The following is the new course structure framework for M.Sc. (CS):
Proposed M.
Sc
. Syllabus
(For new M.
Sc.
students to be admitted in 2013)
Total Credits
–
78
Course Code
Courses
First Semester
L

T

P
Credits
CSC501
Operating Systems
3

1

0
4
CSC502
Computer Organization
3

1

0
4
CSC503
Data Structure & Algorithms (using C)
3

0

3
4
CSC504
Discrete Mathematics
3

1

0
4
CSC505
Mathematical Foundation
3

1

0
4
Foundation Course
Communication Skill
2

0

1
Pass/Fail
Total Credits
20
Second
Semester
CSC521
Database Management Systems
3

0

3
4
CSC522
Object Oriented Programming Methodology
3

0

3
4
CSC523
Theory of Computation
3

1

0
4
CSC524
Software Engineering
3

1

0
4
CSC525
Computer Networks
3

0

3
4
Total Credits
20
Summer Training
Pass/Fail
Third Semester
CSC601
Design & Analysis of Algorithms
3

0

3
4
CSC602
Web Technology & Web Programming
3

0

3
4
CSC603
Computer Graphics & Multimedia
3

0

3
4
CSC604

620
Elective

1
4
CSC604

620
Elective

2
4
Total Credits
20
Fourth
Semester
CSC621
Workshop
(
Business Economics &
Entrepreneurship Development
)
2

0

1
2
CSC6
22
Project in Industry/Institute
16
Total Credits
18
List of
Electives
for
M.Sc. Computer Science
Course Code
Courses
Credits
CS
C
604
Pattern Recognition
4
CS
C
605
Wireless networks
4
CS
C
606
Distributed computing
4
CS
C
607
Data
M
ining
4
CS
C
608
Compiler Design
4
CS
C
609
Applied Stochastic Process
4
CS
C
610
Cryptography &
Network
Security
4
CS
C
611
Image Processing
4
CS
C
612
New Paradigm in Computing
4
CS
C
613
Computational Biology
4
CS
C
614
Soft Computing
4
CS
C
615
Financial Data Analysis and Computing
4
CS
C
616
Information Retrieval
4
CS
C
617
Natural language Processing
4
CS
C
618
Computational Geometry
4
CS
C
619
Mobile Computing
4
CS
C620
Software Project Managements
4
Details Syllabus
CSC501:
OPERATING SYSTEMS
Credits 4
L

T

P:
4

0

0
Course Objectives & Prerequisites:
The course aims to introduce
Operating System Concepts
with emphasis on foundations & design principles.
The course does not require any special prior study except a basic understanding of Digital Computers.
A prior
course in Computer Organization & Architecture and Computer Programming will help increase the
pace of
learning.
UNIT I
Introduction: Introduction to OS. Operating system functions, evaluation of O.S., Different types of O.S.: batch,
multi

programmed, time

sharing, real

time, distributed, parallel.
UNIT II
Processes
:
Concept of processes,
process scheduling, operations on processes, inter

process communication,
Communication in Client

Server Systems, overview & benefits of threads.
UNIT III
Process scheduling:
scheduling criteria, preemptive & non

preemptive scheduling, scheduling algorith
ms.
UNIT IV
Process Synchronization:
background, critical section problem, critical region, synchronization hardware,
classical problems of synchronization, semaphores.
UNIT V
Deadlock:
system model, deadlock characterization, methods for handling deadlocks, deadlock prevention,
deadlock avoidance, deadlock detection, recovery from deadlock.
UNIT VI
Memory
Management: background, logical vs. physical address space, swapping, contiguous
memory
allocation, paging, segmentation.
UNIT VII
Virtual Memory:
background, demand paging, page replacement, page replacement algorithms, allocation of
frames, thrashing.
UNIT VIII
File Systems:
file concept, access methods, directory structure
UNIT IX
Disk Management:
disk structure, disk scheduling (FCFS, SSTF, SCAN, C

SCAN)
Text Books:
1.
Operating System Principles by Silberschatz
A. and Peterson J. L., Wiley
2.
Operating System by Haldar and Aravind, Pearson
3.
Operating Systems by Dhamdhere, TMH
References Books:
1.
Operating Systems by Deitel, Deitel & Choffnes.
2.
Modern Operating Systems by Tanenbaum Pearson Education
3.
Operating System by Stallings Pearson Educatio
CSC 502:
COMPUTER ORGANIZATIO
N
Credits 4
L

T

P: 4

0

0
Course Objectives:
After going through this course a student should be able to:
Design simple circuits and
buses.
Describe the organization of computer
Describe various components of Computer especially personal computer.
Describe the control unit of a computer
Describe the internal working of the computer (including interrupts)
Describe the instruction format
/ set of a computer
Prerequisites:
Number systems, Boolean Algebra, Boolean expressions, Karnaugh Maps, Basic logic gates,
logic diagrams. Combinational circuits, Sequential circuits.
UNIT I
Introduction
:
Function and structure of a computer,
Functional components of a computer, Interconnection of
components, Performance of a computer.
UNIT II
Representation of Instructions
:
Machine instructions, Operands, Addressing modes, Instruction formats,
Instruction sets, Instruction set architectures

CISC and RISC architectures.
UNIT III
Processing Unit
:
Organization of a processor

Registers, ALU and Control unit, Data path in a CPU,
Instruction cycle, Organization of a control unit

Operations of a control unit, Hardwired control unit,
Microprogrammed control unit.
UNIT IV
Memory Subsystem
:
Basic concepts semiconductor RAM memories. Read

only memories, Cache memory
unit

Concept of cache memory, Mapping methods, Organization of a cache memory unit, Fetch and write
mechanisms, Memory m
anagement unit

Concept of virtual memory, Address translation, Hardware support for
memory management.
UNIT V
Input/Output Subsystem
:
Peripheral Devices, Input

Output Interface, Asynchronous data transfer Modes of
Transfer, Priority Interrupt Direct mem
ory Access, Input
–
Output Processor (IOP) Serial communication;
Introduction to peripheral component, Interconnect (PCI) bus. Introduction to standard serial communication
protocols like RS232, USB, IEEE1394
UNIT VI
Pipeline and Vector Processing
:
Parallel Processing, Pipelining, Arithmetic Pipeline, Instruction Pipeline,
RISC Pipeline Vector Processing, Array Processors.
Text Book:
1.
C. Hamacher, Z. Vranesic and S. Zaky, "Computer
Organization", McGraw

Hill,
2002.
2.
M. Morris Mano, “Computer System a
rchitecture”.
References
Books :
1.
W.Stallings, "Computer Organization and Architecture

Designing for Performance", Prentice Hall of
India, 2002.
2.
D.A.Patterson and J.L.Hennessy, "Computer Organization and Design
–
The
3.
Hardware/Software Interface", Morgan
Kaufmann, 1998
4.
J .P.Hayes, "Computer Architecture and Organization", McGraw

Hill, 1998.
CSC503:
DATA STRUCTURE & ALG
ORITHMS
Credits
4
L

T

P: 3:0:3
Objective:
To provide skills on how data may be structured and instructions sequenced in
algorithms and programmes as
well as the relationship between appropriate data and control structures and tasks from the “real world”.
Introduce the student to algorithmic analysis.
Introduce the student to the fundamental data structures.
Introduce the
student to problem solving paradigms.
Course Structure:
Design and Analysis of Algorithms :
Problem Analysis, Concept and Proper Properties of Algorithm.
Elementary Algorithm Development, Algorithm involving Decisions and Loops, Procedures and Functions,
Introduction to Analysis of Algorithm, Testing of an Algorithm and its Efficiency, Flowchart and its
Applications, Sketching Flowchart for various Problems.
Recursion, Sorting, Searching and Merging:
Sorting and Order Statistics: Recursive Procedures an
d
Algorithms, Internal Sorting and Searching Algorithms, External Sorting, Merging, Complexities of Sorting and
Searching Algorithms. Selection, Bubble, Insertion, Merge, Heap, Quick and Radix Sort, Sorting in Linear
Time.
The Notion of Data Structure:
Primitive and Non

Primitive Linear Data Structures, Arrays, Lists, Stacks,
Queues, Linked Lists, Representation and Algorithms for Manipulating Data Structures, Polish Notation,
Applications of Linear Data Structures.
Non

Linear Data Structures:
Hash Tab
les, Trees, Binary Trees, Operations on Binary Trees, Binary Tree
Traversal, Representation and Manipulation of Binary Trees, Binary Search Trees, Heap, Graphs and
Digraphs, Representation and Manipulation of Graphs in Computer, Balancing Trees,
Hash Coding.
Applications of Non

Linear Data Structures. Graph Algorithms: Elementary Graphs Algorithms, Minimum
Spanning Trees, Single

Source Shortest Path, All

Pairs Shortest Paths, Maximum Flow.
Advanced Design and Analysis Techniques:
Dynami
c Programming, Greedy Algorithms, Amortized
Analysis. B

Tress, Binomial Heaps and Fibonacci Heaps,
Text Books:
1.
Introduction to Algorithm, 2e, by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and
Clifford Stein, PHI
2.
Beginning Algorithms by
Simen Harris, James Ross, Wiley India.
3.
Fundamentals of Computer Algorithms by E.Horowitz and S. Sahni, Galgotia
Reference Books:
1.
“Art of Computer Programming, Vol

1” by Knuth, Pearson Education
2.
“An Introduction of Computer Science
–
An Algorithmic Approac
h” by J. P. Tremblay and R.B.
Bunt., TMH
3.
“An Introduction to Data Structures and Non

Numeric Computation” by P G. Brillinger & D. J.
Cohen.
CSC504:
DISCRETE MATHEMATICS
4 Credits L:T:P

4:0:0
Mathematical Logic and
Relations:
Statements, Logical connectives, Truth tables, Equivalence, Inference and
deduction, Predicates, Quantifiers. Relations and their compositions, Equivalence relations, Closures of
relations, Transitive closure and the Warshall’s algorithm, Partia
l ordering relation, Hasse diagram, Recursive
functions.
Semigroups & Monoids:
Semigroups, Monoids, Subsemigroups/monoids, Congruence and quotient
semigroups/monoids, Homomorphism, isomorphism and the basic isomorphism theorem.
Boolean Algebra:
Boolean a
lgebra and their various identities, Homomorphisms and isomorphisms, Atoms and
the Stone’s theorem (finite case), Boolean functions, their simplification and their applications to combinational
circuits.
Combinatorics & Recurrence Relations:
Permutation,
Combination, Principle of inclusion and exclusion,
Recurrence relations, Generating functions
Graph Theory:
Basic concepts of graphs, directed graphs and trees, Adjacency and incidence matrices,
Spanning tree, Kruskal’s
and Prim’s algorithms, Shortest Path, Dijkstra’s algorithm, Planar Graphs, Graph
Coloring, Eulerian and Hamiltonian graphs.
Text Books:
1.
J.P. Trembley and R.P. Manohar, Discrete Mathematical Structures with Applications to
Computer Science, McGraw H
ill.
2.
L.L. Dornhoff and E. F. Hohn, Applied Modern Algebra, McMillan Publishing Co., 1978. .
3.
N. Deo, Graph Theory with Applications to Engineering and Computer Science, Prentice
Hall of India, 1980.
4.
R. Johnsonbaugh, Discrete Mathematics, Pearson Education,
2001.
Reference Books:
1.
R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson Education, 1999.
2.
C.L. Liu, Elements of Discrete Mathematics, McGraw

Hill, 1977
3.
I. Rosen, Discrete Mathematics, Tata McGraw Hill.
4.
B. Kolman, R. Busby, S.C. Ross, Discre
te Mathematical Structures, Prentice Hall of India, 2008
CSC505:
MATHEMATICAL FOUNDAT
ION
Credit 4 L:T:P
–
4:0:0
Functions, continuity and differentiabilit
y
, graphs of f(x), Cartesian equation and graphs of central conics and
conicoids tangent frame graphing, Continuity and Uniform continuity in [a, b], monotone and inverse functions,
functions of bounded variation. Derivatives of one and higher orders and indeterminate forms.
Functions of several variables
:
Partial derivativ
es. Chain rule, Standard Jacobians for change of variables.
Gradient and directional derivatives. Tangent planes and normal.
Vector Calculus
:
Repeated and Multiple integrals, Gradient, Divergence and Curl. Line, surface, and Volume
integrals. Green’s the
orem, Gauss’s divergence theorem and Stoke’s theorem in Cartesian, Spherical polar, and
cylindrical polar coordinates (without proof).
Laplace and Fourier Transforms:
Laplace transforms. Inverse transform. Shifting on the s and t axes,
convolutions, parti
al fractions. Fourier series and Fourier transforms. Solutions of ordinary as well as partial
differential equations by Laplace and Fourier transforms.
Numerical Methods
:
Bisection method, Newton Raphsons and secant methods for roots of nonlinear equation
s.
Polynomial interpolation, divided differences, summation of series, errors in polynomial interpolation,
interpolation by spline functions. Numerical integration, trapezoidal and Simpson’s rules, error formulae,
Gaussian quadrature, numerical differentia
tion.
Text Books:
1.
Mathematical Analysis
:
S. C. Malik
,
Arora Savita
2.
Calculus
:
Early
Transcendentals
:
James Stewart
3.
Introductory Methods o
f Numerical Analysis: S. S. Sastry
Reference Books:
1.
Higher Engineering Mathematics: B.S. Grewal
Foundation Course
: Communication Skills
Communication
–
Meaning
–
Objective and scope
–
Methods of
communication
–
Types
–
Barriers
–
Principles of communication
–
communication process.
Layout of a letter
–
Business Inquires and Replies
–
Quotations
–
Order
–
Execution of orders
–
Cancellation of orders
–
claims
–
Adjustments and settlement of accounts
–
Sales letters
–
Circular
letters.
Collection letters
–
Applications letter
–
Import Export correspondence
–
Bank Correspondence
–
Insurance correspondences.
Reporting writing
–
Reports by Individual
–
committees
–
Annual Reports
–
Press report
–
Speeche
s
–
Preparation of Agenda
–
Minutes.
Internal communication : Short speeches
–
Memo
–
Circulars
–
Notices
–
Explanation to superiors.
Modern means of communication: Intercom
–
Telex
–
Fax
–
Tele Conference
–
Internet
–
Email.
Text
Books :
1.
Essentials of Business Communication

Rajendra Paul & J.S. Korlahalli
2.
Effective Business English & Correspondences

M.S Ramesh & Patsan Shetty
3.
Business Correspondence and Office Management

R.S. Pilai & Bhagavathy
Reference Books:
1.
Business Communicat
ion

R.C. Sharma, Krishnamohan
2.
Effective Letters in Business Law

Shurter
CSC521: DATAB
A
SE MANAGEMENT SYSTEMS Credit 4 L

T

P : 3

0

1
Introduction:
Purpose of database systems, View of data, data models, &
interface, database language,
transaction management, storage management, database administrator, database users, overall system structure,
Classification of Database Management System, Three

Schema Architecture.
Data Modelling:
Entity

Relationsh
ip Model, Basic concepts, design issues, mapping constraints, keys, E

R
diagram, weak entity sets, extended E

R features, design of an E

R database schema, reduction of an E

R
schema to tables.
Relational Model:
Structure of relational databases, relational algebra, tuple relational calculus, domain
relational calculus, extended relational

algebra operations, modification of the database a
nd view, SQL and
Other.
Relational Languages:
Background, basic structure, set operations, aggregate functions, null values, nested
sub

queries, derived database, joined relations, DOL embedded SQL and other SL features.
Integrity Constraints:
Domain
constraints, referential integrity, assertions, triggers and functional
dependencies.
Relational Database Design:
Pitfalls in relational database design, decomposition, normalization using
functional, multi

valued and join dependencies, domain key normal
form and alternative approaches to database
design.
Query Processing:
Overview, catalogue information for cost estimation, measures of query cost, selection
operation, sorting, join operation, other operations, evaluation of expressions, Translating SQL q
uery into
Relational Algebra, transformation of relational expressions, Query Optimization.
Transactions:
Transaction concept, transaction state, System log, Commit point, Desirable Properties of a
Transaction, concurrent executions, serializability, reco
verability, implementation of isolation, transaction
definition in SQL, Testing for serializability. Introduction of Security and Integrity in database.
Text Book:
1.
Database System Concepts, 3
rd
edition, by A.Silberschatz, H. F. Korth, & S. Sudharshan, McG
raw Hill.
2.
Fundamental of Database Systems, by Elmasri, Navathe, Somayajulu, and Gupta, Pearson Education.
3.
An Introduction to database system by C.J. Date, A. Kanana, S. Swamynathan, Pearson Education
Reference Books:
1.
Database management System, by Rajesh
Narang, PHI
2.
Database Systems by Rob, Coronel, Galgotia Publication.
CSC 522
Object Oriented Programming Methodologies
Credits: 4 L

T

P: 3

0

1
Introduction to C++: Object Oriented Technology, Advantages of OOP, Input

output in C++, Tokens,
Keywords, Identifiers, Data Types C++, Derives data types, The
void
data type, Type Modifiers, Typecasting,
Constant, Operator, Precedence of Operators, String
s.
Control Structures: Decision making statements like
if

else, Nested if

else, goto, break, continue, switch case,
Loop statement like
for
loop,
nested for
loop,
while
loop,
do

while
loop.
Functions: Parts of Function, User

defined Functions, Value

Returning Functions,
void
Functions, Value
Parameters, Function overloading, Virtual Functions.
Classes and Data Abstraction: Structure in C++, Class, Built

in Operations on Classes, Assignmen
t Operator
and Classes, Class Scope, Reference parameters and Class Objects(Variables), Member functions, Accessor and
Mutator Functions, Constructors, default Constructor, Destructors.
Overloading & Templates: Operator Overloading, Function Overloading, F
unction Templates, Class Templates
Inheritance: Single & Multiple Inheritance, Virtual Base class, Abstract Class, Pointer and Inheritance,
Overloading Member Function.
Pointers and Arrays: Void Pointers, Pointer to Class, Pointer to Object, The
this
poi
nter, Void Pointer, Arrays.
Exception Handling: The keywords
try, throw and catch,
Creating own Exception Classes, Exception Handling
Techniques (Terminate the Program, Fix the Error and Continue, Log the Error and Continue), and Stack
Unwinding.
Text B
ooks:
1.
Thinking in C++, Volume 1 & 2 by Bruce Eckel, Chuck Allison, Pearson Education.
2.
Mastering C++, 1/e by Venugopal, Tata McGraw Hill.
3.
Object Oriented Programming with C++, 3/e by E. Balagurusamy, Tata McGraw Hill.
4.
Starting Out with Object Oriented Progr
amming in C++, by Tony Gaddis, Wiley India.
Reference Books:
1.
The C++ Programming language 3/e by Bjarne Stroustrup, Pearson Education.
2.
C++ How to Program, 4e, by Deitel, Pearson Education.
3.
Big C++ by Cay Horstmann, Wiley India.
4.
C++ Primer, 3e by Stanley B
. Lippman, Josee Lajoie, Pearson Education.
5.
C++ and Object Oriented Programming Paradigm, 2e by Debasish Jana, PHI.
6.
Programming with C++, 2/e by Ravichandran, Tata McGraw Hill.
CSC523:
THEORY OF COMPUTATIO
N
Credit 4
L

T

P: 4

0

0
Objective:
The objective of the course is to provide an exposition first to the notion of computability, then to the notion of
computational feasibility or tractability.
Course Structure:
Introduction to Automata:
Study and Central c
oncepts of automata theory, An informal picture of finite
automata, deterministic and non

deterministic finite automata, application of finite automata, finite automata
with epsilon transitions.
Regular Expression and Languages:
Regular expression, finit
e automata and regular expressions, applications
of regular expressions, algebraic laws of regular expressions
Properties of Regular Language:
Proving languages not to be regular, closure properties of regular languages,
equivalence and minimization of au
tomata.
Context

free Grammars and Languages:
Parse trees, Applications of context free grammars, Ambiguity in
grammars and languages.
Pushdown Automata:
Pushdown automation (PDA), the language of PDA, equivalence of PDA's and CFG's,
deterministic pushdown automata
Properties of Context

Free Languages:
Normal forms of context free grammars, pumping lemma for context
free languages, closure properties of c
ontext free languages.
Introduction to Turing Machine:
The Turing machine, programming techniques for Turing machine,
extensions to the basic Turing machine, restricted Turing Machines, Turing machines and Computers,
Undecidable
Problem about Turing Machine, Post’s Correspondence Problem.
Intractable Problem:
The Classes
P
&
NP,
NP

Complete Problem, Example of
P& NP
Problem.
Text Book:
1.
Introduction to Automata Theory, Languages, and Computation, by John E. Hopcroft, Rajeev Mo
twani, and
Jeffery D. Ullman, Pearson Education
2.
Theory of Computer Science (Automata, Languages and Computation
)
, 2e, K. L. P. Mishra and N.
Chandrasekharan, Pearson Education.
Reference Books:
1.
Introduction to formal languages, Automata Theory and Computa
tion by Kamla Krithivasan and Rama R,
Pearson Education.
CSC524:
SOFTWARE ENGINEERING
Credit 4 L:T:P
–
4:0:0
Course objectives:
This course is designed to present students with an overview of Software Engineering. Students will be exposed
to techniques that are gaining increasing attention in the industrial and research communities. Students will
apply the software engineering tech
niques to homework assignments and mini

projects throughout the course.
Both individual and group

oriented exercises will be assigned.
Course Structure
Introduction:
S/W Engineering Discipline

Evolution and Impact, Program vs
S/W Product, Emergence of S/W
Engineering
Software Life Cycle Models:
Waterfall, Prototyping, Evolutionary, Spiral models and their comparisons
Software Project Management:
Project Manager responsibilities, Project Planning, Project Size estimation
Metrics, Project estimation Techniques, COCOMO, Staffing Level Estimation, Scheduling, Organization &
Team Structures, Staffing, Risk Management, S/W Configuration Management
Req
uirements Analysis and Specification:
Requirement Gathering and Analysis, SRS, Formal System
Development Techniques, Axiomatic and Algebraic Specification
Software Design:
Overview, Cohesion and Coupling, S/W Design Approaches, Object

Oriented vs. Functio
n

Oriented Design
Function

Oriented S/W Design:
SA/SD Methodology, Structured Analysis, DFDs, Structured Design,
Detailed Design, Design Preview
Object Modeling Using UML:
Overview, UML, UML Diagrams, Use Case Model, Class Diagrams etc
Object

Oriented S
oftware Development:
Design Patterns, Object

Oriented analysis and Design Process, OOD
Goodness Criteria
User Interface Design:
Characteristics, Basic Concepts, Types, Components Based GUI Development, User
Interface Design Methodology
Coding and Testing
:
Coding, Code Review, Testing, Unit Testing, Black Box Testing, White

Box Testing,
Debugging, Program Analysis Tools, Integration Testing, System Testing, General Issues
Software Reliability and Quality Management:
S/W Reliability, Statistical Testing, S
/W Quality, S/W
Quality Management System, ISO 9000, SEI CMM, Personal Software Process, Six Sigma
Computer Aided Software Engineering:
CASE and its Scope, Environment, Support, Other Characteristics
Software Maintenance:
Characteristics, S/W Reverse Engineering, S/W Maintenance Process Models,
Estimation of Maintenance Cost
Software Reuse:
Basic Issues, Reuse Approach, Reuse at Organization Level
Text Books:
1.
Software engineering, by Sommerville, Pearson education.
2.
Fund
amentals of Software Engineering by Rajib Mall, PHI
3.
Software engineering by James F. Peters, Wiley
4.
Software engineering A Practitioner’s Approach by Pressman , MGH
Reference Books:
1.
Software Project Management From Concept to Deployment by Kieron Conway, d
reamtech Press
2.
Software engineering, by Jawadekar, TMH
CSC 525: COMPUTER NETWORKS Credits: 4 L

T

P: 4

0

0
Unit
–
I Computer Networks and the Internet
About The Internet its Protocols, the Network Edge, the Network Core,
Access Networks and Physical
Media, Delay and Loss in Packet

Switched Networks, Protocol Layers and Their Service Models,
Internet Backbones, NAPs and ISPs, A Brief History of Computer Networking and the Internet, ATM.
Unit
–
II Application Layer
Principles of Application

Layer Protocols, the World Wide Web: HTTP, File Transfer: FTP, Electronic
Mail in the Internet, the Internet's Directory Service: DNS, Socket Programming with TCP, Socket
Programming with UDP.
Unit
–
III Transport Layer
Transport

Layer Services and Principles, Multiplexing and De

multiplexing, Connectionless Transport:
UDP, Principles of Reliable of Data Transfer, Connection

Oriented Transport: TCP, Principles of
Congestion Control, TCP Congestion Control.
Unit
–
IV Network Layer
and Routing
Introduction and Network Service Model, Routing Principles, Hierarchical Routing, Internet Protocol,
Routing in the Internet, What is Inside a Router, IPv6, and Multicast Routing.
Unit
–
V Link Layer and Local Area Networks
The Data Link Laye
r: Introduction, Services, Error Detection and Correction, Multiple Access Protocols
and LANs, LAN Addresses and ARP, Ethernet, Hubs, Bridges and Switches, Wireless LANs: IEEE
802.11, the Point

to

Point Protocol, ATM, X.25 and Frame Relay.
Text Books:
1.
Com
puter Networking, by Kurose & Ross, Pearson Education
2.
Computer Network, A system approach; Larry L. Peterson & Bruce. S. Davie .the Morgan
Kaufmann Series.
Reference Books:
1.
Data Communications and Networks, by Forouzan, TMH
2.
Computer Networks, by Tanenbau
m, Pearson Education
3.
Data & Computer Communication, by Willian Stallings, Pearson Education
4.
Networking, All

in

one Desk Reference, 10 Books in 1 by Doug lowe, Wiley
CSC601 :
DESIGN AND ANALYSIS
OF ALGORITHM
Credits: 4 L

T

P: 4

0

0
Introduction and basic concepts :
Complexity measures, worst

case and average

case complexity functions,
problem complexity, quick review of common algorithm design principles.
Sorting and selection:
Finding maximum and minimum, k largest elements in order
; Sorting by selection, heap
sort methods, lower bound for sorting, other sorting algorithms

radix sort, quick sort, merge sort.
Searching and set manipulation:
Searching in static table

binary search, path lengths in binary trees, and
applications,
Huffman tree, optimality of binary search in worst cast and average

case, binary search trees, AVL
and (a, b) trees.
Hashing:
Basic ingredients, analysis of hashing with chaining and with open addressing,
Union

Find problem:
Tree representation of a set,
weighted union and path compression

analysis and
applications.
Graph problems :
Graph searching

BFS, DFS, shortest first search, topological sort; connected and
biconnected components; minimum spanning trees

Kruskal's and Prim's algorithms

Johnson'
s implementation
of Prim's algorithm using priority queue data structures, Single

Source Shortest Path, All

Pairs Shortest Paths.
Backtracking

n

Queen's Problem , Hamiltonian Circuit problem

Travelling and Salesman problem.
String processing:
String s
earching and Pattern matching, Knuth

Morris

Pratt algorithm and its analysis.
Introduction to NP

completeness:
Informal concepts of deterministic and nondeterministic algorithms, P and
NP, NP

completeness, statement of Cook's theorem, some standard NP

com
plete problems, approximation
algorithms.
Text Books:
1.
T. H. Cormen, C.E. Leiserson and R.L.Rivest: Introduction to Algorithms, Prentice Hall of
India, New
Delhi, 1998.
2.
Aho, J. Hopcroft and J. Ullman; The Design and Analysis of Computer Algorithms, A.W.L
, International
Student Edition, Singapore, 1998.
Reference Books:
1.
S. Baase: Computer Algorithms: Introduction to Design and Analysis, 2nd ed., Addison

Wesley, California,
1988.
2.
E. Horowitz and S. Sahni: Fundamental of Computer Algorithms, Galgotia Pub./
Pitman,New
Delhi/London, 1987/1978.
3.
K. Mehlhorn: Data Structures and Algorithms, Vol. 1 and Vol. 2, Springer

Verlag, Berlin, 1984.
4.
Borodin and I. Munro: The Computational Complexity of Algebraic and Numeric Problems, American
Elsevier, New York, 1975.
CS
C602:
WEB TECHNOLOGY AND W
EB PROGRAMMING
C
redit 4
L

T

P: 3

0

3
UNIT I: Introduction and Web Development Strategies
History of Web, Protocols governing Web, Creating Websites for individual and
Corporate World, Cyber Laws,
Web Applications, Writing
Web Projects, Identification
of Objects, Target Users, Web Team, Planning and
Process Development.
UNIT II: HTML, XML and Scripting
List, Tables, Images, Forms, Frames, CSS Document type definition, XML schemes,
Object Models, Presenting
XML, Using XML
Processors: DOM and SAX, Introduction
to Java Script, Object in Java Script, Dynamic
HTML with Java Script.
UNIT III: Java Beans and Web Servers
Introduction to Java Beans, Advantage, Properties, BDK, Introduction to EJB, Java
Beans API Introduction to
S
ervelets, Lifecycle, JSDK, Servlet API, Servlet Packages:
HTTP package, Working with Http request and
response, Security Issues.
UNIT IV: JSP
Introduction to JSP, JSP processing, JSP Application Design, Tomcat Server, Implicit
JSP objects, Conditional
Processing, Declaring variables and methods, Error Handling
and Debugging, Sharing data between JSP pages

Sharing Session and Application Data.
UNIT V: Database Connectivity
Database Programming using JDBC, Studying Javax.sql.*package, accessing a datab
ase
from a JSP page,
Application

specific Database Action, Developing Java Beans in a JSP
page, introduction to Struts framework.
Text Books:
1.
Burdman, “Collaborative Web Development” Addison Wesley.
2.
Chris Bates, “Web Programing Building Internet Applicati
ons”, 2nd Edition, WILEY, Dreamtech
3.
Joel Sklar , “Principal of web Design” Vikash and Thomas Learning
4.
Horstmann, “CoreJava”, Addison Wesley.
Reference Books:
1.
Herbert Schieldt, “The Complete Reference:Java”, TMH.
2.
Hans Bergsten, “Java Server Pages”, SPD
O’Reilly
CSC 603 : COMPUTER GRAPHICS AND MULTIMEDIA Credit 4 L

T

P : 3

0

3
Objectives:
The goal is to provide both theory and practice so that the student will be easily conversant with
techniques of computer graphics and multimedia. On successful completion of the course the students should
have:
1.
Understood the Computer Graphics and the v
arious graphic algorithms.
2.
Understood the 2D and 3D transformations, models and generation techniques
3.
Introduction to multimedia
UNIT I
The origin of computer graphics, Interactive graphics display, new display devices, Points and Lines, DDA,
Bresenham
’s Algorithms, Circles and Ellipse drawing algorithms
UNIT II
Two Dimensional Geometric Transformations: Basic Transformations
–
Matrix Representations

Composite
Transformations. Two Dimensional Viewing: Line Clipping
–
Polygon Clipping
–
Curve Clippin
g
–
Text
Clipping.
UNIT III
Three

Dimensional Concepts

Three Dimensional object Representations

Fractal Geometry Methods
–
Three
Dimensional Geometric and Modeling Transformations: Translation
–
Rotation
–
Scaling. Three Dimensional
Viewing: Viewing
Pipeline
–
Viewing Co

ordinates
–
Projections
–
Clipping.
UNIT IV
Visible
–
Surface Detection Methods, Classification of Visible Surface Detection Algorithms
–
Back Face
Detection

Depth

Buffer Method

A

Buffer Method. Color Models and Color
Applications: RGB
–
YIQ
–
CMY
–
HSV.
UNIT V
Introduction to multimedia, multimedia applications, multimedia hardware, multimedia tools, lossless and lossy
compression, Huffman coding, Animation
TEXT BOOKS
1.
Donald Hearn and M. Pauline Baker, ‘Computer G
raphics C Version’, Prentice
–
Hall of India, Second
Edition.
2.
Hill, Francis S., Computer Graphics Using OpenGL, Prentice

Hall, 2001.
3.
Prabat K Andleigh and Kiran Thakrar, “Multimedia Systems and Design”, PHI, 2003.
4.
Tay Vaughan “ Multimedia: making it work
” Tata McGraw Hill 1999, 4th Edition
REFERENCE BOOKS
1.
Steven Harrington, “Computer Graphics
–
A Programming Approach”, McGraw Hill, second edition.
2.
Multimedia Computing, Communication & Applications, Ralf Steinmetz and Klara Nashtedt. Prentice
Hall.1995(T
B2)
3.
OpenGL programming guide by Woo, Neider, Davis & Shreiner, 3rd Edition 2000, Pearson Education
Asia.
4.
Judith Jeffcoate, “Multimedia in practice technology and Applications”, PHI,1998.
5.
D.D. Hearn, M.P. Baker, Computer Graphics with OpenGL, 3/e, pearson
CSC 604: PATTERN RECOGNITION Credits 4
L

T

P: 4

0

0
Course Objectives:
The objective of this course is to enable the students to understand the fundamentals of:

Pattern recognition. The students should learn to
choose an appropriate feature.

Pattern classification algorithm for a pattern recognition problem, properly implement the algorithm
using modern computing tools such as Matlab, OpenCV, C, C++ and correctly.

Analyze, and report the results using proper
technical terminology
UNIT 1
Overview of Pattern classification and regression, Introduction to Statistical Pattern Recognition, Overview of
Pattern Classifiers, Bayesian decision making and Bayes Classifier, The Bayes Classifier for minimizing Risk,
Est
imating Bayes Error; Minimax and Neymann

Pearson classifiers, Parametric Estimation of Densities,
Implementing Bayes Classifier; Estimation of Class Conditional Densities, Maximum Likelihood estimation of
different densities, Bayesian estimation of paramet
ers of density functions, MAP estimates, Bayesian Estimation
examples; the exponential family of densities and ML estimates, Sufficient Statistics; Recursive formulation of
ML and Bayesian estimates.
UNIT

2
Mixture Densities and EM Algorithm, Mixture De
nsities, ML estimation and EM algorithm, Convergence of
EM algorithm; overview of Nonparametric density estimation, Nonparametric density estimation, Convergence
of EM algorithm; overview of Nonparametric density estimation, Nonparametric estimation, Parze
n Windows,
nearest neighbour methods
UNIT

3
Linear models for classification and regression, Linear Discriminant Functions; Perceptron

Learning
Algorithm and convergence proof, Linear Least Squares Regression; LMS algorithm, AdaLinE and LMS
algorithm;
General nonlinear least

squares regression, Logistic Regression; Statistics of least squares method;
Regularized Least Squares, Fisher Linear Discriminant, Linear Discriminant functions for multi

class case;
multi

class logistic regression
UNIT

4
Overv
iew of statistical learning theory, Empirical Risk Minimization and VC

Dimension, Learning and
Generalization; PAC learning framework, Overview of Statistical Learning Theory; Empirical Risk
Minimization, Consistency of Empirical Risk Minimization, Consist
ency of Empirical Risk Minimization; VC

Dimension, Complexity of Learning problems and VC

Dimension, VC

Dimension Examples; VC

Dimension of
hyperplanes
UNIT

5
Artificial Neural Networks for Classification and regression, Overview of Artificial Neural Ne
tworks,
Multilayer Feedforward Neural networks with Sigmoidal activation functions, Backpropagation Algorithm;
Representational abilities of feedforward networks, Feedforward networks for Classification and Regression;
Backpropagation in Practice, Radial B
asis Function Networks; Gaussian RBF networks, Learning Weights in
RBF networks; K

means clustering algorithm
UNIT

6
Support Vector Machines and Kernel based methods, Support Vector Machines

Introduction, obtaining the
optimal hyperplane, SVM formula
tion with slack variables; nonlinear SVM classifiers, Kernel Functions for
nonlinear SVMs; Mercer and positive definite Kernel, Support Vector Regression and ε

insensitive Loss
function, examples of SVM learning, Overview of SMO and other algorithms for SV
M; ν

SVM and ν

SVR;
SVM as a risk minimizer, Positive Definite Kernels; RKHS; Representer Theorem, Feature Selection, Model
assessment and cross

validation, Feature Selection and Dimensionality Reduction; Principal Component
Analysis, No Free Lunch Theorem
; Model selection and model estimation; Bias

variance trade

off, Assessing
Learnt classifiers; Cross Validation; Boosting and Classifier ensembles, Bootstrap, Bagging and Boosting;
Classifier Ensembles; AdaBoost, LeRisk minimization view of AdaBoost
Test
Books
:
1.
R.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, John Wiley.
REFERENCE BOOKS
1. C.M.Bishop, Neural Networks and Pattern Recognition, Oxford University Press
CSC605
WIRELESS NETWORKS
Credits: 4
L

T

P: 4

0

0
Introduction:
Why wireless, IEEE 802.11
802.11 MAC Fundamentals:
Challenges for MAC, Access mode, Contention based access using DCF,
Fragmentation and reassembly, Frame format, 802.11 graming in detail (DS bits, BSSID, RTS, CTS, control
fr
ame, management frame), Contation based data service, Frame processing and bridging, 802.11 to Ethernet.
WEP:
WEP cryptographic operations, WEP data processing, Problem with WEP, User authentication with
802.1x.
80
2.11i Robust security networks, TKIP,
and CCMP:
Temporal key Integrity protocol, Counter mode with
CCB

MAC, Robust security network operation.
Management operations:
Association, power conservation, timer synchronization, spectrum
management ,Contention free service with PCF.
Physical
Layer:
Physical layer architecture, Radio Link, RF with 802.11, Frequency, GFSK, PLCP, DSSS,
HR/DSSS. 802.11a and 802.11j (OFDM Phy), 802.11g (extended rate PHY), 802.11n: MIMO

OFDM.
Experiencing on 802.11 on Windows OS, Linux
802.11 Access point:
Functio
ns of AP, Power over Ethernet, Selecting AP.
Security Architecture:
Authentication and Access Point, Ensuring secrecy through encryption, selecting
security protocols.
Site planning and project Management:
Network requirement, PHY layer selection and design, Planning
placing AP, Using Antennas to tailor Coverage.
802.11 Network analysis, 802.11 performance tunning.
Text Books:
1.
802.11 Wireless Networks by Mathew S. Gast, SPD
2.
Wireless communications and netw
orks,William Stalling,Pearson Education.
Reference Books :
1.
Mobile Communication, Jochen Schiller.
2.
Wireless Communications: Principles and Practice,Theodore S. Rappaport.
3.
Wireless Communications,(Wiley

IEEE) by Andreas F. Molisch
CSC 606:
DISTRIBUTED COMPUTING
Credits 4
L

T

P: 4

0

0
Objective:
To impart knowledge of distributed Computing and Distributed Environments
Prerequisites:
Knowledge of Operating system
UNIT I
Fundamentals:
Definition, Evolution of distributed Computing System Distributed Computing System Models,
Distributed Operating System, Designing a distributed Operating System, Introduction of distributed computing
environment
UNIT II
Message Passing: Introduction Desirable features, Issues in IPC by message passing, synchronization,
Buffering, Multi datagram messages, encoding and decoding message data.
UNIT III
Remote Procedure Calls: Introduction, The RPC Model, Transparency of RPC
, Implementing RPC mechanism
RPC messages server management, parameter

passing and call semantic, Communication protocols for RPC's.
UNIT IV
Distributed Shared Memory: Introduction, Architecture of DSM Systems Design and implementation,
granularly, struc
ture of shared memory space Consistency models, replacement strategy, Thrashing.
UNIT V
Resource Management: Desirable feature, Task assignment approach, Load

balancing approach, Load

sharing
approach.
UNIT VI
Process Management: Process Migration, Threads.
UNIT VII
Distributed File Systems: Intakes, Desirable features, File models, File accessing models, file

sharing semantic,
File

caching schemes, File replication Fault tolerance, Automatic Transactions, De
sign principle.
Text Book:
1.
Distributed Computing by Liu, Pearson Education.
2.
Distributed Operating Systems: concept and Design by P.K. Sinha, PHI
3.
Distributed Operating System by Tanenbaum, Pearson Education
Reference Books:
1.
Distributed Computing by
Hagit Attiya and Jennifer Welch, Wiley India.
CSC607:
DATA AND WEB MINING
Credit 4
L

T

P: 4

0

0
Objective:
Data and Web mining refers to the automatic discovery of interesting and useful patterns from
the data
associated with the usage, content, and the linkage structure of Web resources. It has quickly become one of the
most popular areas in computing and information systems because of its direct applications in e

commerce,
information retrieval/filter
ing, Web personalization, and recommender systems. The primary focus of this
course is on examining techniques from data mining to extract useful knowledge from Web data. This course
will be focused on a detailed overview of the data mining process and tec
hniques, specifically those that are
most relevant to Web mining. Several topics will be covered such as Web data clustering, classification,
association rules, link analysis, social networks and Web advertising.
Course Structure:
Introduction to data
mining, need for data warehousing and data mining, application potential, keywords and
techniques.
Data Warehousing and On

line analytical Processing (OLAP):
Aggregation operations, models for data
warehousing, star schema, fact and dimension tables , con
ceptualization of data warehouse and
multidimensional databases, Relationship between warehouse and mining.
Data mining primitives:
Data preprocessing, data integration, data transformation. Definition and specification
of a generic data mining task. Desc
ription of Data mining query language with examples.
Association analysis:
Different methods for mining association rules in transaction based data bases.
Illustration of confidence and support. Multidimensional and multilevel association rules. Classific
ation of
association rules. Association rule algorithms
–
A priori and frequent pattern growth.
Classification and Prediction:
Different classification algorithms. Use of genie index, decision tree induction,
Bayesian classification, neural network techni
que of back propagation, fuzzy set theory and genetic algorithms.
Clustering:
Partition based clustering, hierarchical clustering, model based clustering for continuous and
discrete data. Scalability of clustering algorithms. Parallel approaches for clust
ering.
Web mining:
Web usage mining, web content mining, web log attributes.
Data mining issues in object oriented data bases, spatial data bases and multimedia data bases and text data
bases.
Text books:
1.
Data Mining Concepts and Techniques, by J. Han, M. Kamber, Harcourt India.
2.
Data Mining : introductory and Advanced Topics, by M. Dunham, Pearson Pub,
Reference Books:
1.
Data Mining Techniques, by A.K. Pujari, Universities Press.
CSC 608: COMPILER
DESIGN
Credit 4 L

T

P : 4

0

0
Translators, Various phases of compiler, tool based approach to compiler construction.
Lexical analysis: token, lexeme and patterns, difficulties in lexical analysis, error reportin
g, implementation,
regular definition, transition diagrams, LEX.
Syntax Analysis: top down parsing (recursive descent parsing, predictive parsing), operator precedence
parsing, bottom

up parsing (SLR, LALR, Canonical LR), YACC.
Syntax directed definition
s: inherited and synthesized attributes, dependency graph, evaluation order, bottom

up and top

down evaluation of attributes, L

attributed and S

attributed Definitions.
Type checking: type system, type expressions, structural and name equivalence of type
s, type conversion,
overloaded functions and operators, polymorphic functions.
Run time system: storage organization, activation tree, activation record, parameter passing, dynamic storage
allocation, symbol table: hashing, linked list, tree structures.
Intermediate code generation: intermediate representation, translation of declarations, assignments, control
flow, Boolean expressions and procedure calls, implementation issues.
Code generation: issues, basic blocks and flow graphs, register allocation,
code generation, dag representation
of programs, code generation from dags, peephole optimization.
Text books:
1.
Aho, Ullman and Sethi, Principles of Compiler Design, Addison Wesley.
2.
J. P. Trembley and P. G. Sorensen, The
Theory and Practice of Compiler Writing, McGraw Hill.
Reference Books:
1.
Holub, Compiler Design in C, PHI.
CSC609:
APPLIED STOCHASTIC P
ROCESS
Credit 4 L

T

P:4

0

0
Review of Probability Theory:
Elementary Probability, Random variables, Random vectors.
Stochastic Process:
Introduction, Stationarity, Ergodicity,Gaussian and Markovian Process
Markov Chains:
Introduction, properties and applications, Discrete time (Absorption problem, Branching
pr
ocess) and continuous time Markov Chain (Exponential Gamma distribution, Calculation transition function,
Limiting probability and balance equation).
Diffusion Process:
Winner process, Brownian motion with drift, Geometric and Integrated Brownian motion,
Brownian bridge, Ornstein Uhlenbeck Process, The Bessel Process, White noise, First

passage problem.
Poisson Process:
Telegraph signal, nonhomogenous Poisson & Compound
Poisson Process, Doubly Stochastic
Poisson process, Filtered Poisson process, Renewal Process
Queueing Models:
Markovian Queueing models. (M/M/s model) non

Markovian Queueing models (M/G/1
model) embedded Markov chain analysis. Queue with single server
(the model M/M/1, the model M/M/1/c),
queues with many servers (the model M/M/s, the model M/M/s/c and loss system), Networks of queue, Stable
distribution for finite Markovian queues. Machine repairman problem. Replacement and renewal theory.
Text Books:
1.
Applied Stochastic Processes by
Mario Lefebvre
, Springer
2.
Elements of Applied Stochastic Processes by U. Narayan Bhat, Wiley
3.
Stochastic processes and their appln. By Bhattacharya and Waymire, (JW)
Reference Books:
1.
A first course in stochastic processes, by Karlin and Taylor, Academic Press
2.
A second course in stochastic processes by Karlin and Taylor, Taylor, Academic Press
3.
Stochastic Processes by Sheldon M. Ross, Wiley
CSC610: CRYPTOGRAPHY
&
NETWORK
SECURITY
Credits
: 4 L

T

P: 4

0

0
Introduction:
The OSI Security Architecture, Security attack, Security Services, Security Mechanism, A model
for Network Security.
Symmetric Cipher:
Classical Encryption Techniques, Symmetric Cipher Model, Block Cipher Principle
s,
DES, Differential and Linear Cryptanalysis, Block Cipher Design Principle, The Euclidean Algorithm, Finite
field of Form GP(
p
), Advance Encryption Standard (AES), AES Cipher, Multiple Encryption and Triple DES,
Stream Cipher and RC4, Placement of Encryp
tion Function, Traffic Confidentiality, Key Distribution, Random
number generation.
Public Key Encryption and Hash Function:
Fermat’s & Euler’s Theorems, The Chinese Remainder Theorem,
RSA Algorithm, Diffe

Hellman Key Exchange, Elliptic Curve
Cryptography, Massage authentication code,
Security of Hash Functions and MAACs, Secure Hash algorithm, Whirlpool, HMAC, CMAC, Digital Signature.
Network Security Applications:
Kerberos, X.509 Authentication Service, S/MIME, IP Security Architecture,
Enca
psulating Security Payload, Secure Socket Layer (SSL), Transport layer security, Secure Electronic
Transaction.
System Security:
Intrusion detection, Password Management, Virus countermeasure, Denial of Service Attack,
Firewall design principles, Trusted
System.
Text Book:
1.
Cryptography and Network Security: Principles and Practices, 4e, William Stalling, Pearson Education.
2.
Cryptography and Network Security by Behrouz Forouzan, TMH
Reference Book:
1.
Introduction to Cryptography, Johannes A. Buchmann, Sprin
ger
2.
Beginning Cryptography with java by David Hook, Wiley Dreamtech.
3.
Modern Cryptography Theory & Practices by Wenbo Mao, Pearson Education
4.
Cryptography for Database and Internet Application by Nick Galbreath, Wiley Dreamtech
5.
Network Security: Private Comm
unication in a Public World, 2e, by Charlie Kaufman, Radia Perlman, and
Mike Speciner, Pearson Education.
CS
C
6
11
IMAGE PROCESSING Credits 4
L

T

P:
3

0

3
Course Objectives
The objectives of this course are to:

Cover
the basic theory and algorithms that are widely used in digital image processing

Expose students to current technologies and issues that are specific to image processing systems

Develop hands

on experience in using computers to process images

Familia
rize with MATLAB Image Processing Toolbox

Develop critical thinking about shortcomings of the state of the art in image processing
UNIT I DIGITAL IMAGE FUNDAMENTALS AND TRANSFORMS
Elements of visual perception
–
Image sampling and quantization Basic r
elationship between pixels
–
Basic
geometric transformations

Introduction to Fourier Transform and DFT
–
Properties of 2D Fourier Transform
–
FFT
–
Separable Image Transforms

Walsh
–
Hadamard
–
Discrete Cosine Transform, Haar, Slant Karhunen
–
Loeve tran
sforms.
UNIT II IMAGE ENHANCEMENT TECHNIQUES
Spatial Domain methods: Basic grey level transformation
–
Histogram equalization
–
Image subtraction
–
Image
averaging
–
Spatial filtering: Smoothing, sharpening filters
–
Laplacian filters
–
Frequency domain fi
lters :
Smoothing
–
Sharpening filters
–
Homomorphic filtering.
UNIT III IMAGE RESTORATION
Model of Image Degradation/restoration process
–
Noise models
–
Inverse filtering

Least mean square filtering
–
Constrained least mean square filtering
–
Blind ima
ge restoration
–
Pseudo inverse
–
Singular value
decomposition.
UNIT IV IMAGE COMPRESSION
Lossless compression: Variable length coding
–
LZW coding
–
Bit plane coding

predictive coding

DPCM.
Lossy Compression: Transform coding
–
Wavelet coding
–
Basics
of Image compression standards: JPEG,
MPEG,Basics of Vector quantization.
UNIT V IMAGE SEGMENTATION AND REPRESENTATION
Edge detection
–
Thresholding

Region Based segmentation
–
Boundary representation: chair codes

Polygonal
approximation
–
Boundary s
egments
–
boundary descriptors: Simple descriptors

Fourier descriptors

Regional
descriptors
–
Simple descriptors

Texture
TEXT BOOKS
1.
Rafael C Gonzalez, Richard E Woods 2nd Edition, Digital Image Processing

Pearson Education 2003.
REFERENCES
BOOKS
1.
William K Pratt, Digital Image Processing John Willey 2. Image Processing Analysis and Machine Vision
–
Millman Sonka, Vaclav hlavac, Roger Boyle, Broos/colic, Thompson Learniy .
2.
A.K. Jain, PHI, New Delhi

Fundamentals of Digital Image Processing.
3.
Chanda D
utta Magundar
–
Digital Image Processing and Applications, Prentice Hall of India,
CSC612:
NEW PARADIGM IN COMP
UTING
Credit 4 L

T

P:4

0

0
Introduction:
Need of new computing system.
Understanding Cloud Computing:
Beyond the desktop, developing cloud service, cloud computing for
everyone (like for family, community, corporation etc.)
Clod Services:
Collaboration on calendars, schedules, &. Task Management, Collaborating on event
management, Collaborating on projec
t management, Collaborating on word processing, Spreadsheets,
Databases, Collaborating on presentations, Storing and sharing file & other online content, Sharing digital
photographs, etc.
Introduction to Grid Computing:
High Speed Network, Architecture, C
ase study of live project.
Introduction to Bioinformatics computing:
How information is represented and transmitted in biological
system.
Computing in Ad

Hoc Networks & Wireless Sensor Networks.
Text Books:
1.
Cloud Computing by Que, Pearson Education
2.
Bioinformatics Computing by Bryan Bergeron, Pearson Education
Reference Books:
1.
Wireless Sensor Networks by Ananthran Swami, Zhao, Hing etl, Wiley
2.
Ad Hoc Wireless Networks by Ram Murthy and B. S. Manoj, Pearson Education
CSC613 COMPUTATIONA
L BIOLOGY
Credit 4 L

T

P : 3

0

3
Biological Algorithms versus Computer Algorithms, Algorithmic Notations
Algorithm Design Techniques: Exhaustive Search, Greedy Algorithm, Dynamic Programming, Branch

and

Bound Algorithms, Random
ized Algorithms
Machine Learning, Tractable versus Intractable Problems
Introductory Molecular Biology, DNA Analysis, Regulatory Motifs in DNA Sequences, Finding Motifs, Greedy
Approach to Motif finding, Longest Common Subsequences, Global and Local Sequ
ence Alignments, Multiple
Alignment
Gene Prediction, Constructing Algorithms in sub quadratic time, Shortest Superstring Problem
Sequencing by Hybridization, Protein Sequencing and Hybridization, Spectrum Graphs, Spectral Convolution,
Repeat Finding,
Hash Tables, Keyword Trees, Suffix Trees and its Applications
Approximate Pattern Matching, Hierarchical Clustering, Evolutionary Trees, Parsimony Problem, Hidden
Markov Models, Applications of HMM.
Text books:
1.
N. C. Jones, P. A. Pevzner, An
Introduction to Bioinformatics Algorithms, MPI Press 2004.
2.
D. W. Mont, Bioinformatics: Sequence and Genome Analysis, CSHL Press.
Reference Books:
1.
D. Gusfield, Algorithms on Strings, Trees, and Sequences: Computer Science and Computational
Biology, Cambri
dge University Press, 1997.
CSC 614 : SOFTCOMPUTING
Credit 4 L

T

P : 4

0

0
Objectives:
1)
To understand the need of softcomputing
2)
To understand the working of fuzzy, neural and genetic systems
3)
To work with practical examples.
UNIT I
Introduction to softcomputing

relevance, advantage and importance of softcomputing

components of
softcomputing

applications of softcomputing

ability of softcomputing
to handle uncertainty, vagueness,
ambiguity

introduction to computational intelligence

relationship between computational intelligence and
softcomputing
UNIT II
Introduction to fuzzy sets

t

norms

t

conorms

alpha

cuts

distance between fuzzy s
ets, fuzzy numbers

extension principle

interval arithmetic and alpha

cuts

properties of fuzzy arithmetic

fuzzy max and min

inequalities
UNIT III
Introduction to fuzzy logic

applications of fuzzy logic

types of membership functions, fuzzy
inference
system

fuzzifier

defuzzifier

inference engine

rule base, fuzzy rules

mamdani type fuzzy rules

Takagi

Sugeno type fuzzy rules, introduction to type

2 fuzzy logic and its advantages over type

1 fuzzy logic
UNIT IV
Introduction to genet
ic algorithm

applications of genetic algorithm

concepts of genes, chromosomes,
population and its initialization

fitness function

types of selection mechanism, working of roulette wheel
selection

types of crossover operations

working of one poi
nt, two point, multipoint and arithmetic crossovers

mutation

reinsertion

steps of simple genetic algorithm
UNIT V
Introduction to biological neurons

Introduction to artificial neurons

types of transfer functions

architecture
of feedforward ne
ural networks

backpropagation learning algorithm

applications of neural network
Text Books
1.
James J. Buckley, Esfandiar Eslami, An introduction to fuzzy logic and fuzzy sets, Springer International
edition, 2002
2.
S.N. Sivanandam, S.N. Deepa, Introduct
ion to genetic algorithms, Springer, 2008
3.
S. Sivanandam, S. Sumathi, Introduction to Neural Networks using Matlab 6.0, The McGraw

Hill, 2005
4.
S.N. Sivanandam, S.N. Deepa, Principles of Soft Computing, 2
nd
ed., Wiley India
Reference Books
1.
Fuzzy Logic: Intel
ligence, Control, and Information, 1/E, Yen & Langari, 1999, Prentice Hall
2.
Neural Networks and Learning Machines, 3/E, Haykin, 2009, Prentice Hall
3.
Fuzzy Logic and Control: Software and Hardware Applications, Vol. 2, 1/E, Jamshidi, Vadiee &
Ross,
1993, Prentice Hall
4.
Genetic Algorithms in Search, Optimization, and Machine Learning, 1/E, Goldberg, 1989, Addison

Wesley
5.
Timothy J. Ross, Fuzzy logic with engineering applications, 3
rd
ed, Wiley India
CSC615: Financial Data Analysis and Computing
Credit 4
L

T

P : 4

0

0
Understanding volatility and correlation:
Statistical nature of volatility and correlation, Constant and time
varying volatility and correlation models.
Implied volatility and correlation:
Feature of implied volatility, relationship between price and implied
volatility, Moving average model.
GARCH model:
Introduction to GARCH, Univariate GARCH model, Specification and estimation of GARCH
model, Application of GARCH model.
Forecasting vol
atility and correlation:
Evaluation of accuracy of point forecast, confidence interval of
volatility forecast, consequences in uncertainty in volatility and correlation.
Principal component analysis:
Term structure, modeling volatility smiles and skews, D
ata problem using
PCA.
Covariance matrices:
Its application in Risk Management & Investment analysis, Risk Metrics data,
Orthogonal Model
Value at Risk:
Controlling risk in financial market, Advantages and limitation of value

at

risk,VaR model
Time Seri
es model:
Univariate time series model: AR and MA model, multivariate time series. Introduction to
Cointegration
Forecasting high frequency data:
High frequency data, through neural network, price prediction model.
Text Books:
1.
Market Models: A Guide to
Financial Data Analysis by Carol Alexander, Wiley.
2.
Analysis of Financial Data by Gary Koop, Wiley
CSC616:
INFORMATION RETRIEVA
L
Credit 4
L

T

P:4

0

0
Course Objectives:
The main objectives are summarized as shown below:

learn
the important concepts, algorithms, and data/file structures that are necessary to specify, design, and
implement Information Retrieval (IR) systems.
COURSE CONTENT
Unit I: Introduction
Basic Concepts
–
Retrieval Process
–
Modeling
–
Classic Information R
etrieval
–
Set Theoretic, Algebraic and
Probabilistic Models
–
Structured Text Retrieval Models
–
Retrieval Evaluation
–
Word Sense Disambiguation.
Unit II: Querying
Languages
–
Key Word based Querying
–
Pattern Matching
–
Structural Queries
–
Query Operat
ions
–
User
Relevance Feedback
–
Local and Global Analysis
–
Text and Multimedia languages.
Unit III: Text Operations and User Interface
Document Preprocessing
–
Clustering
–
Text Compression

Indexing and Searching
–
Inverted files
–
Boolean
Queries
–
Se
quential searching
–
Pattern matching
–
UserInterface and Visualization
–
Human Computer
Interaction
–
Access Process
–
Starting Points
–
Query Specification

Context
–
User relevance Judgment
–
Interface for Search.
Unit IV: Multimedia Information
Retrieval
Data Models
–
Query Languages
–
Spatial Access Models
–
Generic Approach
–
One Dimensional Time Series
–
Two Dimensional Color Images
–
Feature Extraction.
Unit V: Applications
Searching the Web
–
Challenges
–
Characterizing the Web
–
Search Engines
–
Browsing
–
Meta

searchers
–
Online IR systems
–
Online Public Access Catalogs
–
Digital Libraries
–
Architectural Issues
–
Document
Models, Representations and Access
–
Prototypes an
d Standards.
Text Books:
1.
Ricardo Baeza

Yate, Berthier Ribeiro

Neto, “Modern Information Retrieval”, Pearson Education Asia,
2005.
2.
G.G. Chowdhury, “Introduction to Modern Information Retrieval”, Neal

Schuman Publishers; 2nd edition,
2003.
3.
Daniel Jurafsky
and James H. Martin, “Speech and Language Processing”, Pearson Education, 2000
4.
David A. Grossman, Ophir Frieder, “ Information Retrieval: Algorithms, and Heuristics”, Academic Press,
2000
5.
Charles T. Meadow, Bert R. Boyce, Donald H. Kraft, “Text Information
Retrieval Systems”, Academic
Press, 2000.
CSC617

NATURAL LANGUAGE PRO
CESSING
Cr
edits 4
L

T

P:
4

0

0
Objectives:
At the end of this course, students should have a sound knowledge of the methods used in different
areas of
natural language processing. Students should also be able to use this knowledge to implement simple
natural language processing algorithms and applications.
Prerequisites:
Students should have knowledge of Algorithms, Theory of Computation etc.
UNIT I
Shallow Processing
–
Morphology fundamentals
–
Finite State Machine based Morphology
–
Part of Speech
Tagging and Named Entity tagging
–
Machine learning algorithms for NLP
UNIT II
Parsing
–
Classical Approaches: Top

Down, Bottom

UP and Hybrid Methods
–
Chart Parsing, Early Parsing
–
Statistical Approach: Probabilistic Parsing, Tree Bank
Corpora
UNIT III
Lexical Semantics and/or Discourse Processing
–
Lexicons, Lexical Networks and ontology
–
Word Sense
Disambiguation
–
Coreferences
UNIT IV
Infor
mation Extraction and/or Text Mining
–
Gene Mention Detection
–
Anaphora Resolution in biomedical
texts
–
Event Extraction in biomedical texts
UNIT V
Applications
–
Machine Translation
–
Information Retrieval (cross

lingual)
–
Summarization
–
Question
Answering
UNIT VI
Indian Language Computing
–
Named Entity Recognition
–
Part of Speech Tagging
–
Machine Translation

Cross lingual information access
Textbook:
1.
Speech and Language Processing, by D. Jurafsky and R. Martin (2nd edition)
2.
Natural Lang
uage Understanding : James Allan
References:
1.
Foundations of Statistical NLP: Manning and Schutze
2.
NLP a Panninian Perspective: Bharati, Chaitanya and Sangal
3.
Statistical NLP :Charniak
CSC618:
COMPUTATIONAL GEOMET
RY
Credit 4
L

T

P:
4

0

0
Geometric Data Structures:
Points, Polygons, Edges, Geometric objects in space, Finding intersection of a
line and a triangle.
Incremental Insertion:
Finding star

shaped polygons, finding convex hulls, point enclosure: The ray

shooting
and the sig
ned angle method, line clipping, polygon clipping, triangulating monotone polygons.
Incremental selection:
Off

line & on

line Program in Selection Sort, Finding convex hull: gift wrapping &
Graham scan, Removing hidden surfaces: the depth search algorith
m, intersection of convex polygons, Finding
Delaney triangulations.
Plane

Sweep algorithms:
Finding the intersections of line segments, finding convex hulls: insertion hull
revisited, contour of the union of rectangles, Decomposing polygons into monotone
pieces.
Divide and conquer Algorithms:
Computing the intersection of half planes, Finding the kernel of polygon,
finding Voronoi regions, Merge Hull, closest points, polygon triangulation.
Spatial subdivision Methods:
The Grid method, quad

trees, Two

dimensional search tree, removing hidden
surfaces.
Text Books:
1.
Computational Geometry and Computer Graphics in C++ by Michael J. Laszlo, PHI
2.
Computational Geometry

An Introduction by
Franco P. Preparata
and
Michael Ian Shamos, Springer
3.
Computational Geometry by
Mark de Berg,
Otfried Cheong,
Marc van Kreveld, and
Mark Overmars,
Springer
Reference Books:
1.
Algorithmic Geometry
by
Jean

Daniel Boissonnat,
Mariette Yvinec,
Cambridge University Press.
2.
Computational Geometry in
C
by Joseph
O'Rourke, Cambridge University Press.
CSC 619:
MOBILE COMPUTING
Credits
:
4 L

T

P: 4

0

0
Introduction:
Cellular architecture, Mobile Computing issues and challenges, Architecture issues,
communication
issues, bandwidth management issues, energy issues, information management issues,
Reliability issues, security issues, Social issues, Trust management and anonymity issues, Applications
(horizontal and vertical), Wireless Mobile Network Characteristics, p
ortable characteristics, mobility
characteristics.
Wireless Communication principles:
Multiplexing (SDM, FDM, TDM, CDM) , Modulation, Hidden terminal,
Exposed Terminal.
Digital Cellular Mobile system:
GSM, GPRS, Numbers & Identities for Mobile.
Channel
allocation:
Fixed Channel Allocation, Dynamic Channel Allocation, Hybrid Channel Allocation,
Flexible Channel Allocation.
Location Management:
Location Management Problem, Location management Update principles (No

Update,
Full

Update, Lazy

Update, Selecti
ve

Update), Location management Architecture (two tier, Tree

based,
hierarchical etc.), Location Management Algorithms (Two location, Reporting Cell, Profile

based, etc).
Mobility Models:
Individual mobility model (Random walk, Random way

point, random

di
rection, smooth
random, Gauss

Markov model), Group

based mobility model (Column, Nomadic, Pursue, Reference Point
Group

Mobility model).
Mobile Protocols:
Mobile

IPv.4, Ipv.6, Mobile TCP (m

TCP)
Information Dissemination: Information dissemination through
wireless medium, broadcasting, Push, Pull,
Periodic, on

demand, real

time, variable

sized data broadcasting schemes.
WAP:
WAP architecture, Wireless Mark

up language, WML Script, MMS, Case study of Nokia phone
simulator.
Mobile Payment Models:
Payments
in Mobile environment, e

cash, M

pay, Pay

box, EMPS, e

ticket, Mobile
Computing application development using J2ME platform.
Text Books:
1.
Mobile Communications by Jochen Schiller, Pearson Publications.
2.
Mobile Computing (ed.) by Tomasz Imielinski &
Henry F. Korth, Kluwer Academic Publishers.
3.
Mobile Computing
–
Technology, Application & Service Creation, Asoke Talukder, Roopa Yavagal,
McGraw Hill Publications.
Reference Books :
1.
Mobile commerce and wireless computing systems,Geoffrey Elliott,Nigel
Phillips Pearson
Education.
2.
Mobile Computing
By Dasbit & Sikdar,PHI,
3.
Mobile Computing: Theory and Practice, By Kumkum Garg.
CSC 620: SOFTWARE PROJECT MANAGEMENT Credits: 4 L

T

P: 4

0

0
UNIT I : Introduction
SPM Basic Concepts, Project Management, Project Management: Core Functions, support Functions, Project
Integration Management, Relationships: Knowledge Areas Versus Projects.
UNIT II :
Software Development Process Management
Software Development Process M
anagement, Management of Software workflows, Evaluation of Workflow
Process, Workflow process Templates, Integration of Software Engineering Management and Project Life
Cycle
UNIT III :
Requirements Management
Why Requirements Management, Analysis
of the Problem, User Analysis and Identifying User Needs,
Requirement Specifications, Requirement Assurance Through Right System, Managing Requirements Change.
UNIT IV :
Project Scheduling & Estimation
Project Scheduling, Defining a Task set for the soft
ware project, Defining a task network, Scheduling, Software
Project Estimation, Decomposition Techniques, Empirical Estimation Models, Estimation for object

oriented
Projects, Specialized Estimation Techniques, The make/Buy decision.
UNIT V :
Risk Managem
ent
Reactive vs. Proactive Risk strategies, Software Risk, Risk Identification, Risk Projection, Risk Refinements,
Risk Mitigation, Monitoring, Risk Management, The RMMM plan.
UNIT VI : Quality Management
Quality, Quality Control, Quality Assurance, Cos
t of Quality, Software Quality Assurance : Background Issues,
SQA Activities, Software Technical Reviews : The Review Meeting, Review Reporting and Record Keeping,
Review Guidelines, Sample

Driven Reviews, Formal Approaches to SQA, Statistical Software Qu
ality,
Assurance, A Generic Example, SixSigma for Software Engineering, Software Reliability, Measures of
Reliability and Availability, software Safety, The ISQ Plan.
UNIT VII :
Change Management
Software Configuration Management, The SCM Repository, The
SCM Process, Configuration management for
web engineering.
Text Books:
1.
Software Project Management From Concept to Deployment by Kieron Conway, dreamtech Press
2.
Software Engineering, by Jawadekar, TMH
3.
Software Engineering A Practitioner’s Approach by Press
man , MGH
Reference Books:
1.
Software Engineering, by Sommerville, Pearson education.
2.
Fundamentals of Software Engineering by Rajib Mall, PHI
3.
Software Engineering by James F. Peters, Wiley
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