M.tech. CS (Full Time) Course Curriculum

8/3/2019 M.tech. CS (Full Time) Course Curriculum

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M.Tech.(CSE)

Course Code: 8150Bat c h : 2011

DEPARTMENT OF COMPUTER ENGINEERING & APPLICATIONInstitute of Engineering & Technology

Course Curr ic u lum


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Department of Computer Engineering & Application (Batch 2011)

M.Tech.(CSE)

DEPARTMENT OF COMPUTER ENGINEERING & APPLICATION, Institute of Engineering & Technology

ABOUT THE PROGRAM

The Department of Computer Engineering & Application at GLA University provides cutting

edge research and imparts state of the art education in M Tech (CSE) course. The Department

provides an outstanding research environment complemented by excellent pedagogy. TheDepartment is committed to equip the M Tech(CSE) students with the skills to analyze,

design and develop various applications of computer system with a constant focus on

strengthening analytical thinking, problem solving, research, teamwork, communication skills

and readiness for lifelong learning.

The Institute initiated its undergraduate program in Computer Science and Engineering in

1998. Since then the activities of the Department of Computer Engineering & Application

have proliferated in various directions. A postgraduate course in Computer Science and

Engineering leading to an M Tech (CSE) degree was introduced in the 2010-2011 Session.

The M Tech(CSE) course offered by the department gives a plethora of options to thedesired students through its various full time and part time courses. The Department offers a

Full-Time 2 year programme, comprising of 4 semesters and a Part-Time 3 year programme

comprising of 6 semesters.

The Faculty and the research infrastructure of the department are committed to impart a high

level of learning. This fact is quite evidently reverberated by looking into the combination of

subjects the department is offering. For core courses, the department offers subjects like-

Theory of Computation, Distributed Database Systems, Advanced Data Structures &

Algorithms, Computer System Software, Parallel Computing, Image Processing & Computer

Vision, Embedded Systems, Mobile Adhoc Networks, Intelligent Systems and Advanced

Optimization Techniques.

To provide enough flexibility, a student can opt for a subject of his/her choice from a wide

range of subjects like-Cluster and Grid Computing, Client Server & Middleware

Technologies, Network Performance& Evaluation, Research Methodologies, Computer

Security& Forensics and Data Mining & Warehousing.

To take M Tech (CSE) course to a new level of excellence and to exemplify the quality of

technical education, various seminars and conferences are organized throughout the year.

This ensures that a proper platform is provided to a student and assignments will ensure that a

student is on his\her toes as far as course is concerned.


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M.Tech.(CSE)


COURSE

STRUCTURE


4/21


M.Tech.(CSE)


First Semester

S.NO.

CODE SUBJECT TEACHING SCHEME CREDITS

LECTURE TUTORIALS PRACTICALS

1. MCS111 Theory of Computation 4 0 0 42. MCS112 Distributed Database Systems 4 0 0 4

3. MCS113Advanced Data Structures &Algorithms

4 0 0 4

4. MCS116Software Engineering

Methodologies4 0 0 4

5. MCS115 Parallel Computing 4 0 0 4

6. MCS181 Seminar-I 0 0 2 1

7. MCS183 A D S Lab. 0 0 2 1

TOTAL 20 0 4 22

Second Semester

S.NO.

CODE SUBJECTTEACHING SCHEME

CREDITSLECTURE TUTORIALS PRACTICALS

1. MCS121Image Processing &Computer Vision

4 0 0 4

2. MCS122 Embedded Systems 4 0 0 43. MCS123 Mobile Ad-hoc Networks 4 0 0 4

4. MCS124 Intelligent Systems 4 0 0 4

5. MCS126 Information Retrieval 4 0 0 4

6. MCS191 IPCV Lab. 0 0 2 1

7. MCS193 Seminar-II 0 0 2 1

TOTAL 20 0 4 22


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M.Tech.(CSE)


Third Semester

S.NO.



1. Elective-I 4 0 0 4

2. Elective - II 4 0 0 4

3. MCS282 Colloquium 0 0 4 2

4. MCS281 Thesis Part I 0 0 8 4

5. Total 8 0 12 14

ELECTIVE-1 - Third Semester

S.

NO.



1. MCS 231 Cluster and Grid Computing 4 0 0 4

2. MCS232 Client Server & MiddlewareTechnologies

4 0 0 4

3. MCS233 Information Security 4 0 0 4

ELECTIVE-2 - Third Semester

S.NO.



1. MCS234 Research Methodologies 4 0 0 4

2. MCS235 Pattern Recognition 4 0 0 4

3. MCS236 Data Warehousing & Mining 4 0 0 4

Fourth Semester

S.NO.



1. MCS291 Thesis Part II 0 0 28 14

2. Total 0 0 28 14


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M.Tech.(CSE)


MMCCSS 111111:: TTHHEEOORRYYOOFF CCOOMMPPUUTTAATTIIOONN

Module

No.Contents

Teaching

Hours

I

Automata & LanguagesChomsky Hierarchy of Grammars and the corresponding acceptors, Turing

Machines, Recursive and Recursively Enumerable Languages; Operations onLanguages, closures with respect to the operations.

13

II

Computability TheoryDecidability Decidable languages, The Halting Problem, UndecidableProblems about Turing Machines, Posts Correspondence Problem,

Reducibility.

14

III

Complexity TheoryTime Complexity - Measuring Complexity, The class P, The class NP, NP-

Completeness, Reduction, co-NP, Polynomial Hierarchy

Space Complexity -- Savich's Theorem, The class PSPACE, PSPACECompleteness.Intractability.

13

References:

Michael Sipser - Introduction to The Theory of Computation, CENGAGE Learing John E. Hopcroft, Rajeev Motwani, Jeffery D. Ullman Automata Theory, Languages, and

Computation, Pearson Education John C. Martin Introduction to Languages and the Theory of Computation, McGraw Hill Harry R. Lewis, Christos H. Papadimitriou Elements of the Theory of Computation

Credits: 04 LTP : 400Semester I


7/21


M.Tech.(CSE)


MMCCSS111122:: DDiissttrriibbuutteedd DDaattaabbaassee SSyysstteemmss

Module

No.Contents Teaching Hours

I

Introduction: Introduction to distributed databases, Overview ofRelational DBMS, Relational Database concepts, Normalization,

Integrity rules, comparison of distributed and centralized databasesystems,

Levels of Distributed Transparency: Reference Architecture forDistributed Database, Type of Database Fragmentation, DistributedTransparency, Integrity Constraints,Distributed Database Design: Framework for Distributed Database,Design of Database Fragmentation

13

II

Distributed Query Processing: Representation of database operation

in form of a query, operation in form of a query, operations on a query,unary and binary tree in a query, converting a global query into

fragment query, join and union operations involving a query, aggregatefunctions, parametric queries.Query optimization: Introduction to query optimization, estimation of

profiles of algebraic operations, optimization graphs, reduction ofrelation using semi-join and join operation.

13

III

Transactions Management in Distributed Database: Properties and

goals of transaction management, distributed transactions, recoverymechanism in case of transaction failures, log based recovery, check

pointing, communication and site failures,Concurrency Control in Distributed Database: Serializability andtimestamp in distributed databases. Introduction to distributed

deadlocks, local and global wait for graphs, deadlock detection,prevention of deadlocks.

14

References:

Stefano Ceri and Giuseppe Palgatti, Distributed Database, McGraw Hill Publications, 1985. Korth, Silbertz, Sudarshan, Database Concepts, McGraw Hill Publications Data C J, An Introduction to Database System, Addison Wesley A.Silberchatz Database System Concepts 3rd Edn. McGraw Hill Inc., 1997. Majumdar & Bhattacharya, Database Management System, TMH



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M.Tech.(CSE)


MMCCSS111133:: AADDVVAANNCCEEDD DDAATTAA SSTTRRUUCCTTUURREESS && AALLGGOORRIITTHHMMSS

Module

No.Contents

Teaching

Hours

I

Advanced Data Structures: Data structures, Algorithms, AsymptoticNotations, Recurrence Relations, 2-3 trees, Red-black trees, R-Tree, Splay trees,

Skip lists..13

II

Algorithm Design Techniques: Dynamic Programming: Knapsack problem,Chain matrix multiplication, OBST, Greedy algorithms: Knapsack problem,

shortest paths problem, Divide-and-Conquer Approach: MatrixMultiplication, Convex Hull, Branch and Bound: Knapsack problem,

Assignment Problems, Backtracking: Knapsack problem, sum subsetproblem.

13

III

Advanced Algorithms: Sorting Networks, Polynomials and the FFT,

Probabilistic Algorithms: Numerical Probabilistic Algorithms, Monte Carlo

Algorithms, Computational Complexity: NP-Completeness, Approximation Algorithms: Vertex Cover Problem, TSP Problem, Set Covering Problem.Randomized Algorithms.

14

References:

Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest - Introduction toAlgorithms, Prentice Hall of India.

Gilles Brassard Paul Bratley - Fundamentals of Algorithms, Prentice Hall. RCT Lee, SS Tseng, RC Chang and Y T Tsai - Introduction to the Design and Analysis, Mc

Graw Hill, 2005.

Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekaran - Fundamentals of ComputerAlgorithms , OrientLongman.



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M.Tech.(CSE)


MMCCSS112266:: SSOOFFTTWWAARREE EENNGGIINNEEEERRIINNGG MMEETTHHOODDOOLLOOGGIIEESS

Module

No.Contents

Teaching

Hours

I

Introduction: Motivation Software Attributes Complexity - SoftwareMetrics- Software Process, Requirement Engineering, Formal requirement

specification, requirement modeling and specification Design Metrics andConfiguration Management. Formal Specification and program verification,

introduction to formal methods, Software Process, Requirement Engineering,Formal requirement specification, requirement modeling and specification

13

II

Software Design Patterns

Issues in software design: modularity based cohesion & coupling Functionoriented analysis & design. Software Architecture description languages -Product-line architectures; Component based developmentSoftware Quality EngineeringTesting Techniques Test Case Generation, Software Maintenance schemes

Software testing: strategies and assessment, COTS, Software reliability metrics& modeling, Software quality: models and assurance framework, SoftwareMaintenance,

14

III

Software Metrics - COTS Integration - Distributed, Internet-scale and Web-

based Software EngineeringEmpirical Studies of Software Tools And Methods

Software Reengineering - Software Reuse - Software Safety - EnterpriseArchitectures, Zachman's Framework; Architectural Styles.

Formal specifications Techniques Verification and Validation TheoremProvers - Model checking Temporal logics CTL & LTL and model checking

13

References:

Ghezzi, Jazayeri, Mandrioli, Fundamentals of Software Engineering, 2/E,Pearsonducation,2002

Sommerville, Software Engineering, 6/E,Pearson Education, 2006 Roger S Pressman, Software Engineering A Practitioners Approach , 6/E,MGH, 2005 Schmidt, Stal, Rohnert, and Buschmann, Pattern-Oriented Software Architecture Volume 2:

Patterns for Concurrent and Network ed Objects, Wiley, 2000

Len Bass, Paul Clements, Rick Katzman, Ken Bass, Software Architecture in Practice, 2/E,Addiwon Wesley Professional, 2003.



10/21


M.Tech.(CSE)


MMCCSS111155:: PPAARRAALLLLEELL CCOOMMPPUUTTIINNGG

Module


I

Parallel Computing: Introduction: Need of high speed computing,Techniques to achieve high speed computing memory inter leaving

cache applications & problems performances laws & matrices, PipelineComputers & Parallel Computers, RISC & CISC Systems.

13

II

Distributed & Parallel Processors: Flynns & other Computer

classification Interconnection Networks Static & Dynamic networksvarious design parameters, - Advantages & limitation, various

multiprocessor organizations, and their comparison, Mapping &scheduling concept.

14

III

Parallel Programming: Abstract Machine Models RAM& PRAM,

various parallel algorithms on them: Practical Models for parallel

Computation BSP & Log P Models: Cost Optimal Algorithms: Parallelprogramming Environments: parallel programming Languages.

13

Reference Books:

M.J Quinn- Parallel Computing: Theory and Practice, McGraw Hill KaiHwarg: Parallel Computer Architecture Tata McGraw Hill Edition



11/21


M.Tech.(CSE)


MMCCSS112211:: IIMMAAGGEE PPRROOCCEESSSSIINNGG AANNDD CCOOMMPPUUTTEERR VVIISSIIOONN

Module

No.Contents

Teaching

Hours

I

Digital Image Fundamentals: Image sampling & quantization; Basicrelationships between pixels, Some mathematical tools used in digital image

processing.Image perception: Light, luminance, brightness and contrast, Human Visual

System, Colour representation, Chromaticity diagram, Colour CoordinateSystems.Intensity Transformations and Spatial Filtering: Basic intensityTransformation functions, Histogram processing, Spatial filters, Using FuzzyTechniques for intensity transformations and spatial filtering.

13

II

Object Representation: Chain Codes, Polygonal approximations Using

Minimum-Perimeter, Boundary Descriptors: Some Simple descriptor(Boundary straightness, Bending energy), Shape Numbers, Statistical Moments,

Regional Descriptors: Some Simple Descriptors (Eccentricity, Elongatedness,Rectangularity, Compactness), Topological Descriptors, Texture, MomentInvariants

Object Recognition: Knowledge Representation, patterns and Pattern Classes,Recognition based on decision theoretic Methods, Minimum distance classifierIntroduction to Image Coding Algorithms: Coding Redundancy, Spatial and

Temporal Redundancy, Irrelevant Information, Measuring Image Information,Fidelity Criteria, Lossless compression, Lossy Compression

14

III

3D vision, geometry and radiometry: 3D vision tasks, Geometry for 3Dvision, Radiometry and 3D vision.Use of 3D vision: Shape from X, Full 3D objects, 3D model based vision, 2D

view based representations of a 3D scene.Object Recognition: Patterns and pattern classes, Recognition based ondecision-theoretic methods, structural methods.

13

References:

Digital Image Processing, 3rd Edition, by R.C.Gonzalez and R.E.Woods, Prentice Hall Fundamentals of Digital Image Processing, by Anil K. Jain, Prentice-Hall, 1989 Digital image processing and analysis, Bhabatosh Chanda, D. Dutta Majumder, Prentice-Hall Computer Vision: A Modern Approach, by D. A. Forsyth and J. Ponce, Prentice Hall, Image Processing, Analysis and Machine Vision-Milan Sonka,Vaclav Hlavae, Roger Boyle,

Thomson Digital Image Processing: An Algorithmic Approach, Madhuri A. Joshi, EEE.

Credits: 04 LTP : 400Semester II


12/21


M.Tech.(CSE)


MMCCSS112222:: EEMMBBEEDDDDEEDD SSYYSSTTEEMM

ModuleNo. Contents

TeachingHours

I

Introduction to embedded systems, categories of Embedded Systems;architecture of Embedded Systems; Implementation of Embedded System,Hardware Evolution; Challenges in designing of Embedded System. Skill

required for an Embedded System Designer. Programmable Processor Architecture: Instruction set architecture; memory organization, operand

location and memory addressing. 8051 architecture in brief. Enhancingprocessor performance; Introduction to advanced processor, case studies forselection of processor in hardware design and memory for software design

13

II

Programming in assembly language (ALP) vs. High Level Language - Concepts

of EMBEDDED PROGRAMMING in C++ - Objected Oriented Programming Embedded Programming in C++, C Program, compilers, Cross compiler Optimization of memory codes. Definitions of process, tasks and threads

Clear cut distinction between functions ISRs and tasks by their characteristics Operating System Services- Goals Structures- Kernel - Process Management Memory Management Device Management File System Organization andImplementation I/O Subsystems Interrupt Routines Handling in RTOS,

14

III

REAL TIME OPERATING SYSTEMS : Task scheduling models, Inter processcommunication and synchronization, ,Priority Inversion Problem and Deadlock

Situations, Inter Process communications. Study of Micro C/OS-II or Vx Works

or Any other popular RTOS RTOS System Level Functions Task ServiceFunctions Time Delay Functions Memory Allocation Related Functions

Semaphore Related Functions Mailbox Related Functions Queue RelatedFunctions Case Studies of Programming with RTOS Understanding CaseDefinition Multiple Tasks and their functions Creating a list of tasks

Functions and IPCs Exemplary Coding Steps

13

References:

Rajkamal, Embedded Systems Architecture, Programming and Design, TATA McGraw-Hill,First reprint Oct. 2003

David E.Simon, An Embedded Software Primer, Pearson Education Asia, First Indian Reprint2000.

Steve Heath, Embedded Systems Design, Second Edition-2003, Newnes; Wayne Wolf, Computers as Components; Principles of Embedded Computing System Design

Harcourt India, Morgan Kaufman Publishers, First Indian Reprint 2001

Frank Vahid and Tony Givargis, Embedded Systems Design A unified Hardware /SoftwareIntroduction, John Wiley, 2002



13/21


M.Tech.(CSE)


MMCCSS112233:: MMOOBBIILLEE AADDHHOOCC--NNEETTWWOORRKK

Module

No.Contents

Teaching

Hours

I

Ad Hoc Wireless Networks: Issues in Ad Hoc Wireless Networks, Ad HocWireless Internet; MAC Protocols for Ad Hoc Wireless Networks: Issues in

Designing a MAC Protocol for Ad Hoc Wireless Networks, Classifications ofMAC Protocols; Routing Protocols for Ad Hoc Wireless Networks: Issues in

Designing a Routing Protocol for Ad Hoc Wireless Networks, Classifications ofRouting Protocols, Power Aware Routing Protocols.

13

II

Multi cast routing in Ad Hoc Wireless Networks: Issues in Designing a

Multicast Routing Protocol, Classifications of Multicast Routing Protocols,Energy Efficient Multicasting, Multicasting with Quality of Service Guarantees, Application Dependent Multicast Routing; Security Protocols for Ad Hoc

Wireless Networks: Security in Ad Hoc Wireless Networks. Network SecurityRequirements. Issues and Challenges in Security Provisioning. Network

Security Attacks.

14

III

Key Management. Secure Routing in Ad Hoc Wireless Networks; Energy

Management in Ad Hoc Wireless Networks: Classification of EnergyManagement Schemes, Transmission Power Management Schemes, System

Power Management Schemes.

13

References:

C S. Ram Murthy, B. S. Manoj, Ad Hoc Wireless Networks: Architectures and Protocols,Prentice Hall of India, 2nd ed. 2005.

R. Hekmat, Ad hoc Networks: Fundamental Properties and Network Topologies, Springer,1st ed. 2006.

B. Tavli and W. Heinzelman, Mobile Ad Hoc Networks: Energy Efficient Real Time DataCommunications, Springer, 1st ed. 2006.

G. Anastasi, E. Ancillotti, R. Bernasconi, and E. S. Biagioni, Multi Hop Ad Hoc Newtorks fromTheory to Reality, Nova Science Publishers, 2008



14/21


15/21


M.Tech.(CSE)


MMCCSS112266:: IInnffoorrmmaattiioonn RReettrriieevvaall

Module


I

Introduction: Basic Concepts, Retrieval Process

Modeling A Formal Characterization of IR Models, Classic

Information Retrieval (Boolean model, Vector Model, ProbabilisticModel), Alterative Set Theoretic Models, Alternative Algebraic Models

(Generalized Vector Space Model, Latent Semantic Indexing Model)

14

II

Query Languages and Operations: Keyword based Querying, patternMatching, Structural Queries, User Relevance Feedback

Text Operations: Document Preprocessing, Document Clustering, TextCompression

Evaluation in Information Retrieval: Retrieval Performance

Evaluation (Recall and Precision)

13

III

Searching the Web: Characterizing the web, Search Engines,Centralized and Distributed Architecture, Ranking, Crawling the Web,Browsing, Metaseaches, Searching

IR Applications: summarization and question answering

13

References:

1. Ricardo Baeza-Yate, Berthier Ribeiro-Neto, Modern Information Retrieval, Pearson Education Asia,2005.

2. Chowdhury. G.G., Introduction to Modern Information Retrieval, Neal-Schuman Publishers; 2ndedition, 2003.

3. Daniel Jurafsky and James H. Martin, Speech and Language Processing, Pearson Education, 20004. David A. Grossman, Ophir Frieder, Information Retrieval: Algorithms, and Heuristics, AcademicPress, 2000



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M.Tech.(CSE)


MMCCSS223311:: CCLLUUSSTTEERR AANNDD GGRRIIDD CCOOMMPPUUTTIINNGG

Module

No. Contents

Teaching

Hours

I

Introduction: High Performance Computing (HPC), Grand Challenge Problems,Computational and communication intensive, Parallel Architectures

Classifications SMP, MPP, NUMA, Clusters and Components of a ParallelMachine, Conventional, Supercomputers and its limitations, Multi processor andMulti Computer based Distributed, Systems.

13

II

Characteristics of Cluster: Cluster Components Processor/machine, High

Speed Interconnections goals, topology, latency, bandwidth, ExampleInterconnect: Myrinet, Inifiniband, QsNet, Fast Ethernet, Gigabit Ethernet, Light

weight Messaging system/Light weight communication Protocols, ClusterMiddleware Job/Resource Management System, Load balancing, Scheduling ofparallel processes, Enforcing policies, GUI, Introduction to programming tools

such as PVM, MPI, Cluster Operating Systems Examples: Linux, MOSIX, CONDOR

14

III

Characteristics of Grid: Computational services, Computational Grids, Datagrids/Storage grids, management and applications, Different components of

Grid Grid fabric, Grid middleware, Grid applications and portal, Globus toolkit

Ver.2.4, web services, MDS,GRAM, Grid Security Cryptography, Authentication,Integrity, Digital Signature, Digital Certificates, Certificate Authority, MD 5, RSA,GSI,GSSAPI, Directory Service, LDAP,GRID FTP,GASS Fault Tolerance: Fault

detection and diagnosis of Clusters and Grids. Recent advances in cluster andgrid computing.

13

References:

D. Janakiram, Grid Computing, Tata Mcgraw Hill R. K. Buyya, High Performance Cluster Computing: Programming and Applications, Vol 2,

PHI, P. Jalote, Fault Tolerance in Distributed Systems, Prentice Hall, 1994. J. J. Jos & R. K. Buyya, High Performance Cluster Computing: Architecture and Systems, Vol 1,

PHI, 1999.

R. K. Buyya & C. Szyperski, Cluster Computing, Nova Science, New York, USA, 2001. R. K. Buyya & K. Bubendorfer, Market oriented Grid and Utility Computing, Wiley, 2008. J. Jaseph & C. Fellenstein, Grid Computing, Pearson

Credits: 04 LTP : 400Semester III


17/21


M.Tech.(CSE)


MMCCSS223322:: CCLLIIEENNTT SSEERRVVEERR && MMIIDDDDLLEEWWAARREE TTEECCHHNNOOLLOOGGIIEESS

Module

No. Contents

Teaching

Hours

I

Introduction to client server computing, Evolution of corporate computingmodels from centralized to distributed computing, Client server models and

their benefits. Client-Server File server Database server Group server Object server Web server Middleware General middleware Service specific middleware Client /server building blocks RPC Messaging Peer-to-Peer.Object oriented programming with java.

13

II

Introduction to Distributed Objects Computing standards, OMG, Overview ofRMI/CORBA,Review of java concepts and distributed object programming.

COM to Distributed COM OLE - ActiveX ATL DCOM COMCOM threading models - COM servicesService Oriented architecture (SOA) SOA characteristics, Concept of a service,

Basic & Enterprise Software Models. Web Services Technologies :, Web Servicesand SOA., XML Technologies

14

III

Java Bean Component Model: Events , Properties , MethodsEJB EJB Architecture Overview of EJB Software Architecture Building and

Deploying EJBs EJB Session Beans EJB Entity Beans EJB Clients EJBDeployment

RMI Interfaces Proxy and Stub Marshalling/Unmarshalling Object Creation,Invocation, Destruction Comparison of COM/DCOM with RMI/ CORBACORBA Distributed Systems Purpose Architecture Overview CORBA and

Networking Model CORBA Object Model Building an Application withRMI/CORBA

13

References:

1. Robert Orfali, Dan Harkey, Jeri Edwards, 'The Essential Client/Server Survival Guide', GalgotiaPublication Pvt. Ltd., 2002.

2. Tom Valesky, 'Enterprise JAVA Beans', Pearson Education, 2002.3. Jeremy Rosenberger, 'Teach Yourself CORBA in 14 days', Techmedia, 2000.4. Jason Pritchard, 'COM and CORBA side by side', Addison Wesley, 2000.5. Sudha Sadasivam Distributed Component Architecture, Wiley India edition.6. Thomas Erl Service Oriented Architecture: Concepts , Technology & Design, Prentice Hall7. Brose, A Vogel and K. Duddy, Java programming with CORBA, 3rd Edition, Wiley-dreamtech,

India John Wiley and sons

8. Mowbray, 'Inside CORBA', Pearson Education, 2002.



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M.Tech.(CSE)


MMCCSS223333:: IINNFFOORRMMAATTIIOONN SSEECCUURRIITTYY

Module

No. Contents

Teaching

Hours

I

Introduction- Computer Security, Threats to security, History of Computer security,Computer System Security and Access Controls (System access and data access).Threats - Viruses ,worms , Trojan horse, bombs, trap doors, spoofs, email virus,macro viruses, remedies, Intruders, Malicious software, Firewalls, vulnerabilities &

threats, Network Denial of service attack.

.

13

II

Communication Security- Encryption, classical encryption techniques, data

encryptions standards, advance encryption techniquesNetwork Security- Kerberos,X.509, some network security projects- SDNS, DISNet, Project MAX, Secure NFSSecurity- E-Mail Security, IP security, Web securityServer Security- security for network server, web servers, mobile technologies (java

and java script etc)

13

III

Intrusion Detection Techniques Techniques to provide privacy in Internet

Application and protecting digital contents(music, video, software) from unintended

use, authentication.

System and Application Security- mail security (PGP etc) file System security,

program and security, memory security, Sandboxing.Security threads protection intruders- Viruses-trusted system.

Secure programming languages- concepts structured multiprogramming, shared

classes, cooperating sequential processes, structure of te multiprogramming systemRC-4000 software.

14

References:

Computer Security, Dicter gouman, John Wiley & Sons Computer Security: Art and Science, Mathew Bishop, Addison-Wisley Introduction to computer Security, Mathew Bishop, Addison-Wisley Network security, Kaufman, Perlman and Speciner, Pearson Education



19/21


M.Tech.(CSE)


MMCCSS223344:: RREESSEEAARRCCHH MMEETTHHOODDOOLLOOGGIIEESS

Module

No. Contents

Teaching

Hours

I

Basic principles of design of experiment, Error analysis in experiments.Classification of experimental designs, Design and analysis of one factor

experiments -Completely randomized and randomized complete block designs, Analysis of variance. .

13

II

Estimation of parameters, Residual analysis and model checking, Sample sizeproblem.

Design with two blocking variables, Latin squares, Analysis of data from a Latinsquare.

Experiment with two factors- Introduction, Main effects and interactions, Two-factor analysis of variance, Graphic analysis, Choice of sample size.Design of Experiments with the help of orthogonal arrays, Taguchis Robust

parameter design, Analysis, Noise factors, Tolerance on controlfactors.

14

III

Research Methodology Nature and objective of research, Research topic,

Literature review, Formulation of problem, Research design, Sampling

techniques, Data collection, Statistical and sensitive analysis of data,Interpretation of result and report writing.

13

References:

Probability and Statistics for Engineers and scientists, Walpole, Myers, Myers and Ye, 7th ed,2002, Pearson Education.

Statistics in Research, Bernand Ostle and Richard N.Mensing 3rd ed, 1975, Oxford & IBH PubCo.

Probability and Statistics in Engineering, Hines, Montgomery, Goldsman and Borror, 4th ed,2003, John Wiley & Sons.

Experimental design, Theory & application, Federer, 1955, Oxford & IBH pub Co



20/21


M.Tech.(CSE)


MMCCSS223355:: PPaatttteerrnn RReeccooggnniittiioonn

Module


I

Introduction: Basics of pattern recognition, Design principles ofpattern recognition system, Learning and adaptation, Pattern

recognition approaches, Mathematical foundations Linear algebra,Probability Theory, Expectation, mean and covariance, Normal

distribution, multivariate normal densities, Chi squared test.

14

II

Statistical Patten Recognition: Bayesian Decision Theory, Classifiers,Normal density and discriminant functions,

Parameter estimation methods: Maximum-Likelihood estimation,Bayesian Parameter estimation, Dimension reduction methods -Principal Component Analysis (PCA), Fisher Linear discriminant

analysis, Expectation-maximization (EM), Hidden Markov Models(HMM), Gaussian mixture models.

13

III

Nonparametric Techniques: Density Estimation, Parzen Windows, K-Nearest Neighbor Estimation, Nearest Neighbor Rule, Fuzzy

classification.

Unsupervised Learning & Clustering: Criterion functions for

clustering, Clustering Techniques: Iterative square - error partitionalclustering K means, agglomerative hierarchical clustering, Cluster

validation.

13

References:

1. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, 2nd Edition, John Wiley,2006.2. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2009.3. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th Edition, Academic Press, 2009.

Semester II LTP : 400Credits: 04


21/21


M.Tech.(CSE)


MMCCSS223366:: DDAATTAA WWAARREEHHOOUUSSIINNGG AANNDD MMIINNIINNGG

ModuleNo. Contents TeachingHours

I

Data Warehouse - Introduction: Differences between Operational DatabaseSystems and Data Warehouses, From Tables and Spreadsheets to Data Cubes, Stars,

Snowflakes, and Fact Constellations: Schemas for Multidimensional Databases, DataWarehouse and OLAP Technology for Data Mining Data Warehouse,Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse

Implementation, Further Development of Data Cube Technology,

Data Preprocessing: Needs Preprocessing the Data, Data Cleaning, Data Integration

and Transformation, Data Reduction, Discretization and Concept HierarchyGeneration, Online Data Storage.

.

13

II

Data Mining Techniques: Fundamentals of data mining, Data MiningFunctionalities, Classification of Data Mining systems, Major issues in Data Mining,

Data Mining Primitives, From Data Warehousing to Data Mining,Mining Association Rules in Large Databases: Association Rule Mining, Apriorialgorithm, FP Growth,Classification and Prediction: Issues Regarding Classification and Prediction,Classification by Decision Tree Induction, Bayesian Classification, Rule based

Classification, Prediction: Linear Regression, Nonlinear RegressionCluster Analysis: Types of Data in Cluster Analysis, A Categorization of MajorClustering Methods, Partition algorithms, K-Mean, CLARA, CLARANS;

Hierarchical clustering, DBSCAN, BIRCH, CURE; Outlier Analysis.

14

III

Mining Complex Types of Data: Multidimensional Analysis and Descriptive

Mining of Complex Data Objects, Temporal Mining, Temporal Association Rules,Mining Spatial Databases, Spatial Clustering, Mining Multimedia Databases, Mining

Time-Series and Sequence Data, Web Mining, Text Mining.

13

References:

Data Mining Concepts and Techniques - Jiawei Han and. Micheline Kamber. Data Mining Techniques Arun K Pujari, University Press Building the DataWarehouse- W. H. Inmon, Wiley Dreamtech India Pvt. Ltd.. Data Mining Introductory and advanced topics Margaret H Dunham,

Pearson Education


Documents

M.tech. CS (Full Time) Course Curriculum