – centering and
standardizing – correlation – logarithmic transformations – other transformations – building
regression models – fitting a series of regressions
UNIT II
LOGISTIC AND GENERALIZED LINEAR MODELS
9
Logistic regression – logistic regression coefficients – latent-data formulation – building a
logistic regression model – logistic regression with interactions – evaluating, checking, and
comparing fitted logistic regressions – identifiability and separation – Poisson regression –
logistic-binomial model – Probit regression – multinomial regression – robust regression
using t model – building complex generalized linear models – constructive choice models
UNIT III
SIMULATION AND CAUSAL INFERENCE
9
Simulation of probability models – summarizing linear regressions – simulation of non-linear
predictions – predictive simulation for generalized linear models – fake-data simulation –
simulating and comparing to actual data – predictive simulation to check the fit of a time-
series model – causal inference – randomized experiments – observational studies – causal
inference using advanced models – matching – instrumental variables
UNIT IV
MULTILEVEL REGRESSION
9
Multilevel structures – clustered data – multilevel linear models – partial pooling – group-
level predictors – model building and statistical significance – varying intercepts and slopes –
scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression –
multi-level generalized linear models
UNIT V
DATA COLLECTION AND MODEL UNDERSTANDING
9
Design of data collection – classical power calculations – multilevel power calculations –
power calculation using fake-data simulation – understanding and summarizing fitted models
– uncertainty and variability – variances – R2 and explained variance – multiple comparisons
and statistical significance – analysis of variance – ANOVA and multilevel linear and general
linear models – missing data imputation
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Build and apply linear regression models
Build and apply logistic regression models
Build and apply generalized linear models
Perform simulation using regression models
Perform casual inference from data
Build and apply multilevel regression models
Perform data collection and variance analysis
REFERENCES:
1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and
multilevel/Hierarchical Models", Cambridge University Press, 2006.
2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.
3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012.
244. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second
Edition, Oxford University Press, 2006.
5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and
data mining applications", Academic Press, 2009.
6. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An
Example-based Approach", Third Edition, Cambridge University Press, 2010.
8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer,
2011.
CP7004
IMAGE PROCESSING AND ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the basics of digital images
To understand noise models
To understand spatial domain filters
To understand frequency domain filters
To learn basic image analysis --- segmentation, edge detection, and corner detection
To learn morphological operations and texture analysis
To understand processing of color images
To understand image compression techniques
UNIT I
SPATIAL DOMAIN PROCESSING
9
Introduction to image processing – imaging modalities – image file formats – image sensing
and acquisition – image sampling and quantization – noise models – spatial filtering
operations – histograms – smoothing filters – sharpening filters – fuzzy techniques for spatial
filtering – spatial filters for noise removal
UNIT II
FREQUENCY DOMAIN PROCESSING
9
Frequency domain – Review of Fourier Transform (FT), Discrete Fourier Transform (DFT),
and Fast Fourier Transform (FFT) – filtering in frequency domain – image smoothing –
image sharpening – selective filtering – frequency domain noise filters – wavelets – Haar
Transform – multiresolution expansions – wavelet transforms – wavelets based image
processing
UNIT III
SEGMENTATION AND EDGE DETECTION
9
Thresholding techniques – region growing methods – region splitting and merging – adaptive
thresholding – threshold selection – global valley – histogram concavity – edge detection –
template matching – gradient operators – circular operators – differential edge operators –
hysteresis thresholding – Canny operator – Laplacian operator – active contours – object
segmentation
UNIT IV
INTEREST POINTS, MORPHOLOGY, AND TEXTURE
9
Corner and interest point detection – template matching – second order derivatives –
median filter based detection – Harris interest point operator – corner orientation – local
invariant feature detectors and descriptors – morphology – dilation and erosion –
morphological operators – grayscale morphology – noise and morphology – texture – texture
analysis – co-occurrence matrices – Laws' texture energy approach – Ade's eigen filter
approach
25UNIT V
COLOR IMAGES AND IMAGE COMPRESSION
9
Color models – pseudo colors – full-color image processing – color transformations –
smoothing and sharpening of color images – image segmentation based on color – noise in
color images.
Image Compression – redundancy in images – coding redundancy – irrelevant information in
images – image compression models – basic compression methods – digital image
watermarking.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain image modalities, sensing, acquisition, sampling, and quantization
Explain image noise models
Implement spatial filter operations
Explain frequency domain transformations
Implement frequency domain filters
Apply segmentation algorithms
Apply edge detection techniques
Apply corner and interest point detection algorithms
Apply morphological operations
Perform texture analysis
Analyze color images
Implement image compression algorithms
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction
using Java”, Springer, 2008.
3. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011.
4. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson,
2008.
5. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
6. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
7. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
NE7001
SENSING TECHNIQUES AND SENSORS
LT P C
3003
OBJECTIVES:
To study the sensor characteristics and the fundamental principles of sensing
To understand the sensor interface electronics
To study selected motion-related sensors
To study light and radiation detectors
To study selected temperature sensors
To study selected chemical sensors
UNIT I
PRINCIPLES OF SENSING
9
Data Acquisition – sensor characteristics – electric charges, fields, potentials – capacitance
– magnetism – inductance – resistance – piezoelectric – pyroelectric – Hall effect –
thermoelectric effects – sound waves – heat transfer – light – dynamic models of sensors
UNIT II
OPTICAL COMPONENTS AND INTERFACE ELECTRONICS
9
Radiometry – Photometry – mirrors – lenses – fibre optics – concentrators – Interface
circuits – amplifiers – light-to-voltage – excitation circuits – ADC – Digitization –
Capacitance-to-voltage – bridge circuits – data transmission – noise in sensors and circuits –
calibration – low power sensors
26UNIT III
MOTION RELATED SENSORS
9
Occupancy and motion detectors: ultrasonic – microwave – capacitive detectors –
triboelectric – optoelectronic motion sensors – optical presence sensor – Pressure Gradient
sensorsVelocity and acceleration sensors: Accelerometer characteristics – capacitative
accelerometers – piezoelectric accelerometers – piezoresistive accelerometers – thermal
accelerometers – Gyroscopes – piezoelectric cables – gravitational sensors
UNIT IV
LIGHT AND RADIATION DETECTORS
9
Light Detectors: Photo diodes – photo transistor – photo resistor – cooled detectors – CCD
and CMOS image sensors – thermal detectors – optical design – gas flame detectors
Radiation Detectors: scintillating detectors – ionization detectors – cloud and bubble
chambers
UNIT V
TEMPERATURE AND CHEMICAL SENSORS
9
Temperature Sensors: coupling with objects – temperature reference points – thermo
resistive sensors – thermo electric contact sensors – semiconductor sensors – acoustic
sensors – piezoelectric sensors Chemical sensors: characteristics – classes of chemical
sensors – biochemical sensors – multi-sensor arrays – electronic noses and tongues
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain sensor characteristics
Explain the physics of sensors
Explain optical components of sensors
Apply sensor interface electronics
Choose and use appropriate motion-related sensors
Choose and use appropriate light and radiation detectors
Choose and use appropriate temperature sensors
Choose and use appropriate chemical sensors
REFERENCE:
1. Jacob Fraden, “Handbook of Modern Sensors: Physics, Designs, and Applications”,
Fourth Edition, Springer, 2010.
CP7005
RANDOMIZED ALGORITHMS
LTPC
30 03
OBJECTIVES:
To understand the mathematical foundations needed for understanding and designing
randomized algorithms
To appreciate the need for randomized algorithms
To expose the students to probabilistic methods
To understand the concept of random walk
To expose the students to different types of applications of randomized algorithms
UNIT I
INTRODUCTION TO RANDOMIZED ALGORITHMS
9
Introduction to Randomized Algorithms - Min-cut – Elementary Probability Theory – Models
of Randomized Algorithms – Classification of Randomized Algorithms – Paradigms of the
Design of Randomized Algorithms - Game Theoretic Techniques – Game Tree Evaluation –
Minimax Principle – Randomness and Non Uniformity.
UNIT II
PROBABILISTIC METHODS
9
Moments and Deviations – occupancy Problems – Markov and Chebyshev Inequalities –
Randomized Selection – Two Point Sampling – The Stable Marriage Problem – The
Probabilistic Method – Maximum Satisfiability – Expanding Graphs – Method of Conditional
Probabilities – Markov Chains and Random Walks – 2-SAT Example – Random Walks on
Graphs – Random Connectivity.
27UNIT III
ALGEBRAIC TECHNIQUES AND APPLICATIONS
9
Fingerprinting Techniques – Verifying Polynomial Identities – Perfect Matching in Graphs –
Pattern Matching – Verification of Matrix Multiplication - Data Structuring Problems –
Random Treaps – Skip Lists – Hash Tables.
UNIT IV
GEOMETRIC AND GRAPH ALGORITHMS
9
Randomized Incremental Construction – Convex Hulls – Duality – Trapezoidal
Decompositions – Linear Programming – Graph Algorithms – Min-cut – Minimum Spanning
Trees.
UNIT V HASHING AND ONLINE ALGORITHMS
9
Hashing – Universal Hashing - Online Algorithms – Randomized Online Algorithms - Online
Paging – Adversary Models – Relating the Adversaries – The k-server Problem.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for randomized algorithms
Discuss the classification of randomized algorithms
Present the various paradigms for designing randomized algorithms
Discuss the different probabilistic methods used for designing randomized algorithms
Apply the techniques studied to design algorithms for different applications like matrix
multiplication, hashing, linear programming
REFERENCES:
1. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
2. Juraj Hromkovic,”Design and Analysis of Randomized Algorithms”, Springer, 2010.
3. Michael Mitzenmacher and Eli Upfal, “Probabilty and Computing – Randomized
Algorithms and Probabilistic Analysis”, Cambridge University Press, 2005.
NE7002
MOBILE AND PERVASIVE COMPUTING
LT P C
3003
OBJECTIVES :
To understand the basics of Mobile Computing and Personal Computing
To learn the role of cellular networks in Mobile and Pervasive Computing
To expose to the concept of sensor and mesh networks
To expose to the context aware and wearable computing
To learn to develop applications in mobile and pervasive computing environment
UNIT I
INTRODUCTION
9
Differences between Mobile Communication and Mobile Computing – Contexts and Names
– Functions – Applications and Services – New Applications – Making Legacy Applications
Mobile Enabled – Design Considerations – Integration of Wireless and Wired Networks –
Standards Bodies – Pervasive Computing – Basics and Vision – Principles of Pervasive
Computing – Categories of Pervasive Devices
UNIT II
3G AND 4G CELLULAR NETWORKS
9
Migration to 3G Networks – IMT 2000 and UMTS – UMTS Architecture – User Equipment –
Radio Network Subsystem – UTRAN – Node B – RNC functions – USIM – Protocol Stack –
CS and PS Domains – IMS Architecture – Handover – 3.5G and 3.9G a brief discussion –
4G LAN and Cellular Networks – LTE – Control Plane – NAS and RRC – User Plane –
PDCP, RLC and MAC – WiMax IEEE 802.16d/e – WiMax Internetworking with 3GPP
28UNIT III
SENSOR AND MESH NETWORKS
9
Sensor Networks – Role in Pervasive Computing – In Network Processing and Data
Dissemination – Sensor Databases – Data Management in Wireless Mobile Environments –
Wireless Mesh Networks – Architecture – Mesh Routers – Mesh Clients – Routing – Cross
Layer Approach – Security Aspects of Various Layers in WMN – Applications of Sensor and
Mesh networks
UNIT IV
CONTEXT AWARE COMPUTING & WEARABLE COMPUTING
9
Adaptability – Mechanisms for Adaptation - Functionality and Data – Transcoding –
Location Aware Computing – Location Representation – Localization Techniques –
Triangulation and Scene Analysis – Delaunay Triangulation and Voronoi graphs – Types of
Context – Role of Mobile Middleware – Adaptation and Agents – Service Discovery
Middleware
Health BAN- Medical and Technological Requirements-Wearable Sensors-Intra-BAN
communications
UNIT V
APPLICATION DEVELOPMENT
9
Three tier architecture - Model View Controller Architecture - Memory Management –
Information Access Devices – PDAs and Smart Phones – Smart Cards and Embedded
Controls – J2ME – Programming for CLDC – GUI in MIDP – Application Development ON
Android and iPhone
TOTAL:45 PERIODS
OUTCOMES:
At the end of the course the student should be able to
Design a basic architecture for a pervasive computing environment
Design and allocate the resources on the 3G-4G wireless networks
Analyze the role of sensors in Wireless networks
Work out the routing in mesh network
Deploy the location and context information for application development
Develop mobile computing applications based on the paradigm of context aware
computing and wearable computing
REFERENCES:
1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile Computing: Technology,
Applications and Service Creation”, 2nd ed, Tata McGraw Hill, 2010.
2. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
3. .Pei Zheng and Lionel M Li, ‘Smart Phone & Next Generation Mobile Computing’,
Morgan Kaufmann Publishers, 2006.
4. Frank Adelstein, ‘Fundamentals of Mobile and Pervasive Computing’, TMH, 2005
5. Jochen Burthardt et al, ‘Pervasive Computing: Technology and Architecture of Mobile
Internet Applications’, Pearson Education, 2003
6. Feng Zhao and Leonidas Guibas, ‘Wireless Sensor Networks’, Morgan Kaufmann
Publishers, 2004
7. Uwe Hansmaan et al, ‘Principles of Mobile Computing’, Springer, 2003
8. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
9. Mohammad s. Obaidat et al, “Pervasive Computing and Networking”,John wiley
10. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”,
Wiley, 2009
11. Frank Adelstein Sandeep K. S. Gupta Golden G. Richard III Loren Schwiebert
“Fundamentals of Mobile and Pervasive Computing, “, McGraw-Hill, 2005
29CP7006
PARALLEL PROGRAMMING PARADIGMS
LTPC
3 003
OBJECTIVES:
To understand models of and issues in concurrency in computing
To develop message-passing parallel programs using MPI
To develop shared-memory parallel programs using Pthreads
To develop shared-memory parallel programs using OpenMP
To use GPU for parallel programming using OpenCL and CUDA
UNIT I
FOUNDATIONS OF PARALLEL PROGRAMMING
9
Motivation for parallel programming - Concurrency in computing – basics of processes,
multiprocessing, and threads – cache – cache mappings – caches and programs – virtual
memory – instruction level parallelism – hardware multi-threading – SIMD – MIMD –
interconnection networks – cache coherence – shared-memory model – issues in shared-
memory model – distributed-memory model – issues in distributed-memory model – hybrid
model – I/O – performance of parallel programs – parallel program design
UNIT II
MESSAGE PASSING PARADIGM
9
Basic MPI programming – MPI_Init and MPI_Finalize – MPI communicators – SPMD
programs – message passing – MPI_Send and MPI_Recv – message matching – MPI I/O –
parallel I/O – collective communication – MPI_Reduce – MPI_Allreduce – broadcast –
scatter – gather – allgather – derived types – remote memory access – dynamic process
management – MPI for grids – performance evaluation of MPI programs
UNIT III
SHARED MEMORY PARADIGM: PTHREADS
9
Basics of Pthreads – thread synchronization – critical sections – busy-waiting – mutexes –
semaphores – barriers and condition variables – read-write locks – Caches, cache
coherence and false sharing – thread safety – Pthreads case study
UNIT IV
SHARED MEMORY PARADIGM: OPENMP
9
Basic OpenMP constructs – scope of variabls – reduction clause – parallel for directive –
loops in OpenMP – scheduling loops – synchronization in OpenMP – Case Study:
Producer-Consumer problem – cache issues – threads safety in OpenMP – OpenMP best
practices
UNIT V
GRAPHICAL PROCESSING PARADIGMS: OPENCL AND CUDA
9
Introduction to CUDA – CUDA programming examples – CUDA execution model – CUDA
memory hierarchy – CUDA case study - introduction to OpenCL – OpenCL programming
examples – Programs and Kernels – Buffers and Images – Event model – OpenCL case
study
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain models of parallel programming
Explain hardware level support for concurrency
Explain issues in parallel programming
Develop message-passing parallel programs using MPI framework
Develop shared-memory parallel programs using Pthreads
Develop shared-memory parallel programs using OpenMP
Develop CUDA programs
Develop OpenCL programs
REFERENCES:
1. Peter S. Pacheco, “An introduction to parallel programming”, Morgan Kaufmann,
2011.
2. M. J. Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw Hill,
2003.
3. W. Gropp, E. Lusk, and R. Thakur, “Using MPI-2: Advanced features of the message
passing interface”, MIT Press, 1999.
304. W. Gropp, E. Lusk, and A. Skjellum, “Using MPI: Portable parallel programming with
the message passing interface”, Second Edition, MIT Press, 1999.
5. B. Chapman, G. Jost, and Ruud van der Pas, “Using OpenMP”, MIT Press, 2008.
6. D. R. Butenhof, “Programming with POSIX Threads”, Addison Wesley, 1997.
7. B. Lewis and D. J. Berg, “Multithreaded programming with Pthreads”, Sun
Microsystems Press, 1998.
8. A. Munshi, B. Gaster, T. G. Mattson, J. Fung, and D. Ginsburg, “OpenCL
programming guide”, Addison Wesley, 2011.
9. Rob Farber, “CUDA application design and development”, Morgan Haufmann, 2011.
CP7007
SOFTWARE REQUIREMENTS ENGINEERING
LTPC
3 003
OBJECTIVES:
1. Understand system requirements
2. Identify different types of requirement
3. Generate requirements be elicitation
4. Develop requirements documentation
5. Evaluate the requirements
UNIT I
DOMAIN UNDERSTANDING
9
Introduction – Types of requirements – Requirements engineering process – Validating
requirements – Requirements and design – Requirements and test cases – introduction to
business domain – Problem analysis – Fish bone diagram – Business requirements –
Business process modeling – Business use cases – Business modeling notations – UML
Activity diagrams.
UNIT II
REQUIREMENTS ELICITATION
9
Introduction – Understanding stakeholders' needs – Elicitation techniques – interviews,
questionnaire, workshop, brainstorming, prototyping – Documenting stakeholders' needs
UNIT III
FUNCTIONAL REQUIREMENTS
9
Introduction – Features and Use cases – Use case scenarios – Documenting use cases –
Levels of details – SRS documents.
UNIT IV
QUALITY ATTRIBUTES AND USER EXPERIENCE
9
Quality of solution – Quality attributes – Eliciting quality attributes – Quality attribute
workshop (QAW) – Documenting quality attributes – Six part scenarios – Usability
requirements – Eliciting and documenting usability requirements – Modeling user experience
– Specifying UI design
UNIT V
MANAGING REQUIREMENTS
9
Defining scope of the project – Context diagram – Managing requirements – Requirements
properties – Traceability – Managing changes – Requirements metrics – Requirements
management tools.
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Define a process for requirments engineering
Execute a process for gathering requirments through elicitation techniques.
Validate requirements according to criteria such as feasibility, clarity, preciseness
etc.
Develop and document functional requirements for different types of systems.
Develop and document quality attributes of the system to be implemented
Communicate the requirments to stakeholders
Negotiate with stakeholders in order to agree on a set of requirements.
Detect and resolve feature interactions
31REFERENCES:
1. Axel van Lamsweerde, "Requirements Engineering", Wiley, 2009
2. Gerald Kotonya, Ian Sommerville, "Requirements Engineering: Processes and
Techniques", John Wiley and Sons, 1998
3. Dean Leffingwell and Don Widrig, “Managing Software Requirements: A Use Case
Approach (2nd Edition) ”, Addison-wesley, 2003
4. SEI Report, “Quality Attributes Workshop”,
http://www.sei.cmu.edu/library/abstracts/reports/03tr016.cfm , 2003
5. J Nielsen, “Usability Engineering”, Academic Press, 1993
CP7008
SPEECH PROCESSING AND SYNTHESIS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for speech processing
To understand the basic concepts and algorithms of speech processing and synthesis
To familiarize the students with the various speech signal representation, coding and
recognition techniques
To appreciate the use of speech processing in current technologies and to expose the
students to real– world applications of speech processing
UNIT I
FUNDAMENTALS OF SPEECH PROCESSING
9
Introduction – Spoken Language Structure – Phonetics and Phonology – Syllables and
Words – Syntax and Semantics – Probability, Statistics and Information Theory – Probability
Theory – Estimation Theory – Significance Testing – Information Theory.
UNIT II
SPEECH SIGNAL REPRESENTATIONS AND CODING
9
Overview of Digital Signal Processing – Speech Signal Representations – Short time Fourier
Analysis – Acoustic Model of Speech Production – Linear Predictive Coding – Cepstral
Processing – Formant Frequencies – The Role of Pitch – Speech Coding – LPC Coder.
UNITIII
SPEECH RECOGNITION
9
Hidden Markov Models – Definition – Continuous and Discontinuous HMMs – Practical
Issues – Limitations. Acoustic Modeling – Variability in the Speech Signal – Extracting
Features – Phonetic Modeling – Adaptive Techniques – Confidence Measures – Other
Techniques.
UNITIV
TEXT ANALYSIS
9
Lexicon – Document Structure Detection – Text Normalization – Linguistic Analysis –
Homograph Disambiguation – Morphological Analysis – Letter-to-sound Conversion –
Prosody – Generation schematic – Speaking Style – Symbolic Prosody – Duration
Assignment – Pitch Generation
UNIT V
SPEECH SYNTHESIS
9
Attributes – Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic
Modification of Speech – Source-filter Models for Prosody Modification – Evaluation of TTS
Systems.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the various temporal, spectral and cepstral features required for identifying
speech units – phoneme, syllable and word
Determine and apply Mel-frequency cepstral coefficients for processing all types of
signals
Justify the use of formant and concatenative approaches to speech synthesis
32
Identify the apt approach of speech synthesis depending on the language to be
processed
Determine the various encoding techniques for representing speech.
REFERENCES:
1. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing – A
guide to Theory, Algorithm and System Development”, Prentice Hall PTR, 2001.
2. Thomas F.Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education,
2002.
3. Lawrence Rabiner and Biing-Hwang Juang, “Fundamentals of Speech Recognition”,
Prentice Hall Signal Processing Series, 1993.
4. Sadaoki Furui, “Digital Speech Processing: Synthesis, and Recognition, Second Edition,
(Signal Processing and Communications)”, Marcel Dekker, 2000.
5. Joseph Mariani, “Language and Speech Processing”, Wiley, 2009.
CP7009
MACHINE LEARNING TECHNIQUES
LTPC
3 003
OBJECTIVES:
1. To understand the machine learning theory
2. To implement linear and non-linear learning models
3. To implement distance-based clustering techniques
4. To build tree and rule based models
5. To apply reinforcement learning techniques
UNIT I
FOUNDATIONS OF LEARNING
9
Components of learning – learning models – geometric models – probabilistic models – logic
models – grouping and grading – learning versus design – types of learning – supervised –
unsupervised – reinforcement – theory of learning – feasibility of learning – error and noise –
training versus testing – theory of generalization – generalization bound – approximation-
generalization tradeoff – bias and variance – learning curve
[
UNIT II
LINEAR MODELS
9
Linear classification – univariate linear regression – multivariate linear regression –
regularized regression – Logistic regression – perceptrons – multilayer neural networks –
learning neural networks structures – support vector machines – soft margin SVM – going
beyond linearity – generalization and overfitting – regularization – validation
UNIT III
DISTANCE-BASED MODELS
9
Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchical
clustering – k-d trees – locality sensitive hashing – non-parametric regression – ensemble
learning – bagging and random forests – boosting – meta learning
UNIT IV
TREE AND RULE MODELS
9
Decision trees – learning decision trees – ranking and probability estimation trees –
regression trees – clustering trees – learning ordered rule lists – learning unordered rule lists
– descriptive rule learning – association rule mining – first-order rule learning
UNIT V
REINFORCEMENT LEARNING
9
Passive reinforcement learning – direct utility estimation – adaptive dynamic programming –
temporal-difference learning – active reinforcement learning – exploration – learning an
action-utility function – Generalization in reinforcement learning – policy search –
applications in game playing – applications in robot control
TOTAL : 45 PERIODS
33OUTCOMES:
Upon Completion of the course,the students will be able to
To explain theory underlying machine learning
To construct algorithms to learn linear and non-linear models
To implement data clustering algorithms
To construct algorithms to learn tree and rule-based models
To apply reinforcement learning techniques
REFERENCES:
1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook
Publishers, 2012.
2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of
data”, Cambridge University Press, 2012.
3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012.
4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007.
5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning”,
MIT Press, 2012.
7. T. M. Mitchell, “Machine Learning”, McGraw Hill, 1997.
8. S. Russel and P. Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition,
Prentice Hall, 2009.
CP7010
CONCURRENCY MODELS
LTPC
3 003
OBJECTIVES:
To model concurrency in FSP
To specify and check safety and liveness properties
To understand concurrency architectures and design
To apply linear temporal logic to safety and liveness analysis
To apply Petri nets for concurrency modeling and analysis
UNIT I
FSP AND GRAPH MODELS
9
Concurrency and issues in concurrency – models of concurrency – graphical models – FSP
& LTSA – modeling processes with FSP – concurrency models with FSP – shared action –
structure diagrams – issues with shared objects – modeling mutual exclusion – conditional
synchronization – modeling semaphores – nested monitors – monitor invariants
UNIT II
SAFETY AND LIVENESS PROPERTIES
9
Deadlocks – deadlock analysis in models – dining philosophers problem – safety properties
– single-lane bridge problem – liveness properties – liveness of the single-lane bridge –
readers-writers problem – message passing – asynchronous message passing models –
synchronous message passing models – rendezvous
UNIT III
CONCURRENCY ARCHITECTURES AND DESIGN
9
Modeling dynamic systems – modeling timed systems – concurrent architectures – Filter
pipeline – Supervisor-worker model – announcer-listener model – model-based design –
from requirements to models – from models to implementations – implementing concurrency
in Java – program verification
UNIT IV
LINEAR TEMPORAL LOGIC (LTL)
9
Syntax of LTL – semantics of LTL – practical LTL patterns – equivalences between LTL
statements – specification using LTL – LTL and FSP – Fluent proposition – Temporal
propositions – Fluent Linear Temporal Logic (FLTL) – FLTL assertions in FSP – Database
ring problem
34UNIT V
PETRI NETS
9
Introduction to Petri nets – examples – place-transition nets – graphical and linear algebraic
representations – concurrency & conflict – coverability graphs – decision procedures –
liveness – colored Petri nets (CPN) – modeling & verification using CPN – non-hierarchical
CPN – modeling protocols – hierarchical CPN – timed CPN – applications of Petri Nets
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Develop concurrency models and FSP
State safety and liveness properties in FSP
Verify properties using LTSA tool
Explain concurrency architectures
Design concurrent Java programs from models
Apply Linear Temporal Logic to state safety and liveness properties
Assert LTL properties in FSP and check using LTSA tool
Model and analyze concurrency using Petri nets
REFERENCES:
1. Jeff Magee & Jeff Kramer, “Concurrency: State Models and Java Programs”, Second
Edition, John Wiley, 2006.
2. M. Huth & M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
3. B. Goetz, T. Peierls, J. Bloch, J. Bowbeer, D. Holmes, and D. Lea, “Java Concurrency in
Practice”, Addison-Wesley Professional, 2006.
4. Wolfgang Reisig, “Petri Nets: An Introduction”, Springer, 2011.
5. K. Jensen and L. M. Kristensen, “Colored Petri Nets: Modeling and Validation of
Concurrent Systems”, Springer, 2009.
6. Wolfgang Reisig, “Understanding Petri Nets: Modeling Techniques, Analysis Methods,
Case Studies”, Springer, 2013.
CP7011
REAL TIME SYSTEMS
LTPC
3 003
OBJECTIVES:
To provide good understanding of fundamental concepts in real time systems.
To provide understanding of advanced topics in real time systems.
To provide understanding on basic multi-task scheduling algorithms for periodic,
aperiodic, and sporadic tasks as well as understand the impact of the latter two on
scheduling
To expose to understand capabilities of commercial off-the-shelf R-T kernel.
To expose to real time communications and databases.
UNIT I
INTRODUCTION
9
Real-time systems – Applications – Basic Model – Characteristics – Safety and Reliability –
Real-Time tasks – Timing Constraints – Modelling Timing Constraints.
UNIT II
SCHEDULING REAL-TIME TASKS
9
Concepts – Types of RT Tasks and their Characteristics – Task Scheduling – Clock-Driven
Scheduling – Hybrid Schedulers - Event-Driven Scheduling – EDF Scheduling – RMA –
Issues with RMA – Issues in Using RMA in Practical Situations
35UNIT III
RESOURCE SHARING AMONG RT TASKS & SCHEDULING RT TASKS 9
Resource Sharing Among RT Tasks – Priority Inversion – PIP – HLP – PCP – Types of
Priority Inversions Under PCP – Features of PCP – Issues in using Resource Sharing
Protocol – Handling Task Dependencies – Multiprocessor Task Allocation – Dynamic
Allocation of Tasks – Fault-Tolerant Scheduling of Tasks – Clocks in Distributed RT Systems
– Centralized and Distributed Clock Synchronization.
UNIT IV
COMMERCIAL RT OPERATING SYSTEMS
9
Time Services – Features of RT OS – Unix as a RT OS – Unix Based RT OS – Windows as
a RT OS – POSIX – Survey of RTOS: PSOS – VRTX – VxWorks – QNX - μC/OS-II – RT
Linux – Lynx – Windows CE – Benching RT Systems.
UNIT V
RT COMMUNICATION & DATABASES
9
Examples of Applications Requiring RT Communication – Basic Concepts – RT
Communication in a LAN – Soft & Hard RT Communication in a LAN – Bounded Access
Protocols for LANs – Performance Comparison – RT Communication Over Packet Switched
Networks – QoS Framework – Routing – Resource Reservation – Rate Control – QoS
Models - Examples Applications of RT Databases – RT Databases – Characteristics of
Temporal Data – Concurrency Control in RT Databases – Commercial RT Databases.
TOTAL: 45 PERIODS
OUTCOMES:
Understand the basics and importance of real-time systems
Generate a high-level analysis document based on requirements specifications
Generate a high-level design document based on analysis documentation
Generate a test plan based on requirements specification
Generate a validation plan based on all documentation
Understand basic multi-task scheduling algorithms for periodic, aperiodic, and
sporadic tasks as well as understand the impact of the latter two on scheduling
Understand capabilities of at least one commercial off-the-shelf R-T kernel
REFERENCES:
1. Rajib Mall, "Real-Time Systems: Theory and Practice," Pearson, 2008.
2. Jane W. Liu, "Real-Time Systems" Pearson Education, 2001.
3. Krishna and Shin, "Real-Time Systems," Tata McGraw Hill. 1999.
4. Alan C. Shaw, “Real-Time Systems and Software”, Wiley, 2001.
5. Philip Laplante, “Real-Time Systems Design and Analysis”, 2nd Edition, Prentice Hall
of India.
6. Resource Management in Real-time Systems and Networks, C. Siva Ram Murthy
and G. Manimaran, MIT Press, March 2001.
CP7012
COMPUTER VISION
LTPC
3 003
OBJECTIVES:
To review image processing techniques for computer vision
To understand shape and region analysis
To understand Hough Transform and its applications to detect lines, circles, ellipses
To understand three-dimensional image analysis techniques
To understand motion analysis
To study some applications of computer vision algorithms
UNIT I
IMAGE PROCESSING FOUNDATIONS
9
Review of image processing techniques – classical filtering operations – thresholding
techniques – edge detection techniques – corner and interest point detection – mathematical
morphology – texture
36UNIT II
SHAPES AND REGIONS
9
Binary shape analysis – connectedness – object labeling and counting – size filtering –
distance functions – skeletons and thinning – deformable shape analysis – boundary
tracking procedures – active contours – shape models and shape recognition – centroidal
profiles – handling occlusion – boundary length measures – boundary descriptors – chain
codes – Fourier descriptors – region descriptors – moments
UNIT III
HOUGH TRANSFORM
9
Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line
localization – line fitting – RANSAC for straight line detection – HT based circular object
detection – accurate center location – speed problem – ellipse detection – Case study:
Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial
matched filtering – GHT for ellipse detection – object location – GHT for feature collation
UNIT IV
3D VISION AND MOTION
9
Methods for 3D vision – projection schemes – shape from shading – photometric stereo –
shape from texture – shape from focus – active range finding – surface representations –
point-based representation – volumetric representations – 3D object recognition – 3D
reconstruction – introduction to motion – triangulation – bundle adjustment – translational
alignment – parametric motion – spline-based motion – optical flow – layered motion
UNIT V APPLICATIONS
9
Application: Photo album – Face detection – Face recognition – Eigen faces – Active
appearance and 3D shape models of faces
Application: Surveillance – foreground-background separation – particle filters – Chamfer
matching, tracking, and occlusion – combining views from multiple cameras – human gait
analysis
Application: In-vehicle vision system: locating roadway – road markings – identifying road
signs – locating pedestrians
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Implement fundamental image processing techniques required for computer vision
Perform shape analysis
Implement boundary tracking techniques
Apply chain codes and other region descriptors
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques
Implement motion related techniques
Develop applications using computer vision techniques
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011.
3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge
University Press, 2012.
4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
37NE7202
NETWORK AND INFORMATION SECURITY
LTPC
3 003
OBJECTIVES:
To understand the fundamentals of Cryptography
To acquire knowledge on standard algorithms used to provide confidentiality, integrity
and authenticity.
To understand the various key distribution and management schemes.
To understand how to deploy encryption techniques to secure data in transit across
data networks
To design security applications in the field of Information technology
UNIT I
INTRODUCTION
9
An Overview of Computer Security-Security Services-Security Mechanisms-Security
Attacks-Access Control Matrix, Policy-Security policies, Confidentiality policies, Integrity
policies and Hybrid policies.
UNIT II
CRYPTOSYSTEMS & AUTHENTICATION
9
Classical Cryptography-Substitution Ciphers-permutation Ciphers-Block Ciphers-DES-
Modes of Operation- AES-Linear Cryptanalysis, Differential Cryptanalysis- Hash Function -
SHA 512- Message Authentication Codes-HMAC - Authentication Protocols -
UNIT III
PUBLIC KEY CRYPTOSYSTEMS
9
Introduction to Public key Cryptography- Number theory- The RSA Cryptosystem and
Factoring Integer- Attacks on RSA-The ELGamal
Cryptosystem- Digital Signature
Algorithm-Finite Fields-Elliptic Curves Cryptography- Key management – Session and
Interchange keys, Key exchange and generation-PKI
UNIT IV
SYSTEM IMPLEMENTATION
9
Design Principles, Representing Identity, Access Control Mechanisms, Information Flow and
Confinement Problem
Secure Software Development: Secured Coding - OWASP/SANS Top Vulnerabilities -
Buffer Overflows - Incomplete mediation - XSS - Anti Cross Site Scripting Libraries -
Canonical Data Format - Command Injection - Redirection - Inference – Application Controls
UNIT V
NETWORK SECURITY
9
Secret Sharing Schemes-Kerberos- Pretty Good Privacy (PGP)-Secure Socket Layer (SSL)-
Intruders – HIDS- NIDS - Firewalls - Viruses
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Implement basic security algorithms required by any computing system.
Analyze the vulnerabilities in any computing system and hence be able to design a
security solution.
Analyze the possible security attacks in complex real time systems and their effective
countermeasures
Identify the security issues in the network and resolve it.
Evaluate security mechanisms using rigorous approaches, including theoretical
derivation, modeling, and simulations
Formulate research problems in the computer security field
REFERENCES:
1. William Stallings, “Cryptography and Network Security: Principles and Practices”,
Third Edition, Pearson Education, 2006.
2. Matt Bishop ,“Computer Security art and science ”, Second Edition, Pearson
Education, 2002
3. Wade Trappe and Lawrence C. Washington, “Introduction to Cryptography with
Coding Theory” Second Edition, Pearson Education, 2007
4. Jonathan Katz, and Yehuda Lindell, Introduction to Modern Cryptography, CRC
Press, 2007
385. Douglas R. Stinson, “Cryptography Theory and Practice”, Third Edition, Chapman &
Hall/CRC, 2006
6. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education,
First Edition, 2006.
7. Network Security and Cryptography, Menezes Bernard, Cengage Learning, New
Delhi, 2011
8. Man Young Rhee, Internet Security, Wiley, 2003
9. OWASP top ten security vulnerabilities: http://xml.coverpages.org/OWASP-
TopTen.pdf
CP7013
DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS
LTPC
3 003
OBJECTIVES:
To understand the need for parallel algorithms
To expose the students to different models of parallel computation
To expose the students to parallel sorting and searching algorithms
To understand the application of the concepts studied to different types of problems
To analyze parallel algorithms
UNIT I
INTRODUCTION
9
Introduction to Parallel Algorithms – Models of Parallel Computation – Sorting on an EREW-
SIMD PRAM Computer – Relation between PRAM Models – SIMD Algorithms – MIMD
Algorithms – Selection – Desirable Properties for Parallel Algorithms - Parallel Algorithm for
Selection – Analysis of Parallel Algorithms.
UNIT II
SORTING AND SEARCHING
9
Merging on the EREW and CREW Models - Fast Merging on EREW - Sorting Networks –
Sorting on a Linear Array – Sorting on CRCW, CREW, EREW Models – Searching a Sorted
Sequence – Searching a Random Sequence.
UNIT III
ALGEBRAIC PROBLEMS
9
Generating Permutations and Combinations in Parallel – Matrix Transpositions – Matrix by
Matrix Multiplications – Matrix by Vector multiplication.
UNIT IV
GRAPH THEORY AND COMPUTATIONAL GEOMETRY PROBLEMS
9
Connectivity Matrix – Connected Components – All Pairs Shortest Paths – Minimum
Spanning Trees – Point Inclusion – Intersection, Proximity and Construction Problems -
Sequential Tree Traversal - Basic Design Principles – Algorithm – Analysis.
UNITV
DECISION AND OPTIMIZATION PROBLEMS
9
Computing Prefix Sums – Applications - Job Sequencing with Deadlines – Knapsack
Problem- The Bit Complexity of Parallel Computations.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for parallel algorithms
Discuss the classification of parallel architectures and identify suitable programming
models
Perform sorting on Sorting on CRCW, CREW, EREW Models
Search a sorted as well as random sequence
Develop and analyze algorithms for different applications like matrix multiplication,
shortest path, job sequencing and the knapsack problem.
39REFERENCES:
1. Selim G. Akl, “The Design and Analysis of Parallel Algorithms”, Prentice Hall, New
Jersey, 1989.
2. Michael J. Quinn, “Parallel Computing : Theory & Practice”, Tata McGraw Hill Edition,
2003.
3. Justin R. Smith, “The Design and Analysis of Parallel Algorithms”, Oxford University
Press, USA , 1993.
4. Joseph JaJa, “Introduction to Parallel Algorithms”, Addison-Wesley, 1992.
CP7014
SOFTWARE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
Understand architectural requirements
Identify architectural structures
Develop architectural documentation
Generate architectural alternatives
Evaluate the architecture against the drivers
UNIT I
ARCHITECTURAL DRIVERS
9
Introduction – Standard Definitions of Software Architecture– Architectural structures –
Influence of software architecture on organization – Architecture Business Cycle –
Functional requirements – Technical constraints – Quality Attributes – Quality Attribute
Workshop (QAW) – Documenting Quality Attributes – Six part scenarios
UNIT II
ARCHITECTURAL VIEWS AND DOCUMENTATION
9
Introduction – Standard Definitions for views – Structures and views- Perspectives: Static,
dynamic and physical and the accompanying views – Representing views-available
notations – Good practices in documentation– Documenting the Views using UML – Merits
and Demerits of using visual languages – Need for formal languages - Architectural
Description Languages – ACME
UNIT III
ARCHITECTURAL STYLES
9
Introduction – Data flow styles – Call-return styles – Shared Information styles – Event styles
– Case studies for each style
UNIT IV
ARCHITECTURAL DESIGN
9
Approaches for architectural design – System decomposition – Attributes driven design –
Architecting for specific quality attributes – Performance, Availability – Security –
Architectural conformance
UNIT V
ARCHITECTURE EVALUATION AND SOME SPECIAL TOPICS
9
Need for evaluation – Scenario based evaluation against the drivers – ATAM and its
variations – Case studies in architectural evaluations – SOA and Web services – Cloud
Computing – Adaptive structures
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain key architectural drivers
Explain the influence of architecture on business and technical activities
Identify key architectural structures
Adopt good practices for documenting the architecture
Develop alternative architectures for a given problem
Explain how to use formal languages to specify architecture
Evaluate the architecture against the drivers
Describe the recent trends in software architecture
40REFERENCES:
1. Len Bass, Paul Clements, and Rick Kazman, “Software Architectures Principles and
Practices”, 2n Edition, Addison-Wesley, 2003.
2. Anthony J Lattanze, “Architecting Software Intensive System. A Practitioner's Guide”,
Auerbach Publications, 2010.
3. Paul Clements, Felix Bachmann, Len Bass, David Garlan, James Ivers, Reed Little,
Paulo Merson, Robert Nord, and Judith Stafford, “Documenting Software Architectures.
Views and Beyond”, 2nd Edition, Addison-Wesley, 2010.
4. Paul Clements, Rick Kazman, and Mark Klein, “Evaluating software architectures:
Methods and case studies.”, Addison-Wesley, 2001.
5. David Garlan and Mary Shaw, “Software architecture: Perspectives on an emerging
discipline”, Prentice Hall, 1996.
6. Rajkumar Buyya, James Broberg, and Andrzej Goscinski, “Cloud Computing. Principles
and Paradigms”, John Wiley & Sons, 2011
7. Mark Hansen, “SOA Using Java Web Services”, Prentice Hall, 2007
8. David Garlan, Bradley Schmerl, and Shang-Wen Cheng, “Software Architecture-Based
Self-Adaptation,” 31-56. Mieso K Denko, Laurence Tianruo Yang, and Yan Zang (eds.),
“Autonomic Computing and Networking”. Springer Verlag, 2009.
CP7015
MODEL CHECKING AND PROGRAM VERIFICATION
LTPC
3 003
OBJECTIVES:
To understand automata for model checking
To understand LTL, CTL, and CTL*
To understand timed automata, TCTL, and PCTL
To understand verification of deterministic and recursive programs
To understand verification of object-oriented programs
To understand verification of parallel, distributed, and non-deterministic programs
UNIT I
AUTOMATA AND TEMPORAL LOGICS
9
Automata on finite words – model checking regular properties – automata on infinite words –
Buchi automata – Linear Temporal Logic (LTL) – automata based LTL model checking –
Computational Tree Logic (CTL) – CTL model checking – CTL* model checking
UNIT II
TIMED AND PROBABILISTIC TREE LOGICS
9
Timed automata – timed computational tree logic (TCTL) – TCTL model checking –
probabilistic systems – probabilistic computational tree logic (PCTL) – PCTL model checking
– PCTL* - Markov decision processes
UNIT III
VERIFYING DETERMINISTIC AND RECURSIVE PROGRAMS
9
Introduction to program verification – verification of “while” programs – partial and total
correctness – verification of recursive programs – case study: binary search – verifying
recursive programs with parameters
UNIT IV
VERIFYING OBJECT-ORIENTED AND PARALLEL PROGRAMS
9
Partial and total correctness of object-oriented programs – case study: Insertion in linked
lists – verification of disjoint parallel programs – verifying programs with shared variables –
case study: parallel zero search – verification of synchronization – case study: the mutual
exclusion problem
UNIT V
VERIFYING NON-DETERMINISTIC AND DISTRIBUTED PROGRAMS
9
Introduction to non-deterministic programs – partial and total correctness of non-
deterministic programs – case study: The Welfare Crook Problem – syntax and semantics of
distributed programs – verification of distributed programs – case study: A Transmission
Problem – introduction to fairness
TOTAL : 45 PERIODS
41OUTCOMES:
Upon Completion of the course,the students will be able to
Perform model checking using LTL
Perform model checking using CTL
Perform model checking using CTL*
Perform model checking using TCTL and PCTL
Verify deterministic and recursive programs
Verify object-oriented programs
Verify parallel, distributed, and non-deterministic programs
REFERENCES:
1. C. Baier, J.-P. Katoen, and K. G. Larsen, “Principles of Model Checking”, MIT Press,
2008.
2. E. M. Clarke, O. Grumberg, and D. A. Peled, “Model Checking”, MIT Press, 1999.
3. M. Ben-Ari, “Principles of the SPIN Model Checker”, Springer, 2008.
4. K. R. Apt, F. S. de Boer, E.-R. Olderog, and A. Pnueli, “Verification of Sequential and
Concurrent Programs”, Third Edition, Springer, 2010.
5. M. Huth and M. Ryan, “Logic in Computer Science --- Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
6. B. Berard et al., “Systems and Software Verification: Model-checking techniques and
tools”, Springer, 2010.
7. J. B. Almeida, M. J. Frade, J. S. Pinto, and S. M. de Sousa, “Rigorous Software
Development: An Introduction to Program Verification”, Springer, 2011.
CP7016
EMBEDDED SOFTWARE DEVELOPMENT
LTPC
3 003
OBJECTIVES:
To understand processors and their instruction sets for embedded systems
To understand hardware platform for embedded systems
To design and analyze programs for embedded systems
To design multi-tasking embedded systems with RTOS
To understand overall embedded systems development lifecycle
To understand distributed and multi-processor embedded systems
UNIT I
PROCESSORS AND INSTRUCTION SETS
9
Introduction to embedded computing – overview of embedded system design process –
instruction sets of processors: ARM, PIC, TI C55x, TI C64x – programming I/O – modes and
exceptions – co-processors – memory system – CPU performance – CPU power
consumption
UNIT II
EMBEDDED COMPUTING PLATFORM
9
Basic computing platforms – CPU Bus – memory devices and systems – choosing a
platform – development environments – debugging – consumer electronics architecture –
platform-level performance analysis – design example: Audio Player
UNIT III
PROGRAM DESIGN AND ANALYSIS
9
Components for embedded programs – models of programs – Assembly, linking, and
loading – compiler optimizations – program-level performance analysis – performance
optimization – program-level energy optimization – optimizing program size – program
validation and testing – design example: Digital Still Camera
UNIT IV
PROCESSES AND OPERATING SYSTEMS
9
Multiples tasks and multiple processes – multirate systems – pre-emptive RTOS – priority-
based scheduling – inter-process communication – evaluating OS performance – processes
and power optimization – Case study: Real-time and embedded Linux – design example:
Telephone answering machine
42UNIT V
SYSTEM DESIGN, NETWORKS, AND MULTIPROCESSORS
9
System design methodologies – requirements analysis – specifications – architecture design
– quality assurance – distributed embedded systems – shared-memory multiprocessors –
design example: Video accelerator
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Develop assembley code for processors such as ARM, PIC Microcontroller, TI C55x,
TI C64x, etc.
Choose appropriate hardware platform for a given application
Perform platform-level performance analysis
Design, develop, and debug embedded programs optimized for size or performance
Develop embedded applications using an RTOS
Perform OS-level performance analysis
Employ best practices in embedded software engineering
Develop distributed embedded systems and systems with shared-memory
concurrency
REFERENCES:
1. Marilyn Wolf, “Computers as Components: Principles of Embedded Computing
Systems Design”, Third Edition, Morgan Kaufmann, 2012.
2. Christopher Hallinan, “Embedded Linux Primer: A Practical Real-World Approach”,
Second Edition, Prentice Hall, 2010.
3. Karim Yaghmour et al., “Building Embedded Linux Systems”, O’Reilly, 2008.
4. Arnold S. Berger, “Embedded Systems Design: An Introduction to Processes, Tools,
and Techniques”, CMP Books, 2001.
5. David E. Simon, “An embedded Software Primer”, Addison-Wesley, 1999.
IF7202
CLOUD COMPUTING
LTPC
3 003
OBJECTIVES:
To introduce the broad perceptive of cloud architecture and model
To understand the concept of Virtualization
To be familiar with the lead players in cloud.
To understand the features of cloud simulator
To apply different cloud programming model as per need.
To be able to set up a private cloud.
To understand the design of cloud Services.
To learn to design the trusted cloud Computing system
UNIT I
CLOUD ARCHITECTURE AND MODEL
9
Technologies for Network-Based System – System Models for Distributed and Cloud
Computing – NIST Cloud Computing Reference Architecture.
Cloud Models:- Characteristics – Cloud Services – Cloud models (IaaS, PaaS, SaaS) –
Public vs Private Cloud –Cloud Solutions - Cloud ecosystem – Service management –
Computing on demand.
UNIT II
VIRTUALIZATION
9
Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization -
Virtualization Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O
Devices - Virtual Clusters and Resource management – Virtualization for Data-center
Automation.
43UNIT III
CLOUD INFRASTRUCTURE
9
Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture
Development – Design Challenges - Inter Cloud Resource Management – Resource
Provisioning and Platform Deployment – Global Exchange of Cloud Resources.
UNIT IV
PROGRAMMING MODEL
9
Parallel and Distributed Programming Paradigms – MapReduce , Twister and Iterative
MapReduce – Hadoop Library from Apache – Mapping Applications - Programming Support
- Google App Engine, Amazon AWS - Cloud Software Environments -Eucalyptus, Open
Nebula, OpenStack, Aneka, CloudSim
UNIT V
SECURITY IN THE CLOUD
9
Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security
– Security Governance – Risk Management – Security Monitoring – Security Architecture
Design – Data Security – Application Security – Virtual Machine Security - Identity
Management and Access Control – Autonomic Security.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,the students will be able to
Compare the strengths and limitations of cloud computing
Identify the architecture, infrastructure and delivery models of cloud computing
Apply suitable virtualization concept.
Choose the appropriate cloud player
Choose the appropriate Programming Models and approach.
Address the core issues of cloud computing such as security, privacy and interoperability
Design Cloud Services
Set a private cloud
REFERENCES:
1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From
Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.
2. John W.Rittinghouse and James F.Ransome, “Cloud Computing: Implementation,
Management, and Security”, CRC Press, 2010.
3. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical
Approach”, TMH, 2009.
4. Kumar Saurabh, “ Cloud Computing – insights into New-Era Infrastructure”, Wiley
India,2011.
5. George Reese, “Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud” O'Reilly
6. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and
Processes”, Elsevier/Morgan Kaufmann, 2005.
7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, “Grid and Cloud
Computing – A Business Perspective on Technology and Applications”, Springer.
8. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A comprehensive Guide to
Secure Cloud Computing”, Wiley – India, 2010.
9. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi, ‘Mastering Cloud Computing”,
TMGH,2013.
10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011
11. Michael Miller, Cloud Computing, Que Publishing,2008
12. Nick Antonopoulos, Cloud computing, Springer Publications, 2010
44CP7017
DATA VISUALIZATION TECHNIQUES
LT PC
3 003
COURSE OBJECTIVES:
To introduce visual perception and core skills for visual analysis
To understand visualization for time-series analysis
To understand visualization for ranking analysis
To understand visualization for deviation analysis
To understand visualization for distribution analysis
To understand visualization for correlation analysis
To understand visualization for multivariate analysis
To understand issues and best practices in information dashboard design
UNIT I
CORE SKILLS FOR VISUAL ANALYSIS
9
Information visualization – effective data analysis – traits of meaningful data – visual
perception – making abstract data visible – building blocks of information visualization –
analytical interaction – analytical navigation – optimal quantitative scales – reference lines
and regions – trellises and crosstabs – multiple concurrent views – focus and context –
details on demand – over-plotting reduction – analytical patterns – pattern examples
UNIT II
TIME-SERIES, RANKING, AND DEVIATION ANALYSIS
9
Time-series analysis – time-series patterns – time-series displays – time-series best
practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – best
practices – deviation analysis – deviation analysis displays – deviation analysis best
practices
UNIT III
DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS
9
Distribution analysis – describing distributions – distribution patterns – distribution displays –
distribution analysis best practices – correlation analysis – describing correlations –
correlation patterns – correlation displays – correlation analysis techniques and best
practices – multivariate analysis – multivariate patterns – multivariate displays – multivariate
analysis techniques and best practices
UNIT IV
INFORMATION DASHBOARD DESIGN I
9
Information dashboard – categorizing dashboards – typical dashboard data – dashboard
design issues and best practices – visual perception – limits of short-term memory – visually
encoding data – Gestalt principles – principles of visual perception for dashboard design
UNIT V
INFORMATION DASHBOARD DESIGN II
9
Characteristics of dashboards – key goals in visual design process – dashboard display
media – designing dashboards for usability – meaningful organization – maintaining
consistency – aesthetics of dashboards – testing for usability – case studies: sales
dashboard, CIO dashboard, Telesales dashboard, marketing analysis dashboard
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
Explain principles of visual perception
Apply core skills for visual analysis
Apply visualization techniques for various data analysis tasks
Design information dashboard
REFERENCES:
1. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis",
Analytics Press, 2009.
2. Stephen Few, "Information dashboard design: The effective visual communication of
data", O'Reilly, 2006.
3. Edward R. Tufte, "The visual display of quantitative information", Second Edition,
Graphics Press, 2001.
4. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013.
455. Ben Fry, "Visualizing data: Exploring and explaining data with the processing
environment", O'Reilly, 2008.
6. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking
business intelligence beyond reporting", Wiley, 2010.
7. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley,
2011.
NE7005
PROTOCOLS AND ARCHITECTURE FOR WIRELESS
SENSOR NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION AND OVERVIEW OF WIRELESS SENSOR NETWORKS 9
Background of Sensor Network Technology, Application of Sensor Networks, Challenges for
Wireless Sensor Networks, Mobile Adhoc NETworks (MANETs) and Wireless Sensor
Networks, Enabling Technologies For Wireless Sensor Networks.
UNIT II
ARCHITECTURES
9
Single-node Architecture, Hardware Components & Design Constraints, Operating Systems
and Execution Environments, Introduction to TinyOS and nesC, Network Architecture,
Sensor Network Scenarios, Optimization Goals and Figures of Merit, Design Principles for
WSNs, Service Interfaces of WSNs, Gateway Concepts.
UNIT III
DEPLOYMENT AND CONFIGURATION
9
Localization and Positioning, Coverage and Connectivity, Single-hop and Multi-hop
Localization, Self Configuring Localization Systems, Sensor Management
Network Protocols: Issues in Designing MAC Protocol for WSNs, Classification of MAC
Protocols, S-MAC Protocol, B-MAC Protocol, IEEE 802.15.4 Standard and Zig Bee,
Dissemination Protocol for Large Sensor Network.
UNIT IV
ROUTING PROTOCOLS AND DATA MANIPULATION
9
Issues in Designing Routing Protocols, Classification of Routing Protocols, Energy-Efficient
Routing, Unicast, Broadcast and Multicast, Geographic Routing.
Data Centric and Content based Routing, Storage and Retrieval in Network, Compression
Technologies for WSN, Data Aggregation Technique.
UNIT V
SENSOR NETWORK PLATFORMS AND TOOLS
9
Sensor Node Hardware – Berkeley Motes, Programming Challenges, Node-level Software
Platforms, Node-level Simulators, State-centric Programming.
TOTAL : 45 PERIODS
REFERENCES:
1. Holger Karl & Andreas Willig, “Protocols And Architectures for Wireless Sensor
Networks", John Wiley, 2005.
2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information
Processing Approach", Elsevier, 2007.
3. Raghavendra, Cauligi S, Sivalingam, Krishna M., Zanti Taieb, “Wireless Sensor
Network”, Springer 1st Ed. 2004 (ISBN: 978-4020-7883-5).
4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks- Technology,
Protocols, and Applications”, John Wiley, 2007.
5. N. P. Mahalik, “Sensor Networks and Configuration: Fundamentals, Standards,
Platforms, and Applications” Springer Verlag.
6. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.
46CP7018
LANGUAGE TECHNOLOGIES
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for language processing
To understand the representation and processing of Morphology and Part-of Speech
Taggers
To understand different aspects of natural language syntax and the various methods
used for processing syntax
To understand different methods of disambiguating word senses
To know about various applications of natural language processing
To learn the indexing and searching processes of a typical information retrieval
system and to study NLP based retrieval systems
To gain knowledge about typical text categorization and clustering techniques
UNIT I
INTRODUCTION
9
Natural Language Processing – Mathematical Foundations – Elementary Probability Theory
– Essential information Theory - Linguistics Essentials - Parts of Speech and Morphology –
Phrase Structure – Semantics – Corpus Based Work.
UNIT II
WORDS
9
Collocations – Statistical Inference – n-gram Models – Word Sense Disambiguation –
Lexical Acquisition.
UNIT III
GRAMMAR
9
Markov Models – Part-of-Speech Tagging – Probabilistic Context Free Grammars - Parsing.
UNIT IV
INFORMATION RETRIEVAL
9
Information Retrieval Architecture – Indexing - Storage – Compression Techniques –
Retrieval Approaches – Evaluation - Search Engines - Commercial Search Engine Features
– Comparison - Performance Measures – Document Processing - NLP based Information
Retrieval – Information Extraction.
UNIT V
TEXT MINING
9
Categorization – Extraction Based Categorization – Clustering - Hierarchical Clustering -
Document Classification and Routing - Finding and Organizing Answers from Text Search –
Text Categorization and Efficient Summarization using Lexical Chains – Machine Translation
- Transfer Metaphor - Interlingual and Statistical Approaches.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the different linguistic components of given sentences
Design a morphological analyser for a language of your choice using finite state
automata concepts
Implement a parser by providing suitable grammar and words
Discuss algorithms for word sense disambiguation
Build a tagger to semantically tag words using WordNet
Design an application that uses different aspects of language processing.
REFERENCES:
1. Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural
Language Processing “, MIT Press, 1999.
2. Daniel Jurafsky and James H. Martin, “ Speech and Language Processing” , Pearson,
2008.
3. Ron Cole, J.Mariani, et.al “Survey of the State of the Art in Human Language
Technology”, Cambridge University Press, 1997.
Michael W. Berry, “ Survey of Text Mining: Clustering, Classification and Retrieval”,
Springer Verlag, 2003.
4.
47NE7012
SOCIAL NETWORKS ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the components of the social network
To model and visualize the social network
To mine the users in the social network
To understand the evolution of the social network
To mine the interest of the user
UNIT I
INTRODUCTION
9
Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis -
Development of Social Network Analysis - Key concepts and measures in network analysis -
Discussion networks - Blogs and online communities - Web-based networks
UNIT II
MODELING AND VISUALIZATION
9
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-
Based Representations- Node-Link Diagrams - Hybrid Representations - Modelling and
aggregating social network data – Random Walks and their Applications –Use of Hadoop
and Map Reduce - Ontological representation of social individuals and relationships.
UNIT III
MINING COMMUNITIES
9
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in
Social Networks - Evaluating Communities – Core Methods for Community Detection &
Mining - Applications of Community Mining Algorithms - Node Classification in Social
Networks.
UNIT IV
EVOLUTION
9
Evolution in Social Networks – Framework - Tracing Smoothly Evolving Communities -
Models and Algorithms for Social Influence Analysis - Influence Related Statistics - Social
Similarity and Influence - Influence Maximization in Viral Marketing - Algorithms and
Systems for Expert Location in Social Networks - Expert Location without Graph Constraints
- with Score Propagation – Expert Team Formation - Link Prediction in Social Networks -
Feature based Link Prediction - Bayesian Probabilistic Models - Probabilistic Relational
Models
UNIT V
TEXT AND OPINION MINING
9
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis - Product
review mining – Review Classification – Tracking sentiments towards topics over time
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Work on the internals components of the social network
Model and visualize the social network
Mine the behaviour of the users in the social network
Predict the possible next outcome of the social network
Mine the opinion of the user
REFERENCES:
1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011
2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.
3. Borko Furht, “Handbook of Social Network Technologies and Applications”, Springer, 1st
edition, 2010.
484. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking –
Techniques and applications”, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social Network
Analysis: Trends, Tools and Research Advances”, Springer, 2009.
7. Toby Segaran, “Programming Collective Intelligence”, O’Reilly, 2012
CP7019
MANAGING BIG DATA
LTPC
3 003
OBJECTIVES:
Understand big data for business intelligence
Learn business case studies for big data analytics
Understand nosql big data management
Perform map-reduce analytics using Hadoop and related tools
UNIT I
UNDERSTANDING BIG DATA
9
What is big data – why big data – convergence of key trends – unstructured data – industry
examples of big data – web analytics – big data and marketing – fraud and big data – risk
and big data – credit risk management – big data and algorithmic trading – big data and
healthcare – big data in medicine – advertising and big data – big data technologies –
introduction to Hadoop – open source technologies – cloud and big data – mobile business
intelligence – Crowd sourcing analytics – inter and trans firewall analytics
UNIT II
NOSQL DATA MANAGEMENT
9
Introduction to NoSQL – aggregate data models – aggregates – key-value and document
data models – relationships – graph databases – schemaless databases – materialized
views – distribution models – sharding – master-slave replication – peer-peer replication –
sharding and replication – consistency – relaxing consistency – version stamps – map-
reduce – partitioning and combining – composing map-reduce calculations
UNIT III
BASICS OF HADOOP
9
Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop
pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface
– data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based
data structures
UNIT IV
MAPREDUCE APPLICATIONS
9
MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of
MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and
YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input
formats – output formats
UNIT V
HADOOP RELATED TOOLS
9
Hbase – data model and implementations – Hbase clients – Hbase examples –
praxis.Cassandra – cassandra data model – cassandra examples – cassandra clients –
Hadoop integration.
Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts.
Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –
HiveQL queries.
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe big data and use cases from selected business domains
Explain NoSQL big data management
Install, configure, and run Hadoop and HDFS
Perform map-reduce analytics using Hadoop
Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics
49REFERENCES:
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
NE7011
MOBILE APPLICATION DEVELOPMENT
LTPC
3 003
OBJECTIVES:
1. Understand system requirements for mobile applications
2. Generate suitable design using specific mobile development frameworks
3. Generate mobile application design
4. Implement the design using specific mobile development frameworks
5. Deploy the mobile applications in marketplace for distribution
UNIT I
INTRODUCTION
5
Introduction to mobile applications – Embedded systems - Market and business drivers for
mobile applications – Publishing and delivery of mobile applications – Requirements
gathering and validation for mobile applications
UNIT II
BASIC DESIGN
8
Introduction – Basics of embedded systems design – Embedded OS - Design constraints for
mobile applications, both hardware and software related – Architecting mobile applications
– User interfaces for mobile applications – touch events and gestures – Achieving quality
constraints – performance, usability, security, availability and modifiability.
UNIT III
ADVANCED DESIGN
8
Designing applications with multimedia and web access capabilities – Integration with GPS
and social media networking applications – Accessing applications hosted in a cloud
computing environment – Design patterns for mobile applications.
UNIT IV
TECHNOLOGY I - ANDROID
12
Introduction – Establishing the development environment – Android architecture – Activities
and views – Interacting with UI – Persisting data using SQLite – Packaging and deployment
– Interaction with server side applications – Using Google Maps, GPS and Wifi – Integration
with social media applications.
UNIT V
TECHNOLOGY II - IOS
12
Introduction to Objective C – iOS features – UI implementation – Touch frameworks – Data
persistence using Core Data and SQLite – Location aware applications using Core Location
and Map Kit – Integrating calendar and address book with social media application – Using
Wifi - iPhone marketplace.
TOTAL: 45 PERIODS
OUTCOMES:
Upon the students will be able toCompletion of the course,
1. Describe the requirements for mobile applications
2. Explain the challenges in mobile application design and development
3. Develop design for mobile applications for specific requirements
4. Implement the design using Android SDK
5. Implement the design using Objective C and iOS
6. Deploy mobile applications in Android and iPone marketplace for distribution
50REFERENCES:
1. http://developer.android.com/develop/index.html
2. Jeff McWherter and Scott Gowell, "Professional Mobile Application Development",
Wrox, 2012
3. Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”,
DreamTech, 2012
4. James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2012
5. David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 6
Development: Exploring the iOS SDK”, Apress, 2013.
CP7020
BIO-INSPIRED COMPUTING
LT PC
3 0 03
OBJECTIVES:
Learn evolutionary theory and algorithms
Understand Cellular Automata and artificial life
Learn artificial neural systems and related learning algorithms
Learn developmental and artificial immune systems
Understand behavioral systems especially in the context of Robotics
Understand collective systems such as ACO, PSO, and swarm robotics
UNIT I
EVOLUTIONARY AND CELLULAR SYSTEMS
9
Foundations of evolutionary theory – Genotype – artificial evolution – genetic
representations – initial population – fitness functions – selection and reproduction – genetic
operators – evolutionary measures – evolutionary algorithms – evolutionary electronics –
evolutionary algorithm case study
Cellular systems – cellular automata – modeling with cellular systems – other cellular
systems – computation with cellular systems – artificial life – analysis and synthesis of
cellular systems
UNIT II
NEURAL SYSTEMS
9
Biological nervous systems – artificial neural networks – neuron models – architecture –
signal encoding – synaptic plasticity – unsupervised learning – supervised learning –
reinforcement learning – evolution of neural networks – hybrid neural systems – case study
UNIT III
DEVELOPMENTAL AND IMMUNE SYSTEMS
9
Rewriting systems – synthesis of developmental systems – evolutionary rewriting systems –
evolutionary developmental programs
Biological immune systems – lessons for artificial immune systems – algorithms and
applications – shape space – negative selection algorithm – clonal selection algorithm -
examples
UNIT IV
BEHAVIORAL SYSTEMS
9
Behavior is cognitive science – behavior in AI – behavior based robotics – biological
inspiration for robots – robots as biological models – robot learning – evolution of behavioral
systems – learning in behavioral systems – co-evolution of body and control – towards self
reproduction – simulation and reality
UNIT V
COLLECTIVE SYSTEMS
9
Biological self-organization – Particle Swarm Optimization (PSO) – ant colony optimization
(ACO) – swarm robotics – co-evolutionary dynamics – artificial evolution of competing
systems – artificial evolution of cooperation – case study
TOTAL: 45 PERIODS
51OUTCOMES:
Upon completion of the course, the students will be able to
Implement and apply evolutionary algorithms
Explain cellular automata and artificial life
Implement and apply neural systems
Explain developmental and artificial immune systems
Explain behavioral systems
Implement and apply collective intelligence systems
REFERENCES:
1. D. Floreano and C. Mattiussi, "Bio-Inspired Artificial Intelligence", MIT Press, 2008.
2. F. Neumann and C. Witt, “Bioinspired Computation in combinatorial optimization:
Algorithms and their computational complexity”, Springer, 2010.
3. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010.
4. D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”,
Addison-Wesley, 1989.
5. Simon O. Haykin, “Neural Networks and Learning Machines”, Third Edition, Prentice
Hall, 2008.
6. M. Dorigo and T. Stutzle, “Ant Colony Optimization”, A Bradford Book, 2004.
7. R. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.
CP7021
MEDICAL IMAGE PROCESSING
LT PC
3 0 0 3
COURSE DESCRIPTION:
An advanced graduate level course on medical imaging and medical image analysis. The
course includes topics in medical image formation, medical imaging techniques, such as X-
Ray, Computed Tomography, Magnetic Resonance Imaging, and Nuclear Imaging, image
segmentation, registration, statistical modeling, visualization, and applications of
computational tools for medicine.
OBJECTIVES:
The course will provide the participants with an up-to-date background in current state-of-
the-art in medical imaging and medical image analysis. The aim of the course is to show
how to extract, model, and analyze information from medical data and applications in order
to help diagnosis, treatment and monitoring of diseases through computer science.
UNIT I
INTRODUCTION
9
Introduction to medical imaging technology, systems, and modalities. Brief history;
importance; applications; trends; challenges. Medical Image Formation Principles: X-Ray
physics; X-Ray generation, attenuation, scattering; dose Basic principles of CT;
reconstruction methods; artifacts; CT hardware.
UNIT II
STORAGE AND PROCESSING
9
Medical Image Storage, Archiving and Communication Systems and Formats Picture
archiving and communication system (PACS); Formats: DICOM Radiology Information
Systems (RIS) and Hospital Information Systems (HIS). Medical Image Processing,
Enhancement, Filtering Basic image processing algorithms Thresholding; contrast
enhancement; SNR characteristics; filtering; histogram modeling.
UNIT III
VISUALIZATION
9
Medical Image Visualization Fundamentals of visualization; surface and volume
rendering/visualization; animation; interaction. Magnetic Resonance Imaging (MRI)
Mathematics of MR; spin physics; NMR spectroscopy; imaging principles and hardware;
image artifacts.
52UNIT IV
SEGMENTATION AND CLASSIFICATION
9
Medical Image Segmentation - Histogram-based methods; Region growing and watersheds;
Markov Random Field models; active contours; model-based segmentation. Multi-scale
segmentation; semi-automated methods; clustering-based methods; classification-based
methods; atlas-guided approaches; multi-model segmentation. Medical Image Registration
Intensity-based methods; cost functions; optimization techniques.
UNIT V
NUCLEAR IMAGING
9
PET and SPECT Ultrasound Imaging methods; mathematical principles; resolution; noise
effect; 3D imaging; positron emission tomography; single photon emission tomography;
ultrasound imaging; applications. Medical Image Search and Retrieval Current technology in
medical image search, content-based image retrieval, new trends: ontologies. Applications.
Other Applications of Medical Imaging Validation, Image Guided Surgery, Image Guided
Therapy, Computer Aided Diagnosis/Diagnostic Support Systems.
TOTAL: 45 PERIODS
REFERENCES:
1. Paul Suetens, "Fundamentals of Medical Imaging", Second Edition, Cambridge
University Press, 2009.
2. J. Michael Fitzpatrick and Milan Sonka, "Handbook of Medical Imaging, Volume 2.
Medical Image Processing and Analysis", SPIE Publications, 2009.
3. Kayvan Najarian and Robert Splinter, "Biomedical Signal and Image Processing",
Second Edition, CRC Press, 2005.
4. Geoff Dougherty, "Digital Image Processing for Medical Applications", First Edition,
Cambridge University Press, 2009.
5. Jerry L. Prince and Jonathan Links, "Medical Imaging Signals and Systems", First
Edition, Prentice Hall, 2005.
6. John L. Semmlow, "Biosignal and Medical Image Processing", Second Edition, CRC
Press, 2008.
CP7022
SOFTWARE DESIGN
LTPC
30 03
OBJECTIVES:
Analyze specifications
Describe approaches to design
Develop design documentation
Evaluate the design
UNIT I
SOFTWARE DESIGN PRINCIPLES
9
Introduction – Design process – Managing complexity – Software modeling and notations –
Abstraction – Modularity – Hierarchy – Coupling - Cohesion – Design guidelines and
checklists – Refactoring
UNIT II
OO DESIGN
9
Object model – Classes and objects – Object oriented analysis – Key abstractions and
mechanisms – Object oriented design – Identifying design elements – Detailed design –
Case studies.
UNIT III
DESIGN PATTERNS
9
Introduction to patterns – Design context – Reusable solutions – Documenting reusable
solutions – Standard patterns from GOF book.
UNIT IV
FUNCTION AND SERVICE ORIENTED DESIGNS
9
Structural decomposition – Detailed Design – Function oriented design Case study –
Services – Service identification – Service design – Service composition – choreography and
orchestration – Service oriented design Case study
53UNIT V
USER CENTERED DESIGN AND DESIGN REVIEW
9
Introduction to user centered design – Use in context – Interface and interaction – User
centered design principles – Task analysis – Evaluation – Introduction to design review–
Testing the design – Walk throughs – Review against check lists.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Describe different approaches to desigining a software application
Analyze specifications and identify appropriate design strategies.
Develop an appropriate design for a given set of requirements
Identify applicatble design patterns for the solution
Abstract and document reusable design patterns
Evaluate a given design against the specifications
REFERENCES:
1. Grady Booch et al., "Object Oriented Analysis and Design with Applications", 3rd
Edition, Pearson, 2010.
2. Carlos Otero, "Software Engineering Design: Theory and Practice", CRC Press, 2012
3. David Budgen, "Software Design", 2nd Edtion, Addison Wesley, 2003
4. Alan Shalloway and James R Trott, "Design Patterns Explained: A New Perspective
on Object-Oriented Design", 2nd Edition, Addison-Wesley Professional, 2004
5. Hassan Gomaa, "Software Modeling and Design", Cambridge University Press, 2011
6. Eric Gamma et al., "Design Patterns: Elements of Reusable Object-Oriented
Software", Addison-Wesley Professional, 1994
7. Ian Sommerville, "Software Engineering", 9th Edition, Addison-Wesley, 2010
8. M B Rosson and J M Carroll, "Usability Engineering: Scenario-Based Development
of Human-Computer Interaction", Morgan Kaufmann, 2002
CP7023
RECONFIGURABLE COMPUTING
LTPC
3 003
OBJECTIVES:
To understand the need for reconfigurable computing
To expose the students to various device architectures
To examine the various reconfigurable computing systems
To understand the different types of compute models for programming reconfigurable
architectures
To expose the students to HDL programming and familiarize with the development
environment
To expose the students to the various placement and routing protocols
To develop applications with FPGAs
UNIT I
DEVICE ARCHITECTURE
9
General Purpose Computing Vs Reconfigurable Computing – Simple Programmable Logic
Devices – Complex Programmable Logic Devices – FPGAs – Device Architecture - Case
Studies.
UNIT II
RECONFIGURABLE COMPUTING ARCHITECTURES AND SYSTEMS
9
Reconfigurable Processing Fabric Architectures – RPF Integration into Traditional
Computing Systems – Reconfigurable Computing Systems – Case Studies –
Reconfiguration Management.
UNIT III
PROGRAMMING RECONFIGURABLE SYSTEMS
9
Compute Models - Programming FPGA Applications in HDL – Compiling C for Spatial
Computing – Operating System Support for Reconfigurable Computing.
54UNIT IV
MAPPING DESIGNS TO RECONFIGURABLE PLATFORMS
9
The Design Flow - Technology Mapping – FPGA Placement and Routing – Configuration
Bitstream Generation – Case Studies with Appropriate Tools.
UNIT V
APPLICATION DEVELOPMENT WITH FPGAS
9
Case Studies of FPGA Applications – System on a Programmable Chip (SoPC) Designs.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for reconfigurable architectures
Discuss the architecture of FPGAs
Point out the salient features of different reconfigurable architectures
Build basic modules using any HDL
Develop applications using any HDL and appropriate tools
Design and build an SoPC for a particular application
REFERENCES:
1. Maya B. Gokhale and Paul S. Graham, “Reconfigurable Computing: Accelerating
Computation with Field-Programmable Gate Arrays”, Springer, 2005.
2. Scott Hauck and Andre Dehon (Eds.), “Reconfigurable Computing – The Theory and
Practice of FPGA-Based Computation”, Elsevier / Morgan Kaufmann, 2008.
3. Christophe Bobda, “Introduction to Reconfigurable Computing – Architectures,
Algorithms and Applications”, Springer, 2010.
IF7013
ENERGY AWARE COMPUTING
LTPC
30 03
OBJECTIVES:
This course examines the design of power efficient architecture, power and performance
tradeoffs, restructuring of software and applications and standards for energy aware
Hardware and Software. The objective of this course is:
To know the fundamental principles energy efficient devices
To study the concepts of Energy efficient storage
To introduce energy efficient algorithms
To enable the students to know energy efficient techniques involved to support real-
time systems.
To study Energy aware applications
UNIT I
INTRODUCTION
9
Energy efficient network on chip architecture for multi core system-Energy efficient MIPS
CPU core with fine grained run time power gating – Low power design of Emerging memory
technologies.
UNIT II
ENERGY EFFICIENT STORAGE
9
Disk Energy Management-Power efficient strategies for storage system-Dynamic thermal
management for high performance storage systems-Energy saving technique for Disk
storage systems
UNIT III
ENERGY EFFICIENT ALGORITHMS
9
Scheduling of Parallel Tasks – Task level Dynamic voltage scaling – Speed Scaling –
Processor optimization- Memetic Algorithms – Online job scheduling Algorithms.
55UNIT IV
REAL TIME SYSTEMS
9
Multi processor system – Real Time tasks- Energy Minimization – Energy aware scheduling-
Dynamic Reconfiguration- Adaptive power management-Energy Harvesting Embedded
system
UNIT V
ENERGY AWARE APPLICATIONS
9
On chip network – Video codec Design – Surveillance camera- Low power mobile storage.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Design Power efficient architecture Hardware and Software.
Analyze power and performance trade off between various energy aware storage
devices.
Implement various energy aware algorithms.
Restructure the software and Hardware for Energy aware applications.
Explore the Energy aware applications
REFERENCES:
1. Ishfaq Ah mad, Sanjay Ranka, Handbook of Energy Aware and Green Computing,
Chapman and Hall/CRC, 2012
2. Chong-Min Kyung, Sungioo yoo, Energy Aware system design Algorithms and
Architecture, Springer, 2011.
3. Bob steiger wald ,Chris:Luero, Energy Aware computing, Intel Press,2012.
CP7024
INFORMATION RETRIEVAL TECHNIQUES
LTPC
30 03
OBJECTIVES:
To understand the basics of Information Retrieval with pertinence to modeling, query
operations and indexing
To get an understanding of machine learning techniques for text classification and
clustering
To understand the various applications of Information Retrieval giving emphasis to
Multimedia IR, Web Search
To understand the concepts of digital libraries
UNIT I
INTRODUCTION
8
Motivation – Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean
Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web
Characteristics–The impact of the web on IR ––IR Versus Web Search–Components of a
Search engine
UNIT II
MODELING
10
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term
Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic
Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing
UNIT III
INDEXING
9
Static and Dynamic Inverted Indices – Index Construction and Index Compression Searching
- Sequential Searching and Pattern Matching. Query Operations -Query Languages–Query
Processing - Relevance Feedback and Query Expansion - Automatic Local and Global
Analysis – Measuring Effectiveness and Efficiency.
UNIT IV
CLASSIFICATION AND CLUSTERING
8
Text Classification and Naïve Bayes – Vector Space Classification – Support vector
machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –
Matrix decompositions and latent semantic indexing – Fusion and Meta learning
56UNIT V
SEARCHING AND RANKING
10
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic
Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR:
Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital
Libraries
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Build an Information Retrieval system using the available tools
Identify and design the various components of an Information Retrieval system
Apply machine learning techniques to text classification and clustering which is used
for efficient Information Retrieval
Analyze the Web content structure
Design an efficient search engine
REFERENCES:
1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The
concepts and Technology behind Search (ACM Press Books), Second Edition 2011
2. Christopher D. Manning, PrabhakarRaghavan, HinrichSchutze, Introduction to
Information Retrieval, Cambridge University Press, First South Asian Edition 2012
3. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval
Implementing and Evaluating Search Engines, The MIT Press, Cambridge,
Massachusetts London, England, 2010
CP7025
DATA MINING TECHNIQUES
LTPC
300 3
UNIT I
INTRODUCTION TO DATA MINING
9
Introduction to Data Mining – Data Mining Tasks – Components of Data Mining Algorithms –
Data Mining supporting Techniques – Major Issues in Data Mining – Measurement and Data
– Data Preprocessing – Data sets
UNIT II
OVERVIEW OF DATA MINING ALGORITHMS
9
Overview of Data Mining Algorithms – Models and Patterns – Introduction – The
Reductionist viewpoint on Data Mining Algorithms – Score function for Data Mining
Algorithms- Introduction – Fundamentals of Modeling – Model Structures for Prediction –
Models for probability Distributions and Density functions – The Curve of Dimensionality –
Models for Structured Data – Scoring Patterns – Predictive versus Descriptive score
functions – Scoring Models with Different Complexities – Evaluation of Models and Patterns
– Robust Methods.
UNIT III
CLASSIFICATIONS
9
Classifications – Basic Concepts – Decision Tree induction – Bayes Classification Methods –
Rule Based Classification – Model Evaluation and Selection – Techniques to Improve
Classification Accuracy – Classification: Advanced concepts – Bayesian Belief Networks-
Classification by Back Propagation – Support Vector Machine – Classification using frequent
patterns.
UNIT IV
CLUSTER ANALYSIS
9
Cluster Analysis: Basic concepts and Methods – Cluster Analysis – Partitioning methods –
Hierarchical methods – Density Based Methods – Grid Based Methods – Evaluation of
Clustering – Advanced Cluster Analysis: Probabilistic model based clustering – Clustering
High – Dimensional Data – Clustering Graph and Network Data – Clustering with
Constraints.
57UNIT V
ASSOCIATION RULE MINING AND VISUALIZATION
9
Association Rule Mining – Introduction – Large Item sets – Basic Algorithms – Parallel and
Distributed Algorithms – Comparing Approaches – Incremental Rules – Advanced
Association Rule Techniques – Measuring the Quality of Rules – Visualization of
Multidimensional Data – Diagrams for Multidimensional visualization – Visual Data Mining –
Data Mining Applications – Case Study: WEKA.
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. Jiawei Han, Micheline Kamber , Jian Pei, “Data Mining: Concepts and Techniques”,
Third Edition (The Morgan Kaufmann Series in Data Management Systems), 2012.
2. David J. Hand, Heikki Mannila and Padhraic Smyth “Principles of Data Mining”
(Adaptive Computation and Machine Learning), 2005
3. Margaret H Dunham, “Data Mining: Introductory and Advanced Topics”, 2003
4. Soman, K. P., Diwakar Shyam and Ajay V. “Insight Into Data Mining: Theory And
Practice”, PHI, 2009.
IF7002
BIO INFORMATICS
LTPC
3 003
p
OBJECTIVES:
To get exposed to the domain of bioinformatics
To understand the role of data warehousing and data mining for bioinformatics
To learn to model bioinformatics based applications
To understand how to deploy the pattern matching and visualization techniques in
bioinformatics
To study the Microarray technologies for genome expression
UNIT I
INTRODUCTION
9
Need for Bioinformatics technologies – Overview of Bioinformatics technologies – Structural
bioinformatics – Data format and processing – secondary resources- Applications – Role of
Structural bioinformatics - Biological Data Integration System.
UNIT II
DATAWAREHOUSING AND DATAMINING IN BIOINFORMATICS
9
Bioinformatics data – Data ware housing architecture – data quality – Biomedical data
analysis – DNA data analysis – Protein data analysis – Machine learning – Neural network
architecture- Applications in bioinformatics
UNIT III
MODELING FOR BIOINFORMATICS
9
Hidden markov modeling for biological data analysis – Sequence identification – Sequence
classification – multiple alignment generation – Comparative modeling – Protein modeling –
genomic modeling – Probabilistic modeling – Bayesian networks – Boolean networks -
Molecular modeling – Computer programs for molecular modeling
UNIT IV
PATTERN MATCHING AND VISUALIZATION
9
Gene regulation – motif recognition and motif detection – strategies for motif detection –
Visualization – Fractal analysis – DNA walk models – one dimension – two dimension –
higher dimension – Game representation of Biological sequences – DNA, Protein, Amino
acid sequences
UNIT V
MICROARRAY ANALYSIS
9
Microarray technology for genome expression study – image analysis for data extraction –
preprocessing – segmentation – gridding , spot extraction , normalization, filtering – cluster
analysis – gene network analysis – Compared Evaluation of Scientific Data Management
Systems – Cost Matrix – Evaluation model ,Benchmark , Tradeoffs
TOTAL: 45 PERIODS
58OUTCOMES:
Upon Completion of the course, the students will be able to
Deploy the data warehousing and data mining techniques in Bioinformatics
Model bioinformatics based applications
Deploy the pattern matching and visualization techniques in bioinformatics
Work on the protein sequences
Use the Microarray technologies for genome expression
REFERENCES:
1. Yi-Ping Phoebe Chen (Ed), “Bio Informatics Technologies”, First Indian Reprint, Springer
Verlag, 2007.
2. N.J. Chikhale and Virendra Gomase, "Bioinformatics- Theory and Practice", Himalaya
Publication House, India, 2007
3. Zoe lacroix and Terence Critchlow, “Bio Informatics – Managing Scientific data”, First
Indian Reprint, Elsevier, 2004
4. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003.
5. Arthur M Lesk, “Introduction to Bioinformatics”, Second Edition, Oxford University Press,
2005
6. Burton. E. Tropp, “Molecular Biology: Genes to Proteins “, 4th edition, Jones and Bartlett
Publishers, 2011
7. Dan Gusfield, “Algorithms on Strings Trees and Sequences”, Cambridge University
Press, 1997.
8. P. Baldi, S Brunak , Bioinformatics, “A Machine Learning Approach “, MIT Press, 1998.
CP7026
SOFTWARE QUALITY ASSURANCE
LTPC
3 003
OBJECTIVES:
Describe approaches to quality assurance
Understand quality models
Evaluate the system based on the chosen quality model
UNIT I
INTRODUCTION
9
Introduction – Views on quality – Cost of quality - Quality models – Quality frameworks –
Verification and Validation – Defect taxonomy – Defect management – Statistics and
measurements – IEEE standards – Quality assurance and control processes
UNIT II
VERIFICATION
6
Introduction – Verification techniques – Inspections, reviews, walk-throughs – Case studies
UNIT III
TEST GENERATION
12
Software testing- Validation – Test plan – Test cases - Test Generation – Equivalence
partitioning – Boundary value analysis – Category partition method – Combinatorial
generation - Decision tables – Examples and Case studies
UNIT IV
STRUCTURAL TESTING
12
Introduction – Test adequacy criteria – Control flow graph – Coverages: block, conditions,
multiple conditions, MC/DC, path – Data flow graph – Definition and use coverages – C-use,
P-use, Def-clear, Def-use – Finite state machines – Transition coverage – Fault based
testing – Mutation analysis – Case studies
UNIT V
FUNCTIONAL TESTING
6
Introduction – Test adequacy criteria - Test cases from use cases – Exploratory testing -
Integration, system, acceptance, regression testing – Testing for specific attributes:
Performance, load and stress testing – Usability testing – Security testing - Test automation
– Test oracles
TOTAL : 45 PERIODS
59OUTCOMES:
Upon Completion of the course, the students will be able to
Describe different approaches to testing software applications
Analyze specifications and identify appropriate test generation strategies
Develop an appropriate test design for a given test object
Identify applicatble measurements for the verification and validation effort
Execute the test design
Evaluate the testing effort based on adequate measures
REFERENCES:
1. Boriz Beizer, "Software Testing Techniques", 2nd Edition, DreamTech, 2009.
2. Aditya P. Mathur, "Foundations of Software Testing", Pearson, 2008
3. Mauro Pezze and Michal Young, "Software Testing and Analysis. Process, Principles,
and Techniques", John Wiley 2008
4. Stephen H. Kan, "Metrics and Models in Software Quality Engineering", 2nd Edition,
Pearson, 2003
5. Kshirasagar Naik and Priyadarshi Tripathy (Eds), "Software Testing and Quality
Assurance: Theory and Practice", John Wiley, 2008
6. "Combinatorial Methods in Software Testing", ttp://csrc.nist.gov/groups/SNS/acts/index.html
CP7027
MULTI OBJECTIVE OPTIMIZATION TECHNIQUES
LTPC
3 003
OBJECTIVES:
Learn fundamental principles of Multiobjective Optimization (MOP)
Survey different Multiobjective Optimization algorithms
Introduce various design issues of MOP
Develop and Evaluate MOP Algorithms
Learn Parallel and hybrid MOP Algorithms
Learn other Metaheuristics
UNIT I
INTRODUCTION AND CLASSICAL APPROACHES
9
Multiobjective optimization: Introduction - Multiobjective optimization problem-principles –
Difference between single and multiobjective optimization – Dominance and Pareto
Optimality , Classical Methods – Weighted Sum -
Constraint method – Weighted Metric
methods – Benson’s method - Value Function - Goal Programming methods – Interactive
Methods
UNIT II
MOP EVOLUTIONARY ALGORITHMS
9
Generic MOEA - Various MOEAs: MOGA, NSGA-II, NPGA, PAES, SPEA2, MOMGA, micro
GA - Constrained MOEAs: Penalty Function approach - Constrained Tournament – Ray –
Tai –Seow’s Method.
UNIT III
THEORETICAL ISSUES
9
Fitness Landscapes - Fitness Functions - Pareto Ranking - Pareto Niching and Fitness
Sharing - Recombination Operators - Mating Restriction - Solution Stability and Robustness
- MOEA Complexity - MOEA Scalability - Running Time Analysis - MOEA Computational
Cost - No Free Lunch Theorem.
UNIT IV
MOEA TESTING, ANALYSIS, AND PARALLELIZATION
9
MOEA Experimental Measurements – MOEA Statistical Testing Approaches – MOEA Test
Suites - MOEA Parallelization: Background – Paradigms – Issues - MOEA Local Search
Techniques.
60UNIT V
APPLICATIONS AND ALTERNATIVE METAHEURISTICS
9
Scientific Applications: Computer Science and Computer Engineering - Alternative
Metaheuristics: Simulated Annealing – Tabu Search and Scatter Search – Ant System –
Distributed Reinforcement Learning – Particle Swarm Optimization – Differential Evolution –
Artificial Immune Systems - Other Heuristics.
TOTAL:45 PERIODS
OUTCOMES:
Upon Completion of the course,students will be able to
Explain MOP principles
Explain classical methods to solve MOP problems
Be familiar with and explain structures of different MOP algorithms
Solve constrained MOP problems
Explain various design issues of MOP algorithms
Perform a evaluation and analysis of MOP algorithm results
Explain parallelization of MOP algorithms
Develop parallel and hybrid MOP algorithms
Identify various real time MOP applications
Explain other search algorithms
REFERENCES:
1. Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen, “Evolutionary
Algorithms for Solving Multi-objective Problems”, Second Edition, Springer, 2007.
2. Kalyanmoy Deb, “ Multi-Objective Optimization Using Evolutionary Algorithms”, John
Wiley, 2002.
3. Aimin Zhoua, Bo-Yang Qub, Hui Li c, Shi-Zheng Zhaob, Ponnuthurai Nagaratnam
Suganthan b, Qingfu Zhangd, “Multiobjective evolutionary algorithms: A survey of the
state of the art”, Swarm and Evolutionary Computation (2011) 32–49.
4. E Alba, M Tomassini, “Parallel and evolutionary algorithms”, Evolutionary Computation,
IEEE Transactions on 6 (5), 443-462.
5. Crina Grosan, Ajith Abraham, “Hybrid Evolutionary Algorithms: Methodologies,
Architectures, and Reviews”, Studies in Computational Intelligence, Vol. 75, Springer,
2007.
6. Christian Blum and Andrea Roli. 2003. Metaheuristics in combinatorial optimization:
Overview and conceptual comparison. ACM Comput. Surv. 35, 3 (September 2003),
268-308.
CP7028
ENTERPRISE APPLICATION INTEGRATION
LTPC
30 03
OBJECTIVES:
Describe approaches to enterprise application integration
Understand the integration middleware
Evaluate the integration approaches suitable for a given problem
UNIT I
INTRODUCTION
6
Requirements for EAI - Challenges in EAI – Integration with legacy systems – Integration
with partners - Heterogeneous environment – Implementation approaches – Web services,
messaging, ETL, direct data integration – Middleware requirements – Approaches to
integration – services oriented and messaging.
UNIT II
INTEGRATION PATTERNS
6
Introduction to integration patterns – Architecture for application integration – Integration
patterns – Point to point, broker, message bus, publish/subscribe, Challenges in
performance, security, reliability - Case studies
61UNIT III
SERVICE ORIENTED INTEGRATION
12
Business process integration - Composite applications-services – Web services – Service
choreography and orchestration - Business process modeling - BPMN, Business process
execution - BPEL – Middleware infrastructure - Case studies
UNIT IV
MESSAGING BASED INTEGRATION
9
Messaging – Synchronous and asynchronous – Message structure – Message oriented
middleware – Reliability mechanisms – Challenges – Messaging infrastructure – Java
Messaging Services – Case studies
UNIT V
ENTERPRISE SERVICE BUS
12
Enterprise Service Bus – routing, scalable connectivity, protocol and message
transformations, data enrichment, distribution, correlation, monitoring – Deployment
configurations – Global ESB, Directly connected, Federated, brokered ESBs – Application
server based – Messaging system based – Hardware based ESBs – Support to SOA,
message based and event based integrations - Case studies.
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe different approaches to integration enterprise applications
Analyze specifications and identify appropriate integration approaches
Develop a suitable integration design for a given problem
Identify appropriate integration middleware for a given problem
Evaluate the integration approaches against specified requirements
REFERENCES:
1. George Mentzas and Andreas Frezen (Eds), "Semantic Enterprise Application
Integration for Business Processes: Service-oriented Frameworks", Business Science
Reference, 2009
2. Waseem Roshen, "SOA Based Enterprise Integration", Tata McGrawHill, 2009.
3. G
Hohpe
and
B
Woolf,
"Enterprise
Integration Patterns: Designing,
Building, and Deploying Messaging Solutions", Addison Wesley Professional,
2003
4. D
Linthicum, "Next
Generation
Application
Integration:
From
Simple
Information to Web Services", Addison Wesley, 2003
5. Martin Fowler, "Patterns of Enterprise Application Architecture", Addison- Wesley, 2003
6. Kapil Pant and Matiaz Juric, "Business Process Driven SOA using BPMN and BPEL:
From Business Process Modeling to Orchestration and Service Oriented Architecture",
Packt Publishing, 2008
CP7029
INFORMATION STORAGE MANAGEMENT
LTPC
3 003
UNIT I
INTRODUCTION TO STORAGE TECHNOLOGY
9
Review data creation and the amount of data being created and understand the value of
data to a business, challenges in data storage and data management, Solutions available for
data storage, Core elements of a data center infrastructure, role of each element in
supporting business activities
62UNIT II
STORAGE SYSTEMS ARCHITECTURE
9
Hardware and software components of the host environment, Key protocols and concepts
used by each component ,Physical and logical components of a connectivity environment
,Major physical components of a disk drive and their function, logical constructs of a physical
disk, access characteristics, and performance Implications, Concept of RAID and its
components, Different RAID levels and their suitability for different application environments:
RAID 0, RAID 1, RAID 3, RAID 4, RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and
contrast integrated and modular storage systems ,Iligh-level architecture and working of an
intelligent storage system
UNIT III
INTRODUCTION TO NETWORKED STORAGE
9
Evolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS,
and IP-SAN, Benefits of the different networked storage options, understand the need for
long-term archiving solutions and describe how CAS full fill the need, understand the
appropriateness of the different networked storage options for different application
environments
UNIT IV
INFORMATION AVAILABILITY, MONITORING & MANAGING
DATACENTER
9
List reasons for planned/unplanned outages and the impact of downtime, Impact of
downtime - Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO
and RPO, Identify single points of failure in a storage infrastructure and list solutions to
mitigate these failures, Architecture of backup/recovery and the different backup/ recovery
topologies, replication technologies and their role in ensuring information availability and
business continuity, Remote replication technologies and their role in providing disaster
recovery and business continuity capabilities. Identify key areas to monitor in a data center,
Industry standards for data center monitoring and management, Key metrics to monitor for
different components in a storage infrastructure, Key management tasks in a data center
UNIT V
SECURING STORAGE AND STORAGE VIRTUALIZATION
9
Information security, Critical security attributes for information systems, Storage security
domains, List and analyzes the common threats in each domain, Virtualization technologies,
block-level and file-level virtualization technologies and processes
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. EMC Corporation, Information Storage and Management, Wiley, India.
2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill ,
Osborne, 2003.
3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001.
4. Additional resource material on www.emc.com/resource-library/resource-library.esp
CP7030
ROBOTICS
OBJECTIVES:
To understand robot locomotion and mobile robot kinematics
To understand perception in robotics
To understand mobile robot localization
To understand mobile robot mapping
To understand simultaneous localization and mapping (SLAM)
To understand robot planning and navigation
63
L T P C
3 0 0 3UNIT I
LOCOMOTION AND KINEMATICS
9
Introduction to Robotics – key issues in robot locomotion – legged robots – wheeled mobile
robots – aerial mobile robots – introduction to kinematics – kinematics models and
constraints – robot maneuverability
UNIT II
ROBOT PERCEPTION
9
Sensors for mobile robots – vision for robotics – cameras – image formation – structure from
stereo – structure from motion – optical flow – color tracking – place recognition – range data
UNIT III
MOBILE ROBOT LOCALIZATION
9
Introduction to localization – challenges in localization – localization and navigation – belief
representation – map representation – probabilistic map-based localization – Markov
localization – EKF localization – UKF localization – Grid localization – Monte Carlo
localization – localization in dynamic environments
UNIT IV
MOBILE ROBOT MAPPING
9
Autonomous map building – occupancy grip mapping – MAP occupancy mapping – SLAM –
extended Kalman Filter SLAM – graph-based SLAM – particle filter SLAM – sparse extended
information filter – fastSLAM algorithm
UNIT V
PLANNING AND NAVIGATION
9
Introduction to planning and navigation – planning and reacting – path planning – obstacle
avoidance techniques – navigation architectures – basic exploration algorithms
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain robot locomotion
Apply kinematics models and constraints
Implement vision algorithms for robotics
Implement robot localization techniques
Implement robot mapping techniques
Implement SLAM algorithms
Explain planning and navigation in robotics
REFERENCES:
1. Roland Seigwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, “Introduction to
autonomous mobile robots”, Second Edition, MIT Press, 2011.
2. Sebastian Thrun, Wolfram Burgard, and Dieter Fox, “Probabilistic Robotics”, MIT Press,
2005.
3. Howie Choset et al., “Principles of Robot Motion: Theory, Algorithms, and
Implementations”, A Bradford Book, 2005.
4. Gregory Dudek and Michael Jenkin, “Computational Principles of Mobile Robotics”,
Second Edition, Cambridge University Press, 2010.
5. Maja J. Mataric, “The Robotics Primer”, MIT Press, 2007.
CP7031
COMPILER OPTIMIZATION TECHNIQUES
LTPC
3003
OBJECTIVES:
To understand the optimization techniques used in compiler design.
To be aware of the various computer architectures that support parallelism.
To become familiar with the theoretical background needed for code optimization.
To understand the techniques used for identifying parallelism in a sequential program.
To learn the various optimization algorithms.
64UNIT I
INTRODUCTION
9
Language Processors - The Structure of a Compiler – The Evolution of Programming
Languages- The Science of Building a Compiler – Applications of Compiler Technology
Programming Language Basics - The Lexical Analyzer Generator -Parser Generator -
Overview of Basic Blocks and Flow Graphs - Optimization of Basic Blocks - Principle
Sources of Optimization.
UNIT II
INSTRUCTION-LEVEL PARALLELISM
9
Processor Architectures – Code-Scheduling Constraints – Basic-Block Scheduling –Global
Code Scheduling – Software Pipelining.
UNIT III
OPTIMIZING FOR PARALLELISM AND LOCALITY-THEORY
9
Basic Concepts – Matrix-Multiply: An Example - Iteration Spaces - Affine Array Indexes –
Data Reuse Array data dependence Analysis.
UNITIV
OPTIMIZING FOR PARALLELISM AND LOCALITY – APPLICATION
9
Finding Synchronization - Free Parallelism – Synchronization Between Parallel Loops –
Pipelining – Locality Optimizations – Other Uses of Affine Transforms.
UNIT V
INTERPROCEDURAL ANALYSIS
9
Basic Concepts – Need for Interprocedural Analysis – A Logical Representation of Data
Flow – A Simple Pointer-Analysis Algorithm – Context Insensitive Interprocedural Analysis -
Context-Sensitive Pointer-Analysis - Datalog Implementation by Binary Decision Diagrams.
TOTAL: 45 PERIODS
REFERENCES:
1. Alfred V. Aho, Monica S.Lam, Ravi Sethi, Jeffrey D.Ullman, “Compilers:Principles,
Techniques and Tools”, Second Edition, Pearson Education,2008.
2. Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern Architectures: A
Dependence-based Approach”, Morgan Kaufmann Publishers, 2002.
3. Steven S. Muchnick, “Advanced Compiler Design and Implementation”,Morgan
Kaufmann Publishers - Elsevier Science, India, Indian Reprint 2003.
65ANNA UNIVERSITY, CHENNAI
AFFILIATED INSTITUTIONS
REGULATIONS - 2013
M.E. COMPUTER SCIENCE AND ENGINEERING
I - IV SEMESTERS (FULL TIME) CURRICULUM AND SYLLABUS
405
THEORY
Course Code
MA7155
CP7101
CP7102
CP7103
M.E. Computer Science
and Engineering
SEMESTER I
Course Title
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 4 2
0 0 2 1
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
L T P C
0 0 2 1
0 0 4 2
18 1 6 22
L T P C
3 1 0 4
3 0 0 3
3 0 0 3
3 0 0 3
Total L T P C
0 0 12 6
12 1 12 19
Total L T P C
0 0 24 12
0 0 24 12
Applied Probability and Statistics
Design and Management of Computer Networks
Advanced Data Structures and Algorithms
Multicore Architectures
Elective I
Elective II
PRACTICAL
Course Code
Course Title
CP7111
Advanced Data Structures Laboratory
CP7112
Case Study - Network Design (Team Work)
Total
SEMESTER II
THEORY
Course Code
CP7201
CP7202
CP7203
CP7204
Course Title
Theoretical Foundations of Computer Science
Advanced Databases
Principles of Programming Languages
Advanced Operating Systems
Elective III
Elective IV
PRACTICAL
Course Code
Course Title
CP7211
Advanced Databases Laboratory
CP7212
Case Study - Operating Systems Design (Team Work)
Total
SEMESTER III
THEORY
Course Code
Course Title
CP7301
Software Process and Project Management
Elective V
Elective VI
Elective VII
PRACTICAL
Course Code
CP7311
Project Work (Phase I)
Course Title
SEMESTER IV
PRACTICAL
Course Code
CP7411
Project Work (Phase II)
Course Title
TOAL NO OF CREDITS 75ELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER I
ELECTIVE-I Course Title L T P C
Course Code Formal Models of Software Systems 3 0 0 3
SE7103 Performance Evaluation of Computer Systems 3 0 0 3
CP7001 Probabilistic Reasoning Systems 3 0 0 3
CP7002 Data Analysis and Business Intelligence 3 0 0 3
CP7003 Image Processing and Analysis 3 0 0 3
CP7004 Sensing Techniques and Sensors 3 0 0 3
NE7001
ELECTIVE-II Course Title Randomized Algorithms L T P C
Course Code Mobile and Pervasive Computing 3 0 0 3
CP7005 Parallel Programming Paradigms 3 0 0 3
NE7002 Software Requirements Engineering 3 0 0 3
CP7006 Speech Processing and Synthesis 3 0 0 3
CP7007 Machine Learning Techniques 3 0 0 3
CP7008 3 0 0 3
CP7009 36 0 0 36
Total
SEMESTER II
ELECTIVE-III Course Title L T P C
Course Code Concurrency Models 3 0 0 3
CP7010 Real Time Systems 3 0 0 3
CP7011 Computer Vision 3 0 0 3
CP7012 Network and Information Security 3 0 0 3
NE7202 Design and Analysis of Parallel Algorithms 3 0 0 3
CP7013 Software Architectures 3 0 0 3
CP7014
ELECTIVE-IV Course Title L T P C
Course Code Model Checking and Program Verification 3 0 0 3
CP7015 Embedded Software Development 3 0 0 3
CP7016 Cloud Computing 3 0 0 3
IF7202 Data Visualization Techniques 3 0 0 3
CP7017 Protocols and Architecture for Wireless Sensor Networks 3 0 0 3
NE7005 Language Technologies 3 0 0 3
CP7018 36 0 0 36
TotalELECTIVES
405
M.E. Computer Science
and Engineering
SEMESTER III
ELECTIVE-V Social Network Analysis
Course Code Managing Big Data
NE7012 Mobile Application Development
CP7019 Bio-inspired Computing
NE7011 Medical Image Processing
CP7020 Software Design
CP7021
CP7022
ELECTIVE-VI Reconfigurable Computing
Course Code Energy Aware Computing
CP7023 Information Retrieval Techniques
IF7013 Data Mining Techniques
CP7024 Bio Informatics
CP7025 Software Quality Assurance
IF7002
CP7026
ELECTIVE-VII
Course Code
CP7027
CP7028
CP7029
CP7030
CP7031
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
Course Title L T P C
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
3 0 0 3
51 0 0 51
Multiobjective Optimization Techniques
Enterprise Application Integration
Information Storage Management
Robotics
Compiler Optimization Techniques
Total
TOAL NO OF CREDITS 123PROGRAM EDUCATIONAL OBJECTIVES (PEO):
Graduates of this M. E. Computer Science and Engineering will be able to
1. Apply the necessary mathematical tools and fundamental & advanced knowledge of
computer science & engineering
2. Develop computer/software/network systems understanding the importance of social,
business, technical, environmental, and human context in which the systems would
work
3. Articulate fundamental concepts, design underpinnings of computer/software/network
systems, and research findings to train professionals or to educate engineering
students
4. Contribute effectively as a team member/leader, using common tools and
environment, in computer science and engineering projects, research, or education
5. Pursue life-long learning and research in selected fields of computer science &
engineering and contribute to the growth of those fields and society at large
PROGRAM OUTCOMES:
Apply knowledge of mathematics, science, engineering fundamentals and an
engineering specialization to the conceptualization of engineering models.
Identify, formulate, research literature and solve complex engineering problems
reaching substantiated conclusions using first principles of mathematics and
engineering sciences.
Design solutions for complex engineering problems and design systems,
components or processes that meet specified needs with appropriate consideration
for public health and safety, cultural, societal, and environmental considerations.
Conduct investigations of complex problems including design of experiments,
analysis and interpretation of data, and synthesis of information to provide valid
conclusions.
Create, select and apply appropriate techniques, resources, and modern engineering
tools, including prediction and modeling, to complex engineering activities, with an
understanding of the limitations.
Function effectively as an individual, and as a member or leader in diverse teams
and in multi-disciplinary settings.
Communicate effectively on complex engineering activities with the engineering
community and with society at large, such as being able to comprehend and write
effective reports and design documentation, make effective presentations, and give
and receive clear instructions.
Demonstrate understanding of the societal, health, safety, legal and cultural issues
and the consequent responsibilities relevant to engineering practice.
Understand and commit to professional ethics and responsibilities and norms of
engineering practice.
Understand the impact of engineering solutions in a societal context and demonstrate
knowledge of and need for sustainable development.
Demonstrate a knowledge and understanding of management and business
practices, such as risk and change management, and understand their limitations.
Recognize the need for, and have the ability to engage in independent and life-long
learning.
1MA7155
APPLIED PROBABILITY AND STATISTICS
L T PC
3 1 0 4
OBJECTIVES:
To introduce the basic concepts of one dimensional and two dimensional Random
Variables.
To provide information about Estimation theory, Correlation, Regression and Testing of
hypothesis.
To enable the students to use the concepts of multivariate normal distribution and
principle components analysis.
UNIT I
ONE DIMENSIONAL RANDOM VARIABLES
9+3
Random variables - Probability function – Moments – Moment generating functions and
their properties – Binomial, Poisson, Geometric, Uniform, Exponential, Gamma and Normal
distributions – Functions of a Random Variable.
UNIT II
TWO DIMENSIONAL RANDOM VARIABLES
9+3
Joint distributions – Marginal and Conditional distributions – Functions of two dimensional
random variables – Regression Curve – Correlation.
UNIT III
ESTIMATION THEORY
9+3
Unbiased Estimators – Method of Moments – Maximum Likelihood Estimation - Curve fitting
by Principle of least squares – Regression Lines.
UNIT IV
TESTING OF HYPOTHESES
9+3
Sampling distributions - Type I and Type II errors - Tests based on Normal, t,2 and F
distributions for testing of mean, variance and proportions – Tests for Independence of
attributes and Goodness of fit.
UNIT V
MULTIVARIATE ANALYSIS
9+3
Random Vectors and Matrices - Mean vectors and Covariance matrices - Multivariate
Normal density and its properties - Principal components Population
principal
components - Principal components from standardized variables.
TOTAL 45+15=60 PERIODS
OUTCOME:
The student will able to acquire the basic concepts of Probability and Statistical
techniques for solving mathematical problems which will be useful in solving Engineering
problems
REFERENCES:
1 Jay L. Devore, “Probability and Statistics For Engineering and the Sciences”,Thomson
and Duxbury, 2002.
2.Richard Johnson. ”Miller & Freund’s Probability and Statistics for Engineer”, Prentice
–
Hall , Seventh Edition, 2007.
3.Richard A. Johnson and Dean W. Wichern, “Applied Multivariate Statistical Analysis”,
Pearson Education, Asia, Fifth Edition, 2002.
4.Gupta S.C. and Kapoor V.K.”Fundamentals of Mathematical Statistics”, Sultan an Sons,
2001.
5.Dallas E Johnson , “Applied Multivariate Methods for Data Analysis”, Thomson an Duxbury
press,1998.
2CP7101
DESIGN AND MANAGEMENT OF COMPUTER NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION TO NETWORK MANAGEMENT
9
Overview of Analysis, Architecture and Design Process-System Methodology, Service
methodology, Service Description - Service characteristics - Performance Characteristics -
Network supportability - Requirement analysis – User Requirements – Application
Requirements – Device Requirements – Network Requirements – Other Requirements -
Requirement specification and map.
UNIT II
REQUIREMENTS ANALYSIS
9
Requirement Analysis Process – Gathering and Listing Requirements- Developing service
metrics – Characterizing behavior – Developing RMA requirements – Developing delay
Requirements - Developing capacity Requirements - Developing supplemental performance
Requirements –Requirements mapping – Developing the requirements specification
UNIT III
FLOW ANALYSIS
9
Individual and Composite Flows – Critical Flows - Identifying and developing flows – Data
sources and sinks – Flow models- Flow prioritization – Flow specification algorithms –
Example Applications of Flow Analysis
UNIT IV
NETWORK ARCHITECTURE
9
Architecture and design – Component Architectures – Reference Architecture – Architecture
Models – System and Network Architecture – Addressing and Routing Architecture –
Addressing and Routing Fundamentals – Addressing Mechanisms – Addressing Strategies
– Routing Strategies – Network Management Architecture – Network Management
Mechanisms Performance Architecture – Performance Mechanisms – Security and Privacy
Architecture – Planning security and privacy Mechanisms
UNIT V
NETWORK DESIGN
9
Design Concepts – Design Process - Network Layout – Design Traceability – Design Metrics
– Logical Network Design – Topology Design – Bridging, Switching and Routing Protocols-
Physical Network Design – Selecting Technologies and Devices for Campus and Enterprise
Networks – Optimizing Network Design
TOTAL : 45 PERIODS
REFERENCES:
1. Network Analysis, Architecture, and Design By James D. McCabe, Morgan Kaufmann,
Third Edition, 2007.ISBN-13: 978-0123704801
2. Computer Networks: A Systems Approach by Larry L. Peterson, Bruce S. Davie - 2007,
Elsevier Inc.
3. Top-down Network Design: [a Systems Analysis Approach to Enterprise Network
Design] By Priscilla Oppenheimer, Cisco Press , 3rd Edition, ISBN-13: 978-1-58720-
283-4 ISBN-10: 1-58720-283-2
4. Integrated Management of Networked Systems: Concepts, Architectures, and Their
Operational Application (The Morgan Kaufmann Series in Networking), Heinz-Gerd
Hegering, Sebastian Abeck, and Bernhard Neumair, 1999.
5. “Network Design and Management” – by Steven T.Karris, Orchard publications, Second
edition, Copyright 2009, ISBN 978-1-934404-15-7
6. “Network Design, Management and Technical Perspective”, Teresa C. Mann-Rubinson
and Kornel Terplan, CRC Press, 1999
7. “Ethernet Networks-Design, Implementation, Operation and Management by Gilbert
Held, John Wiley and sons, Fourth Edition
8. James Kurose and Keith Ross, “Computer Networking: A Top-Down Approach Featuring
the Internet”, 1999
3CP7102
ADVANCED DATA STRUCTURES AND ALGORITHMS
LTPC
3003
OBJECTIVES:
To understand the principles of iterative and recursive algorithms.
To learn the graph search algorithms.
To study network flow and linear programming problems.
To learn the hill climbing and dynamic programming design techniques.
To develop recursive backtracking algorithms.
To get an awareness of NP completeness and randomized algorithms.
To learn the principles of shared and concurrent objects.
To learn concurrent data structures.
UNIT I
ITERATIVE AND RECURSIVE ALGORITHMS
9
Iterative Algorithms: Measures of Progress and Loop Invariants-Paradigm Shift: Sequence of
Actions versus Sequence of Assertions- Steps to Develop an Iterative Algorithm-Different
Types of Iterative Algorithms--Typical Errors-Recursion-Forward versus Backward- Towers
of Hanoi-Checklist for Recursive Algorithms-The Stack Frame-Proving Correctness with
Strong Induction- Examples of Recursive Algorithms-Sorting and Selecting Algorithms-
Operations on Integers- Ackermann’s Function- Recursion on Trees-Tree Traversals-
Examples- Generalizing the Problem - Heap Sort and Priority Queues-Representing
Expressions.
UNIT II
OPTIMISATION ALGORITHMS
9
Optimization Problems-Graph Search Algorithms-Generic Search-Breadth-First Search-
Dijkstra’s Shortest-Weighted-Path -Depth-First Search-Recursive Depth-First Search-Linear
Ordering of a Partial Order- Network Flows and Linear Programming-Hill Climbing-Primal
Dual Hill Climbing- Steepest Ascent Hill Climbing-Linear Programming-Recursive
Backtracking-Developing Recursive Backtracking Algorithm- Pruning Branches-Satisfiability
UNIT III
DYNAMIC PROGRAMMING ALGORITHMS
9
Developing a Dynamic Programming Algorithm-Subtle Points- Question for the Little Bird-
Subinstances and Subsolutions-Set of Substances-Decreasing Time and Space-Number of
Solutions-Code. Reductions and NP-Completeness-Satisfiability-Proving NP-Completeness-
3-Coloring- Bipartite Matching. Randomized Algorithms-Randomness to Hide Worst Cases-
Optimization Problems with a Random Structure.
UNIT IV
SHARED OBJECTS AND CONCURRENT OBJECTS
9
Shared Objects and Synchronization -Properties of Mutual Exclusion-The Mora l- The
Producer–Consumer Problem -The Readers–Writers Problem-Realities of Parallelization-
Parallel Programming- Principles- Mutual Exclusion-Time- Critical Sections--Thread
Solutions-The Filter Lock-Fairness-Lamport’s Bakery Algorithm-Bounded Timestamps-Lower
Bounds on the Number of Locations-Concurrent Objects- Concurrency and Correctness-
Sequential Objects-Quiescent Consistency- Sequential Consistency-Linearizability- Formal
Definitions- Progress Conditions- The Java Memory Model
UNIT V
CONCURRENT DATA STRUCTURES
9
Practice-Linked Lists-The Role of Locking-List-Based Sets-Concurrent Reasoning- Coarse-
Grained Synchronization-Fine-Grained Synchronization-Optimistic Synchronization- Lazy
Synchronization-Non-Blocking Synchronization-Concurrent Queues and the ABA Problem-
Queues-A Bounded Partial Queue-An Unbounded Total Queue-An Unbounded Lock-Free
Queue-Memory Reclamation and the ABA Problem- Dual Data Structures- Concurrent
Stacks and Elimination- An Unbounded Lock-Free Stack- Elimination-The Elimination
Backoff Stack
TOTAL : 45 PERIODS
4OUTCOMES:
Upon completion of the course, the students will be able to
Design and apply iterative and recursive algorithms.
Design and implement optimisation algorithms in specific applications.
Design appropriate shared objects and concurrent objects for applications.
Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7103
MULTICORE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
To understand the recent trends in the field of Computer Architecture and identify
performance related parameters
To appreciate the need for parallel processing
To expose the students to the problems related to multiprocessing
To understand the different types of multicore architectures
To expose the students to warehouse-scale and embedded architectures
UNIT I
FUNDAMENTALS OF QUANTITATIVE DESIGN AND ANALYSIS
9
Classes of Computers – Trends in Technology, Power, Energy and Cost – Dependability –
Measuring, Reporting and Summarizing Performance – Quantitative Principles of Computer
Design – Classes of Parallelism - ILP, DLP, TLP and RLP - Multithreading - SMT and CMP
Architectures – Limitations of Single Core Processors - The Multicore era – Case Studies of
Multicore Architectures.
UNIT II
DLP IN VECTOR, SIMD AND GPU ARCHITECTURES
9
Vector Architecture - SIMD Instruction Set Extensions for Multimedia – Graphics Processing
Units - Detecting and Enhancing Loop Level Parallelism - Case Studies.
UNIT III
TLP AND MULTIPROCESSORS
9
Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues -
Performance Issues – Synchronization Issues – Models of Memory Consistency -
Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks.
UNIT IV
RLP AND DLP IN WAREHOUSE-SCALE ARCHITECTURES
9
Programming Models and Workloads for Warehouse-Scale Computers – Architectures for
Warehouse-Scale Computing – Physical Infrastructure and Costs – Cloud Computing –
Case Studies.
5UNIT V
ARCHITECTURES FOR EMBEDDED SYSTEMS
9
Features and Requirements of Embedded Systems – Signal Processing and Embedded
Applications – The Digital Signal Processor – Embedded Multiprocessors - Case Studies.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the limitations of ILP and the need for multicore architectures
Discuss the issues related to multiprocessing and suggest solutions
Point out the salient features of different multicore architectures and how they exploit
parallelism
Critically analyze the different types of inter connection networks
Discuss the architecture of GPUs, warehouse-scale computers and embedded
processors
REFERENCES:
1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative
Approach”, Morgan Kaufmann / Elsevier, 5th edition, 2012.
2. Kai Hwang, “Advanced Computer Architecture”, Tata McGraw-Hill Education, 2003
3. Richard Y. Kain, “Advanced Computer Architecture a Systems Design Approach”,
Prentice Hall, 2011.
4. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/
Software Approach” , Morgan Kaufmann / Elsevier, 1997.
CP7111
ADVANCED DATA STRUCTURES LABORATORY
L T PC
0 0 4 2
OBJECTIVES:
To learn to implement iterative and recursive algorithms.
To learn to design and implement algorithms using hill climbing and dynamic
programming techniques.
To learn to implement shared and concurrent objects.
To learn to implement concurrent data structures.
LAB EXERCISES:
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week or two two-hour sessions per
week. There will be about 15 exercises in a semester. It is recommended that all
implementations are carried out in Java. If C or C++ has to be used, then the threads library
will be required for concurrency. Exercises should be designed to cover the following topics:
Implementation of graph search algorithms.
Implementation and application of network flow and linear programming problems.
Implementation of algorithms using the hill climbing and dynamic programming
design techniques.
Implementation of recursive backtracking algorithms.
Implementation of randomized algorithms.
Implementation of various locking and synchronization mechanisms for concurrent
linked lists, concurrent queues, and concurrent stacks.
Developing applications involving concurrency.
TOTAL :60 PERIODS
6OUTCOMES:
Upon completion of the course, the students will be able to
1. Design and apply iterative and recursive algorithms.
2. Design and implement algorithms using the hill climbing and dynamic programming
and recursive backtracking techniques.
3. Design and implement optimisation algorithms for specific applications.
4. Design and implement randomized algorithms.
5. Design appropriate shared objects and concurrent objects for applications.
6. Implement and apply concurrent linked lists, stacks, and queues.
REFERENCES:
1. Jeff Edmonds, “How to Think about Algorithms”, Cambridge University Press, 2008.
2. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”, Morgan Kaufmann,
2008.
3. Steven S. Skiena, “The Algorithm Design Manual”, Springer, 2008.
4. Peter Brass, “Advanced Data Structures”, Cambridge University Press, 2008.
5. S. Dasgupta, C. H. Papadimitriou, and U. V. Vazirani, “Algorithms” , McGrawHill, 2008.
6. J. Kleinberg and E. Tardos, "Algorithm Design“, Pearson Education, 2006.
7. T. H. Cormen, C.2 E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms“,
PHI Learning Private Limited, 2012.
8. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
9. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and Analysis of Computer
Algorithms”, Addison-Wesley, 1975.
10. A. V. Aho, J. E. Hopcroft, and J. D. Ullman,”Data Structures and Algorithms”,
Pearson,2006.
CP7112
CASE STUDY: NETWORK DESIGN (Team Work)
LT PC
0 0 21
CASE STUDY : 1
Analyzing the performance of various configurations and protocols in LAN.
1.1. Establishing a Local Area Network (LAN): The main objective is to set up a Local
Area Network,concepts involved in this network are IP addressing and the Address
Resolution Protocol (ARP). The required equipments are192.168.1.1 ,192.168.1.2,
192.168.1.3,Host A Host B Host C, Switch/HUB, three PC`s equipped with at least one
NIC, one HUB or Switch and the necessary cables. Once the physical LAN is set up the
hosts need to be configured using the ifconfig command. To verify communication
among the machines the ping command is used. Next, to manipulate the routing tables
at the hosts to understand how machines know where to send packets. Since the ifconfig
command places a default route into the routing tables this route must be deleted. to
́blindfoldˇ the machine. The ping command is used again to show that communication is
no longer available. To re-establish communication the routes are put back into the
routing table one host at a time. Communication is once again verified using the ping
command.
1.2. Connecting two LANs using multi-router topology with static routes:
The main objective is to extend routing connection by using multiple routers. The
concepts include IP addressing and basic network routing principles. Connect two LANs
topology. During router configuration attention is paid to the types of interfaces as
additional issues are involved with set-up. For example, the serial interfaces require
clocking mechanisms to be set correctly. Once the interfaces are working the ping
command is used to check for communication between LANs. The failure of
communication illustrates the need for routes to be established inside the routing
infrastructure. Static routes are used to show how packets can be transported through
any reasonable route. It is run trace route on two different configurations to demonstrate
the implementation of different routes.
71.3 Analyzing the performance of various configurations and protocols
Original TCP versus the above modified one: To compare the performance between the
operation of TCP with congestion control and the operation of TCP as implemented . The
main objective is for students to examine how TCP responds to a congested network.
The concepts involved in the lab include network congestion and the host responsibilites
for communicating over a network. This lab requires three PC’s connected to a
switch.One PC is designated as the target host and the other two PC’s will transfer a file
from the targethost using FTP. A load is placed on the network to simulate congestion
and the file is transfered, first by the host using the normal TCP and then by the host
using the modified version. This procedure is performed multiple times to determine
average statistics. The students are then asked to summarize the results and draw
conclusions about the performance differences and the underlying implications for hosts
operating in a network environment.
Case Study 2:
RIP and OSPF Redistribution
This case study addresses the issue of integrating Routing Information Protocol (RIP)
networks with Open Shortest Path First (OSPF) networks. Most OSPF networks also use
RIP to communicate with hosts or to communicate with portions of the internetwork that do
not use OSPF. This case study should provide examples of how to complete the following
phases in redistributing information between RIP and OSPF networks, including the
following topics:
• Configuring a RIP Network
• Adding OSPF to the Center of a RIP Network
• Adding OSPF Areas
• Setting Up Mutual Redistribution
‘Case Study 3:
Dial-on-Demand Routing
This case study should describe the use of DDR to connect a worldwide network that
consists of a central site located in Mumbai and remote sites located in Chennai, Bangalore,
and Hyderabad. The following scenarios should be considered:
• Having the Central Site Dial Out
Describe the central and remote site configurations for three setups: a central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Include examples of the usage of rotary groups and access lists.
• Having the Central and Remote Sites Dial In and Dial Out
Describe the central and remote site configurations for three setups: central site with one
interface per remote site, a single interface for multiple remote sites, and multiple interfaces
for multiple remote sites. Also describes the usage of Point-to-Point Protocol (PPP)
encapsulation and the Challenge Handshake Authentication Protocol (CHAP).
• Having Remote Sites Dial Out
A common configuration is one in which the remote sites place calls to the central site but
the central site does not dial out. In a “star” topology, it is possible for all of the remote
routers to have their serial interfaces on the same subnet as the central site serial interface.
• Using DDR as a Backup to Leased Lines
Describes the use of DDR as a backup method to leased lines and provides examples of
how to use floating static routes on single and shared interfaces.
• Using Leased Lines and Dial Backup
Describes the use of Data Terminal Ready (DTR) dialing and V.25bis dialing with leased
lines.
Case Study 4:
Network Security
This case study should provide the specific actions you can take to improve the security of
your network. Before going into specifics, however, you should understand the following
basic concepts that are essential to any security system:
8• Know your enemy
This case study refers to attackers or intruders. Consider who might want to circumvent
your security measures and identify their motivations. Determine what they might want to
do and the damage that they could cause to your network. Security measures can never
make it impossible for a user to perform unauthorized tasks with a computer system. They
can only make it harder. The goal is to make sure the network security controls are beyond
the attacker’s ability or motivation.
• Count the cost
Security measures almost always reduce convenience, especially for sophisticated users.
Security can delay work and create expensive administrative and educational overhead. It
can use significant computing resources and require dedicated hardware. When you
design your security measures, understand their costs and weigh those costs against the
potential benefits. To do that, you must understand the costs of the measures themselves
and the costs and likelihoods of security breaches. If you incur security costs out of
proportion to the actual dangers, you have done yourself a disservice.
• Identify your assumptions
Every security system has underlying assumptions. For example, you might assume that
your network is not tapped, or that attackers know less than you do, that they are using
standard software, or that a locked room is safe. Be sure to examine and justify your
assumptions. Any hidden assumption is a potential security hole.
• Control your secrets
Most security is based on secrets. Passwords and encryption keys, for example, are
secrets. Too often, though, the secrets are not really all that secret. The most important
part of keeping secrets is knowing the areas you need to protect. What knowledge would
enable someone to circumvent your system? You should jealously guard that knowledge
and assume that everything else is known to your adversaries. The more secrets you
have, the harder it will be to keep all of them. Security systems should be designed so that
only a limited number of secrets need to be kept.
Know your weaknesses
Every security system has vulnerabilities. You should understand your system’s weak
points and know how they could be exploited. You should also know the areas that present
the largest danger and prevent access to them immediately. Understanding the weak
points is the first step toward turning them into secure areas.
Limit the scope of access
You should create appropriate barriers inside your system so that if intruders access one
part of the system, they do not automatically have access to the rest of the system. The
security of a system is only as good as the weakest security level of any single host in the
system.
• Remember physical security Physical access to a computer (or a router) usually gives a
sufficiently sophisticated user total control over that computer. Physical access to a
network link usually allows a person to tap that link, jam it, or inject traffic into it. It
makes no sense to install complicated software security measures when access to the
hardware is not controlled.
Case Study 5:
Controlling Traffic Flow
In this case study, the firewall router allows incoming new connections to one or more
communication servers or hosts. Having a designated router act as a firewall is desirable
because it clearly identifies the router’s purpose as the external gateway and avoids
encumbering other routers with this task. In the event that the internal network needs to
isolate itself, the firewall router provides the point of isolation so that the rest of the internal
network structure is not affected. Connections to the hosts are restricted to incoming file
transfer protocol (FTP) requests and email services. The incoming Telnet, or modem
connections to the communication server are screened by the communication server running
TACACS username authentication.
9Case Study 6:
Defining Access Lists
Access lists define the actual traffic that will be permitted or denied, whereas an access
group applies an access list definition to an interface. Access lists can be used to deny
connections that are known to be a security risk and then permit all other connections, or to
permit those connections that are considered acceptable and deny all the rest. For firewall
implementation, the latter is the more secure method. In this case study, incoming email and
news are permitted for a few hosts, but FTP, Telnet, and rlogin services are permitted only to
hosts on the firewall subnet. IP extended access lists (range 100 to 199) and transmission
control protocol (TCP) or user datagram protocol (UDP) port numbers are used to filter
traffic. When a connection is to be established for email, Telnet, FTP, and so forth, the
connection will attempt to open a service on a specified port number. You can, therefore,
filter out selected types of connections by denying packets that are attempting to use that
service. An access list is invoked after a routing decision has been made but before the
packet is sent out on an interface. The best place to define an access list is on a preferred
host using your favorite text editor. You can create a file that contains the access-list
commands, place the file (marked readable) in the default TFTP directory, and then network
load the file onto the router.
Case Study 7:
Configuring a fire wall
Consider a Fire wall communication server with single inbound modem.
modem to ensure security for LAN
Configure the
Case Study 8:
Integrating EIGRP (Enhanced Interior Gateway Routing Protocol) into Existing
Networks:
The case study should provide the benefits and considerations involved in integrating
Enhanced IGRP into the following types of internetworks:
• IP—The existing IP network is running IGRP
• Novell IPX—The existing IPX network is running RIP and SAP
• AppleTalk—The existing AppleTalk network is running the Routing Table Maintenance
Protocol (RTMP)
When integrating Enhanced IGRP into existing networks, plan a phased implementation.
Add Enhanced IGRP at the periphery of the network by configuring Enhanced IGRP on a
boundary router on the backbone off the core network. Then integrate Enhanced IGRP into
the core network.
CP7201
THEORETICAL FOUNDATIONS OF COMPUTER SCIENCE
LTPC
310 4
OBJECTIVES:
To review sets, relations, functions, and other foundations
To understand propositional and predicate logics and their applications
To understand lambda calculus and functional programming
To understand graph structures and their applications
To understand formal models of computation, computability, and decidability
UNIT I
FOUNDATIONS
9
Sets – relations – equivalence relations – partial orders – functions – recursive functions –
sequences – induction principle – structural induction – recursive algorithms – counting –
pigeonhole principle – permutations and combinations – recurrence relations
10UNIT II
LOGIC AND LOGIC PROGRAMMING
9
Propositional logic – syntax – interpretations and models – deduction theorems – normal
forms – inference rules – SAT solvers - predicate logic – syntax – proof theory – semantics
of predicate logic – undecidability of predicate logic – inferences in first-order logic – logic
programming – definite programs – SLD resolution – normal programs – SLDNF resolution –
introduction to Prolog
UNIT III
LAMBDA CALCULUS AND FUNCTIONAL PROGRAMMING
9
Lambda notation for functions – syntax – curried functions – parametric polymorphism –
lambda reduction – alpha reduction – beta reduction – beta abstraction – extensionality
theorem – delta reduction – reduction strategies – normal forms – Church-Rosser Theorems
– pure lambda calculus – constants – arithmetic – conditionals – Iteration – recursion –
introduction to functional programming
UNIT IV
GRAPH STRUCTURES
9
Tree Structures – Graph structures – graph representations – regular graph structures –
random graphs – Connectivity – Cycles – Graph Coloring – Cliques, Vertex Covers,
Independent sets – Spanning Trees – network flows – matching
UNIT V
STATE MACHINES
9
Languages and Grammars – Finite State Machines – State machines and languages –
Turing Machines – Computational Complexity – computability – Decidability – Church's
Thesis
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able
To explain sets, relations, functions
To conduct proofs using induction, pigeonhole principle, and logic
To apply counting, permutations, combinations, and recurrence relations
To apply recursive functions and lambda calculus
To explain logic programming and functional programming principles
To apply sequential structures, tree structures, and graph structures
To explain computational models, computability, and complexity
REFERENCES:
1. Uwe Schoning, “Logic for Computer Scientists”, Birkhauser, 2008.
2. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
3. John Harrison, “Handbook of Practical Logic and Automated Reasoning”, Cambridge
University Press, 2009.
4. Greg Michaelson, “An introduction to functional programming through lambda
calculus”, Dover Publications, 2011.
5. Kenneth Slonneger and Barry Kurtz, “Formal syntax and semantics of programming
languages”, Addison Wesley, 1995.
6. Kenneth H. Rosen, “Discrete Mathematics and its applications”, Seventh Edition,
Tata McGraw Hill, 2011.
7. Sriram Pemmaraju and Steven Skiena, “Computational Discrete Mathematics”,
Cambridge University Press, 2003.
8. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
9. Norman L. Biggs, “Discrete Mathematics”, Second Edition, Oxford University Press,
2002.
10. Juraj Hromkovic, “Theoretical Computer Science”, Springer, 1998.
11. J. E. Hopcroft, Rajeev Motwani, and J. D. Ullman, “Introduction to Automata Theory,
Languages, and Computation”, Third Edition, Pearson, 2008.
11CP7202
ADVANCED DATABASES
LTPC
3 003
OBJECTIVES:
To learn the modeling and design of databases.
To acquire knowledge on parallel and distributed databases and its applications.
To study the usage and applications of Object Oriented database
To understand the principles of intelligent databases.
To understand the usage of advanced data models.
To learn emerging databases such as XML, Cloud and Big Data.
To acquire inquisitive attitude towards research topics in databases.
UNIT I
PARALLEL AND DISTRIBUTED DATABASES
9
Database System Architectures: Centralized and Client-Server Architectures – Server
System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O
Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism –
Design of Parallel Systems- Distributed Database Concepts - Distributed Data Storage –
Distributed Transactions – Commit Protocols – Concurrency Control – Distributed Query
Processing – Case Studies
UNIT II
OBJECT AND OBJECT RELATIONAL DATABASES
9
Concepts for Object Databases: Object Identity – Object structure – Type Constructors –
Encapsulation of Operations – Methods – Persistence – Type and Class Hierarchies –
Inheritance – Complex Objects – Object Database Standards, Languages and Design:
ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems: Object
Relational features in SQL/Oracle – Case Studies.
UNIT III
INTELLIGENT DATABASES
9
Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)- Taxonomy- Applications-
Design Principles for Active Rules- Temporal Databases: Overview of Temporal Databases-
TSQL2- Deductive Databases: Logic of Query Languages – Datalog- Recursive Rules-
Syntax and Semantics of Datalog Languages- Implementation of Rules and Recursion-
Recursive Queries in SQL- Spatial Databases- Spatial Data Types- Spatial Relationships-
Spatial Data Structures-Spatial Access Methods- Spatial DB Implementation.
UNIT IV
ADVANCED DATA MODELS
9
Mobile Databases: Location and Handoff Management - Effect of Mobility on Data
Management - Location Dependent Data Distribution - Mobile Transaction Models -
Concurrency Control - Transaction Commit Protocols- Multimedia Databases- Information
Retrieval- Data Warehousing- Data Mining- Text Mining.
UNIT V
EMERGING TECHNOLOGIES
9
XML Databases: XML-Related Technologies-XML Schema- XML Query Languages- Storing
XML in Databases-XML and SQL- Native XML Databases- Web Databases- Geographic
Information Systems- Biological Data Management- Cloud Based Databases: Data Storage
Systems on the Cloud- Cloud Storage Architectures-Cloud Data Models- Query Languages-
Introduction to Big Data-Storage-Analysis.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Select the appropriate high performance database like parallel and distributed
database
Model and represent the real world data using object oriented database
Design a semantic based database to meaningful data access
Embed the rule set in the database to implement intelligent databases
Represent the data using XML database for better interoperability
Handle Big data and store in a transparent manner in the cloud
To solve the issues related to the data storage and retrieval
12REFERENCES:
1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition,
Pearson Education/Addison Wesley, 2007.
2. Thomas Cannolly and Carolyn Begg, “Database Systems, A Practical Approach to
Design, Implementation and Management”, Third Edition, Pearson Education, 2007.
3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”,
Fifth Edition, McGraw Hill, 2006.
4. C.J.Date, A.Kannan and S.Swamynathan, ”An Introduction to Database Systems”,
Eighth Edition, Pearson Education, 2006.
5. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”,
McGraw Hill, Third Edition 2004.
CP7203
PRINCIPLES OF PROGRAMMING LANGUAGES
LTPC
3003
OBJECTIVES:
To understand and describe syntax and semantics of programming languages
To understand data, data types, and basic statements
To understand call-return architecture and ways of implementing them
To understand object-orientation, concurrency, and event handling in programming
languages
To develop programs in non-procedural programming paradigms
UNIT I
SYNTAX AND SEMANTICS
9
Evolution of programming languages – describing syntax – context-free grammars – attribute
grammars – describing semantics – lexical analysis – parsing – recursive-decent – bottom-
up parsing
UNIT II
DATA, DATA TYPES, AND BASIC STATEMENTS
9
Names – variables – binding – type checking – scope – scope rules – lifetime and garbage
collection – primitive data types – strings – array types – associative arrays – record types –
union types – pointers and references – Arithmetic expressions – overloaded operators –
type conversions – relational and boolean expressions – assignment statements – mixed-
mode assignments – control structures – selection – iterations – branching – guarded
statements
UNIT III
SUBPROGRAMS AND IMPLEMENTATIONS
9
Subprograms – design issues – local referencing – parameter passing – overloaded
methods – generic methods – design issues for functions – semantics of call and return –
implementing simple subprograms – stack and dynamic local variables – nested
subprograms – blocks – dynamic scoping
UNIT IV
OBJECT-ORIENTATION, CONCURRENCY, AND EVENT HANDLING
9
Object-orientation – design issues for OOP languages – implementation of object-oriented
constructs – concurrency – semaphores – monitors – message passing – threads –
statement level concurrency – exception handling – even handling
UNIT V
FUNCTIONAL AND LOGIC PROGRAMMING LANGUAGES
9
Introduction to lambda calculus – fundamentals of functional programming languages –
Programming with Scheme – Programming with ML – Introduction to logic and logic
programming – Programming with Prolog – multi-paradigm languages
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe syntax and semantics of programming languages
Explain data, data types, and basic statements of programming languages
Design and implement subprogram constructs
13
Apply object-oriented, concurrency, and event handling programming constructs
Develop programs in Scheme, ML, and Prolog
Understand and adopt new programming languages
REFERENCES:
1. Robert W. Sebesta, “Concepts of Programming Languages”, Tenth Edition, Addison
Wesley, 2012.
2. Michael L. Scott, “Programming Language Pragmatics”, Third Edition, Morgan
Kaufmann, 2009.
3. R. Kent Dybvig, “The Scheme programming language”, Fourth Edition, MIT Press, 2009.
4. Jeffrey D. Ullman, “Elements of ML programming”, Second Edition, Prentice Hall, 1998.
5. Richard A. O'Keefe, “The craft of Prolog”, MIT Press, 2009.
6. W. F. Clocksin and C. S. Mellish, “Programming in Prolog: Using the ISO Standard”, Fifth
Edition, Springer, 2003.
CP7204
ADVANCED OPERATING SYSTEMS
LTPC
3 003
OBJECTIVES:
To learn the fundamentals of Operating Systems
To gain knowledge on Distributed operating system concepts that includes architecture,
Mutual exclusion algorithms, Deadlock detection algorithms and agreement protocols
To gain insight on to the distributed resource management components viz. the
algorithms for implementation of distributed shared memory, recovery and commit
protocols
To know the components and management aspects of Real time, Mobile operating
systems
UNIT I
FUNDAMENTALS OF OPERATING SYSTEMS
9
Overview – Synchronization Mechanisms – Processes and Threads - Process Scheduling –
Deadlocks: Detection, Prevention and Recovery – Models of Resources – Memory
Management Techniques.
UNIT II
DISTRIBUTED OPERATING SYSTEMS
9
Issues in Distributed Operating System – Architecture – Communication Primitives –
Lamport’s Logical clocks – Causal Ordering of Messages – Distributed Mutual Exclusion
Algorithms – Centralized and Distributed Deadlock Detection Algorithms – Agreement
Protocols.
UNIT III
DISTRIBUTED RESOURCE MANAGEMENT
9
Distributed File Systems – Design Issues - Distributed Shared Memory – Algorithms for
Implementing Distributed Shared memory–Issues in Load Distributing – Scheduling
Algorithms – Synchronous and Asynchronous Check Pointing and Recovery – Fault
Tolerance – Two-Phase Commit Protocol – Nonblocking Commit Protocol – Security and
Protection.
UNIT IV
REAL TIME AND MOBILE OPERATING SYSTEMS
9
Basic Model of Real Time Systems - Characteristics- Applications of Real Time Systems –
Real Time Task Scheduling - Handling Resource Sharing - Mobile Operating Systems –
Micro Kernel Design - Client Server Resource Access – Processes and Threads - Memory
Management - File system.
UNIT V
CASE STUDIES
9
Linux System: Design Principles - Kernel Modules - Process Management Scheduling -
Memory Management - Input-Output Management - File System - Interprocess
Communication. iOS and Android: Architecture and SDK Framework - Media Layer -
Services Layer - Core OS Layer - File System.
14OUTCOMES:
Upon Completion of the course, the students should be able to:
Discuss the various synchronization, scheduling and memory management issues
Demonstrate the Mutual exclusion, Deadlock detection and agreement protocols of
Distributed operating system
Discuss the various resource management techniques for distributed systems
Identify the different features of real time and mobile operating systems
Install and use available open source kernel
Modify existing open source kernels in terms of functionality or features used
REFERENCES:
1. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
2. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,
Seventh Edition, John Wiley & Sons, 2004.
3. Daniel P Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
4. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
5. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload
media, 2011.
CP7211
ADVANCED DATABASE LABORATORY
LT P C
0 0 21
OBJECTIVES:
To learn to work on distributed data bases
To understand and work on object oriented databases
To gain knowledge in parallel data base by experimenting it
To learn to work on active database
To study and explore deductive database
To work on the data mining tool weka
To represent and work with the database using XML
DISTRIBUTED DATABASE:
1. Consider a distributed database for a bookstore with 4 sites called S1, S2, S3 and S4.
Consider the following relations:
Books ( ISBN, primary Author, topic, total Stock, price )
Book Store (store No, city, state, zip, inventoryValue )
Stock (store No, ISBN, Qty )
Total Stock is the total number of books in stock and inventory Value is the total
inventory value for the store in dollars.
Consider that Books are fragmented by price amounts into:
F1: Books: price up to $20
F2: Books: price from $20.01 to $50
F3: Books: price from $50.01 to $100
F4: Books: price $100.01 and above
Similarly, Book Stores are divided by ZIP codes into:
15S1: Bookstore: Zip up to 25000
S2: Bookstore: Zip 25001 to 50000
S3: Bookstore: Zip 50001 to 75000
S4: Bookstore: Zip 75001 to 99999
Task: Write SQL query for the following
1. Insert and Display details in each table.
2. Find the total number of books in stock where price is between $15 and $55.
3. Update the book price of book No=1234 from $45 to $55 at site S3.
4. Find total number of book at site S2.
2. Implement deadlock detection algorithm for distributed database using wait-for graph
and test with the following information.
Consider five transactions T1, T2, T3, T4 and T5 with
T1 initiated at site S1 and spawning an agent at site S2
T2 initiated at site S3 and spawning an agent at site S1
T3 initiated at site S1 and spawning an agent at site S3
T4 initiated at site S2 and spawning an agent at site S3
T5 initiated at site S3
The locking information for these transactions is shown in the following table
Transactions
T1
T1
T2
T2
T3
T3
T4
T4
T5
Data items locked
by transactions
X1
X6
X4
X5
X2
-
X7
X8
X3
Data items transaction is
waiting for
X8
X2
X1
-
X7
X3
-
X5
X7
Site involved
in operations
S1
S2
S1
S3
S1
S3
S2
S3
S3
Produce local wait for graph for each of the sites and construct global wait for graph and
check for dead lock.
OBJECT ORIENTED DATABASE:
3. A University wants to track persons associated with them. A person can be an
Employee or Student. Employees are Faculty, Technicians and Project associates.
Students are Full time students, Part time students and Teaching Assistants.
a) Design an Enhanced Entity Relationship (EER) Model for university database.
Write OQL for the following
i.
Insert details in each object.
ii.
Display the Employee details.
iii.
Display Student Details.
iv.
Modify person details.
v.
Delete person details.
b)
Extend the design by incorporating the following information.
Students are registering for courses which are handled by instructor researchers
(graduate students). Faculty are advisors to graduate students. Instructor researchers’ class
16is a category with super class of faculty and graduate students. Faculty are having
sponsored research projects with a grant supporting instruction researchers. Grants are
sanctioned by different agencies. Faculty belongs to different departments. Department is
chaired by a faculty. Implement for the Insertion and Display of details in each class.
PARALLEL DATABASE:
4. Consider the application for University Counselling for Engineering Colleges. The
college, department and vacancy details are maintained in 3 sites. Students are
allocated colleges in these 3 sites simultaneously. Implement this application using
parallel database [State any assumptions you have made].
5. There are 5 processors working in a parallel environment and producing output. The
output record contains college details and students mark information. Implement
parallel join and parallel sort algorithms to get the marks from different colleges of the
university and publish 10 ranks for each discipline.
ACTIVE DATABASE:
6. Create triggers and assertions for Bank database handling deposits and loan and
admission database handling seat allocation and vacancy position. Design the above
relational database schema and implement the following triggers and assertions.
a. When a deposit is made by a customer, create a trigger for updating
customers account and bank account
b. When a loan is issued to the customer, create a trigger for updating
customer’s loan account and bank account.
c. Create assertion for bank database so that the total loan amount does not
exceed the total balance in the bank.
d. When an admission is made, create a trigger for updating the seat allocation
details and vacancy position.
DEDUCTIVE DATABASE:
7. Construct a knowledge database for kinship domain (family relations) with facts.
Extract the following relations using rules.
Parent, Sibling, Brother, Sister, Child, Daughter, Son, Spouse, Wife, husband,
Grandparent, Grandchild, Cousin, Aunt and Uncle.
WEKA TOOL:
8. Work with Weka tool classification and clustering algorithms using the given training
data and test with the unknown sample. Also experiment with different scenarios and
large data set
RID Age Income Student
1 youth high no
2 youth high no
3 middle_aged high no
4 senior medium no
5 senior low yes
6 senior low yes
7 middle_aged low yes
8 youth medium no
9 youth low yes
10 senior medium yes
11 Youth medium yes
12 middle_aged medium no
13 middle_aged high yes
14 senior medium no
17
Credit_
rating
fair
excellent
fair
fair
fair
excellent
excellent
fair
fair
fair
excellent
excellent
fair
excellent
Class: buys_
computer
no
no
yes
yes
yes
no
yes
no
yes
yes
yes
yes
yes
noQUERY PROCESSING
9. Implement Query Optimizer with Relational Algebraic expression construction and
execution plan generation for choosing an efficient execution strategy for processing
the given query.
Also design employee database and test the algorithm with following sample queries.
a) Select empid, empname from employee where experience > 5
b) Find all managers working at London Branch
XML
10. Design XML Schema for the given company database
Department ( deptName, deptNo, deptManagerSSN, deptManagerStartDate,
deptLocation )
Employee ( empName, empSSN, empSex, empSalary, empBirthDate,
empDeptNo, empSupervisorSSN, empAddress, empWorksOn)
Project ( projName, projNo, projLocation, projDeptNo, projWorker )
a. Implement the following queries using XQuery and XPath
i.
ii.
iii.
iv.
b.
Retrieve the department name, manager name, and manager salary for every
department’
Retrieve the employee name, supervisor name and employee salary for each
employee who works in the Research Department.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project.
Retrieve the project name, controlling department name, number of employees
and total hours worked per week on the project for each project with more than
one employee working on it
Implement a storage structure for storing XML database and test with the above
schema.
TOTAL : 30 PERIODS
OUTCOMES:
Work on distributed databases
Create and work on object oriented databases
Create and work with parallel database
Experiment on active database
Explore the features of deductive database
To work on weka tool for clustering and classification
Represent the database using XML and work on it
CP7212
CASE STUDY – OPERATING SYSTEMS DESIGN
(Team Work)
L T PC
0 0 4 2
OBJECTIVES:
1. To develop capabilities to work at systems level
2. To learn about issues in designing and implementing modern operating systems
3. To understand team formation, team issues, and allocating roles and responsibilities
4. To make effective presentations on the work done
5. To develop effective written communication skills
LAB EXERCISES:
A team of three or four students will work on assigned case study / mini-project. Case Study
/ Mini-project can be designed on the following lines:
1. Development of a reasonably sized dynamically loadable kernel module for Linux
kernel
182. Study educational operating systems such as Minix (http://www.minix3.org/), Weenix
(http://weenix.cs.brown.edu/mediawiki/index.php/Weenix) and develop reasonably
sized interesting modules for them
3. Study the Android open source operating system for mobile devices
(http://source.android.com/) and develop / modify some modules.
4. Study any embedded and real-time operating system such as eCos
(http://ecos.sourceware.org/) and develop / modify some modules.
TOTAL : 60 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Develop assigned modules of operating systems design carrying out coding, testing,
and documentation work involved.
Describe team issuses and apply suitable methods to resolve the same.
Demonstrate individual competence in building medium size operating system
components.
Demonstrate ethical and professional attributes of a computer engineer.
Prepare suitable plan with clear statements of deliverables, and track the same.
Make individual presentation of the work carried out.
Prepare well-organized written documents to communicate individual work
accomplished.
REFERENCES:
1. Watts S. Humphrey, “Introduction to Team Software Process”, Addison-Wesley, SEI
Series in Software Engineering, 1999.
2. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems
– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,
2001.
3. T. W. Doeppner, “Operating Systems in Depth: Design and Programming”, Wiley, 2010.
4. S. Tanenbaum and A. S. Woodhull, “Operating Systems Design and Implementation”,
Third Edition, Prentice Hall, 2006.
5. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne, “Operating System Concepts”,
Ninth Edition, John Wiley & Sons, 2012.
6. Daniel P. Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition,
O’Reilly, 2005.
7. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.
CP7301
SOFTWARE PROCESS AND PROJECT MANAGEMENT
LTPC
3104
OBJECTIVES:
1.
2.
3.
4.
5.
To
To
To
To
To
understand overall SDLC and adopt suitable processes
elicite, analyze, prioritize, and manage both functional and quality requirements
estimate efforts required, plan, and track the plans
understand and apply configuration and quality management techniques
evaluate, manage, and design processes
(A mini-project can be chosen by the instructor and use it as a context for the
tutorials)
UNIT I
DEVELOPMENT LIFE CYCLE PROCESSES
9
Overview of software development life cycle – introduction to processes – Personal Software
Process (PSP) – Team software process (TSP) – Unified processes – agile processes –
choosing the right process Tutorial: Software development using PSP
19UNIT II
REQUIREMENTS MANAGEMENT
9
Functional requirements and quality attributes – elicitation techniques – Quality Attribute
Workshops (QAW) – analysis, prioritization, and trade-off – Architecture Centric
Development Method (ACDM) – requirements documentation and specification – change
management – traceability of requirements
Tutorial: Conduct QAW, elicit, analyze, prioritize, and document requirements using ACDM
UNIT III
ESTIMATION, PLANNING, AND TRACKING
9
Identifying and prioritizing risks – risk mitigation plans – estimation techniques – use case
points – function points – COCOMO II – top-down estimation – bottom-up estimation – work
breakdown structure – macro and micro plans – planning poker – wideband delphi –
documenting the plan – tracking the plan – earned value method (EVM)
Tutorial: Estimation, planning, and tracking exercises
UNIT IV
CONFIGURATION AND QUALITY MANAGEMENT
9
identifying artifacts to be configured – naming conventions and version control –
configuration control – quality assurance techniques – peer reviews – Fegan inspection –
unit, integration, system, and acceptance testing – test data and test cases – bug tracking –
causal analysis
Tutorial: version control exercises, development of test cases, causal analysis of defects
UNIT V
SOFTWARE PROCESS DEFINITION AND MANAGEMENT
9
Process elements – process architecture – relationship between elements – process
modeling – process definition techniques – ETVX (entry-task-validation-exit) – process
baselining – process assessment and improvement – CMMI – Six Sigma
Tutorial: process measurement exercises, process definition using ETVX
TOTAL 45+15=60 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
1. Explain software development life cycle
2. Adopt a suitable process for software development
3. Elicit functional and quality requirements
4. Analyze, prioritize, and manage requirements
5. Perform trade-off among conflicting requirements
6. Identify and prioritize risks and create mitigation plans
7. Estimate the efforts required for software development
8. Perform planning and tracking activities
9. Control the artifacts during software development
10. Perform various tests to ensure quality
11. Define new processes based on the needs
12. Adopt best practices for process improvement
REFERENCES:
1. Pankaj Jalote, “Software Project Management in Practice”, Pearson, 2002.
2. Chris F. Kemerer, “Software Project Management – Readings and Cases”, McGraw Hill,
1997.
3. Watts S. Humphrey, “PSP: A self-improvement process for software engineers”,
Addison-Wesley, 2005.
4. Watts S. Humphrey, “Introduction to the Team Software Process”, Addison-Wesley,
2000.
5. Orit Hazzan and Yael Dubinsky, “Agile software engineering”, Springer, 2008.
6. James R. Persse, “Process Improvement Essentials”, O’Reilly, 2006.
7. Roger S. Pressman, “Software Engineering – A Practitioner’s Approach”, Seventh
Edition, McGraw Hill, 2010.
20SE7103
FORMAL MODELS OF SOFTWARE SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the basic elements of Z
To understand relations, functions, and logical structures in Z
To understand Z schemas and schema calculus
To learn selected Z case studies
To understand Z schema refinement
UNIT I
FOUNDATIONS OF Z
9
Understanding formal methods – motivation for formal methods – informal requirements to
formal specifications – validating formal specifications – Overview of Z specification – basic
elements of Z – sets and types – declarations – variables – expressions – operators –
predicates and equations
UNIT II
STRUCTURES IN Z
9
Tuples and records – relations, tables, databases – pairs and binary relations – functions –
sequences – propositional logic in Z – predicate logic in Z – Z and boolean types – set
comprehension – lambda calculus in Z – simple formal specifications – modeling systems
and change
UNIT III
Z SCHEMAS AND SCHEMA CALCULUS
9
Z schemas – schema calculus – schema conjunction and disjunction – other schema
calculus operators – schema types and bindings – generic definitions – free types – formal
reasoning – checking specifications – precondition calculation – machine-checked proofs
UNIT IV
Z CASE STUDIES
9
Case Study: Text processing system – Case Study: Eight Queens – Case Study: Graphical
User Interface – Case Study: Safety critical protection system – Case Study: Concurrency
and real time systems
UNIT V
Z REFINEMENT
9
Refinement of Z specification – generalizing refinements – refinement strategies – program
derivation and verification – refinement calculus – data structures – state schemas –
functions and relations – operation schemas – schema expressions – refinement case study
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Apply the basic elements of Z
Develop relational, functional, and logical Z structures
Develop Z schema as models of software systems
Perform verifications and conduct proofs using Z models
Refine Z models towards implementing software systems
REFERENCES:
1. Jonathan Jacky, “The way of Z: Practical programming with formal methods”,
Cambridge University Press, 1996.
2. Antoni Diller, “Z: An introduction to formal methods”, Second Edition, Wiley, 1994.
3. Jim Woodcock and Jim Davies, “Using Z – Specification, Refinement, and Proof”,
Prentice Hall, 1996.
4. J. M. Spivey, “The Z notation: A reference manual”, Second Edition, Prentice Hall,
1992.
5. M. Ben-Ari, “Mathematical logic for computer science”, Second Edition, Springer,
2003.
6. M. Huth and M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
systems”, Second Edition, Cambridge University Press, 2004.
21CP7001
PERFORMANCE EVALUATION OF COMPUTER SYSTEMS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for performance evaluation of
computer systems
To understand the metrics used for performance evaluation
To understand the analytical modeling of computer systems
To enable the students to develop new queueing analysis for both simple and complex
systems
To appreciate the use of smart scheduling and introduce the students to analytical
techniques for evaluating scheduling policies
UNIT I
OVERVIEW OF PERFORMANCE EVALUATION
9
Need for Performance Evaluation in Computer Systems – Overview of Performance
Evaluation Methods – Introduction to Queueing – Probability Review – Generating Random
Variables for Simulation – Sample Paths, Convergence and Averages – Little’s Law and
other Operational Laws – Modification for Closed Systems.
UNIT II
MARKOV CHAINS AND SIMPLE QUEUES
9
Discrete-Time Markov Chains – Ergodicity Theory – Real World Examples – Google, Aloha
– Transition to Continuous-Time Markov Chain – M/M/1 and PASTA.
UNIT III
MULTI-SERVER AND MULTI-QUEUE SYSTEMS
9
Server Farms: M/M/k and M/M/k/k – Capacity Provisioning for Server Farms – Time
Reversibility and Burke’s Theorem – Networks of Queues and Jackson Product Form –
Classed and Closed Networks of Queues.
UNIT IV
REAL-WORLD WORKLOADS
9
Case Study of Real-world Workloads – Phase-Type Distributions and Matrix-Alalytic
Methods – Networks with Time-Sharing Servers – M/G/1 Queue and the Inspection Paradox
– Task Assignment Policies for Server Farms.
UNIT V
SMART SCHEDULING IN THE M/G/1
9
Performance Metrics – Scheduling Non-Preemptive and Preemptive Non-Size-Based
Policies - . Scheduling Non-Preemptive and Preemptive Size-Based Policies – Scheduling -
SRPT and Fairness.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for performance evaluation and the metrics used for it
Discuss open and closed queueing networks
Define Little’e law and other operational laws
Apply the operational laws to open and closed systems
Use discrete-time and continuous-time Markov chains to model real world systems
Develop analytical techniques for evaluating scheduling policies
REFERENCES:
1. Mor Harchol - Balter, “Performance Modeling and Design of Computer Systems –
Queueing Theory in Action”, Cambridge University Press, 2013.
2. Raj Jain, “The Art of Computer Systems Performance Analysis: Techniques for
Experimental Design, Measurement, Simulation and Modeling”, Wiley-Interscience,
1991.
3. Lieven Eeckhout, “Computer Architecture Performance Evaluation Methods”, Morgan
and Claypool Publishers, 2010.
4. Paul J. Fortier and Howard E. Michel, “Computer Systems Performance Evaluation and
Prediction”, Elsevier, 2003.
5. David J. Lilja, “Measuring Computer Performance: A Practitioner’s Guide”, Cambridge
University Press, 2000.
226. Krishna Kant, “Introduction to Computer System Performance Evaluation”, McGraw-Hill,
1992.
7. K. S. Trivedi, “Probability and Statistics with Reliability, Queueing and Computer Science
Applications”, John Wiley and Sons, 2001.
CP7002
PROBABILISTIC REASONING SYSTEMS
LT P C
3 0 0 3
OBJECTIVES:
To construct and reason with Bayesian networks
To reason with temporal models
To make exact and approximate inferences with graphical models
To understand learning of parameters for probabilistic graphical models
To understand actions and decisions with probabilistic graphical models
UNIT I
REPRESENTATION
9
Probability Theory, Graphs, Bayesian network representation: Bayes networks,
Independence in graphs – Undirected graphical models: Parameterization, Markov Network
independencies – Conditional Bayesian networks.
UNIT II
TEMPLATE BASED REPRESENTATION
9
Temporal models (Dynamic Bayesian networks , Hidden Markov Models) – Directed
probabilistic models for object-relational domains – Inference in temporal models: Kalman
filters.
UNIT III
INFERENCE
9
Exact inference: Variable elimination – Exact inference: Clique trees (Junction trees) –
Approximate inference: Forward sampling, Importance sampling, MCMC – MAP inference:
Variable elimination for MAP, Max-product in clique trees.
UNIT IV
LEARNING
9
Learning graphical models – Parameter estimation: maximum-likelihood estimation, MLE for
Bayesian networks, Bayesian parameter estimation – Structure learning in Bayesian
networks: Constraint based, structure scores, structure search – Partially observed data:
Parameter estimation, Learning models with hidden variables – Learning undirected
models: Maximum likelihood
UNIT V
ACTIONS AND DECISIONS
Causality – Utilities and decisions – Structured decision problems
9
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Construct Bayesian networks
Reason with Bayesian networks
Reason with Dynamic networks and Hidden Markov Models
Conduct inferences with Bayesian networks
Implement algorithms to learn probabilistic graphical models
Explain actions and decisions with probabilistic graphical models
REFERENCES:
1. Daphne Koller and Nir Friedman, “Probabilistic Graphical Models: Principles and
Techniques”, MIT Press, 2009.
2. David Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
3. Adnan Darwiche, “Modeling and Reasoning with Bayesian networks”, Cambridge
University Press, 2009.
4. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
5. Stuart Russel and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Third
Edition, Prentice Hall, 2009.
23CP7003
DATA ANALYSIS AND BUSINESS INTELLIGENCE
LTPC
3 003
OBJECTIVES:
To understand linear regression models
To understand logistic regression models
To understand generalized linear models
To understand simulation using regression models
To understand causal inference
To understand multilevel regression
To understand data collection and model understanding
UNIT I
LINEAR REGRESSION
9
Introduction to data analysis – Statistical processes – statistical models – statistical inference
– review of random variables and probability distributions – linear regression – one predictor
– multiple predictors – prediction and validation – linear transformations – centering and
standardizing – correlation – logarithmic transformations – other transformations – building
regression models – fitting a series of regressions
UNIT II
LOGISTIC AND GENERALIZED LINEAR MODELS
9
Logistic regression – logistic regression coefficients – latent-data formulation – building a
logistic regression model – logistic regression with interactions – evaluating, checking, and
comparing fitted logistic regressions – identifiability and separation – Poisson regression –
logistic-binomial model – Probit regression – multinomial regression – robust regression
using t model – building complex generalized linear models – constructive choice models
UNIT III
SIMULATION AND CAUSAL INFERENCE
9
Simulation of probability models – summarizing linear regressions – simulation of non-linear
predictions – predictive simulation for generalized linear models – fake-data simulation –
simulating and comparing to actual data – predictive simulation to check the fit of a time-
series model – causal inference – randomized experiments – observational studies – causal
inference using advanced models – matching – instrumental variables
UNIT IV
MULTILEVEL REGRESSION
9
Multilevel structures – clustered data – multilevel linear models – partial pooling – group-
level predictors – model building and statistical significance – varying intercepts and slopes –
scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression –
multi-level generalized linear models
UNIT V
DATA COLLECTION AND MODEL UNDERSTANDING
9
Design of data collection – classical power calculations – multilevel power calculations –
power calculation using fake-data simulation – understanding and summarizing fitted models
– uncertainty and variability – variances – R2 and explained variance – multiple comparisons
and statistical significance – analysis of variance – ANOVA and multilevel linear and general
linear models – missing data imputation
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Build and apply linear regression models
Build and apply logistic regression models
Build and apply generalized linear models
Perform simulation using regression models
Perform casual inference from data
Build and apply multilevel regression models
Perform data collection and variance analysis
REFERENCES:
1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and
multilevel/Hierarchical Models", Cambridge University Press, 2006.
2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.
3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012.
244. Davinderjit Sivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second
Edition, Oxford University Press, 2006.
5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and
data mining applications", Academic Press, 2009.
6. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An
Example-based Approach", Third Edition, Cambridge University Press, 2010.
8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer,
2011.
CP7004
IMAGE PROCESSING AND ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the basics of digital images
To understand noise models
To understand spatial domain filters
To understand frequency domain filters
To learn basic image analysis --- segmentation, edge detection, and corner detection
To learn morphological operations and texture analysis
To understand processing of color images
To understand image compression techniques
UNIT I
SPATIAL DOMAIN PROCESSING
9
Introduction to image processing – imaging modalities – image file formats – image sensing
and acquisition – image sampling and quantization – noise models – spatial filtering
operations – histograms – smoothing filters – sharpening filters – fuzzy techniques for spatial
filtering – spatial filters for noise removal
UNIT II
FREQUENCY DOMAIN PROCESSING
9
Frequency domain – Review of Fourier Transform (FT), Discrete Fourier Transform (DFT),
and Fast Fourier Transform (FFT) – filtering in frequency domain – image smoothing –
image sharpening – selective filtering – frequency domain noise filters – wavelets – Haar
Transform – multiresolution expansions – wavelet transforms – wavelets based image
processing
UNIT III
SEGMENTATION AND EDGE DETECTION
9
Thresholding techniques – region growing methods – region splitting and merging – adaptive
thresholding – threshold selection – global valley – histogram concavity – edge detection –
template matching – gradient operators – circular operators – differential edge operators –
hysteresis thresholding – Canny operator – Laplacian operator – active contours – object
segmentation
UNIT IV
INTEREST POINTS, MORPHOLOGY, AND TEXTURE
9
Corner and interest point detection – template matching – second order derivatives –
median filter based detection – Harris interest point operator – corner orientation – local
invariant feature detectors and descriptors – morphology – dilation and erosion –
morphological operators – grayscale morphology – noise and morphology – texture – texture
analysis – co-occurrence matrices – Laws' texture energy approach – Ade's eigen filter
approach
25UNIT V
COLOR IMAGES AND IMAGE COMPRESSION
9
Color models – pseudo colors – full-color image processing – color transformations –
smoothing and sharpening of color images – image segmentation based on color – noise in
color images.
Image Compression – redundancy in images – coding redundancy – irrelevant information in
images – image compression models – basic compression methods – digital image
watermarking.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain image modalities, sensing, acquisition, sampling, and quantization
Explain image noise models
Implement spatial filter operations
Explain frequency domain transformations
Implement frequency domain filters
Apply segmentation algorithms
Apply edge detection techniques
Apply corner and interest point detection algorithms
Apply morphological operations
Perform texture analysis
Analyze color images
Implement image compression algorithms
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. W. Burger and M. Burge, “Digital Image Processing: An Algorithmic Introduction
using Java”, Springer, 2008.
3. John C. Russ, “The Image Processing Handbook”, Sixth Edition, CRC Press, 2011.
4. R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Third Edition, Pearson,
2008.
5. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
6. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
7. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
NE7001
SENSING TECHNIQUES AND SENSORS
LT P C
3003
OBJECTIVES:
To study the sensor characteristics and the fundamental principles of sensing
To understand the sensor interface electronics
To study selected motion-related sensors
To study light and radiation detectors
To study selected temperature sensors
To study selected chemical sensors
UNIT I
PRINCIPLES OF SENSING
9
Data Acquisition – sensor characteristics – electric charges, fields, potentials – capacitance
– magnetism – inductance – resistance – piezoelectric – pyroelectric – Hall effect –
thermoelectric effects – sound waves – heat transfer – light – dynamic models of sensors
UNIT II
OPTICAL COMPONENTS AND INTERFACE ELECTRONICS
9
Radiometry – Photometry – mirrors – lenses – fibre optics – concentrators – Interface
circuits – amplifiers – light-to-voltage – excitation circuits – ADC – Digitization –
Capacitance-to-voltage – bridge circuits – data transmission – noise in sensors and circuits –
calibration – low power sensors
26UNIT III
MOTION RELATED SENSORS
9
Occupancy and motion detectors: ultrasonic – microwave – capacitive detectors –
triboelectric – optoelectronic motion sensors – optical presence sensor – Pressure Gradient
sensorsVelocity and acceleration sensors: Accelerometer characteristics – capacitative
accelerometers – piezoelectric accelerometers – piezoresistive accelerometers – thermal
accelerometers – Gyroscopes – piezoelectric cables – gravitational sensors
UNIT IV
LIGHT AND RADIATION DETECTORS
9
Light Detectors: Photo diodes – photo transistor – photo resistor – cooled detectors – CCD
and CMOS image sensors – thermal detectors – optical design – gas flame detectors
Radiation Detectors: scintillating detectors – ionization detectors – cloud and bubble
chambers
UNIT V
TEMPERATURE AND CHEMICAL SENSORS
9
Temperature Sensors: coupling with objects – temperature reference points – thermo
resistive sensors – thermo electric contact sensors – semiconductor sensors – acoustic
sensors – piezoelectric sensors Chemical sensors: characteristics – classes of chemical
sensors – biochemical sensors – multi-sensor arrays – electronic noses and tongues
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain sensor characteristics
Explain the physics of sensors
Explain optical components of sensors
Apply sensor interface electronics
Choose and use appropriate motion-related sensors
Choose and use appropriate light and radiation detectors
Choose and use appropriate temperature sensors
Choose and use appropriate chemical sensors
REFERENCE:
1. Jacob Fraden, “Handbook of Modern Sensors: Physics, Designs, and Applications”,
Fourth Edition, Springer, 2010.
CP7005
RANDOMIZED ALGORITHMS
LTPC
30 03
OBJECTIVES:
To understand the mathematical foundations needed for understanding and designing
randomized algorithms
To appreciate the need for randomized algorithms
To expose the students to probabilistic methods
To understand the concept of random walk
To expose the students to different types of applications of randomized algorithms
UNIT I
INTRODUCTION TO RANDOMIZED ALGORITHMS
9
Introduction to Randomized Algorithms - Min-cut – Elementary Probability Theory – Models
of Randomized Algorithms – Classification of Randomized Algorithms – Paradigms of the
Design of Randomized Algorithms - Game Theoretic Techniques – Game Tree Evaluation –
Minimax Principle – Randomness and Non Uniformity.
UNIT II
PROBABILISTIC METHODS
9
Moments and Deviations – occupancy Problems – Markov and Chebyshev Inequalities –
Randomized Selection – Two Point Sampling – The Stable Marriage Problem – The
Probabilistic Method – Maximum Satisfiability – Expanding Graphs – Method of Conditional
Probabilities – Markov Chains and Random Walks – 2-SAT Example – Random Walks on
Graphs – Random Connectivity.
27UNIT III
ALGEBRAIC TECHNIQUES AND APPLICATIONS
9
Fingerprinting Techniques – Verifying Polynomial Identities – Perfect Matching in Graphs –
Pattern Matching – Verification of Matrix Multiplication - Data Structuring Problems –
Random Treaps – Skip Lists – Hash Tables.
UNIT IV
GEOMETRIC AND GRAPH ALGORITHMS
9
Randomized Incremental Construction – Convex Hulls – Duality – Trapezoidal
Decompositions – Linear Programming – Graph Algorithms – Min-cut – Minimum Spanning
Trees.
UNIT V HASHING AND ONLINE ALGORITHMS
9
Hashing – Universal Hashing - Online Algorithms – Randomized Online Algorithms - Online
Paging – Adversary Models – Relating the Adversaries – The k-server Problem.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for randomized algorithms
Discuss the classification of randomized algorithms
Present the various paradigms for designing randomized algorithms
Discuss the different probabilistic methods used for designing randomized algorithms
Apply the techniques studied to design algorithms for different applications like matrix
multiplication, hashing, linear programming
REFERENCES:
1. Rajeev Motwani and Prabhakar Raghavan, “Randomized Algorithms”, Cambridge
University Press, 1995.
2. Juraj Hromkovic,”Design and Analysis of Randomized Algorithms”, Springer, 2010.
3. Michael Mitzenmacher and Eli Upfal, “Probabilty and Computing – Randomized
Algorithms and Probabilistic Analysis”, Cambridge University Press, 2005.
NE7002
MOBILE AND PERVASIVE COMPUTING
LT P C
3003
OBJECTIVES :
To understand the basics of Mobile Computing and Personal Computing
To learn the role of cellular networks in Mobile and Pervasive Computing
To expose to the concept of sensor and mesh networks
To expose to the context aware and wearable computing
To learn to develop applications in mobile and pervasive computing environment
UNIT I
INTRODUCTION
9
Differences between Mobile Communication and Mobile Computing – Contexts and Names
– Functions – Applications and Services – New Applications – Making Legacy Applications
Mobile Enabled – Design Considerations – Integration of Wireless and Wired Networks –
Standards Bodies – Pervasive Computing – Basics and Vision – Principles of Pervasive
Computing – Categories of Pervasive Devices
UNIT II
3G AND 4G CELLULAR NETWORKS
9
Migration to 3G Networks – IMT 2000 and UMTS – UMTS Architecture – User Equipment –
Radio Network Subsystem – UTRAN – Node B – RNC functions – USIM – Protocol Stack –
CS and PS Domains – IMS Architecture – Handover – 3.5G and 3.9G a brief discussion –
4G LAN and Cellular Networks – LTE – Control Plane – NAS and RRC – User Plane –
PDCP, RLC and MAC – WiMax IEEE 802.16d/e – WiMax Internetworking with 3GPP
28UNIT III
SENSOR AND MESH NETWORKS
9
Sensor Networks – Role in Pervasive Computing – In Network Processing and Data
Dissemination – Sensor Databases – Data Management in Wireless Mobile Environments –
Wireless Mesh Networks – Architecture – Mesh Routers – Mesh Clients – Routing – Cross
Layer Approach – Security Aspects of Various Layers in WMN – Applications of Sensor and
Mesh networks
UNIT IV
CONTEXT AWARE COMPUTING & WEARABLE COMPUTING
9
Adaptability – Mechanisms for Adaptation - Functionality and Data – Transcoding –
Location Aware Computing – Location Representation – Localization Techniques –
Triangulation and Scene Analysis – Delaunay Triangulation and Voronoi graphs – Types of
Context – Role of Mobile Middleware – Adaptation and Agents – Service Discovery
Middleware
Health BAN- Medical and Technological Requirements-Wearable Sensors-Intra-BAN
communications
UNIT V
APPLICATION DEVELOPMENT
9
Three tier architecture - Model View Controller Architecture - Memory Management –
Information Access Devices – PDAs and Smart Phones – Smart Cards and Embedded
Controls – J2ME – Programming for CLDC – GUI in MIDP – Application Development ON
Android and iPhone
TOTAL:45 PERIODS
OUTCOMES:
At the end of the course the student should be able to
Design a basic architecture for a pervasive computing environment
Design and allocate the resources on the 3G-4G wireless networks
Analyze the role of sensors in Wireless networks
Work out the routing in mesh network
Deploy the location and context information for application development
Develop mobile computing applications based on the paradigm of context aware
computing and wearable computing
REFERENCES:
1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile Computing: Technology,
Applications and Service Creation”, 2nd ed, Tata McGraw Hill, 2010.
2. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
3. .Pei Zheng and Lionel M Li, ‘Smart Phone & Next Generation Mobile Computing’,
Morgan Kaufmann Publishers, 2006.
4. Frank Adelstein, ‘Fundamentals of Mobile and Pervasive Computing’, TMH, 2005
5. Jochen Burthardt et al, ‘Pervasive Computing: Technology and Architecture of Mobile
Internet Applications’, Pearson Education, 2003
6. Feng Zhao and Leonidas Guibas, ‘Wireless Sensor Networks’, Morgan Kaufmann
Publishers, 2004
7. Uwe Hansmaan et al, ‘Principles of Mobile Computing’, Springer, 2003
8. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley,2010.
9. Mohammad s. Obaidat et al, “Pervasive Computing and Networking”,John wiley
10. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”,
Wiley, 2009
11. Frank Adelstein Sandeep K. S. Gupta Golden G. Richard III Loren Schwiebert
“Fundamentals of Mobile and Pervasive Computing, “, McGraw-Hill, 2005
29CP7006
PARALLEL PROGRAMMING PARADIGMS
LTPC
3 003
OBJECTIVES:
To understand models of and issues in concurrency in computing
To develop message-passing parallel programs using MPI
To develop shared-memory parallel programs using Pthreads
To develop shared-memory parallel programs using OpenMP
To use GPU for parallel programming using OpenCL and CUDA
UNIT I
FOUNDATIONS OF PARALLEL PROGRAMMING
9
Motivation for parallel programming - Concurrency in computing – basics of processes,
multiprocessing, and threads – cache – cache mappings – caches and programs – virtual
memory – instruction level parallelism – hardware multi-threading – SIMD – MIMD –
interconnection networks – cache coherence – shared-memory model – issues in shared-
memory model – distributed-memory model – issues in distributed-memory model – hybrid
model – I/O – performance of parallel programs – parallel program design
UNIT II
MESSAGE PASSING PARADIGM
9
Basic MPI programming – MPI_Init and MPI_Finalize – MPI communicators – SPMD
programs – message passing – MPI_Send and MPI_Recv – message matching – MPI I/O –
parallel I/O – collective communication – MPI_Reduce – MPI_Allreduce – broadcast –
scatter – gather – allgather – derived types – remote memory access – dynamic process
management – MPI for grids – performance evaluation of MPI programs
UNIT III
SHARED MEMORY PARADIGM: PTHREADS
9
Basics of Pthreads – thread synchronization – critical sections – busy-waiting – mutexes –
semaphores – barriers and condition variables – read-write locks – Caches, cache
coherence and false sharing – thread safety – Pthreads case study
UNIT IV
SHARED MEMORY PARADIGM: OPENMP
9
Basic OpenMP constructs – scope of variabls – reduction clause – parallel for directive –
loops in OpenMP – scheduling loops – synchronization in OpenMP – Case Study:
Producer-Consumer problem – cache issues – threads safety in OpenMP – OpenMP best
practices
UNIT V
GRAPHICAL PROCESSING PARADIGMS: OPENCL AND CUDA
9
Introduction to CUDA – CUDA programming examples – CUDA execution model – CUDA
memory hierarchy – CUDA case study - introduction to OpenCL – OpenCL programming
examples – Programs and Kernels – Buffers and Images – Event model – OpenCL case
study
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Explain models of parallel programming
Explain hardware level support for concurrency
Explain issues in parallel programming
Develop message-passing parallel programs using MPI framework
Develop shared-memory parallel programs using Pthreads
Develop shared-memory parallel programs using OpenMP
Develop CUDA programs
Develop OpenCL programs
REFERENCES:
1. Peter S. Pacheco, “An introduction to parallel programming”, Morgan Kaufmann,
2011.
2. M. J. Quinn, “Parallel programming in C with MPI and OpenMP”, Tata McGraw Hill,
2003.
3. W. Gropp, E. Lusk, and R. Thakur, “Using MPI-2: Advanced features of the message
passing interface”, MIT Press, 1999.
304. W. Gropp, E. Lusk, and A. Skjellum, “Using MPI: Portable parallel programming with
the message passing interface”, Second Edition, MIT Press, 1999.
5. B. Chapman, G. Jost, and Ruud van der Pas, “Using OpenMP”, MIT Press, 2008.
6. D. R. Butenhof, “Programming with POSIX Threads”, Addison Wesley, 1997.
7. B. Lewis and D. J. Berg, “Multithreaded programming with Pthreads”, Sun
Microsystems Press, 1998.
8. A. Munshi, B. Gaster, T. G. Mattson, J. Fung, and D. Ginsburg, “OpenCL
programming guide”, Addison Wesley, 2011.
9. Rob Farber, “CUDA application design and development”, Morgan Haufmann, 2011.
CP7007
SOFTWARE REQUIREMENTS ENGINEERING
LTPC
3 003
OBJECTIVES:
1. Understand system requirements
2. Identify different types of requirement
3. Generate requirements be elicitation
4. Develop requirements documentation
5. Evaluate the requirements
UNIT I
DOMAIN UNDERSTANDING
9
Introduction – Types of requirements – Requirements engineering process – Validating
requirements – Requirements and design – Requirements and test cases – introduction to
business domain – Problem analysis – Fish bone diagram – Business requirements –
Business process modeling – Business use cases – Business modeling notations – UML
Activity diagrams.
UNIT II
REQUIREMENTS ELICITATION
9
Introduction – Understanding stakeholders' needs – Elicitation techniques – interviews,
questionnaire, workshop, brainstorming, prototyping – Documenting stakeholders' needs
UNIT III
FUNCTIONAL REQUIREMENTS
9
Introduction – Features and Use cases – Use case scenarios – Documenting use cases –
Levels of details – SRS documents.
UNIT IV
QUALITY ATTRIBUTES AND USER EXPERIENCE
9
Quality of solution – Quality attributes – Eliciting quality attributes – Quality attribute
workshop (QAW) – Documenting quality attributes – Six part scenarios – Usability
requirements – Eliciting and documenting usability requirements – Modeling user experience
– Specifying UI design
UNIT V
MANAGING REQUIREMENTS
9
Defining scope of the project – Context diagram – Managing requirements – Requirements
properties – Traceability – Managing changes – Requirements metrics – Requirements
management tools.
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Define a process for requirments engineering
Execute a process for gathering requirments through elicitation techniques.
Validate requirements according to criteria such as feasibility, clarity, preciseness
etc.
Develop and document functional requirements for different types of systems.
Develop and document quality attributes of the system to be implemented
Communicate the requirments to stakeholders
Negotiate with stakeholders in order to agree on a set of requirements.
Detect and resolve feature interactions
31REFERENCES:
1. Axel van Lamsweerde, "Requirements Engineering", Wiley, 2009
2. Gerald Kotonya, Ian Sommerville, "Requirements Engineering: Processes and
Techniques", John Wiley and Sons, 1998
3. Dean Leffingwell and Don Widrig, “Managing Software Requirements: A Use Case
Approach (2nd Edition) ”, Addison-wesley, 2003
4. SEI Report, “Quality Attributes Workshop”,
http://www.sei.cmu.edu/library/abstracts/reports/03tr016.cfm , 2003
5. J Nielsen, “Usability Engineering”, Academic Press, 1993
CP7008
SPEECH PROCESSING AND SYNTHESIS
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for speech processing
To understand the basic concepts and algorithms of speech processing and synthesis
To familiarize the students with the various speech signal representation, coding and
recognition techniques
To appreciate the use of speech processing in current technologies and to expose the
students to real– world applications of speech processing
UNIT I
FUNDAMENTALS OF SPEECH PROCESSING
9
Introduction – Spoken Language Structure – Phonetics and Phonology – Syllables and
Words – Syntax and Semantics – Probability, Statistics and Information Theory – Probability
Theory – Estimation Theory – Significance Testing – Information Theory.
UNIT II
SPEECH SIGNAL REPRESENTATIONS AND CODING
9
Overview of Digital Signal Processing – Speech Signal Representations – Short time Fourier
Analysis – Acoustic Model of Speech Production – Linear Predictive Coding – Cepstral
Processing – Formant Frequencies – The Role of Pitch – Speech Coding – LPC Coder.
UNITIII
SPEECH RECOGNITION
9
Hidden Markov Models – Definition – Continuous and Discontinuous HMMs – Practical
Issues – Limitations. Acoustic Modeling – Variability in the Speech Signal – Extracting
Features – Phonetic Modeling – Adaptive Techniques – Confidence Measures – Other
Techniques.
UNITIV
TEXT ANALYSIS
9
Lexicon – Document Structure Detection – Text Normalization – Linguistic Analysis –
Homograph Disambiguation – Morphological Analysis – Letter-to-sound Conversion –
Prosody – Generation schematic – Speaking Style – Symbolic Prosody – Duration
Assignment – Pitch Generation
UNIT V
SPEECH SYNTHESIS
9
Attributes – Formant Speech Synthesis – Concatenative Speech Synthesis – Prosodic
Modification of Speech – Source-filter Models for Prosody Modification – Evaluation of TTS
Systems.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the various temporal, spectral and cepstral features required for identifying
speech units – phoneme, syllable and word
Determine and apply Mel-frequency cepstral coefficients for processing all types of
signals
Justify the use of formant and concatenative approaches to speech synthesis
32
Identify the apt approach of speech synthesis depending on the language to be
processed
Determine the various encoding techniques for representing speech.
REFERENCES:
1. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, “Spoken Language Processing – A
guide to Theory, Algorithm and System Development”, Prentice Hall PTR, 2001.
2. Thomas F.Quatieri, “Discrete-Time Speech Signal Processing”, Pearson Education,
2002.
3. Lawrence Rabiner and Biing-Hwang Juang, “Fundamentals of Speech Recognition”,
Prentice Hall Signal Processing Series, 1993.
4. Sadaoki Furui, “Digital Speech Processing: Synthesis, and Recognition, Second Edition,
(Signal Processing and Communications)”, Marcel Dekker, 2000.
5. Joseph Mariani, “Language and Speech Processing”, Wiley, 2009.
CP7009
MACHINE LEARNING TECHNIQUES
LTPC
3 003
OBJECTIVES:
1. To understand the machine learning theory
2. To implement linear and non-linear learning models
3. To implement distance-based clustering techniques
4. To build tree and rule based models
5. To apply reinforcement learning techniques
UNIT I
FOUNDATIONS OF LEARNING
9
Components of learning – learning models – geometric models – probabilistic models – logic
models – grouping and grading – learning versus design – types of learning – supervised –
unsupervised – reinforcement – theory of learning – feasibility of learning – error and noise –
training versus testing – theory of generalization – generalization bound – approximation-
generalization tradeoff – bias and variance – learning curve
[
UNIT II
LINEAR MODELS
9
Linear classification – univariate linear regression – multivariate linear regression –
regularized regression – Logistic regression – perceptrons – multilayer neural networks –
learning neural networks structures – support vector machines – soft margin SVM – going
beyond linearity – generalization and overfitting – regularization – validation
UNIT III
DISTANCE-BASED MODELS
9
Nearest neighbor models – K-means – clustering around medoids – silhouttes – hierarchical
clustering – k-d trees – locality sensitive hashing – non-parametric regression – ensemble
learning – bagging and random forests – boosting – meta learning
UNIT IV
TREE AND RULE MODELS
9
Decision trees – learning decision trees – ranking and probability estimation trees –
regression trees – clustering trees – learning ordered rule lists – learning unordered rule lists
– descriptive rule learning – association rule mining – first-order rule learning
UNIT V
REINFORCEMENT LEARNING
9
Passive reinforcement learning – direct utility estimation – adaptive dynamic programming –
temporal-difference learning – active reinforcement learning – exploration – learning an
action-utility function – Generalization in reinforcement learning – policy search –
applications in game playing – applications in robot control
TOTAL : 45 PERIODS
33OUTCOMES:
Upon Completion of the course,the students will be able to
To explain theory underlying machine learning
To construct algorithms to learn linear and non-linear models
To implement data clustering algorithms
To construct algorithms to learn tree and rule-based models
To apply reinforcement learning techniques
REFERENCES:
1. Y. S. Abu-Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook
Publishers, 2012.
2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of
data”, Cambridge University Press, 2012.
3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012.
4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007.
5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University
Press, 2012.
6. M. Mohri, A. Rostamizadeh, and A. Talwalkar, “Foundations of Machine Learning”,
MIT Press, 2012.
7. T. M. Mitchell, “Machine Learning”, McGraw Hill, 1997.
8. S. Russel and P. Norvig, “Artificial Intelligence: A Modern Approach”, Third Edition,
Prentice Hall, 2009.
CP7010
CONCURRENCY MODELS
LTPC
3 003
OBJECTIVES:
To model concurrency in FSP
To specify and check safety and liveness properties
To understand concurrency architectures and design
To apply linear temporal logic to safety and liveness analysis
To apply Petri nets for concurrency modeling and analysis
UNIT I
FSP AND GRAPH MODELS
9
Concurrency and issues in concurrency – models of concurrency – graphical models – FSP
& LTSA – modeling processes with FSP – concurrency models with FSP – shared action –
structure diagrams – issues with shared objects – modeling mutual exclusion – conditional
synchronization – modeling semaphores – nested monitors – monitor invariants
UNIT II
SAFETY AND LIVENESS PROPERTIES
9
Deadlocks – deadlock analysis in models – dining philosophers problem – safety properties
– single-lane bridge problem – liveness properties – liveness of the single-lane bridge –
readers-writers problem – message passing – asynchronous message passing models –
synchronous message passing models – rendezvous
UNIT III
CONCURRENCY ARCHITECTURES AND DESIGN
9
Modeling dynamic systems – modeling timed systems – concurrent architectures – Filter
pipeline – Supervisor-worker model – announcer-listener model – model-based design –
from requirements to models – from models to implementations – implementing concurrency
in Java – program verification
UNIT IV
LINEAR TEMPORAL LOGIC (LTL)
9
Syntax of LTL – semantics of LTL – practical LTL patterns – equivalences between LTL
statements – specification using LTL – LTL and FSP – Fluent proposition – Temporal
propositions – Fluent Linear Temporal Logic (FLTL) – FLTL assertions in FSP – Database
ring problem
34UNIT V
PETRI NETS
9
Introduction to Petri nets – examples – place-transition nets – graphical and linear algebraic
representations – concurrency & conflict – coverability graphs – decision procedures –
liveness – colored Petri nets (CPN) – modeling & verification using CPN – non-hierarchical
CPN – modeling protocols – hierarchical CPN – timed CPN – applications of Petri Nets
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Develop concurrency models and FSP
State safety and liveness properties in FSP
Verify properties using LTSA tool
Explain concurrency architectures
Design concurrent Java programs from models
Apply Linear Temporal Logic to state safety and liveness properties
Assert LTL properties in FSP and check using LTSA tool
Model and analyze concurrency using Petri nets
REFERENCES:
1. Jeff Magee & Jeff Kramer, “Concurrency: State Models and Java Programs”, Second
Edition, John Wiley, 2006.
2. M. Huth & M. Ryan, “Logic in Computer Science – Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
3. B. Goetz, T. Peierls, J. Bloch, J. Bowbeer, D. Holmes, and D. Lea, “Java Concurrency in
Practice”, Addison-Wesley Professional, 2006.
4. Wolfgang Reisig, “Petri Nets: An Introduction”, Springer, 2011.
5. K. Jensen and L. M. Kristensen, “Colored Petri Nets: Modeling and Validation of
Concurrent Systems”, Springer, 2009.
6. Wolfgang Reisig, “Understanding Petri Nets: Modeling Techniques, Analysis Methods,
Case Studies”, Springer, 2013.
CP7011
REAL TIME SYSTEMS
LTPC
3 003
OBJECTIVES:
To provide good understanding of fundamental concepts in real time systems.
To provide understanding of advanced topics in real time systems.
To provide understanding on basic multi-task scheduling algorithms for periodic,
aperiodic, and sporadic tasks as well as understand the impact of the latter two on
scheduling
To expose to understand capabilities of commercial off-the-shelf R-T kernel.
To expose to real time communications and databases.
UNIT I
INTRODUCTION
9
Real-time systems – Applications – Basic Model – Characteristics – Safety and Reliability –
Real-Time tasks – Timing Constraints – Modelling Timing Constraints.
UNIT II
SCHEDULING REAL-TIME TASKS
9
Concepts – Types of RT Tasks and their Characteristics – Task Scheduling – Clock-Driven
Scheduling – Hybrid Schedulers - Event-Driven Scheduling – EDF Scheduling – RMA –
Issues with RMA – Issues in Using RMA in Practical Situations
35UNIT III
RESOURCE SHARING AMONG RT TASKS & SCHEDULING RT TASKS 9
Resource Sharing Among RT Tasks – Priority Inversion – PIP – HLP – PCP – Types of
Priority Inversions Under PCP – Features of PCP – Issues in using Resource Sharing
Protocol – Handling Task Dependencies – Multiprocessor Task Allocation – Dynamic
Allocation of Tasks – Fault-Tolerant Scheduling of Tasks – Clocks in Distributed RT Systems
– Centralized and Distributed Clock Synchronization.
UNIT IV
COMMERCIAL RT OPERATING SYSTEMS
9
Time Services – Features of RT OS – Unix as a RT OS – Unix Based RT OS – Windows as
a RT OS – POSIX – Survey of RTOS: PSOS – VRTX – VxWorks – QNX - μC/OS-II – RT
Linux – Lynx – Windows CE – Benching RT Systems.
UNIT V
RT COMMUNICATION & DATABASES
9
Examples of Applications Requiring RT Communication – Basic Concepts – RT
Communication in a LAN – Soft & Hard RT Communication in a LAN – Bounded Access
Protocols for LANs – Performance Comparison – RT Communication Over Packet Switched
Networks – QoS Framework – Routing – Resource Reservation – Rate Control – QoS
Models - Examples Applications of RT Databases – RT Databases – Characteristics of
Temporal Data – Concurrency Control in RT Databases – Commercial RT Databases.
TOTAL: 45 PERIODS
OUTCOMES:
Understand the basics and importance of real-time systems
Generate a high-level analysis document based on requirements specifications
Generate a high-level design document based on analysis documentation
Generate a test plan based on requirements specification
Generate a validation plan based on all documentation
Understand basic multi-task scheduling algorithms for periodic, aperiodic, and
sporadic tasks as well as understand the impact of the latter two on scheduling
Understand capabilities of at least one commercial off-the-shelf R-T kernel
REFERENCES:
1. Rajib Mall, "Real-Time Systems: Theory and Practice," Pearson, 2008.
2. Jane W. Liu, "Real-Time Systems" Pearson Education, 2001.
3. Krishna and Shin, "Real-Time Systems," Tata McGraw Hill. 1999.
4. Alan C. Shaw, “Real-Time Systems and Software”, Wiley, 2001.
5. Philip Laplante, “Real-Time Systems Design and Analysis”, 2nd Edition, Prentice Hall
of India.
6. Resource Management in Real-time Systems and Networks, C. Siva Ram Murthy
and G. Manimaran, MIT Press, March 2001.
CP7012
COMPUTER VISION
LTPC
3 003
OBJECTIVES:
To review image processing techniques for computer vision
To understand shape and region analysis
To understand Hough Transform and its applications to detect lines, circles, ellipses
To understand three-dimensional image analysis techniques
To understand motion analysis
To study some applications of computer vision algorithms
UNIT I
IMAGE PROCESSING FOUNDATIONS
9
Review of image processing techniques – classical filtering operations – thresholding
techniques – edge detection techniques – corner and interest point detection – mathematical
morphology – texture
36UNIT II
SHAPES AND REGIONS
9
Binary shape analysis – connectedness – object labeling and counting – size filtering –
distance functions – skeletons and thinning – deformable shape analysis – boundary
tracking procedures – active contours – shape models and shape recognition – centroidal
profiles – handling occlusion – boundary length measures – boundary descriptors – chain
codes – Fourier descriptors – region descriptors – moments
UNIT III
HOUGH TRANSFORM
9
Line detection – Hough Transform (HT) for line detection – foot-of-normal method – line
localization – line fitting – RANSAC for straight line detection – HT based circular object
detection – accurate center location – speed problem – ellipse detection – Case study:
Human Iris location – hole detection – generalized Hough Transform (GHT) – spatial
matched filtering – GHT for ellipse detection – object location – GHT for feature collation
UNIT IV
3D VISION AND MOTION
9
Methods for 3D vision – projection schemes – shape from shading – photometric stereo –
shape from texture – shape from focus – active range finding – surface representations –
point-based representation – volumetric representations – 3D object recognition – 3D
reconstruction – introduction to motion – triangulation – bundle adjustment – translational
alignment – parametric motion – spline-based motion – optical flow – layered motion
UNIT V APPLICATIONS
9
Application: Photo album – Face detection – Face recognition – Eigen faces – Active
appearance and 3D shape models of faces
Application: Surveillance – foreground-background separation – particle filters – Chamfer
matching, tracking, and occlusion – combining views from multiple cameras – human gait
analysis
Application: In-vehicle vision system: locating roadway – road markings – identifying road
signs – locating pedestrians
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Implement fundamental image processing techniques required for computer vision
Perform shape analysis
Implement boundary tracking techniques
Apply chain codes and other region descriptors
Apply Hough Transform for line, circle, and ellipse detections
Apply 3D vision techniques
Implement motion related techniques
Develop applications using computer vision techniques
REFERENCES:
1. E. R. Davies, “Computer & Machine Vision”, Fourth Edition, Academic Press, 2012.
2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer 2011.
3. Simon J. D. Prince, “Computer Vision: Models, Learning, and Inference”, Cambridge
University Press, 2012.
4. Mark Nixon and Alberto S. Aquado, “Feature Extraction & Image Processing for
Computer Vision”, Third Edition, Academic Press, 2012.
5. D. L. Baggio et al., “Mastering OpenCV with Practical Computer Vision Projects”,
Packt Publishing, 2012.
6. Jan Erik Solem, “Programming Computer Vision with Python: Tools and algorithms
for analyzing images”, O'Reilly Media, 2012.
37NE7202
NETWORK AND INFORMATION SECURITY
LTPC
3 003
OBJECTIVES:
To understand the fundamentals of Cryptography
To acquire knowledge on standard algorithms used to provide confidentiality, integrity
and authenticity.
To understand the various key distribution and management schemes.
To understand how to deploy encryption techniques to secure data in transit across
data networks
To design security applications in the field of Information technology
UNIT I
INTRODUCTION
9
An Overview of Computer Security-Security Services-Security Mechanisms-Security
Attacks-Access Control Matrix, Policy-Security policies, Confidentiality policies, Integrity
policies and Hybrid policies.
UNIT II
CRYPTOSYSTEMS & AUTHENTICATION
9
Classical Cryptography-Substitution Ciphers-permutation Ciphers-Block Ciphers-DES-
Modes of Operation- AES-Linear Cryptanalysis, Differential Cryptanalysis- Hash Function -
SHA 512- Message Authentication Codes-HMAC - Authentication Protocols -
UNIT III
PUBLIC KEY CRYPTOSYSTEMS
9
Introduction to Public key Cryptography- Number theory- The RSA Cryptosystem and
Factoring Integer- Attacks on RSA-The ELGamal
Cryptosystem- Digital Signature
Algorithm-Finite Fields-Elliptic Curves Cryptography- Key management – Session and
Interchange keys, Key exchange and generation-PKI
UNIT IV
SYSTEM IMPLEMENTATION
9
Design Principles, Representing Identity, Access Control Mechanisms, Information Flow and
Confinement Problem
Secure Software Development: Secured Coding - OWASP/SANS Top Vulnerabilities -
Buffer Overflows - Incomplete mediation - XSS - Anti Cross Site Scripting Libraries -
Canonical Data Format - Command Injection - Redirection - Inference – Application Controls
UNIT V
NETWORK SECURITY
9
Secret Sharing Schemes-Kerberos- Pretty Good Privacy (PGP)-Secure Socket Layer (SSL)-
Intruders – HIDS- NIDS - Firewalls - Viruses
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Implement basic security algorithms required by any computing system.
Analyze the vulnerabilities in any computing system and hence be able to design a
security solution.
Analyze the possible security attacks in complex real time systems and their effective
countermeasures
Identify the security issues in the network and resolve it.
Evaluate security mechanisms using rigorous approaches, including theoretical
derivation, modeling, and simulations
Formulate research problems in the computer security field
REFERENCES:
1. William Stallings, “Cryptography and Network Security: Principles and Practices”,
Third Edition, Pearson Education, 2006.
2. Matt Bishop ,“Computer Security art and science ”, Second Edition, Pearson
Education, 2002
3. Wade Trappe and Lawrence C. Washington, “Introduction to Cryptography with
Coding Theory” Second Edition, Pearson Education, 2007
4. Jonathan Katz, and Yehuda Lindell, Introduction to Modern Cryptography, CRC
Press, 2007
385. Douglas R. Stinson, “Cryptography Theory and Practice”, Third Edition, Chapman &
Hall/CRC, 2006
6. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education,
First Edition, 2006.
7. Network Security and Cryptography, Menezes Bernard, Cengage Learning, New
Delhi, 2011
8. Man Young Rhee, Internet Security, Wiley, 2003
9. OWASP top ten security vulnerabilities: http://xml.coverpages.org/OWASP-
TopTen.pdf
CP7013
DESIGN AND ANALYSIS OF PARALLEL ALGORITHMS
LTPC
3 003
OBJECTIVES:
To understand the need for parallel algorithms
To expose the students to different models of parallel computation
To expose the students to parallel sorting and searching algorithms
To understand the application of the concepts studied to different types of problems
To analyze parallel algorithms
UNIT I
INTRODUCTION
9
Introduction to Parallel Algorithms – Models of Parallel Computation – Sorting on an EREW-
SIMD PRAM Computer – Relation between PRAM Models – SIMD Algorithms – MIMD
Algorithms – Selection – Desirable Properties for Parallel Algorithms - Parallel Algorithm for
Selection – Analysis of Parallel Algorithms.
UNIT II
SORTING AND SEARCHING
9
Merging on the EREW and CREW Models - Fast Merging on EREW - Sorting Networks –
Sorting on a Linear Array – Sorting on CRCW, CREW, EREW Models – Searching a Sorted
Sequence – Searching a Random Sequence.
UNIT III
ALGEBRAIC PROBLEMS
9
Generating Permutations and Combinations in Parallel – Matrix Transpositions – Matrix by
Matrix Multiplications – Matrix by Vector multiplication.
UNIT IV
GRAPH THEORY AND COMPUTATIONAL GEOMETRY PROBLEMS
9
Connectivity Matrix – Connected Components – All Pairs Shortest Paths – Minimum
Spanning Trees – Point Inclusion – Intersection, Proximity and Construction Problems -
Sequential Tree Traversal - Basic Design Principles – Algorithm – Analysis.
UNITV
DECISION AND OPTIMIZATION PROBLEMS
9
Computing Prefix Sums – Applications - Job Sequencing with Deadlines – Knapsack
Problem- The Bit Complexity of Parallel Computations.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for parallel algorithms
Discuss the classification of parallel architectures and identify suitable programming
models
Perform sorting on Sorting on CRCW, CREW, EREW Models
Search a sorted as well as random sequence
Develop and analyze algorithms for different applications like matrix multiplication,
shortest path, job sequencing and the knapsack problem.
39REFERENCES:
1. Selim G. Akl, “The Design and Analysis of Parallel Algorithms”, Prentice Hall, New
Jersey, 1989.
2. Michael J. Quinn, “Parallel Computing : Theory & Practice”, Tata McGraw Hill Edition,
2003.
3. Justin R. Smith, “The Design and Analysis of Parallel Algorithms”, Oxford University
Press, USA , 1993.
4. Joseph JaJa, “Introduction to Parallel Algorithms”, Addison-Wesley, 1992.
CP7014
SOFTWARE ARCHITECTURES
LTPC
3 003
OBJECTIVES:
Understand architectural requirements
Identify architectural structures
Develop architectural documentation
Generate architectural alternatives
Evaluate the architecture against the drivers
UNIT I
ARCHITECTURAL DRIVERS
9
Introduction – Standard Definitions of Software Architecture– Architectural structures –
Influence of software architecture on organization – Architecture Business Cycle –
Functional requirements – Technical constraints – Quality Attributes – Quality Attribute
Workshop (QAW) – Documenting Quality Attributes – Six part scenarios
UNIT II
ARCHITECTURAL VIEWS AND DOCUMENTATION
9
Introduction – Standard Definitions for views – Structures and views- Perspectives: Static,
dynamic and physical and the accompanying views – Representing views-available
notations – Good practices in documentation– Documenting the Views using UML – Merits
and Demerits of using visual languages – Need for formal languages - Architectural
Description Languages – ACME
UNIT III
ARCHITECTURAL STYLES
9
Introduction – Data flow styles – Call-return styles – Shared Information styles – Event styles
– Case studies for each style
UNIT IV
ARCHITECTURAL DESIGN
9
Approaches for architectural design – System decomposition – Attributes driven design –
Architecting for specific quality attributes – Performance, Availability – Security –
Architectural conformance
UNIT V
ARCHITECTURE EVALUATION AND SOME SPECIAL TOPICS
9
Need for evaluation – Scenario based evaluation against the drivers – ATAM and its
variations – Case studies in architectural evaluations – SOA and Web services – Cloud
Computing – Adaptive structures
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain key architectural drivers
Explain the influence of architecture on business and technical activities
Identify key architectural structures
Adopt good practices for documenting the architecture
Develop alternative architectures for a given problem
Explain how to use formal languages to specify architecture
Evaluate the architecture against the drivers
Describe the recent trends in software architecture
40REFERENCES:
1. Len Bass, Paul Clements, and Rick Kazman, “Software Architectures Principles and
Practices”, 2n Edition, Addison-Wesley, 2003.
2. Anthony J Lattanze, “Architecting Software Intensive System. A Practitioner's Guide”,
Auerbach Publications, 2010.
3. Paul Clements, Felix Bachmann, Len Bass, David Garlan, James Ivers, Reed Little,
Paulo Merson, Robert Nord, and Judith Stafford, “Documenting Software Architectures.
Views and Beyond”, 2nd Edition, Addison-Wesley, 2010.
4. Paul Clements, Rick Kazman, and Mark Klein, “Evaluating software architectures:
Methods and case studies.”, Addison-Wesley, 2001.
5. David Garlan and Mary Shaw, “Software architecture: Perspectives on an emerging
discipline”, Prentice Hall, 1996.
6. Rajkumar Buyya, James Broberg, and Andrzej Goscinski, “Cloud Computing. Principles
and Paradigms”, John Wiley & Sons, 2011
7. Mark Hansen, “SOA Using Java Web Services”, Prentice Hall, 2007
8. David Garlan, Bradley Schmerl, and Shang-Wen Cheng, “Software Architecture-Based
Self-Adaptation,” 31-56. Mieso K Denko, Laurence Tianruo Yang, and Yan Zang (eds.),
“Autonomic Computing and Networking”. Springer Verlag, 2009.
CP7015
MODEL CHECKING AND PROGRAM VERIFICATION
LTPC
3 003
OBJECTIVES:
To understand automata for model checking
To understand LTL, CTL, and CTL*
To understand timed automata, TCTL, and PCTL
To understand verification of deterministic and recursive programs
To understand verification of object-oriented programs
To understand verification of parallel, distributed, and non-deterministic programs
UNIT I
AUTOMATA AND TEMPORAL LOGICS
9
Automata on finite words – model checking regular properties – automata on infinite words –
Buchi automata – Linear Temporal Logic (LTL) – automata based LTL model checking –
Computational Tree Logic (CTL) – CTL model checking – CTL* model checking
UNIT II
TIMED AND PROBABILISTIC TREE LOGICS
9
Timed automata – timed computational tree logic (TCTL) – TCTL model checking –
probabilistic systems – probabilistic computational tree logic (PCTL) – PCTL model checking
– PCTL* - Markov decision processes
UNIT III
VERIFYING DETERMINISTIC AND RECURSIVE PROGRAMS
9
Introduction to program verification – verification of “while” programs – partial and total
correctness – verification of recursive programs – case study: binary search – verifying
recursive programs with parameters
UNIT IV
VERIFYING OBJECT-ORIENTED AND PARALLEL PROGRAMS
9
Partial and total correctness of object-oriented programs – case study: Insertion in linked
lists – verification of disjoint parallel programs – verifying programs with shared variables –
case study: parallel zero search – verification of synchronization – case study: the mutual
exclusion problem
UNIT V
VERIFYING NON-DETERMINISTIC AND DISTRIBUTED PROGRAMS
9
Introduction to non-deterministic programs – partial and total correctness of non-
deterministic programs – case study: The Welfare Crook Problem – syntax and semantics of
distributed programs – verification of distributed programs – case study: A Transmission
Problem – introduction to fairness
TOTAL : 45 PERIODS
41OUTCOMES:
Upon Completion of the course,the students will be able to
Perform model checking using LTL
Perform model checking using CTL
Perform model checking using CTL*
Perform model checking using TCTL and PCTL
Verify deterministic and recursive programs
Verify object-oriented programs
Verify parallel, distributed, and non-deterministic programs
REFERENCES:
1. C. Baier, J.-P. Katoen, and K. G. Larsen, “Principles of Model Checking”, MIT Press,
2008.
2. E. M. Clarke, O. Grumberg, and D. A. Peled, “Model Checking”, MIT Press, 1999.
3. M. Ben-Ari, “Principles of the SPIN Model Checker”, Springer, 2008.
4. K. R. Apt, F. S. de Boer, E.-R. Olderog, and A. Pnueli, “Verification of Sequential and
Concurrent Programs”, Third Edition, Springer, 2010.
5. M. Huth and M. Ryan, “Logic in Computer Science --- Modeling and Reasoning about
Systems”, Second Edition, Cambridge University Press, 2004.
6. B. Berard et al., “Systems and Software Verification: Model-checking techniques and
tools”, Springer, 2010.
7. J. B. Almeida, M. J. Frade, J. S. Pinto, and S. M. de Sousa, “Rigorous Software
Development: An Introduction to Program Verification”, Springer, 2011.
CP7016
EMBEDDED SOFTWARE DEVELOPMENT
LTPC
3 003
OBJECTIVES:
To understand processors and their instruction sets for embedded systems
To understand hardware platform for embedded systems
To design and analyze programs for embedded systems
To design multi-tasking embedded systems with RTOS
To understand overall embedded systems development lifecycle
To understand distributed and multi-processor embedded systems
UNIT I
PROCESSORS AND INSTRUCTION SETS
9
Introduction to embedded computing – overview of embedded system design process –
instruction sets of processors: ARM, PIC, TI C55x, TI C64x – programming I/O – modes and
exceptions – co-processors – memory system – CPU performance – CPU power
consumption
UNIT II
EMBEDDED COMPUTING PLATFORM
9
Basic computing platforms – CPU Bus – memory devices and systems – choosing a
platform – development environments – debugging – consumer electronics architecture –
platform-level performance analysis – design example: Audio Player
UNIT III
PROGRAM DESIGN AND ANALYSIS
9
Components for embedded programs – models of programs – Assembly, linking, and
loading – compiler optimizations – program-level performance analysis – performance
optimization – program-level energy optimization – optimizing program size – program
validation and testing – design example: Digital Still Camera
UNIT IV
PROCESSES AND OPERATING SYSTEMS
9
Multiples tasks and multiple processes – multirate systems – pre-emptive RTOS – priority-
based scheduling – inter-process communication – evaluating OS performance – processes
and power optimization – Case study: Real-time and embedded Linux – design example:
Telephone answering machine
42UNIT V
SYSTEM DESIGN, NETWORKS, AND MULTIPROCESSORS
9
System design methodologies – requirements analysis – specifications – architecture design
– quality assurance – distributed embedded systems – shared-memory multiprocessors –
design example: Video accelerator
TOTAL : 45 PERIODS
OUTCOMES:
Upon Completion of the course, the students will be able to
Develop assembley code for processors such as ARM, PIC Microcontroller, TI C55x,
TI C64x, etc.
Choose appropriate hardware platform for a given application
Perform platform-level performance analysis
Design, develop, and debug embedded programs optimized for size or performance
Develop embedded applications using an RTOS
Perform OS-level performance analysis
Employ best practices in embedded software engineering
Develop distributed embedded systems and systems with shared-memory
concurrency
REFERENCES:
1. Marilyn Wolf, “Computers as Components: Principles of Embedded Computing
Systems Design”, Third Edition, Morgan Kaufmann, 2012.
2. Christopher Hallinan, “Embedded Linux Primer: A Practical Real-World Approach”,
Second Edition, Prentice Hall, 2010.
3. Karim Yaghmour et al., “Building Embedded Linux Systems”, O’Reilly, 2008.
4. Arnold S. Berger, “Embedded Systems Design: An Introduction to Processes, Tools,
and Techniques”, CMP Books, 2001.
5. David E. Simon, “An embedded Software Primer”, Addison-Wesley, 1999.
IF7202
CLOUD COMPUTING
LTPC
3 003
OBJECTIVES:
To introduce the broad perceptive of cloud architecture and model
To understand the concept of Virtualization
To be familiar with the lead players in cloud.
To understand the features of cloud simulator
To apply different cloud programming model as per need.
To be able to set up a private cloud.
To understand the design of cloud Services.
To learn to design the trusted cloud Computing system
UNIT I
CLOUD ARCHITECTURE AND MODEL
9
Technologies for Network-Based System – System Models for Distributed and Cloud
Computing – NIST Cloud Computing Reference Architecture.
Cloud Models:- Characteristics – Cloud Services – Cloud models (IaaS, PaaS, SaaS) –
Public vs Private Cloud –Cloud Solutions - Cloud ecosystem – Service management –
Computing on demand.
UNIT II
VIRTUALIZATION
9
Basics of Virtualization - Types of Virtualization - Implementation Levels of Virtualization -
Virtualization Structures - Tools and Mechanisms - Virtualization of CPU, Memory, I/O
Devices - Virtual Clusters and Resource management – Virtualization for Data-center
Automation.
43UNIT III
CLOUD INFRASTRUCTURE
9
Architectural Design of Compute and Storage Clouds – Layered Cloud Architecture
Development – Design Challenges - Inter Cloud Resource Management – Resource
Provisioning and Platform Deployment – Global Exchange of Cloud Resources.
UNIT IV
PROGRAMMING MODEL
9
Parallel and Distributed Programming Paradigms – MapReduce , Twister and Iterative
MapReduce – Hadoop Library from Apache – Mapping Applications - Programming Support
- Google App Engine, Amazon AWS - Cloud Software Environments -Eucalyptus, Open
Nebula, OpenStack, Aneka, CloudSim
UNIT V
SECURITY IN THE CLOUD
9
Security Overview – Cloud Security Challenges and Risks – Software-as-a-Service Security
– Security Governance – Risk Management – Security Monitoring – Security Architecture
Design – Data Security – Application Security – Virtual Machine Security - Identity
Management and Access Control – Autonomic Security.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,the students will be able to
Compare the strengths and limitations of cloud computing
Identify the architecture, infrastructure and delivery models of cloud computing
Apply suitable virtualization concept.
Choose the appropriate cloud player
Choose the appropriate Programming Models and approach.
Address the core issues of cloud computing such as security, privacy and interoperability
Design Cloud Services
Set a private cloud
REFERENCES:
1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing, From
Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.
2. John W.Rittinghouse and James F.Ransome, “Cloud Computing: Implementation,
Management, and Security”, CRC Press, 2010.
3. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing, A Practical
Approach”, TMH, 2009.
4. Kumar Saurabh, “ Cloud Computing – insights into New-Era Infrastructure”, Wiley
India,2011.
5. George Reese, “Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud” O'Reilly
6. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and
Processes”, Elsevier/Morgan Kaufmann, 2005.
7. Katarina Stanoevska-Slabeva, Thomas Wozniak, Santi Ristol, “Grid and Cloud
Computing – A Business Perspective on Technology and Applications”, Springer.
8. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A comprehensive Guide to
Secure Cloud Computing”, Wiley – India, 2010.
9. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi, ‘Mastering Cloud Computing”,
TMGH,2013.
10. Gautam Shroff, Enterprise Cloud Computing, Cambridge University Press, 2011
11. Michael Miller, Cloud Computing, Que Publishing,2008
12. Nick Antonopoulos, Cloud computing, Springer Publications, 2010
44CP7017
DATA VISUALIZATION TECHNIQUES
LT PC
3 003
COURSE OBJECTIVES:
To introduce visual perception and core skills for visual analysis
To understand visualization for time-series analysis
To understand visualization for ranking analysis
To understand visualization for deviation analysis
To understand visualization for distribution analysis
To understand visualization for correlation analysis
To understand visualization for multivariate analysis
To understand issues and best practices in information dashboard design
UNIT I
CORE SKILLS FOR VISUAL ANALYSIS
9
Information visualization – effective data analysis – traits of meaningful data – visual
perception – making abstract data visible – building blocks of information visualization –
analytical interaction – analytical navigation – optimal quantitative scales – reference lines
and regions – trellises and crosstabs – multiple concurrent views – focus and context –
details on demand – over-plotting reduction – analytical patterns – pattern examples
UNIT II
TIME-SERIES, RANKING, AND DEVIATION ANALYSIS
9
Time-series analysis – time-series patterns – time-series displays – time-series best
practices – part-to-whole and ranking patterns – part-to-whole and ranking displays – best
practices – deviation analysis – deviation analysis displays – deviation analysis best
practices
UNIT III
DISTRIBUTION, CORRELATION, AND MULTIVARIATE ANALYSIS
9
Distribution analysis – describing distributions – distribution patterns – distribution displays –
distribution analysis best practices – correlation analysis – describing correlations –
correlation patterns – correlation displays – correlation analysis techniques and best
practices – multivariate analysis – multivariate patterns – multivariate displays – multivariate
analysis techniques and best practices
UNIT IV
INFORMATION DASHBOARD DESIGN I
9
Information dashboard – categorizing dashboards – typical dashboard data – dashboard
design issues and best practices – visual perception – limits of short-term memory – visually
encoding data – Gestalt principles – principles of visual perception for dashboard design
UNIT V
INFORMATION DASHBOARD DESIGN II
9
Characteristics of dashboards – key goals in visual design process – dashboard display
media – designing dashboards for usability – meaningful organization – maintaining
consistency – aesthetics of dashboards – testing for usability – case studies: sales
dashboard, CIO dashboard, Telesales dashboard, marketing analysis dashboard
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
Explain principles of visual perception
Apply core skills for visual analysis
Apply visualization techniques for various data analysis tasks
Design information dashboard
REFERENCES:
1. Stephen Few, "Now you see it: Simple Visualization techniques for quantitative analysis",
Analytics Press, 2009.
2. Stephen Few, "Information dashboard design: The effective visual communication of
data", O'Reilly, 2006.
3. Edward R. Tufte, "The visual display of quantitative information", Second Edition,
Graphics Press, 2001.
4. Nathan Yau, "Data Points: Visualization that means something", Wiley, 2013.
455. Ben Fry, "Visualizing data: Exploring and explaining data with the processing
environment", O'Reilly, 2008.
6. Gert H. N. Laursen and Jesper Thorlund, "Business Analytics for Managers: Taking
business intelligence beyond reporting", Wiley, 2010.
7. Evan Stubbs, "The value of business analytics: Identifying the path to profitability", Wiley,
2011.
NE7005
PROTOCOLS AND ARCHITECTURE FOR WIRELESS
SENSOR NETWORKS
LTPC
3 003
UNIT I
INTRODUCTION AND OVERVIEW OF WIRELESS SENSOR NETWORKS 9
Background of Sensor Network Technology, Application of Sensor Networks, Challenges for
Wireless Sensor Networks, Mobile Adhoc NETworks (MANETs) and Wireless Sensor
Networks, Enabling Technologies For Wireless Sensor Networks.
UNIT II
ARCHITECTURES
9
Single-node Architecture, Hardware Components & Design Constraints, Operating Systems
and Execution Environments, Introduction to TinyOS and nesC, Network Architecture,
Sensor Network Scenarios, Optimization Goals and Figures of Merit, Design Principles for
WSNs, Service Interfaces of WSNs, Gateway Concepts.
UNIT III
DEPLOYMENT AND CONFIGURATION
9
Localization and Positioning, Coverage and Connectivity, Single-hop and Multi-hop
Localization, Self Configuring Localization Systems, Sensor Management
Network Protocols: Issues in Designing MAC Protocol for WSNs, Classification of MAC
Protocols, S-MAC Protocol, B-MAC Protocol, IEEE 802.15.4 Standard and Zig Bee,
Dissemination Protocol for Large Sensor Network.
UNIT IV
ROUTING PROTOCOLS AND DATA MANIPULATION
9
Issues in Designing Routing Protocols, Classification of Routing Protocols, Energy-Efficient
Routing, Unicast, Broadcast and Multicast, Geographic Routing.
Data Centric and Content based Routing, Storage and Retrieval in Network, Compression
Technologies for WSN, Data Aggregation Technique.
UNIT V
SENSOR NETWORK PLATFORMS AND TOOLS
9
Sensor Node Hardware – Berkeley Motes, Programming Challenges, Node-level Software
Platforms, Node-level Simulators, State-centric Programming.
TOTAL : 45 PERIODS
REFERENCES:
1. Holger Karl & Andreas Willig, “Protocols And Architectures for Wireless Sensor
Networks", John Wiley, 2005.
2. Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information
Processing Approach", Elsevier, 2007.
3. Raghavendra, Cauligi S, Sivalingam, Krishna M., Zanti Taieb, “Wireless Sensor
Network”, Springer 1st Ed. 2004 (ISBN: 978-4020-7883-5).
4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks- Technology,
Protocols, and Applications”, John Wiley, 2007.
5. N. P. Mahalik, “Sensor Networks and Configuration: Fundamentals, Standards,
Platforms, and Applications” Springer Verlag.
6. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.
46CP7018
LANGUAGE TECHNOLOGIES
LTPC
3 003
OBJECTIVES:
To understand the mathematical foundations needed for language processing
To understand the representation and processing of Morphology and Part-of Speech
Taggers
To understand different aspects of natural language syntax and the various methods
used for processing syntax
To understand different methods of disambiguating word senses
To know about various applications of natural language processing
To learn the indexing and searching processes of a typical information retrieval
system and to study NLP based retrieval systems
To gain knowledge about typical text categorization and clustering techniques
UNIT I
INTRODUCTION
9
Natural Language Processing – Mathematical Foundations – Elementary Probability Theory
– Essential information Theory - Linguistics Essentials - Parts of Speech and Morphology –
Phrase Structure – Semantics – Corpus Based Work.
UNIT II
WORDS
9
Collocations – Statistical Inference – n-gram Models – Word Sense Disambiguation –
Lexical Acquisition.
UNIT III
GRAMMAR
9
Markov Models – Part-of-Speech Tagging – Probabilistic Context Free Grammars - Parsing.
UNIT IV
INFORMATION RETRIEVAL
9
Information Retrieval Architecture – Indexing - Storage – Compression Techniques –
Retrieval Approaches – Evaluation - Search Engines - Commercial Search Engine Features
– Comparison - Performance Measures – Document Processing - NLP based Information
Retrieval – Information Extraction.
UNIT V
TEXT MINING
9
Categorization – Extraction Based Categorization – Clustering - Hierarchical Clustering -
Document Classification and Routing - Finding and Organizing Answers from Text Search –
Text Categorization and Efficient Summarization using Lexical Chains – Machine Translation
- Transfer Metaphor - Interlingual and Statistical Approaches.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the different linguistic components of given sentences
Design a morphological analyser for a language of your choice using finite state
automata concepts
Implement a parser by providing suitable grammar and words
Discuss algorithms for word sense disambiguation
Build a tagger to semantically tag words using WordNet
Design an application that uses different aspects of language processing.
REFERENCES:
1. Christopher D.Manning and Hinrich Schutze, “ Foundations of Statistical Natural
Language Processing “, MIT Press, 1999.
2. Daniel Jurafsky and James H. Martin, “ Speech and Language Processing” , Pearson,
2008.
3. Ron Cole, J.Mariani, et.al “Survey of the State of the Art in Human Language
Technology”, Cambridge University Press, 1997.
Michael W. Berry, “ Survey of Text Mining: Clustering, Classification and Retrieval”,
Springer Verlag, 2003.
4.
47NE7012
SOCIAL NETWORKS ANALYSIS
LTPC
3 003
OBJECTIVES:
To understand the components of the social network
To model and visualize the social network
To mine the users in the social network
To understand the evolution of the social network
To mine the interest of the user
UNIT I
INTRODUCTION
9
Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis -
Development of Social Network Analysis - Key concepts and measures in network analysis -
Discussion networks - Blogs and online communities - Web-based networks
UNIT II
MODELING AND VISUALIZATION
9
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-
Based Representations- Node-Link Diagrams - Hybrid Representations - Modelling and
aggregating social network data – Random Walks and their Applications –Use of Hadoop
and Map Reduce - Ontological representation of social individuals and relationships.
UNIT III
MINING COMMUNITIES
9
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in
Social Networks - Evaluating Communities – Core Methods for Community Detection &
Mining - Applications of Community Mining Algorithms - Node Classification in Social
Networks.
UNIT IV
EVOLUTION
9
Evolution in Social Networks – Framework - Tracing Smoothly Evolving Communities -
Models and Algorithms for Social Influence Analysis - Influence Related Statistics - Social
Similarity and Influence - Influence Maximization in Viral Marketing - Algorithms and
Systems for Expert Location in Social Networks - Expert Location without Graph Constraints
- with Score Propagation – Expert Team Formation - Link Prediction in Social Networks -
Feature based Link Prediction - Bayesian Probabilistic Models - Probabilistic Relational
Models
UNIT V
TEXT AND OPINION MINING
9
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis - Product
review mining – Review Classification – Tracking sentiments towards topics over time
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Work on the internals components of the social network
Model and visualize the social network
Mine the behaviour of the users in the social network
Predict the possible next outcome of the social network
Mine the opinion of the user
REFERENCES:
1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011
2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.
3. Borko Furht, “Handbook of Social Network Technologies and Applications”, Springer, 1st
edition, 2010.
484. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking –
Techniques and applications”, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social Network
Analysis: Trends, Tools and Research Advances”, Springer, 2009.
7. Toby Segaran, “Programming Collective Intelligence”, O’Reilly, 2012
CP7019
MANAGING BIG DATA
LTPC
3 003
OBJECTIVES:
Understand big data for business intelligence
Learn business case studies for big data analytics
Understand nosql big data management
Perform map-reduce analytics using Hadoop and related tools
UNIT I
UNDERSTANDING BIG DATA
9
What is big data – why big data – convergence of key trends – unstructured data – industry
examples of big data – web analytics – big data and marketing – fraud and big data – risk
and big data – credit risk management – big data and algorithmic trading – big data and
healthcare – big data in medicine – advertising and big data – big data technologies –
introduction to Hadoop – open source technologies – cloud and big data – mobile business
intelligence – Crowd sourcing analytics – inter and trans firewall analytics
UNIT II
NOSQL DATA MANAGEMENT
9
Introduction to NoSQL – aggregate data models – aggregates – key-value and document
data models – relationships – graph databases – schemaless databases – materialized
views – distribution models – sharding – master-slave replication – peer-peer replication –
sharding and replication – consistency – relaxing consistency – version stamps – map-
reduce – partitioning and combining – composing map-reduce calculations
UNIT III
BASICS OF HADOOP
9
Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop
pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface
– data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based
data structures
UNIT IV
MAPREDUCE APPLICATIONS
9
MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of
MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and
YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input
formats – output formats
UNIT V
HADOOP RELATED TOOLS
9
Hbase – data model and implementations – Hbase clients – Hbase examples –
praxis.Cassandra – cassandra data model – cassandra examples – cassandra clients –
Hadoop integration.
Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts.
Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –
HiveQL queries.
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe big data and use cases from selected business domains
Explain NoSQL big data management
Install, configure, and run Hadoop and HDFS
Perform map-reduce analytics using Hadoop
Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics
49REFERENCES:
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
NE7011
MOBILE APPLICATION DEVELOPMENT
LTPC
3 003
OBJECTIVES:
1. Understand system requirements for mobile applications
2. Generate suitable design using specific mobile development frameworks
3. Generate mobile application design
4. Implement the design using specific mobile development frameworks
5. Deploy the mobile applications in marketplace for distribution
UNIT I
INTRODUCTION
5
Introduction to mobile applications – Embedded systems - Market and business drivers for
mobile applications – Publishing and delivery of mobile applications – Requirements
gathering and validation for mobile applications
UNIT II
BASIC DESIGN
8
Introduction – Basics of embedded systems design – Embedded OS - Design constraints for
mobile applications, both hardware and software related – Architecting mobile applications
– User interfaces for mobile applications – touch events and gestures – Achieving quality
constraints – performance, usability, security, availability and modifiability.
UNIT III
ADVANCED DESIGN
8
Designing applications with multimedia and web access capabilities – Integration with GPS
and social media networking applications – Accessing applications hosted in a cloud
computing environment – Design patterns for mobile applications.
UNIT IV
TECHNOLOGY I - ANDROID
12
Introduction – Establishing the development environment – Android architecture – Activities
and views – Interacting with UI – Persisting data using SQLite – Packaging and deployment
– Interaction with server side applications – Using Google Maps, GPS and Wifi – Integration
with social media applications.
UNIT V
TECHNOLOGY II - IOS
12
Introduction to Objective C – iOS features – UI implementation – Touch frameworks – Data
persistence using Core Data and SQLite – Location aware applications using Core Location
and Map Kit – Integrating calendar and address book with social media application – Using
Wifi - iPhone marketplace.
TOTAL: 45 PERIODS
OUTCOMES:
Upon the students will be able toCompletion of the course,
1. Describe the requirements for mobile applications
2. Explain the challenges in mobile application design and development
3. Develop design for mobile applications for specific requirements
4. Implement the design using Android SDK
5. Implement the design using Objective C and iOS
6. Deploy mobile applications in Android and iPone marketplace for distribution
50REFERENCES:
1. http://developer.android.com/develop/index.html
2. Jeff McWherter and Scott Gowell, "Professional Mobile Application Development",
Wrox, 2012
3. Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”,
DreamTech, 2012
4. James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2012
5. David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 6
Development: Exploring the iOS SDK”, Apress, 2013.
CP7020
BIO-INSPIRED COMPUTING
LT PC
3 0 03
OBJECTIVES:
Learn evolutionary theory and algorithms
Understand Cellular Automata and artificial life
Learn artificial neural systems and related learning algorithms
Learn developmental and artificial immune systems
Understand behavioral systems especially in the context of Robotics
Understand collective systems such as ACO, PSO, and swarm robotics
UNIT I
EVOLUTIONARY AND CELLULAR SYSTEMS
9
Foundations of evolutionary theory – Genotype – artificial evolution – genetic
representations – initial population – fitness functions – selection and reproduction – genetic
operators – evolutionary measures – evolutionary algorithms – evolutionary electronics –
evolutionary algorithm case study
Cellular systems – cellular automata – modeling with cellular systems – other cellular
systems – computation with cellular systems – artificial life – analysis and synthesis of
cellular systems
UNIT II
NEURAL SYSTEMS
9
Biological nervous systems – artificial neural networks – neuron models – architecture –
signal encoding – synaptic plasticity – unsupervised learning – supervised learning –
reinforcement learning – evolution of neural networks – hybrid neural systems – case study
UNIT III
DEVELOPMENTAL AND IMMUNE SYSTEMS
9
Rewriting systems – synthesis of developmental systems – evolutionary rewriting systems –
evolutionary developmental programs
Biological immune systems – lessons for artificial immune systems – algorithms and
applications – shape space – negative selection algorithm – clonal selection algorithm -
examples
UNIT IV
BEHAVIORAL SYSTEMS
9
Behavior is cognitive science – behavior in AI – behavior based robotics – biological
inspiration for robots – robots as biological models – robot learning – evolution of behavioral
systems – learning in behavioral systems – co-evolution of body and control – towards self
reproduction – simulation and reality
UNIT V
COLLECTIVE SYSTEMS
9
Biological self-organization – Particle Swarm Optimization (PSO) – ant colony optimization
(ACO) – swarm robotics – co-evolutionary dynamics – artificial evolution of competing
systems – artificial evolution of cooperation – case study
TOTAL: 45 PERIODS
51OUTCOMES:
Upon completion of the course, the students will be able to
Implement and apply evolutionary algorithms
Explain cellular automata and artificial life
Implement and apply neural systems
Explain developmental and artificial immune systems
Explain behavioral systems
Implement and apply collective intelligence systems
REFERENCES:
1. D. Floreano and C. Mattiussi, "Bio-Inspired Artificial Intelligence", MIT Press, 2008.
2. F. Neumann and C. Witt, “Bioinspired Computation in combinatorial optimization:
Algorithms and their computational complexity”, Springer, 2010.
3. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010.
4. D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”,
Addison-Wesley, 1989.
5. Simon O. Haykin, “Neural Networks and Learning Machines”, Third Edition, Prentice
Hall, 2008.
6. M. Dorigo and T. Stutzle, “Ant Colony Optimization”, A Bradford Book, 2004.
7. R. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.
CP7021
MEDICAL IMAGE PROCESSING
LT PC
3 0 0 3
COURSE DESCRIPTION:
An advanced graduate level course on medical imaging and medical image analysis. The
course includes topics in medical image formation, medical imaging techniques, such as X-
Ray, Computed Tomography, Magnetic Resonance Imaging, and Nuclear Imaging, image
segmentation, registration, statistical modeling, visualization, and applications of
computational tools for medicine.
OBJECTIVES:
The course will provide the participants with an up-to-date background in current state-of-
the-art in medical imaging and medical image analysis. The aim of the course is to show
how to extract, model, and analyze information from medical data and applications in order
to help diagnosis, treatment and monitoring of diseases through computer science.
UNIT I
INTRODUCTION
9
Introduction to medical imaging technology, systems, and modalities. Brief history;
importance; applications; trends; challenges. Medical Image Formation Principles: X-Ray
physics; X-Ray generation, attenuation, scattering; dose Basic principles of CT;
reconstruction methods; artifacts; CT hardware.
UNIT II
STORAGE AND PROCESSING
9
Medical Image Storage, Archiving and Communication Systems and Formats Picture
archiving and communication system (PACS); Formats: DICOM Radiology Information
Systems (RIS) and Hospital Information Systems (HIS). Medical Image Processing,
Enhancement, Filtering Basic image processing algorithms Thresholding; contrast
enhancement; SNR characteristics; filtering; histogram modeling.
UNIT III
VISUALIZATION
9
Medical Image Visualization Fundamentals of visualization; surface and volume
rendering/visualization; animation; interaction. Magnetic Resonance Imaging (MRI)
Mathematics of MR; spin physics; NMR spectroscopy; imaging principles and hardware;
image artifacts.
52UNIT IV
SEGMENTATION AND CLASSIFICATION
9
Medical Image Segmentation - Histogram-based methods; Region growing and watersheds;
Markov Random Field models; active contours; model-based segmentation. Multi-scale
segmentation; semi-automated methods; clustering-based methods; classification-based
methods; atlas-guided approaches; multi-model segmentation. Medical Image Registration
Intensity-based methods; cost functions; optimization techniques.
UNIT V
NUCLEAR IMAGING
9
PET and SPECT Ultrasound Imaging methods; mathematical principles; resolution; noise
effect; 3D imaging; positron emission tomography; single photon emission tomography;
ultrasound imaging; applications. Medical Image Search and Retrieval Current technology in
medical image search, content-based image retrieval, new trends: ontologies. Applications.
Other Applications of Medical Imaging Validation, Image Guided Surgery, Image Guided
Therapy, Computer Aided Diagnosis/Diagnostic Support Systems.
TOTAL: 45 PERIODS
REFERENCES:
1. Paul Suetens, "Fundamentals of Medical Imaging", Second Edition, Cambridge
University Press, 2009.
2. J. Michael Fitzpatrick and Milan Sonka, "Handbook of Medical Imaging, Volume 2.
Medical Image Processing and Analysis", SPIE Publications, 2009.
3. Kayvan Najarian and Robert Splinter, "Biomedical Signal and Image Processing",
Second Edition, CRC Press, 2005.
4. Geoff Dougherty, "Digital Image Processing for Medical Applications", First Edition,
Cambridge University Press, 2009.
5. Jerry L. Prince and Jonathan Links, "Medical Imaging Signals and Systems", First
Edition, Prentice Hall, 2005.
6. John L. Semmlow, "Biosignal and Medical Image Processing", Second Edition, CRC
Press, 2008.
CP7022
SOFTWARE DESIGN
LTPC
30 03
OBJECTIVES:
Analyze specifications
Describe approaches to design
Develop design documentation
Evaluate the design
UNIT I
SOFTWARE DESIGN PRINCIPLES
9
Introduction – Design process – Managing complexity – Software modeling and notations –
Abstraction – Modularity – Hierarchy – Coupling - Cohesion – Design guidelines and
checklists – Refactoring
UNIT II
OO DESIGN
9
Object model – Classes and objects – Object oriented analysis – Key abstractions and
mechanisms – Object oriented design – Identifying design elements – Detailed design –
Case studies.
UNIT III
DESIGN PATTERNS
9
Introduction to patterns – Design context – Reusable solutions – Documenting reusable
solutions – Standard patterns from GOF book.
UNIT IV
FUNCTION AND SERVICE ORIENTED DESIGNS
9
Structural decomposition – Detailed Design – Function oriented design Case study –
Services – Service identification – Service design – Service composition – choreography and
orchestration – Service oriented design Case study
53UNIT V
USER CENTERED DESIGN AND DESIGN REVIEW
9
Introduction to user centered design – Use in context – Interface and interaction – User
centered design principles – Task analysis – Evaluation – Introduction to design review–
Testing the design – Walk throughs – Review against check lists.
TOTAL : 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Describe different approaches to desigining a software application
Analyze specifications and identify appropriate design strategies.
Develop an appropriate design for a given set of requirements
Identify applicatble design patterns for the solution
Abstract and document reusable design patterns
Evaluate a given design against the specifications
REFERENCES:
1. Grady Booch et al., "Object Oriented Analysis and Design with Applications", 3rd
Edition, Pearson, 2010.
2. Carlos Otero, "Software Engineering Design: Theory and Practice", CRC Press, 2012
3. David Budgen, "Software Design", 2nd Edtion, Addison Wesley, 2003
4. Alan Shalloway and James R Trott, "Design Patterns Explained: A New Perspective
on Object-Oriented Design", 2nd Edition, Addison-Wesley Professional, 2004
5. Hassan Gomaa, "Software Modeling and Design", Cambridge University Press, 2011
6. Eric Gamma et al., "Design Patterns: Elements of Reusable Object-Oriented
Software", Addison-Wesley Professional, 1994
7. Ian Sommerville, "Software Engineering", 9th Edition, Addison-Wesley, 2010
8. M B Rosson and J M Carroll, "Usability Engineering: Scenario-Based Development
of Human-Computer Interaction", Morgan Kaufmann, 2002
CP7023
RECONFIGURABLE COMPUTING
LTPC
3 003
OBJECTIVES:
To understand the need for reconfigurable computing
To expose the students to various device architectures
To examine the various reconfigurable computing systems
To understand the different types of compute models for programming reconfigurable
architectures
To expose the students to HDL programming and familiarize with the development
environment
To expose the students to the various placement and routing protocols
To develop applications with FPGAs
UNIT I
DEVICE ARCHITECTURE
9
General Purpose Computing Vs Reconfigurable Computing – Simple Programmable Logic
Devices – Complex Programmable Logic Devices – FPGAs – Device Architecture - Case
Studies.
UNIT II
RECONFIGURABLE COMPUTING ARCHITECTURES AND SYSTEMS
9
Reconfigurable Processing Fabric Architectures – RPF Integration into Traditional
Computing Systems – Reconfigurable Computing Systems – Case Studies –
Reconfiguration Management.
UNIT III
PROGRAMMING RECONFIGURABLE SYSTEMS
9
Compute Models - Programming FPGA Applications in HDL – Compiling C for Spatial
Computing – Operating System Support for Reconfigurable Computing.
54UNIT IV
MAPPING DESIGNS TO RECONFIGURABLE PLATFORMS
9
The Design Flow - Technology Mapping – FPGA Placement and Routing – Configuration
Bitstream Generation – Case Studies with Appropriate Tools.
UNIT V
APPLICATION DEVELOPMENT WITH FPGAS
9
Case Studies of FPGA Applications – System on a Programmable Chip (SoPC) Designs.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Identify the need for reconfigurable architectures
Discuss the architecture of FPGAs
Point out the salient features of different reconfigurable architectures
Build basic modules using any HDL
Develop applications using any HDL and appropriate tools
Design and build an SoPC for a particular application
REFERENCES:
1. Maya B. Gokhale and Paul S. Graham, “Reconfigurable Computing: Accelerating
Computation with Field-Programmable Gate Arrays”, Springer, 2005.
2. Scott Hauck and Andre Dehon (Eds.), “Reconfigurable Computing – The Theory and
Practice of FPGA-Based Computation”, Elsevier / Morgan Kaufmann, 2008.
3. Christophe Bobda, “Introduction to Reconfigurable Computing – Architectures,
Algorithms and Applications”, Springer, 2010.
IF7013
ENERGY AWARE COMPUTING
LTPC
30 03
OBJECTIVES:
This course examines the design of power efficient architecture, power and performance
tradeoffs, restructuring of software and applications and standards for energy aware
Hardware and Software. The objective of this course is:
To know the fundamental principles energy efficient devices
To study the concepts of Energy efficient storage
To introduce energy efficient algorithms
To enable the students to know energy efficient techniques involved to support real-
time systems.
To study Energy aware applications
UNIT I
INTRODUCTION
9
Energy efficient network on chip architecture for multi core system-Energy efficient MIPS
CPU core with fine grained run time power gating – Low power design of Emerging memory
technologies.
UNIT II
ENERGY EFFICIENT STORAGE
9
Disk Energy Management-Power efficient strategies for storage system-Dynamic thermal
management for high performance storage systems-Energy saving technique for Disk
storage systems
UNIT III
ENERGY EFFICIENT ALGORITHMS
9
Scheduling of Parallel Tasks – Task level Dynamic voltage scaling – Speed Scaling –
Processor optimization- Memetic Algorithms – Online job scheduling Algorithms.
55UNIT IV
REAL TIME SYSTEMS
9
Multi processor system – Real Time tasks- Energy Minimization – Energy aware scheduling-
Dynamic Reconfiguration- Adaptive power management-Energy Harvesting Embedded
system
UNIT V
ENERGY AWARE APPLICATIONS
9
On chip network – Video codec Design – Surveillance camera- Low power mobile storage.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Design Power efficient architecture Hardware and Software.
Analyze power and performance trade off between various energy aware storage
devices.
Implement various energy aware algorithms.
Restructure the software and Hardware for Energy aware applications.
Explore the Energy aware applications
REFERENCES:
1. Ishfaq Ah mad, Sanjay Ranka, Handbook of Energy Aware and Green Computing,
Chapman and Hall/CRC, 2012
2. Chong-Min Kyung, Sungioo yoo, Energy Aware system design Algorithms and
Architecture, Springer, 2011.
3. Bob steiger wald ,Chris:Luero, Energy Aware computing, Intel Press,2012.
CP7024
INFORMATION RETRIEVAL TECHNIQUES
LTPC
30 03
OBJECTIVES:
To understand the basics of Information Retrieval with pertinence to modeling, query
operations and indexing
To get an understanding of machine learning techniques for text classification and
clustering
To understand the various applications of Information Retrieval giving emphasis to
Multimedia IR, Web Search
To understand the concepts of digital libraries
UNIT I
INTRODUCTION
8
Motivation – Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean
Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web
Characteristics–The impact of the web on IR ––IR Versus Web Search–Components of a
Search engine
UNIT II
MODELING
10
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term
Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic
Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing
UNIT III
INDEXING
9
Static and Dynamic Inverted Indices – Index Construction and Index Compression Searching
- Sequential Searching and Pattern Matching. Query Operations -Query Languages–Query
Processing - Relevance Feedback and Query Expansion - Automatic Local and Global
Analysis – Measuring Effectiveness and Efficiency.
UNIT IV
CLASSIFICATION AND CLUSTERING
8
Text Classification and Naïve Bayes – Vector Space Classification – Support vector
machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –
Matrix decompositions and latent semantic indexing – Fusion and Meta learning
56UNIT V
SEARCHING AND RANKING
10
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic
Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR:
Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital
Libraries
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Build an Information Retrieval system using the available tools
Identify and design the various components of an Information Retrieval system
Apply machine learning techniques to text classification and clustering which is used
for efficient Information Retrieval
Analyze the Web content structure
Design an efficient search engine
REFERENCES:
1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The
concepts and Technology behind Search (ACM Press Books), Second Edition 2011
2. Christopher D. Manning, PrabhakarRaghavan, HinrichSchutze, Introduction to
Information Retrieval, Cambridge University Press, First South Asian Edition 2012
3. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval
Implementing and Evaluating Search Engines, The MIT Press, Cambridge,
Massachusetts London, England, 2010
CP7025
DATA MINING TECHNIQUES
LTPC
300 3
UNIT I
INTRODUCTION TO DATA MINING
9
Introduction to Data Mining – Data Mining Tasks – Components of Data Mining Algorithms –
Data Mining supporting Techniques – Major Issues in Data Mining – Measurement and Data
– Data Preprocessing – Data sets
UNIT II
OVERVIEW OF DATA MINING ALGORITHMS
9
Overview of Data Mining Algorithms – Models and Patterns – Introduction – The
Reductionist viewpoint on Data Mining Algorithms – Score function for Data Mining
Algorithms- Introduction – Fundamentals of Modeling – Model Structures for Prediction –
Models for probability Distributions and Density functions – The Curve of Dimensionality –
Models for Structured Data – Scoring Patterns – Predictive versus Descriptive score
functions – Scoring Models with Different Complexities – Evaluation of Models and Patterns
– Robust Methods.
UNIT III
CLASSIFICATIONS
9
Classifications – Basic Concepts – Decision Tree induction – Bayes Classification Methods –
Rule Based Classification – Model Evaluation and Selection – Techniques to Improve
Classification Accuracy – Classification: Advanced concepts – Bayesian Belief Networks-
Classification by Back Propagation – Support Vector Machine – Classification using frequent
patterns.
UNIT IV
CLUSTER ANALYSIS
9
Cluster Analysis: Basic concepts and Methods – Cluster Analysis – Partitioning methods –
Hierarchical methods – Density Based Methods – Grid Based Methods – Evaluation of
Clustering – Advanced Cluster Analysis: Probabilistic model based clustering – Clustering
High – Dimensional Data – Clustering Graph and Network Data – Clustering with
Constraints.
57UNIT V
ASSOCIATION RULE MINING AND VISUALIZATION
9
Association Rule Mining – Introduction – Large Item sets – Basic Algorithms – Parallel and
Distributed Algorithms – Comparing Approaches – Incremental Rules – Advanced
Association Rule Techniques – Measuring the Quality of Rules – Visualization of
Multidimensional Data – Diagrams for Multidimensional visualization – Visual Data Mining –
Data Mining Applications – Case Study: WEKA.
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. Jiawei Han, Micheline Kamber , Jian Pei, “Data Mining: Concepts and Techniques”,
Third Edition (The Morgan Kaufmann Series in Data Management Systems), 2012.
2. David J. Hand, Heikki Mannila and Padhraic Smyth “Principles of Data Mining”
(Adaptive Computation and Machine Learning), 2005
3. Margaret H Dunham, “Data Mining: Introductory and Advanced Topics”, 2003
4. Soman, K. P., Diwakar Shyam and Ajay V. “Insight Into Data Mining: Theory And
Practice”, PHI, 2009.
IF7002
BIO INFORMATICS
LTPC
3 003
p
OBJECTIVES:
To get exposed to the domain of bioinformatics
To understand the role of data warehousing and data mining for bioinformatics
To learn to model bioinformatics based applications
To understand how to deploy the pattern matching and visualization techniques in
bioinformatics
To study the Microarray technologies for genome expression
UNIT I
INTRODUCTION
9
Need for Bioinformatics technologies – Overview of Bioinformatics technologies – Structural
bioinformatics – Data format and processing – secondary resources- Applications – Role of
Structural bioinformatics - Biological Data Integration System.
UNIT II
DATAWAREHOUSING AND DATAMINING IN BIOINFORMATICS
9
Bioinformatics data – Data ware housing architecture – data quality – Biomedical data
analysis – DNA data analysis – Protein data analysis – Machine learning – Neural network
architecture- Applications in bioinformatics
UNIT III
MODELING FOR BIOINFORMATICS
9
Hidden markov modeling for biological data analysis – Sequence identification – Sequence
classification – multiple alignment generation – Comparative modeling – Protein modeling –
genomic modeling – Probabilistic modeling – Bayesian networks – Boolean networks -
Molecular modeling – Computer programs for molecular modeling
UNIT IV
PATTERN MATCHING AND VISUALIZATION
9
Gene regulation – motif recognition and motif detection – strategies for motif detection –
Visualization – Fractal analysis – DNA walk models – one dimension – two dimension –
higher dimension – Game representation of Biological sequences – DNA, Protein, Amino
acid sequences
UNIT V
MICROARRAY ANALYSIS
9
Microarray technology for genome expression study – image analysis for data extraction –
preprocessing – segmentation – gridding , spot extraction , normalization, filtering – cluster
analysis – gene network analysis – Compared Evaluation of Scientific Data Management
Systems – Cost Matrix – Evaluation model ,Benchmark , Tradeoffs
TOTAL: 45 PERIODS
58OUTCOMES:
Upon Completion of the course, the students will be able to
Deploy the data warehousing and data mining techniques in Bioinformatics
Model bioinformatics based applications
Deploy the pattern matching and visualization techniques in bioinformatics
Work on the protein sequences
Use the Microarray technologies for genome expression
REFERENCES:
1. Yi-Ping Phoebe Chen (Ed), “Bio Informatics Technologies”, First Indian Reprint, Springer
Verlag, 2007.
2. N.J. Chikhale and Virendra Gomase, "Bioinformatics- Theory and Practice", Himalaya
Publication House, India, 2007
3. Zoe lacroix and Terence Critchlow, “Bio Informatics – Managing Scientific data”, First
Indian Reprint, Elsevier, 2004
4. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003.
5. Arthur M Lesk, “Introduction to Bioinformatics”, Second Edition, Oxford University Press,
2005
6. Burton. E. Tropp, “Molecular Biology: Genes to Proteins “, 4th edition, Jones and Bartlett
Publishers, 2011
7. Dan Gusfield, “Algorithms on Strings Trees and Sequences”, Cambridge University
Press, 1997.
8. P. Baldi, S Brunak , Bioinformatics, “A Machine Learning Approach “, MIT Press, 1998.
CP7026
SOFTWARE QUALITY ASSURANCE
LTPC
3 003
OBJECTIVES:
Describe approaches to quality assurance
Understand quality models
Evaluate the system based on the chosen quality model
UNIT I
INTRODUCTION
9
Introduction – Views on quality – Cost of quality - Quality models – Quality frameworks –
Verification and Validation – Defect taxonomy – Defect management – Statistics and
measurements – IEEE standards – Quality assurance and control processes
UNIT II
VERIFICATION
6
Introduction – Verification techniques – Inspections, reviews, walk-throughs – Case studies
UNIT III
TEST GENERATION
12
Software testing- Validation – Test plan – Test cases - Test Generation – Equivalence
partitioning – Boundary value analysis – Category partition method – Combinatorial
generation - Decision tables – Examples and Case studies
UNIT IV
STRUCTURAL TESTING
12
Introduction – Test adequacy criteria – Control flow graph – Coverages: block, conditions,
multiple conditions, MC/DC, path – Data flow graph – Definition and use coverages – C-use,
P-use, Def-clear, Def-use – Finite state machines – Transition coverage – Fault based
testing – Mutation analysis – Case studies
UNIT V
FUNCTIONAL TESTING
6
Introduction – Test adequacy criteria - Test cases from use cases – Exploratory testing -
Integration, system, acceptance, regression testing – Testing for specific attributes:
Performance, load and stress testing – Usability testing – Security testing - Test automation
– Test oracles
TOTAL : 45 PERIODS
59OUTCOMES:
Upon Completion of the course, the students will be able to
Describe different approaches to testing software applications
Analyze specifications and identify appropriate test generation strategies
Develop an appropriate test design for a given test object
Identify applicatble measurements for the verification and validation effort
Execute the test design
Evaluate the testing effort based on adequate measures
REFERENCES:
1. Boriz Beizer, "Software Testing Techniques", 2nd Edition, DreamTech, 2009.
2. Aditya P. Mathur, "Foundations of Software Testing", Pearson, 2008
3. Mauro Pezze and Michal Young, "Software Testing and Analysis. Process, Principles,
and Techniques", John Wiley 2008
4. Stephen H. Kan, "Metrics and Models in Software Quality Engineering", 2nd Edition,
Pearson, 2003
5. Kshirasagar Naik and Priyadarshi Tripathy (Eds), "Software Testing and Quality
Assurance: Theory and Practice", John Wiley, 2008
6. "Combinatorial Methods in Software Testing", ttp://csrc.nist.gov/groups/SNS/acts/index.html
CP7027
MULTI OBJECTIVE OPTIMIZATION TECHNIQUES
LTPC
3 003
OBJECTIVES:
Learn fundamental principles of Multiobjective Optimization (MOP)
Survey different Multiobjective Optimization algorithms
Introduce various design issues of MOP
Develop and Evaluate MOP Algorithms
Learn Parallel and hybrid MOP Algorithms
Learn other Metaheuristics
UNIT I
INTRODUCTION AND CLASSICAL APPROACHES
9
Multiobjective optimization: Introduction - Multiobjective optimization problem-principles –
Difference between single and multiobjective optimization – Dominance and Pareto
Optimality , Classical Methods – Weighted Sum -
Constraint method – Weighted Metric
methods – Benson’s method - Value Function - Goal Programming methods – Interactive
Methods
UNIT II
MOP EVOLUTIONARY ALGORITHMS
9
Generic MOEA - Various MOEAs: MOGA, NSGA-II, NPGA, PAES, SPEA2, MOMGA, micro
GA - Constrained MOEAs: Penalty Function approach - Constrained Tournament – Ray –
Tai –Seow’s Method.
UNIT III
THEORETICAL ISSUES
9
Fitness Landscapes - Fitness Functions - Pareto Ranking - Pareto Niching and Fitness
Sharing - Recombination Operators - Mating Restriction - Solution Stability and Robustness
- MOEA Complexity - MOEA Scalability - Running Time Analysis - MOEA Computational
Cost - No Free Lunch Theorem.
UNIT IV
MOEA TESTING, ANALYSIS, AND PARALLELIZATION
9
MOEA Experimental Measurements – MOEA Statistical Testing Approaches – MOEA Test
Suites - MOEA Parallelization: Background – Paradigms – Issues - MOEA Local Search
Techniques.
60UNIT V
APPLICATIONS AND ALTERNATIVE METAHEURISTICS
9
Scientific Applications: Computer Science and Computer Engineering - Alternative
Metaheuristics: Simulated Annealing – Tabu Search and Scatter Search – Ant System –
Distributed Reinforcement Learning – Particle Swarm Optimization – Differential Evolution –
Artificial Immune Systems - Other Heuristics.
TOTAL:45 PERIODS
OUTCOMES:
Upon Completion of the course,students will be able to
Explain MOP principles
Explain classical methods to solve MOP problems
Be familiar with and explain structures of different MOP algorithms
Solve constrained MOP problems
Explain various design issues of MOP algorithms
Perform a evaluation and analysis of MOP algorithm results
Explain parallelization of MOP algorithms
Develop parallel and hybrid MOP algorithms
Identify various real time MOP applications
Explain other search algorithms
REFERENCES:
1. Carlos A. Coello Coello, Gary B. Lamont, David A. Van Veldhuizen, “Evolutionary
Algorithms for Solving Multi-objective Problems”, Second Edition, Springer, 2007.
2. Kalyanmoy Deb, “ Multi-Objective Optimization Using Evolutionary Algorithms”, John
Wiley, 2002.
3. Aimin Zhoua, Bo-Yang Qub, Hui Li c, Shi-Zheng Zhaob, Ponnuthurai Nagaratnam
Suganthan b, Qingfu Zhangd, “Multiobjective evolutionary algorithms: A survey of the
state of the art”, Swarm and Evolutionary Computation (2011) 32–49.
4. E Alba, M Tomassini, “Parallel and evolutionary algorithms”, Evolutionary Computation,
IEEE Transactions on 6 (5), 443-462.
5. Crina Grosan, Ajith Abraham, “Hybrid Evolutionary Algorithms: Methodologies,
Architectures, and Reviews”, Studies in Computational Intelligence, Vol. 75, Springer,
2007.
6. Christian Blum and Andrea Roli. 2003. Metaheuristics in combinatorial optimization:
Overview and conceptual comparison. ACM Comput. Surv. 35, 3 (September 2003),
268-308.
CP7028
ENTERPRISE APPLICATION INTEGRATION
LTPC
30 03
OBJECTIVES:
Describe approaches to enterprise application integration
Understand the integration middleware
Evaluate the integration approaches suitable for a given problem
UNIT I
INTRODUCTION
6
Requirements for EAI - Challenges in EAI – Integration with legacy systems – Integration
with partners - Heterogeneous environment – Implementation approaches – Web services,
messaging, ETL, direct data integration – Middleware requirements – Approaches to
integration – services oriented and messaging.
UNIT II
INTEGRATION PATTERNS
6
Introduction to integration patterns – Architecture for application integration – Integration
patterns – Point to point, broker, message bus, publish/subscribe, Challenges in
performance, security, reliability - Case studies
61UNIT III
SERVICE ORIENTED INTEGRATION
12
Business process integration - Composite applications-services – Web services – Service
choreography and orchestration - Business process modeling - BPMN, Business process
execution - BPEL – Middleware infrastructure - Case studies
UNIT IV
MESSAGING BASED INTEGRATION
9
Messaging – Synchronous and asynchronous – Message structure – Message oriented
middleware – Reliability mechanisms – Challenges – Messaging infrastructure – Java
Messaging Services – Case studies
UNIT V
ENTERPRISE SERVICE BUS
12
Enterprise Service Bus – routing, scalable connectivity, protocol and message
transformations, data enrichment, distribution, correlation, monitoring – Deployment
configurations – Global ESB, Directly connected, Federated, brokered ESBs – Application
server based – Messaging system based – Hardware based ESBs – Support to SOA,
message based and event based integrations - Case studies.
OUTCOMES:
Upon Completion of the course,the students will be able to
Describe different approaches to integration enterprise applications
Analyze specifications and identify appropriate integration approaches
Develop a suitable integration design for a given problem
Identify appropriate integration middleware for a given problem
Evaluate the integration approaches against specified requirements
REFERENCES:
1. George Mentzas and Andreas Frezen (Eds), "Semantic Enterprise Application
Integration for Business Processes: Service-oriented Frameworks", Business Science
Reference, 2009
2. Waseem Roshen, "SOA Based Enterprise Integration", Tata McGrawHill, 2009.
3. G
Hohpe
and
B
Woolf,
"Enterprise
Integration Patterns: Designing,
Building, and Deploying Messaging Solutions", Addison Wesley Professional,
2003
4. D
Linthicum, "Next
Generation
Application
Integration:
From
Simple
Information to Web Services", Addison Wesley, 2003
5. Martin Fowler, "Patterns of Enterprise Application Architecture", Addison- Wesley, 2003
6. Kapil Pant and Matiaz Juric, "Business Process Driven SOA using BPMN and BPEL:
From Business Process Modeling to Orchestration and Service Oriented Architecture",
Packt Publishing, 2008
CP7029
INFORMATION STORAGE MANAGEMENT
LTPC
3 003
UNIT I
INTRODUCTION TO STORAGE TECHNOLOGY
9
Review data creation and the amount of data being created and understand the value of
data to a business, challenges in data storage and data management, Solutions available for
data storage, Core elements of a data center infrastructure, role of each element in
supporting business activities
62UNIT II
STORAGE SYSTEMS ARCHITECTURE
9
Hardware and software components of the host environment, Key protocols and concepts
used by each component ,Physical and logical components of a connectivity environment
,Major physical components of a disk drive and their function, logical constructs of a physical
disk, access characteristics, and performance Implications, Concept of RAID and its
components, Different RAID levels and their suitability for different application environments:
RAID 0, RAID 1, RAID 3, RAID 4, RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and
contrast integrated and modular storage systems ,Iligh-level architecture and working of an
intelligent storage system
UNIT III
INTRODUCTION TO NETWORKED STORAGE
9
Evolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS,
and IP-SAN, Benefits of the different networked storage options, understand the need for
long-term archiving solutions and describe how CAS full fill the need, understand the
appropriateness of the different networked storage options for different application
environments
UNIT IV
INFORMATION AVAILABILITY, MONITORING & MANAGING
DATACENTER
9
List reasons for planned/unplanned outages and the impact of downtime, Impact of
downtime - Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO
and RPO, Identify single points of failure in a storage infrastructure and list solutions to
mitigate these failures, Architecture of backup/recovery and the different backup/ recovery
topologies, replication technologies and their role in ensuring information availability and
business continuity, Remote replication technologies and their role in providing disaster
recovery and business continuity capabilities. Identify key areas to monitor in a data center,
Industry standards for data center monitoring and management, Key metrics to monitor for
different components in a storage infrastructure, Key management tasks in a data center
UNIT V
SECURING STORAGE AND STORAGE VIRTUALIZATION
9
Information security, Critical security attributes for information systems, Storage security
domains, List and analyzes the common threats in each domain, Virtualization technologies,
block-level and file-level virtualization technologies and processes
TOTAL: 45 PERIODS
REFERENCE BOOKS:
1. EMC Corporation, Information Storage and Management, Wiley, India.
2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill ,
Osborne, 2003.
3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001.
4. Additional resource material on www.emc.com/resource-library/resource-library.esp
CP7030
ROBOTICS
OBJECTIVES:
To understand robot locomotion and mobile robot kinematics
To understand perception in robotics
To understand mobile robot localization
To understand mobile robot mapping
To understand simultaneous localization and mapping (SLAM)
To understand robot planning and navigation
63
L T P C
3 0 0 3UNIT I
LOCOMOTION AND KINEMATICS
9
Introduction to Robotics – key issues in robot locomotion – legged robots – wheeled mobile
robots – aerial mobile robots – introduction to kinematics – kinematics models and
constraints – robot maneuverability
UNIT II
ROBOT PERCEPTION
9
Sensors for mobile robots – vision for robotics – cameras – image formation – structure from
stereo – structure from motion – optical flow – color tracking – place recognition – range data
UNIT III
MOBILE ROBOT LOCALIZATION
9
Introduction to localization – challenges in localization – localization and navigation – belief
representation – map representation – probabilistic map-based localization – Markov
localization – EKF localization – UKF localization – Grid localization – Monte Carlo
localization – localization in dynamic environments
UNIT IV
MOBILE ROBOT MAPPING
9
Autonomous map building – occupancy grip mapping – MAP occupancy mapping – SLAM –
extended Kalman Filter SLAM – graph-based SLAM – particle filter SLAM – sparse extended
information filter – fastSLAM algorithm
UNIT V
PLANNING AND NAVIGATION
9
Introduction to planning and navigation – planning and reacting – path planning – obstacle
avoidance techniques – navigation architectures – basic exploration algorithms
TOTAL: 45 PERIODS
OUTCOMES:
Upon Completion of the course,the students will be able to
Explain robot locomotion
Apply kinematics models and constraints
Implement vision algorithms for robotics
Implement robot localization techniques
Implement robot mapping techniques
Implement SLAM algorithms
Explain planning and navigation in robotics
REFERENCES:
1. Roland Seigwart, Illah Reza Nourbakhsh, and Davide Scaramuzza, “Introduction to
autonomous mobile robots”, Second Edition, MIT Press, 2011.
2. Sebastian Thrun, Wolfram Burgard, and Dieter Fox, “Probabilistic Robotics”, MIT Press,
2005.
3. Howie Choset et al., “Principles of Robot Motion: Theory, Algorithms, and
Implementations”, A Bradford Book, 2005.
4. Gregory Dudek and Michael Jenkin, “Computational Principles of Mobile Robotics”,
Second Edition, Cambridge University Press, 2010.
5. Maja J. Mataric, “The Robotics Primer”, MIT Press, 2007.
CP7031
COMPILER OPTIMIZATION TECHNIQUES
LTPC
3003
OBJECTIVES:
To understand the optimization techniques used in compiler design.
To be aware of the various computer architectures that support parallelism.
To become familiar with the theoretical background needed for code optimization.
To understand the techniques used for identifying parallelism in a sequential program.
To learn the various optimization algorithms.
64UNIT I
INTRODUCTION
9
Language Processors - The Structure of a Compiler – The Evolution of Programming
Languages- The Science of Building a Compiler – Applications of Compiler Technology
Programming Language Basics - The Lexical Analyzer Generator -Parser Generator -
Overview of Basic Blocks and Flow Graphs - Optimization of Basic Blocks - Principle
Sources of Optimization.
UNIT II
INSTRUCTION-LEVEL PARALLELISM
9
Processor Architectures – Code-Scheduling Constraints – Basic-Block Scheduling –Global
Code Scheduling – Software Pipelining.
UNIT III
OPTIMIZING FOR PARALLELISM AND LOCALITY-THEORY
9
Basic Concepts – Matrix-Multiply: An Example - Iteration Spaces - Affine Array Indexes –
Data Reuse Array data dependence Analysis.
UNITIV
OPTIMIZING FOR PARALLELISM AND LOCALITY – APPLICATION
9
Finding Synchronization - Free Parallelism – Synchronization Between Parallel Loops –
Pipelining – Locality Optimizations – Other Uses of Affine Transforms.
UNIT V
INTERPROCEDURAL ANALYSIS
9
Basic Concepts – Need for Interprocedural Analysis – A Logical Representation of Data
Flow – A Simple Pointer-Analysis Algorithm – Context Insensitive Interprocedural Analysis -
Context-Sensitive Pointer-Analysis - Datalog Implementation by Binary Decision Diagrams.
TOTAL: 45 PERIODS
REFERENCES:
1. Alfred V. Aho, Monica S.Lam, Ravi Sethi, Jeffrey D.Ullman, “Compilers:Principles,
Techniques and Tools”, Second Edition, Pearson Education,2008.
2. Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern Architectures: A
Dependence-based Approach”, Morgan Kaufmann Publishers, 2002.
3. Steven S. Muchnick, “Advanced Compiler Design and Implementation”,Morgan
Kaufmann Publishers - Elsevier Science, India, Indian Reprint 2003.
65
