Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Courses

M.Tech. (Computer Science and Engineering)

---

Duration: 2 Years

Eligibility: Graduation

Course Structure

Course Code	Course Title
Semester - I
PCSA22	Software Engineering and Project Management
PCSA23	Computer Network & Distributed Systems
PCSA24	Operating Systems
******	Elective I
******	Elective II
Practical
PCSA25	Computer Network Lab
PCSA26	Operating System Lab
Semester - II
PCSA27	Advanced  Object Oriented Design
PCSA28	Data on Web
PCSA29	Advanced DBMS
******	Elective III
******	Elective IV
Practical
PCSA30	Advanced Object Oriented Design Lab
PCSA31	DBMS Lab
Semester - III
******	Elective V
******	Elective VI
******	Seminar
Practical
PCSA45	Project Phase I
Semester - IV
PCSA32	Project Phase II
List of Electives
PCSA33	Artificial Intelligence
PCSA34	Natural Language Processing
PCSA35	Modeling and Simulation
PCSA36	Advanced Computer Graphics
PCSA37	Robotics
PCSA38	Parallel Computing
PCSA39	Computational Intelligence
PCSA40	Advanced Computer Architecture
PMAA01	Mathematical Foundations of Computer Science

Course Detail

Semester – I

PCSA22   Software Engineering and Project Management  

Unit – I                                                                                                                       

Lifecycle Methodologies - Development models - Waterfall, V-Model, Incremental, Spiral - Development Techniques - Rapid prototype, clean room, Object Oriented – Military/aerospace and commercial development standards.

Unit – II                                                                                                                     

Phase specific activities - software specification and requirements analysis – Software architecture – Comparison and selection - Software Design - Coding and Unit Testing - Integration Testing - System Testing - Operational turn-over.

Unit-III                                                                                                                      

Lifecycle activities - Baseline management – Software Quality Management – Software Configuration Management – Software Reliability.

Software Project Management Processes

Unit – IV                                                                                                                     

Planning – Integration and change control – Scope Management – Quality Management.

Unit – V                                                                                                                      

Risk Management – Schedule Management – Communications Management.

 

PCSA23   Computer Networks and Distributed Systems                 

Unit – I                                                                                                                       

Introduction: overview of computer networks – Seven-layer architecture – TCP/IP suite of protocols – MAC protocols for high-speed LAN – MAN and wireless LANs - FDDI, DQDB,HIPPI, Gigabit Ethernet, Wireless Ethernet.

Unit – II                                                                                                                     

Fast access technologies - ADSL, Cable Modem - IPv6 – basic protocol - extensions and options – support for QoS – Neighbour discovery – Auto-configuration - Routing - Application Programming Interface for IPv6 – 6bone.

Unit – III                                                                                                                    

Mobility in networks – Mobile IP – Security related issues – IP Multicasting – Multicast Routing Protocols – Address assignments

Unit – IV                                                                                                                     

Session discovery - TCP extensions for high-speed networks – Transaction-oriented applications – Network Security at various layers – Authentication header – Key distribution protocols – Digital signatures – Digital certificates.

Unit – V                                                                                                                      

Distributed System taxonomy – Service models – naming and binding remote procedure calls (RPC) – object brokers – distributed file system design - distributed file system case studies: NFS, AFS – Clock synchronization – distributed transactions – mutual exclusion – election algorithms – distributed memory and memory consistency models – distributed deadlocks.

 

PCSA24  Operating Systems                                                    

Unit – I                                                                                                 

User level Specification of OS – Fundamental Concepts of Multiprogrammed OS – Basic concepts and Techniques for Implementation of  Multiprogrammed OS Processes and the Kernel – Micro kernel Architecture of OS Multiprocessor – Multimedia – Real-Time OS Management and Control of Processes.

Unit – II                                                                                                

Basic concept of Threads – Types of Threads – Models of Thread Implementation – Traditional and Real-Time Signals – Clocks – Timers and Callouts – Thread Scheduling for Unix – Windows – Real-Time OS – Real-Time Scheduling.

Unit – III                                                                                                          

Inter process/Inter thread Synchronization and Communication – Mutual Exclusion/Critical Section – Problem – Semaphores – Monitors – Mailbox Deadlocks.

Unit – IV                                                                                                           

Concepts and Implementation of Virtual Memory (32-bit and 64-bit), Physical Memory Management – File Organization – File System Interface and Virtual File Systems – Implementation of File Systems.

Unit – V                                                                                                 

I/O Software: Interrupt Service Routines and Device Drivers – Protection and Security – Case Study of UNIX, Windows and Real-Time OS

 

 PCSA25   Computer Networking Lab               

• Retrieving data with URLs
• Implementation of Socket Programming
• Using TCP/IP
• Using UDP
• Implementation of FTP
• Implementation of ECHO/PING/TALK
• Implementation of Remote command Execution
• Implementation of ARP
• Implementation of  RARP
• Implementation of RMI / RPC
• Implementation of Shortest Path Routing Algorithm
• Implementation of Sliding Window Protocol

 

PCSA26 Operating Systems Lab                                            

• Implement the following CPU Scheduling Algorithms.
  • i) FCFS   ii) Round Robin   iii) Shortest Job First.
• Implement the Mutual Exclusion Problem Using Dekker’s Algorithm.
• Implement Inter Process Communication Problem (Producer-Consumer/Reader- Writer Problem ) using Semaphores.
• Implement Best fit, First Fit Algorithm for Memory Management.
• Implement Memory Allocation with Pages.
• Implement FIFO page Replacement Algorithm.
• Implement LRU page Replacement Algorithm.
• Implement the creation of Shared memory Segment.
• Implement File Locking.
•  Implement Banker’s algorithm.

 

Semester - II

PCSA27  Advanced Object Oriented Design                         

Unit – I                                                                                                 

Object orientation paradigm – Object oriented analysis: Basic concepts – use cases – analysis – stereotypes and objects – analysis patterns.

Unit – II                                                                                                

Object modeling languages – object oriented design: basic concepts – design stereotypes and objects – design patterns – Object oriented programming: basic concepts – idioms – most common object oriented programming languages - C++, Java.

Unit – III                                                                                                          

Application frameworks – Object oriented case tools – State transition and interaction diagrams – Testing of object oriented programs.

Unit – IV                                                                                                           

Advanced approaches to object-oriented analysis and design – Frameworks and design patterns – Design for reusability.

Unit – V                                                                                                 

Advanced object-oriented programming techniques – Design using object-oriented databases and distributed object architectures – Design of software agents.

 

PCSA28  Data On Web                                                           

Unit – I                                                                                               

Introduction to advanced web technology – Technological issues: XML processing – RDF processing.

Unit – II                                                                                             

Middleware technologies - CORBA, IIOP – RMI – RPC.

Unit – III                                                                                            

Taxonomies and ontologies for advanced web applications - Ontology modelling – Languages for representing ontologies on the web.

Unit – IV                                                                                             

Rules and inferences – Web services – Design and modeling of web services.

Unit – V                                                                                              

Technologies for Implementing web services – Current applications of advanced web technologies.

 

PCSA2 Advanced Dbms                                                                     

Unit – I                                                                                                           

Database Concepts – Client/Server Computing – RDBMS Technologies – Codd’s Rules – Data Models.

Unit – II                                                                                                         

Normalization Techniques – ER Diagrams – Data Flow Diagrams – Data Integrity – Data Security – Database recovery & backup.

Unit – III                                                                                                        

SQL and PL/SQL – Overview of Oracle – SQL *Plus – DDL – DML and DCL – Tables – Indexes and Views.

Unit – IV                                                                                                         

Clusters – Sequences and Snapshots – PL/SQL – Cursors – Stored Procedures – Triggers.

Unit – V                                                                                                          

Oracle 11g Architecture – Oracle 11g Architecture – Database Creation – User Creation and Management

 

PCSA30  Advanced Object Oriented Design Lab                              

Prepare the following documents for two or three of the experiments listed below and develop the software engineering methodology.

• Program Analysis and Project Planning.
  • Thorough study of the problem – Identify project scope, Objectives, Infrastructure.
• Software requirement Analysis
  • Describe the individual Phases / Modules of the project, Identify deliverables.
• Data Modeling
  • Use work products – Data dictionary, Use diagrams and activity diagrams, build and test lass diagrams, Sequence diagrams and add interface to class diagrams.
• Software Development and Debugging
• Software Testing
  • Prepare test plan, perform validation testing, Coverage analysis, memory leaks, develop test case hierarchy, Site check and Site monitor.

 

PCSA31  Database Management Systems Lab                                                                                          

• Study of Backend Tool – Oracle.    
• Data Definition Language (DDL) commands in RDBMS.
• Data Manipulation Language (DML) and Data Control Language (DCL) commands in RDBMS.
• High-level language extension with Cursors.
• High level language extension with Triggers
• Procedures and Functions.
• Embedded SQL.
• Database design using E-R model and Normalization.
• Mini project (Application Development using Oracle and Visual Basic)
  • Inventory Control System.
  • Material Requirement Processing
  • Hospital Management System
  • Railway Reservation System
  • Personal Information System
  •  Web Based User Identification System
  •  Time-table Management System

 

List of Electives

PCSA33  Artificial Intelligence                                          

Unit – I                                                                                                           

Introduction

Overview of Artificial Intelligence – Problems of AI – AI technique – Tic  Tac Toe problem.

Intelligent Agents

Agents & environment – nature of environment – structure of agents – goal based agents – utility based agents – learning agents.

Problem Solving

Problem – Problem Space & Search – Defining the problem as state space search – production system – problem characteristics – issues in the design of search programs

Unit – II                                                                                                         

Search Techniques

Solving problems by searching: Problem solving agents – searching for solutions – uniform search strategies: breadth first search – depth first search – depth limited search – bi-directional search – comparing uniform strategies.

Unit – III                                                                                                        

Adversarial Search

Games – optimal decisions & strategies in games – the minimum search procedure – alpha-beta pruning – additional refinements – iterative deepening.

Knowledge & Reasoning

Knowledge Representation issues – representation & mapping – approaches to knowledge representation – issues in knowledge representation.

Representing knowledge using rules

Procedural verses declarative knowledge – logic programming – forward verses backward reasoning – matching – control knowledge.

Unit – IV                                                                                                         

Probabilistic reasoning

Representing knowledge in an uncertain domain – the semantics of Bayesian networks – Dempster-Shafer theory – Fuzzy sets & fuzzy logics.

Planning

Overview – components of a planning system – Goal stack planning – Hierarchical planning – other planning techniques.

Unit – V                                                                                                          

Natural Language Processing

Introduction – Syntactic processing – semantic analysis – discourse & pragmatic processing.

Learning

Forms of learning – inductive learning – learning decision trees – explanation based learning – learning using relevance information - neural net learning & genetic learning.

Expert Systems

Representing and using domain knowledge – expert system shells – knowledge acquisition.

 

PCSA34   Natural Language Processing                                           

Unit – I                                                                                                                       

Computational framework for natural language – LFG framework – GPSG or Panlni.

Unit – II                                                                                                                     

Partial description of English or an Indian language in the framework.

Unit – III                                                                                                                    

Lexicon – algorithms and data structures for implementation of the framework.

Unit – IV                                                                                                                     

Introduction to semantics and knowledge representation – some applications like machine translation.

Unit – V                                                                                                                      

Database interface

 

PCSA35 Modelling and Simulation                                        

Unit – I                                                                                                           

Introduction to Probability theory – Random variables – Commonly used continuous and discrete distribution – Introduction to Stochastic Process – Poisson Process.

Unit – II                                                                                                         

Markov Process – Steady state and transient analysis – Psuedo random numbers: Methods of Generation and testing – Methods for generating continuous and discrete distributions – Methods for generating Poisson Process.

Unit – III                                                                                                        

Building blocks of Simulation – Data Structures and Algorithms – Introduction to Probabilistic modelling.

Unit – IV                                                                                                         

Maximum Likelihood Variance Reduction techniques

Antithetic variates – Control variates – Common random numbers – importance sampling.

Unit – V                                                                                                          

Analysis of Simulation Results

Confidence intervals – Design of experiments  Markov chain  Monte Carlo techniques.

 

PCSA36   Advanced Computer Graphics                                           

Unit – I                                                                                                           

3D Object Representaion – Visible surface algorithm.

Unit – II                                                                                                         

Curves and surfaces in computer graphics – Introduction to ray – tracing and radiosity methods.

Unit – III                                                                                                        

Anti- aliasing – Shadow generation – Texture mapping.

Unit –IV                                                                                                          

Effects – fractals.

Unit – V                                                                                                          

Image coding – Color.

 

PCSA37   Robotics                                                                 

Unit – I                                                                                                           

Introduction:

Classifications of robots – basic robot components – manipulator end effectors – Controllers – Power unit – sensing device – specifications of robot systems – accuracy precision and repeatability.

Unit – II                                                                                                         

Robot Motion Analysis:

Manipulator Kinematics – Inverse manipulator Kinematics – Manipulator Dynamics – Newton – Eulor and Lagrange formulation – Trajector generation.

Unit – III                                                                                                        

Robotic Sensing Device:

Position – Velocity and acceleration sensor – proximity and range sensor – touch and slip sensor – tactile sensors – force and torque sensor.

Robotic Vision systems

Imaging components – Picture coding – Object recognition – training and vision system – review of existing vision systems.

Unit – IV                                                                                                         

Robotics Programming:

Methods of robot programming – types of programming – Robotics programming languages – artificial intelligence.

Unit – V                                                                                                          

Robot applications:

Material transfer and machine loading/unloading – Processing applications – Welding and painting assembly and inspection – Future robotics applications and related technologies developments.

Economics analysis of Robotics

Robotics project analysis – Life cycle costs – data required for economics analysis – Methods of economic analysis.

 

PCSA38   Parallel Computing                                                

Unit – I                                                                                                           

Fundamental theoretical issues in designing parallel algorithms and architecture – parallel computers based on interconnection networks such as Hyper cubes – Shuffle – Exchanges – Trees – Meshes – butterfly networks – parallel algorithm for arithmetic – Linear algebra – sorting – Fourier Transforms – recurrence evaluations and dense graph problem.

Unit – II                                                                                                         

Use of Graph embedding techniques to compare different networks – shared memory based parallel computers.

Unit – III                                                                                                        

Algorithms for list ranking – maximal independent set – arithmetic expression evaluation – convex hull problems and others.

Unit – IV                                                                                                         

Message Routing on Multi dimensional meshes – butterfly networks – hyper cubes – Shuffle exchange networks – fat trees and others.

Simulation of shared memory on networks – Routing on expander-based networks – Limits to parallelizability and P-completeness.

Unit – V                                                                                                          

Thompson grid model for VLSI Layouts for standard interconnection networks – Lower bound techniques for area and area time-squared tradeoffs – Area-Universal networks.

 

PCSA3   Computational Intelligence                                                 

Unit – I

Introduction to Computational Intelligence/ soft computing: Soft verses Hard Computing – various paradigms of computing.

Foundations of Biological Neural Networks:

Introduction to Neural networks – Humans and computers – Organization of Brain – Biological Neuron – Biological and Artificial Neuron Models  - Hodgkin-Huxley Neuron Model – Integrate-and-Fire Neuron Model – Spiking Neuron Model – Characteristics of ANN - learning , Generation , Memory , Abstraction , Applications - McCulloch-Pitts Model – Historical Developments.

Unit – II                                                                                                                     

Essentials of Artificial Neural Networks: Introduction – Artificial Neuron Model – Operations of Artificial Neuron – Types of Neuron Activation Function – ANN Architectures – Classification Taxonomy of ANN – Connectivity – Feed forward, feedback, Single and Multi-Layer - Neural Dynamics - Activation and Synaptic - Learning Strategy - Supervised, Unsupervised, Reinforcement - Learning Rules - Error Correction, Pattern Association/Memory, Function Approximation , Prediction, Optimization.

Unit – III                                                                                                                    

Neural Architectures with Supervised Learning: Single Layer Feed Forward Neural Networks  - Perception - Multilayer Feed forward Neural Networks - Back propagation learning - Radial Basis – Function Networks – Support Vector Machines – Simulated Annealing – Boltzmann Machine – Feedback Recurrent Networks and Dynamical Systems.

Associative Memories:

Matrix memories – Bidirectional Associative Memory – Hopfield Neural Network.

Unit – IV                                                                                                                     

Neural Architectures with Unsupervised Learning:

Competitive learning – Principal component Analysis Networks (PCA) – Kohonen’s Self-Organizing Maps – Linear Vector Quantization – Adaptive Resonance Theory(ART) Networks – Independent Component Analysis Networks(ICA)

Reinforcement Learning: Markov Decision Processes, Value Functions – Bellman Optimality Criterion – Policy and Value Iterations – Q-Learning – Td Learning.

Unit – V                                                                                                                      

Fuzzy Logic:

Basic Concepts – fuzzy set theory – basic operations – fuzzification – defuzzification – neurofuzzy approach – applications

Evolutionary and Genetic Algorithms:

Basic concepts of evolutionary computing , genetic operators – fitness function and selection – genetic programming – other models of evolution and learning – and colony systems – swarm intelligence – applications.

Rough Set Theory:

Basic concepts – indiscernability relation – lower and upper approximation – decision systems based on rough approximation – applications.

 

PCSA40   Advanced Computer Architecture                         

Unit – I                                                                                     

Computation model – The concept of Computer Architecture – Introduction to Parallel Processing.

Unit – II                                                                                                

Introduction to ILP Processing – Pipelined Processors – VLIW Architecture – Super Scalar Processors

Unit – III                                                                                                          

Code Scheduling for ILP-processors – Introduction to Data Parallel Architecture – SIMD Architecture – MIMD Architecture.

Unit – IV                                                                                                           

Vector Architecture – Multi threaded Architecture.

Unit – V                                                                                                 

Distributed Memory MIMD Architecture – Shared memory MIMD Architecture.

 

PMAA01  Mathematical Foundations Of Computer Science 

Unit-I  Fundamental Structures 

Set Theory: Relationships between sets – Operations on sets – Set identities – Principle of inclusion and exclusion – Minsets Relations: Binary relations – Partial orderings – Equivalence relations Functions: Properties of functions – composition of functions – inverse functions, permutation  functions

Unit-II: Logic   

Propositional logic – logical connectives – truth tables – normal forms (conjunctive and disjunctive) – predicate logic – universal and existential quantifiers – proof techniques – direct and indirect – proof by contradiction – mathematical induction

Unit-III: Combinatorics                                                                                                         

Basics of counting – counting arguments – pigeonhole principle – permutations and combinations – recursion and recurrence relations – generating functions

Unit-IV: Discrete Probability

Finite probability – probability distributions – conditional probability – independence – Bayes’ theorem – mathematical induction                                                                                  

Unit-V: Languages, Grammars and Machines

Alphabet and words – languages – regular expressions and regular languages – finite state automata – classes of grammars – context-sensitive and context-free grammars – regular grammars -  finite state machines.