master of technology (cse) 2 years programme ... (cse) old syllabus...master of technology (cse) 2...

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Master of Technology (CSE)

2 YEARS PROGRAMME

CHOICE BASED CREDIT SYSTEM

w.e.f. JULY 2011

(70:30)

Department

of

Computer Science and Engineering

GJUS & T, HISAR-125001

2

M.Tech. (CSE)

SCHEME OF EXAMINATION

(TWO-YEAR PROGRAMME)

Credit Based System w.e.f July 2011

SEMESTER –1

Paper Nomenclature of the Courses Total credits

CSL-711 Data Structures & Computer Algorithms 4

CSL-712 Computer Organization & Architecture 4

CSL-713 Advanced DBMS 4

CSL-714 Computer Networks 4

CSL-715 Distributed Operating System 4

CSP-711 Data Structures & Algorithms Lab 2

CSP-712 ADBMS Lab 2

Total: 24

SEMESTER –II

Paper Nomenclature of the Courses Total Credits

CSL-721 Software Project Management 4

CSL-722 Theory of Computation 4

CSL-723 Design & Development of

Microprocessor Based Systems 4

CSL-724 High Speed Networks 4

Programme Elective-I 4

CSP-721 Lab Based on Current Technologies 2

CSP-722 Microprocessor Lab 2

Total: 24

List of Electives for Programme Elective –I

CSL-725 Soft Computing

CSL-726 Advanced Computer Architecture

CSL-727 Embedded Systems

CSL-728 Computer Graphics

CSL-729 Compiler Construction

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SEMESTER –III

Paper Nomenclature of Paper Total Credits

CSD-731 Dissertation & Seminar –I 4

Programme Elective-II 4

Programme Elective-III 4

Total: 12

CSD-731 (Dissertation & Seminar –I):

To be evaluated by committee constituted by Chairman, CSE.

List of Electives for Programme Elective –II & III

CSL-731 Mobile & Wireless Communication

CSL-732 Performance Modelling

CSL-733 Security of Information Systems

CSL-734 Data Mining Concepts & Techniques

CSL-735 Research Methodologies

CSL-736 Neural Networks

SEMESTER –IV

Paper Nomenclature of Paper Total Credits

CSD-741 Dissertation & Seminar-II 10

CSD-741 (Dissertation & Seminar –II):

To be evaluated jointly by internal supervisor and external examiner.

Total Credits for all Semester 70 Note: One credit in theory papers is equivalent to 1 hour

classroom teaching per week and one credit in

practical/lab course is equivalent to 2 hour practical/lab

work per week. A teacher will conduct practical class in a

group of 10-15 students.

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Data Structures and Computer Algorithms

General Course Information:

Course Code: CSL711

Course Credits: 4

Type: Compulsory

Contact Hours: 4 hours/week

Mode: Lectures

Examination Duration: 3 hours

Course Assessment Methods: Two minor

examinations each of 20 marks, class performance

measured through percentage of lectures attended,

assignment and quizzes (10 marks), and end

semester examination of 70 marks.

For the end semester examination, total 8

questions are to be set by the examiner/teacher

covering the entire syllabus uniformly. A

candidate is required to attempt any five questions.

All the questions carry equal marks.

Pre-requisites:

Students are expected to have knowledge of fundamental concept of data structures and the importance of

data structures in developing and implementing efficient algorithms

The objectives of this course are to:

1. Analyze algorithms and determine their time complexity.

2. Effectively choose the data structures that efficiently model the information in a problem.

By the end of the course a student is expected to:

1. Understand the concepts of data structures, data types and apply various data structures such as

stacks, queues, trees and graphs to solve various computing problems using C-programming

language.

2. Analyse algorithms and determine their time complexity.

3. Design an algorithm to solve a given problem.

4. Effectively choose the data structures that efficiently model the information in a problem.

5. Understand and apply appropriate algorithmic techniques for solving problems.

Syllabus

A review of data types, abstract data types, data and storage structures, arrays, linked lists, queues and their

applications.

Binary tree, operations on binary tree, applications of binary tree, representations of binary tree, tree

traversals, threading and their implementation, binary search tree, Trees in general, height balanced trees,

red and black trees, splay trees, multi-way search trees, B- trees

Graphs, their representation and applications, path matrix and shortest path, graph traversals. spanning

trees and related algorithms

Comparative study of sorting techniques with their complexities, Dynamic Memory management, hashing

Solving recurrence relations

Principles and techniques of algorithm analysis:

Algorithm design techniques: Divide and conquer, greedy method, Dynamic programming, Backtracking.

(Two examples of each technique should be discussed with a complexity analysis).

Text and Reference Books:

1. Yedidyah Langsam, Moshe J. Augenstein, Aaron M.Tenenbaum, Data structure using C and C++,

Second Ed. , Pearsons Education Asia, 1996.

2. Robert Sedgewick, Algorithms in C, Addison Wesley, 1999.

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3. A. V. Aho, John E. Hopcroft, Jeffery D. Ullman, Data Structures and Algorithms, Pearson

Education Asia, 1983.

4. Ellis Horowitz, Sartaj Sahni, S. Rajasekaran, Fundamentals of Computer Algorithm, Second Ed.,

Universities Press, 2008.

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Computer Organization and Architecture


Course Code: CSL712

Course Credits: 4

Type: Compulsory


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge in Digital Electronics


1. Understand the key factors for designing cost effective basic computer.

2. Understand the different types of memories and input/output data transfer techniques.

3. Make the students to identify where, when and how enhancements of computer performance

can be accomplished.


1. Describe the structure and functioning of a digital computer, including its overall system

architecture, digital components, CPU organization.

2. Design the basic computer system.

3. Explain the difference between micro programmed and hardwired control units.

4. Describe the organization of memory and input/output devices.

Syllabus

Register Transfer and Microoperations

Register Transfer Language, Register Transfer, Bus and Memory Transfers, Arithmetic Microoperations,

Logic Microoperations, Shift Microoperations, Arithmetic Logic Shift Unit.

Basic Computer Organization and Design

Instruction Codes, Computer Registers, Computer Instructions, Timing and Control, Instruction Cycle,

Memory reference instructions, Input Output and Interrupt, Complete Computer Description, Design of

Basic Computer, Design of Accumulator Logic.

Microprogrammed Control

Control Memory, Address Sequencing, micro-program Example, Design of Control Unit.

Central Processing Unit

General Register Organization, Stack Organization, Instruction Formats, Addressing Modes, Data Transfer

and Manipulation, Program Control, Reduced Instruction Set Computer(RISC),

Pipeline and Vector Processing

Parallel Processing, Pipelining, Arithmetic Pipeline, Instruction Pipeline, RISC Pipeline, Vector

Processing, Array Processors.

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Computer Arithmetic

Introduction, Addition and Subtraction, Multiplication Algorithms, Division Algorithms, Floating Point

Arithmetic Operations, Decimal Arithmetic Unit, Decimal Arithmetic Operations.

Input Output Organization

Peripheral Devices, Input-Output Interface, Asynchronous Data Transfer, Modes of Transfer, Priority

Interrupt, Direct Memory Access(DMA), Input Output Processor (IOP), Serial Communication.

Memory Organization

Memory Hierarchy, Main Memory, Auxiliary Memory, Associative Memory, Cache Memory, Virtual

Memory, Memory Management Hardware.

Multiprocessors

Characteristics of Multiprocessors, Interconnection Structures, Interprocessor Arbitration, Interprocessor

Communication and Synchronization, Cache Coherence.


1. M. Mano, Computer System Architecture, Third Ed. , Prentice Hall , 1993.

2. J. Hayes, Computer Architecture and Organization, Third Ed. , McGraw-Hill , 1998.

3. Hennessy and Patterson, Computer Architecture: A Quantitative Approach, Fifth Ed., Morgan

Kaufmann, 2006.

4. Computer Organization & Design: The Hardware/Software Interface, Patterson and Hennessy,

Fourth Ed. Morgan Kaufmann, 2005.

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Advanced DBMS


Course Code: CSL713

Course Credits: 4

Type: Compulsory


Mode: Lectures












Pre-requisites:

Overview of basic DBMS concepts .


1. Educate students with fundamental concepts of Database Management System.

2. Study Database Design methodology.

3. Design Database and Normalize data.

4. Understand the concept of Recovery, Data Mining and Data Warehouse.

By the end of the course a student is expected to be able to:

1. Understand what is involved in the design of a database.

2. Understanding the models used for structuring data in database systems.

3. Critically evaluate and contrast the alternative designs and architectures for databases

4. Analyse the background processes involved in queries and transactions, and explain how these

impact on database operation and design.

Syllabus

Introduction:

Architecture, Advantages, Disadvantages, Data models, relational algebra, SQL, Normal forms.

Query Processing: General strategies for query processing, transformations, expected size, statistics in estimation, query

improvement, query evaluation, view processing, query processor.

Recovery: Reliability, transactions, recovery in centralized DBMS, reflecting updates, buffer management, logging

schemes, disaster recovery.

Concurrency: Introduction, serializability, concurrency control, locking schemes, timestamp based order, optimistic

scheduling, multiversion techniques, deadlock.

Object Oriented Data base Development:

Introduction, Object definition language, creating object instances, Object query language.

Distributed Databases:

Basic concepts, options for distributing a database, distributed DBMS.

Data warehousing:

Introduction, basic concepts, data warehouse architecture, data characteristics, reconciled data layer, data

transformation, derived data layer, user interface.

Object Relational Databases: Basic concepts, enhanced SQL, advantages of object relational approach.

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1. Bipin C. Desai, An Introduction to database systems, Galgotia Publications, 1991.

2. Jeffray A. Hoffer, Mary B. Prescott, Fred R. Mcfadden, Modern Database Management, Sixth Ed.,

LPE Pearson Education, 2002.

3. M. Tamer & Valduriez, Principles of distributed database systems, Second Ed., LPE Pearson

education, 1999.

4. Abraham Silberschatz, Henry Korth, S. Sudarshan, Database Systems Concepts, Fourth Ed.,

McGraw Hill, 1997.

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Computer Networks


Course Code: CSL714

Course Credits: 4

Type: Compulsory


Mode: Lectures



examinations each of 20 marks, Class

Performance measured through percentage of

lectures attended (4 marks) Assignment and quiz

(6 marks), and end semester examination of 70

marks.

For the end semester examination eight questions

are to be set by the examiner. A candidate is

required to attempt any five questions. All the

questions carry equal marks.

Pre-requisites:

Undergraduate Computer Networks course


1. Understand the basic topology of computer networks.

2. Understand the working of different types of networking devices..

3. Provide a broad coverage of computer networks.


1. Understand and analyse existing modern computer networks architectures and protocols.

2. Design new protocols in networking.

3. Understand the role of protocols in networking.

4. Design subnet masks to fulfil networking requirements.

Syllabus

Introduction

Uses of Computer Networks, Network Hardware, Network Software, Reference Models.

Physical Layer

The Theoretical Basis of Data Communications-- The Maximum Data Rate of a Channel. Introduction to

Transmission Media. Basics of Wireless Transmission, Communication Satellites, The Public Switched

Telephone Network, Structure of Telephone network, The Local Loop, Modems, ADSL, Wireless, Trunks

and Multiplexing, Switching.

Data Link Layer

Data Link Layer Design Issues, Error Detection and Correction, Elementary Data Link Protocols, One bit

Sliding Window Protocols, Example Data Link Protocol: HDLC- High Level Data Link Control.

Medium Access Control Sublayer

Introduction, The Channel Allocation Problem, Multiple Access protocols---- ALOHA, Carrier Sense

Multiple Access Protocols, Collision Free Protocols.

Ethernet: Ethernet Cabling, Manchester Encoding, The Ethernet MAC Sublayer Protocol, The Binary

Exponential Backoff Algorithm, Performance. Data Link Layer Switching: Bridges from 802.x to 802.y,

Local Internetworking, Spanning Tree Bridges, Remote Bridges, Repeaters, Hubs, Bridges, Switches,

Routers, Gateways, Virtual LAN’s.

The Network Layer

Routing Algorithms, Congestion Control Algorithms, Internetworking,The Network Layer in Internet: IP

Protocol, IP Addresses, Internet Control Protocols, Internet Multicasting, Mobile IP, IPv6

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The Transport Layer

Elements of Transport Protocols, Introduction to Internet Transport Protocols: UDP, TCP --- Introduction,

TCP Service Model, TCP Protocol, TCP Segment Header, TCP Connection Establishment, TCP

Connection Release, TCP congestion control, TCP timer management.

The Application Layer

Introduction to DNS, Electronic Mail, WWW, Multimedia.


1. Andrew S. Tanenbaum , Computer Networks, Fifth Ed., PHI, 2010.

2. Forouzan, Data Communication and networking, Fourth Ed.,TMH, 2007.

3. William Stalling, Data & Comp. Communication, Eighth Ed., LPE Pearson Education, 2007.

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Distributed Operating System


Course Code: CSL715

Course Credits: 4

Type: Compulsory


Mode: Lectures



examinations each of 20 marks, Class

Performance measured through percentage of

lectures attended (4 marks) Assignment and quiz

(6 marks), and end semester examination of 70

marks.

For the end semester examination nine questions

are to be set by the examiner. Total 8 questions are

to be set by the examiner/teacher covering the

entire syllabus uniformly. A candidate is required

to attempt any five questions. All questions shall

carry equal marks.

Pre-requisites:

Basic knowledge of Operating System, Computer Networks & Network Programming

Course objectives:

1. To examine the fundamental principles of distributed systems.

2. To provide students hands on experience on developing distributed protocols.

Course Outcomes: By the end of the course a student is expected to:

1. Understand issues in design of distributed computing and models of Distributed Computing

System.

2. Understand message passing mechanism in Distributed Computing Environment.

3. Understand & implement Remote Procedure Calls mechanism for distributed communication.

4. Classify distributed shared memory models, Design and Implement distributed file systems.

5. Study and implement synchronization algorithms in distributed computing.

6. Study and understand Process management and Naming & security issues in distributed

computing.

7. Design synchronization algorithms in distributed computing.

8. Understand Process management and Naming & security issues in distributed computing.

Syllabus

Operating system, types of O.S., Distributed Computing System, Evolution of Distributed Operating

System, Distributed Commuting System Models, Distributed Operating System, Issues in designing a

Distributed Operating System.

Desirable Features of a message passing system, Synchronization, Buffering, Multidatagram messages,

Encoding and Decoding of Message Data, Process Addressing, Failure Handling, Group Communication.

The RPC Model, Transparency of RPC, Stub generation, RPC Messages, Marshaling Arguments and

Results, Server Management, Parameter-Passing Semantics, Call Semantics, Communication Protocols for

RPCs, Complicated RPCs, Client Server Binding, Exception Handling, Security.

General Architecture of DSM Systems, Design and Implementation Issues of DSM, Granularity, Structure

of Shared Memory Space, Consistency Models, Replacement Strategy, Trashing.

Clock Synchronization, Event Ordering, Mutual Exclusion, Deadlock, Election Algorithms.

Features of a global scheduling algorithm, Task assignment Approach, Load Balancing Approach, Load

Sharing Approach.

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Process Migration, threads.

File models, File Accessing Models, File Sharing Semantics, File Caching Schemes, File Replication, Fault

Tolerance.Naming, Security


1. Pradeep K. Sinha , Distributed Operating System Concept and Design, PHI, First Ed. , 1998.

2. Andrew S. Tanenbaum, Distributed Operating System, Pearson Education Asia, First Ed. , 1995.

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Data Structures and Computer Algorithms Lab.


Course Code: CSP-711 *Course Credits: 2

Type: Compulsory


Mode: Experimental Lab.

*In lab work one credit is equivalent to

two hours

Course Assessment Methods (internal: 30;

external: 70) An internal practical examination is conducted by the

course coordinator.

The end semester practical examination is conducted

jointly by external and internal examiners. External

examiner is appointed by the COE of the university from

the panel of examiners approved by BOSR of the

Department of Computer Science and Engineering, Hisar

and the internal examiner is appointed by the Chairperson

of the Department.

Pre-requisites:

Programming in C/C++

The objectives of this lab course are to:

1. Develop skills to design and analyze linear and nonlinear data structures.

2. Apply appropriate data structures to solve unseen problems.

3. Practically experience the complexity analysis of sorting techniques.


1. Implement linear and non-linear data structures like linked lists, trees and graphs.

2. Practically select and apply appropriate data structures and algorithms using C/C++ programming

language to solve unforeseen problems.

3. Analyse the time and space efficiency of various data structures and computer algorithms for lab

experiments.

4. Be able to tackle unforeseen problems.

5. Be able to compare/contrast different algorithms for a problem.

Students are required to submit eight to ten assignments. The lab. assignments are evenly spread over the

semester. Every student is required to prepare a file of lab. experiments done.

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Advanced DBMS Lab.



Type: Compulsory




two hours

Course Assessment Methods (internal: 30;

external: 70) An internal practical examination is conducted by the

course coordinator.







of the Department.

Pre-requisites:

Basic Queries of SQL.

The objectives of this lab. course are to:

1. Understand the concepts of good database design and maintain it effectively.

2. Present SQL and procedural interfaces to SQL comprehensively.

3. Motivate the students to relate one or more commercial product environments as they related to

developer tasks.


1. Design and implement a database schema for a given problem-domain.

2. Declare and enforce integrity constraints on a database.

3. Maintain the database using PL/SQL programming including stored procedures, stored functions,

cursors and triggers.

4. Understand the issues involved in a database design

5. Design and implement a database schema for a given problem-domain.

Students are required to submit eight to ten assignments. The lab. assignments are evenly spread over the

semester. Every student is required to prepare a file of lab. experiments done.

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Software Project Management


Course Code: CSL721

Course Credits: 4

Type: Compulsory


Mode: Lectures












Pre-requisites:

Basic Knowledge of software engineering principles, software development life cycles, software models,

planning issues, and estimation process.

Course Objectives:

1. To discuss the various aspects of project management.

2. To understand the tasks in software project management.

3. To describe the requirements of project plan.

Course Outcomes (COs): By the end of the course student is expected to be able to

1. Understand and practice the process models, project life cycle models and the metrics road map

along with typical metrics strategy used in software project management.

2. Understand risk management analysis techniques that identify the factors that put a project on risk

and to quantify the likely effect of risk on project timescales.

3. Understand and use process and activities related to configuration management, Software Quality

Assurance, project initiation and completion criteria for the project intimation phase.

4. Demonstrate use of tools and techniques for project planning and tracking, estimation along with

the activities involved in testing phase and maintenance in software project management.

Syllabus

Project Life Cycle Models: Project Life Cycle Model, A Framework for studying different life cycle

models, The waterfall model, The prototyping model, The rapid Application Development (RAD) model,

The spiral model and its variants. Process Models: Characteristics of a process, what constitutes a

effective process, why are the processes important, process models, Common misconceptions about

processes.

Metrices : The metrics roadmap, A typical metrics strategy, what should you measure, Set targets and

track them, Understanding and trying to minimize variability, Act on data, people and organizational issues

in metrics programs, Common pitfalls to watch out for in Metrics programs, Metrics Implementation

Checklist and tools.

Software Configuration Management: The processes and activities of software configuration management,

configuration status accounting, Configuration Audit, Software configuration management in

geographically distributed teams, Metrics in software configuration management, Software Quality

Assurance: how do you define quality, why is quality important in software, quality control and quality

assurance, Cost and benefits of quality, software quality analyst’s functions, some popular misconceptions

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about the SQA’s role, Software Quality assurance tools, Organizational Structures, Profile of a successful

SQA, Measures of SQA success, pitfalls to watch out for in the SQA’s role.

Risk management : What is risk management and why it is important? Risk Management Cycle, Risk

Identification: Common Tools and Techniques Risk quantification, Risk Monitoring , Risk mitigation,

Risks and mitigation in the context of Global Project Teams. Some Practical Techniques in Risk

Management, Metrics in risk management.

Project Initiation: Activities during Project initiation, Outputs, quality records and completion criteria for

the project intimation phase. Interfaces to the process database.

Project planning and Tracking: Components of project planning and tracking, the “What” part of a project

plan, The “What Cost” part of a Project plan, The “When” part of project planning, The “How” part of

project planning , The “By whom” part of project management plan, putting it all together: The software

project management plan Activities specific to project tracking, Interfaces to the process database.

Project Closure: When does project closure happen. Why should we explicitly do a Closure? An Effective

Closure process, Issues that Get Discussed During Closure, metrics for project Closure, Interfaces to the

process Database.

Estimation: What is estimation? When and why is estimation done? The three phases of estimation,

Estimation Methodology, Formal models for size estimation. Translating size estimate into effort Estimate,

translating effort estimates into schedule estimates, Common challenges during estimation, Metrics for the

estimation processes. Design and Development Phases: some differences in our chosen approach, silent

features of design, Evolving an Architecture/ Blueprint, design for Reusability, Technology choices /

constraints, design of standards, Design of portability, user interface issues. Design for testability, design

for Diagnosability, Design for maintainability, Design for Install ability Inter-operability design, Inter-

operability design, Challenges during design and development phases, Skill sets for design and

development, Metrics for design and development phase. Project Management in Testing phase; What is

testing, What are the activities that make up Testing? Test scheduling and type of test, people issues in

testing, Management structures for testing in global teams, metrics for Testing phase. Project management

in the maintenance phase. Activities during the maintenance phase, management issues during the

maintenance Phase, Configuration management during the maintenance phase, Skill sets for people in the

maintenance phase, Estimating size, effort and people resources for the maintenance phase, Advantages of

using geographically distributed teams for the maintenance phase , metrics for the maintenance phase.


1. Managing Global Software project, Gopalaswamy Ramesh ,TMH Publishing Company, New Delhi.

(2001)

2. Controlling Software Project Management, Tom Demarco, Measurement, Prentice Hall , New jersey.

91982).

3. Principals of Software Engineering management, Tom Glib, Finzi Susannah, Addison Wesley,

England.

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Theory of Computation


Course Code: CSL722

Course Credits: 4

Type: Compulsory


Mode: Lectures












Pre-requisites:

Set theory, Mathematical Induction, Structural Induction and Predicates

Objectives:

1. To be able to construct FSA and the equivalent regular expressions.

2. To be able to construct PDA and equivalent CFG.


1. Understand the relationship between Automata and Regular Expressions, and Context Free

Grammar and Push down Automata. Abstract model of computation in the form of Turing

Machine and application of Turing Machine.

2. Understand the application of the theory of finite automata and context free grammars in the

design of programming language and compilers in the interest of more practical inclination.

3. Understand mathematical and computational principals that are foundations of the Computer

Science.

4. Construct pushdown automata and equivalence context free grammars.

Syllabus

Finite Automata, Deterministic finite automata, Non deterministic finite automata, finite automata with

epsilon transitions. Application of finite automata.

Regular Expressions, finite automata and regular expressions, algebraic laws of regular expressions,

Application of regular expression.

Context free grammars, The language of a grammar, sentential form, parse trees, ambiguity in grammars

and languages, Applications of context free grammar.

Normal forms for context free grammar, Chomsky normal form,The pumping lemma for context free

languages. Decision properties of context free language.

Push down automata, Languages of a PDA, parsing and pushdown automation.

Turing machine,Programming techniques for turing machine, restricted turing machines, turing machine

and computers.


1. John E. Hopcroft, Jefrey D. Ullman, Introduction to Automata Theory, Languages and

Computation, Narosa Publication House, 2004.

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2. K.L.P. Mishra, N. Chandrasekaran,, Theory of Computer Science automata, languages, and

computation, Third Ed. , PHI, 2008.

3. Peter Linz, Introduction to formal languages and automata, Third Ed., Jones and Barlett

Publishers, 2001.

4. Ramond Greenlaw, H. James Hoover, Fundamentals of the Theory of Computation- principles and

practice, Morgan Kaufmann Publishers, 1998.

5. H.R. Lewis, C.H. Papaditriou, Elements of Theory of Computation, Second Ed., Pearson

Education, 2005.

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Design and Development of Microprocessor Based Systems


Course Code: CSL723

Course Credits: 4

Type: Compulsory


Mode: Lectures












Pre-requisites:

Basic knowledge of Digital Electronics, Computer Organization


1. Provide the insight and develop the understanding of the architectures of microprocessors

including the advanced ones.

2. Cultivate the ability to write the programs by mastering the assembly language programming

using various concepts like addressing modes, assemblers, directives, operators, interrupts.

3. Understand the hardware specifications of various processors and demonstrate the basic

understanding of operations between the microprocessor and input/output and/or memory

devices.


1. Describe the features and use of the real and protected modes of microprocessors.

2. Do the application and system programming.

3. Design memory, input/output and interrupt interfaces to the microprocessors.

4. Design the microprocessor based control systems and can develop the software to

control them.

Syllabus

Architecture of 8086/8088- Introduction, Digital Computers, Microprocessors, 8086/8088 internal

Architecture, Memory Organization, Addressing Modes, Assembly directives, Introduction, Symbols,

Variables and constants, Data Definition and storage allocation directives, structures, records, Assigning

Names to Expressions, Segment Definition, Alignment Directives, Value Returning Attribute Operators.

The 8086/8088 Instructions- Introduction, Instruction Formats, Instruction execution Timing, assembler

instruction format, Data transfer Instruction, Arithmetic Instruction, Branch Instruction, and conditional

and unconditional, loop instructions, NOP and HLT instructions, Flag manipulation instructions, logical

instructions, Shift and Rotate Instructions, String Instructions, Assembly Language Programming.

Advanced Processors - Introduction, Intel 80286, Intel 80386, Intel 80486, Intel Pentium and Intel P6

processor - Internal Block Diagram Only.

I/O Programming - Fundamentals, I/O Considerations, Programmed I/O, Interrupt I/O, Block Transfer

and DMA. I/O Design Example.

Basic 8086/88 Minimum Mode, maximum mode, Interrupt priority Management, based on single 8259A

and multiple 8259, I/O interfaces, Asynchronous , Synchronous , 8251A Programmable Communications

interface, 8255 A Programmable Peripheral Interface.

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Microprocessor Applications - Data Acquisition System, Temperature Monitoring, Speed Control etc. Software and Tools to be learnt: MASM / TASM


1. INTEL Microprocessors ,Barry B. Brey, 8th Edition, Prentice-Hall Inc., U.S.A., 2008.

2. Microcomputer systems: The 8086 /8088 Family architecture, Programming and Design , Yu-

cheng Liu, Glenn A. Gibson, Second Edition, Prentice Hall of India, 2003

3. The 80386, 80486, and Pentium Microprocessor: Hardware, Software, and Interfacing , Walter A.

Triebel, Prentice-Hall Inc., U.S.A., 1998.

4. Intel Microprocessors: Architecture, Programming and Interfacing , K. Ray and K.M. Bhurchandi,

McGraw Hill Inc., 2001.

5. Multi-Core Programming , Shameem Akhter and Jason Roberts, Intel Press, 2006.

6. Modern Processor Design , John Paul Shen, McGraw-Hill Professional, 2004.

7. Microprocessors and Interfacing: Programming and Hardware, Douglas V. Hall.

8. The Pentium Microprocessor , James L. Antonakos , Pearson Education , 1997.

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High Speed Networks

Course Code: CSL724

Course Credits: 4

Type: Compulsory


Mode: Lectures












Pre-requisites:

Basic knowledge of computer networks, layers of OSI reference model, protocols at different layers of OSI

reference model.

Objectives of this course are to:

1. Learn about different high speed communication technologies like 10 G Ethernet, WiFi,

WiMAX, Fiber Channel, GSM, CDMA, ATM, ISDN and Frame Relay.


1. Have knowledge of different high speed communication technologies like 10 G Ethernet,

WiFi, WiMAX, Fiber Channel, GSM, CDMA, ATM, ISDN and Frame Relay.

2. Start research for improvement of performance of these technologies.

Syllabus

HIGH SPEED LAN

Gigabit Ethernet overview of fast Ethernet, IEEE 802.3z standard, protocol architecture,

network design using Gigabit Ethernet, applications, 10GB Ethernet.

Wireless Networks Existing and Emerging standards, Wireless LAN(802.11), Broadband

Wireless(802.16), Bluetooth(802.15) their architecture, protocol stack and frame format. Mobile

Networks

Fibre Channel Fibre channel characteristics, topology, ports, layered model, session

management, flow control, addressing, SAN.

HIGH SPEED WAN

Frame Relay Protocol architecture, frame format, routing.

ISDN and B-ISDN Channels, interfaces, addressing, protocol architecture, services.

ATM Virtual circuits, cell switching, reference model, traffic management

.

PERFORMANCE ANALYSIS and QoS IN COMPUTER NETWORKS

N/W analysis and modeling Probability and network queuing models(Little’s theorem, M/M/1,

M/M/m, M/M/∞, M/G/1), modeling network as a graph.

Open queuing network(Jackson’s Theorem) and closed queuing networks, managing network

performance.

QOS Protocols Overview of QoS protocols(RSVP, RTP).

INTERNET SUITE OF PROTOCOLS

Internet Layer IPV4 and IPV6, IP addressing, ARP, IP routing(OSPF and BGP), internet

multicasting, mobile IP.

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Transport Layer UDP/TCP protocols and architecture, TCP connection management, wireless

TCP.

Application Layer DNS, FTP, Voice over IP, audio and video compression.


1. Tere Parnell, Building high speed Networks, Osborne/McGraw-Hill, 1999

2. William stalling, High Speed Networks and Internets, Pearson Education, 2002

24

Soft Computing Lab.



Type: Compulsory




two hours

Course Assessment Methods (internal: 30; external:

70) An internal practical examination is conducted by the

course coordinator.







of the Department.

Pre-requisites:

Programming in C/C++

The objectives of this lab. course are to:

1. Give students a hand on training to implement soft computing techniques.

2. Apply genetic algorithms to the unseen problems.

3. Learn the tools to apply other soft computing techniques like neural net and fuzzy logic.


1. Practically apply Genetic Algorithms using C/C++ programming language to optimize some

benchmark functions.

2. Use the tools like MATLAB to implement the GAs, ANN and FL systems.

3. Be able to design novel GAs to solve optimization problems.

4. Have knowledge of fuzzy and neural tools.

Students are required to implement GA by breaking the whole programme into eight to ten modules. The

lab. assignments are evenly spread over the semester. Every students is required to prepare a file of lab.

experiments done. At the end, they learn basics of MATLAB and do mini projects in groups.

25

Microprocessor Lab



Type: Compulsory


Mode: Experimental Lab


two hours

Course Assessment Methods (internal: 30; external:

70) An internal evaluation is done by the course coordinator.







of the Department.

Pre-requisites:

Knowledge of assembly language

The objectives of this laboratory course are to:

1. Make students write 8086 assembly language programs using different types of instructions.

2. Learn the code conversion while inputting the data from keyboard and displaying it on monitor.

3. Understand the uses of different interrupt functions.


1. Be able to develop the assembly language programs.

2. Describe the internal architecture of an X86 processor showing the general purpose registers, the

segment registers, the ALU, the flags register, the instruction pointer (IP) register, and the

instruction register.

3. Be able to write code for interfacing of peripherals/devices with processor.

Students are given eight to ten laboratory assignments with soft and hard deadlines. The lab assignments

are evenly spread over the semester. The assignments may include a mini project. Every student is required

to prepare a file of laboratory experiments done.

Software and Tools to be learnt: MASM/TASM

26

Soft Computing


Course Code: CSL725

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge of programming and data structures


1. Study the need and applications of soft computing .

2. Provide knowledge of Genetic Algorithms, Fuzzy Logic and Neural Networks.

3. Design genetic algorithms for different kind of problems.

4. Design systems with a combination of soft computing techniques.


1. Have in-depth understanding of some of the soft computing techniques.

2. Identify the situations for which it is beneficial to apply soft computing techniques.

3. Apply suitable soft computing techniques for the problems which could not be otherwise

efficiently solved.

4. Describe suitable soft computing algorithms for the problems which could not be otherwise solved

efficiently.

5. Design the hybrid systems involving more than one soft computing techniques.

Syllabus

Introduction to genetic algorithms: Representation, initial population, evaluation function, genetic

operators, working of genetic algorithms using optimization of a simple function, study of parameters of

genetic algorithms and its performance, sampling mechanisms, mathematical foundations of genetic

algorithms and schemata processing, computer implementation of genetic algorithms and applications of

genetic algorithms.

Parallel and distributed genetic algorithms.

Introduction to genetic based machine learning: Classifier system, rule and message system, the bucket

brigade algorithm and application of genetic based learning.

Introduction to neural networks and fuzzy sets and logic

Evolutionary strategies: Comparison of genetic algorithm and evolutionary strategies, multi modal and

multi objective function optimization, the transport problem and traveling salesman problem.

27


1. David E. Goldberg, Genetic Algorithms in Search, Optimization and machine learning, Thirteenth

Ed., Addison Wesley, 2007.

2. Zbigniew Michalewicz, Genetic algorithms +Data Structures = Evolution Programs, Third Ed.,

Springers-Verlag, 1999.

3. Handbook of genetic algorithms, Edited by Lawrence Davis, Van Nostrand Reinhold, New York,

2010.

28

Advanced Computer Architecture


Course Code: CSL726

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge in Computer Organization


1. Study fundamental issues in architecture design.

2. Provide detailed coverage of current and emerging trends in computer architectures.

3. Study the pipeline processes and design.

4. Study superscalar and super pipeline design concepts.


1. Understand pipelining, instruction set architectures, memory addressing.

2. Analyse performance trade-offs in computer design.

3. Explain advanced issues in design of computer processors, caches, and memory.

4. Explain and design pipelining and superscalar design.

5. Explain vector processing principles and cache coherence.

Syllabus

Parallel Computer Models

The state of Computing, Multiprocesors and Multicomputers, Multivector and SIMD Computers, PRAM

and VLSI Models.

Program and Network Properties

Conditions of Parallelism, Program Partitioning and Scheduling, Program Flow Mechanism, System

Interconnect Architecture.

Processors and Memory Hierarchy

Advanced Processor Technology, Superscalar and vector Processors, Memory Hierarchy Technology,

Virtual Memory Technology.

Bus, Cache, and Shared Memory

Backplane Bus Systems, Cache Memory Organizations, Shared-Memory Organizations, Sequential and

Weak Consistency Models.

Pipelining and Superscalar Techniques

Linear Pipeline Processors, Nonlinear Pipelene Processors, Instruction Pipeline Design, Arithmetic Pipeline

Design, Superscalar and Superpipeline Design.

29

Multiprocessors and Multicomputers

Multiprocessor System Interconnects Cache Coherence and Synchronization Mechanisms, Message-

Passing Mechanisms.

Multivector, Scalable, Multithreaded, Data Flow Architecture

Vector Processing principles, Multivector Multiprocessors, Compound Vector Processing, Principles of

Multithreading, Dataflow and Hybrid Architectures.


1. Kai Hwang Advanced Computer Architecture, Second Ed., McGraw Hill, 2011.

2. Micheal J. Flynn, Computer Architecture: Pipelined and Parallel Processor Design, Jones & Bartlett

Learning, 1998.

3. John L. Hennessy, David A. Patterson, Computer Architecture: A Quantitative Approach, Fifth Ed.,

Elsevier, 2002.

https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22John+L.+Hennessy%22&source=gbs_metadata_r&cad=6

https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22David+A.+Patterson%22&source=gbs_metadata_r&cad=6

30

Embedded Systems


Course Code: CSL727

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge in Microprocessor, Operating System


1. Study the basics of Embedded Systems and Real Time Systems.

2. Study the 16 and 32 bit and microcontrollers and DSP hardware.

3. Study embedded software development tools and compilers.


1. Explain the characteristics of Embedded Systems

2. Design and implement Embedded Systems and Real Time Systems.

3. Apply the embedded software tools.

4. Describe the communication protocols with reference to embedded systems.

Syllabus

Real time operating system overview, Exposure to Windows CE, QNX, Micro kernels and c/OS of

introduction to process models. Interrupt routines in an RTOs environment, encapsulation semaphores and

queues, hard real-time scheduling considerations, saving memory space.

16 and 32 bit microprocessor and microcontroller and DSP hardware with reference to Embedded system.

Embedded software development tools and compilers - host and target machines linkers/locators for

embedded software, cross compilers, cross assemblers and tool chairs, gce compiler, basic concept of

device drivers, serial communication interface device driver.

System synthesis of Hardware / Software co-emulation, simulation speed of emulators, JTAG OCD.

Communication protocols with special reference to embedded system. TCP/IP, VDP wireless protocols,

IRDA, Blue tooth IEEE 8.8.11.


1. An Embedded System Primer, Devid E Simon, Addison Wesley, 1999.

2. Programming for Embedded System, Dreamtech Software Team, John Wiley, 2002.

3. TCP/IP Lean: Web Servers for Embedded Systems, Jeramy Bentham, 2002.

4. Real Time Programming: A Guide to 32 bit Embedded Development, Rick Grehan, AW, 1999.

5. Embedded Systems Architecture: A Comprehensive Guide for Engineers and Programmers

(Embedded Technology), Tammy Noergaard, Elsevier, Newnes.

31

6. Embedded Systems, Architecture, Programming, and Design. (2/e), Raj Kamal, Tata McGraw Hill,

2008.

7. Introduction to Embedded Systems, K.V. Shibu, Tata McGraw, 2009.

32

Computer Graphics


Course Code: CSL728

Course Credits: 4

Type: Elective I


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge in 2-D and 3-D Geometric.

Course Objectives:

1. To understand the applications of computer graphic concepts in the development of computer

games, information visualization and business applications.

2. To understand techniques of hidden surface elimination, polygon clipping and line clipping.

Course Outcomes:

After the completion of course students will be able to:

1. Create 2-D images and to perform various transformation.

2. Learn techniques for removal of hidden lines/ surfaces.

3. Use shading techniques to improve the quality of the projected images.

4. Perform image manipulation, storage and animation (conventional and computer assisted)

5. Use multimedia authoring tools.

Syllabus

Basic raster graphics algorithms for drawing 2D Primitive, linear, circles, ellipses. Arcs, clipping, clipping

circles, ellipses and polygon.

Geometric transformation: 2D, 3D transformations, Window-to-viewport transformations.

Graphics Hardware: Display techniques, Input Devices, Image Scanners, Hard Copy

Shading Techniques: Transparencies, Shadows, Object reflection, Gouraud and Phony Shading Techniques.

Visible Surface determination techniques for visible line determination, Z- Buffer algorithm, Scanline

algorithm, Algorithm for oct-tres, algorithm for curve surfaces visible surfaces, ray tracing, recursive ray

tracing.

Image manipulation and Storage: File formats for BMP, GIF, TIFF, IPEG, MPEG-II.

Animation: Conventional and Computer Assisted Animation, Methods of Controlling Animation,

Tweening, Morphing.

Multimedia: Applications, Components, Hypertext, Hypermedia, Authoring Tools.

33

Text and Reference Books

1. James D. Foley, Computer Graphics Principles and Practices, Addison Wesley.

2. Hearn and Baker, Computer Graphics, Pearson Education India.

3. N. Krishnamurty, Introduction to Computer Graphics, Tata McGraw Hill, 2002.

https://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22James+D.+Foley%22&source=gbs_metadata_r&cad=8

34

Compiler Construction General Course Information:

Course Code: CSL729

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Concepts of communication and computer network


1. Understand the use of regular expression and automata for the design of Lexical analyzer.

2. Understand the context free grammar for the development of automatic parsers.

3. Enable the students to understand generation of codes for target machine.


1. Design a compiler for given set of tokens.

2. Understand the working of software like Lex and YACC. 3. Understand a compiler structure and identify the relationship between different phases.

4. Understand the compiler optimization process.

Syllabus

Compilers and Translators, Lexical analysis, Syntax analysis, Intermediate code generation, Optimization,

Code generation.

Context free grammars, derivation and parse trees, capabilities of context free grammars.

Parsers, Shift reduce parsing, operator precedence parsing, topdown parsing, predictive parsers.

LR parsers, The canonical collection of LR(0) items, constructing SLR parsing tables, constructing LR

parsing table, constructing LALR parsing tables, ambiguous grammars usages, implementation of LR

parsing tables, constructing LALR sets of items.

Syntax directed translation schemes and their implementation, post fix notation, parse tree and syntax trees,

quadruples and triples, assignment statements, Boolean expressions, flow control alteration, postfix

translation, translation with top-down parsers, array references in arithmetic expressions, procedure calls,

declarations, case statements, record structures, PL/I style structures.

The contents of a symbol table, data structures for symbol tables, representing scope information.

Error, lexical phase errors, synthetic phase errors, semantic errors.

Sources of optimization, Loop optimization, DAG representation of logic blocks, loop invariant

computations, induction variable elimination, machine code generation.


1. Affred V. Aho, Jaffrey D. Ullman, Compilers: Principles, Techniques and Tools, Pearson Education

India, 2003. 2. Ravi Sethi, Compiler: Principles, Techniques and Tools, Pearson Education India.

35

Dissertation and Seminar-I


Course Code: CSD-731 *Course Credits: 4

Type: Compulsory

Contact Hours: 2 hours/week with

supervisor

Mode: One- to- one discussions with

the supervisor

Course Assessment Methods (internal assessment:

100) Every student is allotted a supervisor at the beginning of

the third semester and is required to present his/her

dissertation synopsis using power point presentation

towards the end of third semester. The presentation is

evaluated by a committee of senior teachers constituted

by the Chairperson of the Department.

The objectives of Dissertation and Seminar-I are to train students to:

1. Do literature survey to identify a research problem of appropriate level and size.

2. Understand the process of research.

3. Plan and write dissertation synopsis.

4. Communicate and discuss research ideas.

Outcomes for Dissertation and Seminar-I:

By the end of this phase every students is expected to display the evidence of having learnt:

1. Planning research including steps like identifying research problem, selecting appropriate

research methods, exposure to appropriate research tools.

2. Writing a suitable research synopsis/proposal.

3. Communicating effectively verbally and in writing.

4. Discussing novel ideas critically and openly, and improving the research proposal in the light of

the feedback given by others.

5. MS Office tools for writing and presenting the research proposals.

6. Do literature survey about a research domain, identify research problem, select appropriate

research methods and tools.

7. Achieve proficiency in Word Processing tools for writing and presenting the research proposals.

36

Mobile and Wireless Communication


Course Code: CSL731

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Concepts of communication and computer network


1. Understand the working of wireless channel and working of wireless MAC layer protocols.

2. Understand the routing protocols for wireless networks and mobility protocols for TCP layer.

3. Enable the students to understand working of wireless network along with communication

concepts.


1. Design Mac and routing protocols for wireless networks.

2. Understand the design of future concepts of wireless networks.

3. Understand the issues in mobile and wireless communication.

4. Analyse the improved data services in mobile networks.

Syllabus

Introduction:

Applications, history, market, reference model and overview. Wireless Transmission--- Frequencies,

signals, antennas, signal propagation, multiplexing, modulation, spread spectrum, cellular system.

MAC and Telecommunication system:

Specialized MAC, SDMA, FDMA, TDMA- fixed TDM, classical ALOHA, slotted ALOHA, CSMA,

DAMA, PRMA, reservation TDMA. Collision avoidance, polling inhibit sense multiple access.CDMA,

comparison, GSM- mobile services, architecture, radio interface, protocol, localization, calling, handover,

security, new data services, Introduction to WLL.

Satellite and Broadcast Systems:

History, Applications, GEO, LEO, MEO, routing, localization , handover in satellite system. Digital audio

and video broadcasting.

Wireless LAN:

IEEE 802.11-System and protocol architecture, physical layer. MAC layerand management. Bluetooth---

User scenarios, physical layer, MAC layer, networking, security and link management.

Mobile network Layer:

Mobile IP- goals, assumption, requirement, entities, terminology, IP packet delivery, Agent advertisement

and discovery, registration, tunneling, encapsulation, optimization , reverse tunneling, IPV6.

DHCP. Adhoc Networks—routing , destination sequence distance vector, dynamic source routing,

hierarchical algorithm, alternative metric.

Mobile Transport Layer:

Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP fast retransmission/ recovery,

transmission/time out freezing, selective retransmission, Transaction oriented TCP.

37

Support for Mobility:

File System, WWW-HTP, HTML, system architecture. WAP – architecture, wireless datagram, protocol,

wireless transport layer security, wireless transaction protocol, application environment, telephony

application.


1. Jochen Schiller, Mobile Communication, Pearson Education, Second Ed. , 2003.

2. Lee, Mobile Cellular Telecommunications, McGraw Hill, Second Ed. , 2009.

38

Performance Modelling General Course Information:

Course Code: CSL732

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Probability, Random Variable and distributions.


1. Understand the basic concepts of queueing theory..

2. Understand the real time problems on the basis of Markovian and general queues.


1. Model the queueing systems.

2. Map the real world problem with the help of queueing theory.

Syllabus

Overview of probability and stochastic process

Probability, Random Variables, Stochastic Processes

Queuing Paradigm and elements of Performance issues

How queue behave?

Why queuing analysis?

Queuing Theory and models

Case Study : Performance model of a distributed file service; Single Bus Multiprocessor

modeling, Performance model of a shared medium packet switch

Single Queuing System

M/M/P Q.S., Poisson process, Foundation of the Poisson process, The Markov Property, Exponential

Service times, Foundation of the M/M/1 Q.S., Flows and balancing, Little’s law, Performance measures.

State-dependent Q.S.

M/M/1/N q.s. : the finite buffer case

M/M/ q.s. infinite member of servers

M/M/M q.s. : m parallel servers with a queue (Erlang C formula)

M/M/m/m queue : A loss system (Erlang B formula)

Central server CPU model

Network of queue and Simulation of Communication networks:

Open networks (The product from solution)

Closed queuing networks

Norton’s equivalent for queuing networks

Simulation of communication networks

Network Traffic Modelling

Continuous Tine Models- Poisson process, Generally Modulated Poisson process, Markov

modulated Poisson Process.

Auto regressive – Moving average model

Fluid flow approximation model

Self-similar data traffic

39

Continuous-time definition, Long-range dependence, Spectral density, Examples :World wide

traffic, TCP, FTP, TELNET traffic

Burstiness

Network Performance

Text and Reference Books :

1. Thomas G. Robertazzi, Computer networks and systems- queuing theory and performance

evaluation, Third Ed. , Springer, 2000

2. Kishore S. Trivedi, Probability and Statistics with reliability, queuing and computer Sc.

Applications”, PHI, 1988

40

Security of Information Systems


Course Code: CSL733

Course Credits: 4

Type: Elective


Mode: Lectures










candidate is required to attempt any five

questions. All the questions carry equal marks.

Pre-requisites:

Students are expected to have knowledge of programming in any language

Objectives:

1. To understand several security issues.

2. To understand several cryptographic algorithms.

3. To understand the social legal and ethical implications of modern security systems.


1. Identify the range and importance of security systems applications in modern organizations,

2. Understand the different cryptographic algorithms and their relevance to the creation of an

effective security system,

3. Examine the alternative methods for security systems development and acquisition, and their

suitability in particular circumstances

4. Outline different types of applications used in practice, as well as the technical infrastructures

upon which they rely

5. Identify the social, legal and ethical implications of modern security systems used

6. Outline technologies such as wireless and bluetooth applications.

Syllabus

UNIT 1 CRYPTOGRAPHY

Overview of Information Security Basic Concepts, Cryptosystems, Crypto analysis, Ciphers and

Cipher modes.

Symmetric Key Cryptography DES, AES.

Asymmetric Key Cryptography RSA algorithm, Key management protocols, Diffie Hellman

Algorithm.

Digital Signature Digital Signatures, Public Key Infrastructure.

UNIT II SYSTEM SECURITY

Program Security Security problems in Coding, Malicious Logic, Protection.

Database Security Access Controls, Security and Integrity Threats, Defence Mechanisms.

OS Security Protection of System Resources, Models for OS security.

.Net Security User based security, Code access security, form authentication.

UNIT III NETWORK and INTERNET SECURITY

LAN Security Threats, Authentication and access control, Secured communication Mechanisms

(IPSec, Kerberos, Biometric, PKI), Secured Design for LAN.

Firewall and IDS Firewall Techniques, Firewall Architecture, Types of IDS, IDS Tools.

Email and Transaction Security Mechanisms Privacy Enhanced Mail(PEM), S/MIME, SET

41

protocol, Client-Server Security on web.

UNIT IV WIRELESS SECURITY

Wi-Fi and IEEE 802.11 Security Protocol architecture, WEP, Access controls.

Wireless Transport Layer Security Transport Layer Security, SSL, IPSEC, WAP security.

Bluetooth Security Protocol architecture, Attacks, Security architecture.


1. Charles P. Pfleeger, Security in Computing, Fourth Ed., Prentic- Hall International, 2006.

2. Bruce Schneier, Applied Cryptography Protocols, Algorithms, and Source Code in C, Second Ed.,

John Wiley and Sons, 1995.

3. Rolf Oppliger, Security Technologies for World Wide Web, Second Ed., Artech House, 2002.

42

Data Mining Concepts & Techniques


Course Code: CSL734

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge of programming and data structures


1. Study the need and applications of data mining .

2. Provide knowledge of data mining tasks.

3. Design enhance data mining algorithms for different kind of data mining problems.

4. Provide in-depth understanding of data and interpretation of the results of data mining tasks .


1. Have in-depth understanding of data mining tasks.

2. Understand the data, apply an appropriate data mining task as required in a given problem and

interpret results.

3. Enhance the functioning of data mining algorithms.

4. Apply pre-processing techniques to make data ready for mining.

Syllabus

Introduction to data mining and its importance, data mining, functionalities, Data warehouse,

multidimensional data model, data warehouse architecture.

Data Pre-processing: Data cleaning, Data integration and transformations, data reduction, Dicretization and

concept hierarchy generation, feature selection.

Association rule mining, apriori algorithm and finding frequent item sets, mining multilevel association

rules. Association mining correlation analysis. Mining fuzzy association rules.

Classification and prediction, Quantifying interestingness of rules: subjective and objective interestingness,

classification by decision tree induction, Baysain classification, Classification by back propagation, case

based reasoning, rough set approach.

Clustering Methods: hierarchical models, k-means algorithm and density based methods, Outlier and

exception discovery. Introduction to data streams.

Evolutionary Algorithms in data mining: Encoding, Fitness functions and GA operators.

Introduction data mining tools.

43


1. Jiawei Han, Micheline Kamber, Data Mining Concepts and Techniques, Third Ed. , Morgan

Kaufmann, 2011

2. Pujari, A. K. Data Mining Techniques, First Ed., Universities Press (India) Limited, 2001.

3. Pieter Adriaans and Dolf Zantinge , Data Mining, Pearson Education Asia, 2000.

44

Research Methodologies General Course Information:

Course Code: CSL735

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Set theory, matrix and calculus.


1. Understand the basic concepts of research and survey of literature..

2. Understand the statistical techniques for the analysis of the research data.

3. Know the discrete and continuous Random Variable sand their distributions.


1. Understand the concepts of research methodology.

2. Apply the probability distributions to the computer science problems.

3. Conduct research experiments and apply the statistical tests to validate the results of research

experiments.

4. Practice research ethics.

Syllabus

Scientific Research: Nature and objective of research, types and methods of research; historical,

descriptive, and experimental.

Study and formulation of research problem; feasibility, preparation and presentation of research proposal.

Information and how to deal with it.

Nature and use of argument.

Scientific methods and responsible conduct in research.

Statistical analysis: Measures of central tendency and dispersion; mean, median, mode, range, mean and

standard deviations.

Random variables and probability: Probability and probability distributions; binomial, poisson, geometric,

negative binomial uniform exponential, normal and log normal distribution.

Test of hypothesis: basic ideas of testing hypothesis, test of significance based on normal, t and chi-square

distributions, analysis of variance technique.


1. Wayne C. Booth, Joseph M. Williams, et.AL. The Craft of Research: Chicago Guides to Writing

Edition and Publishing, Second Ed., University of Chicagos Press, 2003.

2. Johnson R.A., Probability and Statistics, Fifth Ed. , PHI, 2002.

3. Nicholas Williman, Your Research Project, Third Ed., SAGE, 2011.

4. Peter Medawar, Advice to A Young Scientist, First Ed., Basic Books, 1981.

5. Santiago Ramon Y. Cajal, Advice for a Young Investigator, MIT Press, 2004.

45

Neural Networks


Course Code: CSL736

Course Credits: 4

Type: Elective


Mode: Lectures












Pre-requisites:

Students are expected to have knowledge of Linear Algebra, Probability


1. Make the students understand the role of neural networks in engineering for computational

learning.

2. Present the basic network architectures.

3. Provide knowledge of supervised learning in neural networks.

4. Provide knowledge of unsupervised learning using neural networks.


1. Design single layer and multilayer feed-forward neural networks.

2. Understand optimization techniques, steepest descent, gradient method, conjugate gradient,

backpropagation.

3. Understand Kohonen rule, competitive learning, self-organizing feature maps.

4. Perform training of various neural networks and analyze their performance.

Syllabus

Introduction: Objective, History, Applications, Biological Inspiration.

Neuron Model and Network Architectures: Neuron Model, Transfer Functions, Network Architectures.

Perceptron Learning Rule: Learning Rules, Perceptron Architecture, Perceptron Learning Rule, Training

Multiple-Neuron Perceptrons.

Signal and Weight vector Spaces and Linear Transformations for Neural Networks: Linear Vector Spaces,

Spanning a Space, Inner Product, Norm, Orthogonality, Vector Expansions, Linear Tranformations, Matrix

Representations, Change of Basis, Eigenvalue and Eigenvectors.

Supervised Hebbian Learning: The Hebb Rule, Performance Analysis, Application, Variations of Hebbian

Learning.

46

Performance Surfaces and Optimization: Taylor Series, Directional Derivatives, Necessary Condition for

Optimality, Quadratic Functions, Optimization Techniques, Steepest Descent, Newton’s Method,

Conjugate Gradient Method.

Backpropagation: The Backpropagation Algorithm; Performance Index, Chain Rule, Example, Drawbacks

of Backpropagation, Heuristic Modifications; Momentum, Conjugate Gradient, Levenberg-Marquardt

Algorithm.

Associative Learning and Competitive Networks: Simple Associative Network, Unsupervised Hebb Rule,

Kohonen Rule, Competitive Learning Rule, Self Organization Feature Maps.


1. M. T. Hagan, H. B. Demuth and MH. Beale, Neural Network Design, Thomson Learning, 2002.

2. Simon Haykin, Neural Networks – A Comprehensive Foundation, Third Ed., Pearson, 2008.

3. Bishop, C.M., Neural Networks for Pattern Recognition, First Ed., Oxford University Press, Oxford,

UK, 1996.

4. N. Nikolaev, H. Iba, Adaptive Learning of Polynomial Networks: Genetic Programming,

Backpropagation and Bayesian Methods, Springer, 2006.

5. Satish Kumar, Neural Networks: A Classroom Approach, Second Ed. , Tata McGraw-Hill, 2013

47

Dissertation and Seminar-II


Course Code: CSD-741 *Course Credits: 10

Type: Compulsory

Contact Hours: 2 hours/week with

supervisor

Mode: One- to- one discussions with

the supervisor

Course Assessment Methods (Joint evaluation: 100) Fourth semester is dedicated to carry out the research

proposal submitted at the end of third semester. The

students submit their dissertation by 30th of June and it is

jointly evaluated by internal and external examiners. The

supervisor of a student acts as an internal examiner and

the external examiner is appointed by COE from panel of

experts approved by the BOS of the Department.

The objectives of Dissertation and Seminar-II are to train students to:

1. Make students learn to conduct independent, original, and significant research.

2. Try out novel and innovative research ideas

3. Select suitable research methods and tools.

4. Enhance the functionality of research tools

5. Conducted suitable experiments and discuss the results in the light of similar works done by

other.

6. Understand the scope and relevance of their work.

7. Write a dissertation.

8. Publish research papers

9. Know the ethics of research

Outcomes for Dissertation and Seminar-II: By the end of this phase every students is expected to be

able to

1. Handle research problems independently.

2. Analyse and review the existing literature on a research question.

3. Read research material/papers critically and make original comments on it.

4. Design and conduct experiments.

5. Interpret data and result, and critically evaluate empirical evidence.

6. Use research methods efficiently.

7. Use modern research tools.

8. Write dissertation and technical reports.

9. Publish research papers.

10. Understand the social relevance of research.

11. Communicate research ideas verbally and in writing.

12. Discuss ideas in a group and accept critical comments.

13. Comprehend the social relevance and impact of the research.

14. Understand and follow ethical research practices.

The research on problem formulated after review of literature done in 3rd semester should be

continued in this semester. Student should do their research at GJU Hisar only. However they may visit

other organizations for any kind of help in their dissertation with prior approval of Chairman on

48

recommendation of supervisor. Before submission of final dissertation student is required to present

himself/herself for pre-submission seminar in front of departmental committee.

Student shall present before submission a paper on any topic related to his/her work in a Seminar/

Conference/ Symposia or should have published a paper in a journal or proceedings of a Seminar/

Conference/ Symposia without which dissertation could not be submitted. Paper accepted for publication

will be considered to have been published for this purpose.

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