Proposed M. Sc. Syllabus

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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