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Complete syllabus of M.Tech. Semester I - Computer Science and Technology (Shivaji University)
Posted Date: 17 Dec 2007 Resource Type: Articles/Knowledge Sharing Category: Syllabus
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Posted By: Arun Jadhav Member Level: Diamond
Rating:  Points: 1
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Syllabus
M. Tech. Semester I - Computer Science and Technology
Paper-I- Theory of Computer Science
Theory: 100 Marks,
Term Work: 25 Marks
Section – I
1. Introduction: Mathematical notions and terminology of sets, sequences and
tuples, functions and relations, graphs, strings and languages. Boolean logic
properties and representation. Definition. Theorems and types of proofs
formal proofs, deductive, reduction to definition, proof by construction,
contradiction, induction, counter-examples.
2. Regular languages: Finite automata, DFA, NFA. Equivalence of DFA and
NFA. An application, Regular expressions and languages, applications.
3. Context-free languages: CFGs, Applications, Ambiguity removal, Pushdown
automata and Equivalence with CFGs.
4. Turing machine: Turing machines, variants of TMs, programming techniques
for TMs, TMs and computers.
Section – II
5. Decidability: Decidable languages, decidable problems concerning Contextfree
languages. The halting problem – Diagonalization method, halting
problem is undecidable.
6. Reducibility: Undecidable problems from language theory. Regular
expressions, Turing machines, Reduction, A simple undecidable problem
(PCP), mapping reducibility and other undecidable problems.
7. Computability: Primitive recursive functions, more examples, the recursion
theorem.
8. Computational complexity: Tractable and Interact able problems, Growth
rates of functions, Time complexity of TM of TM, Tractable decision problems,
Theory of Optimization.
Books:
1) Introduction to Theory of Computation - Michael Spicer (Thomson Brools Cole )
2) Introduction to Automata Theory, Languages and Computation - J. E. Hoperoft, Rajeev Motawani and J.D. Ullman( Pearson Education Asia) 2nd
Edition.
References:
1) Discrete Mathematical structures with applications to computer science - J. P. Thembloy and R. Manohar.
2) Theory of Computer Science – E. V. Krishnamoorthy
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Responses
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| Author: Arun Jadhav 17 Dec 2007 | Member Level: Diamond Points : 1 | Paper-II- Advanced Operating System
Theory: 100 Marks,
Term Work: 25Marks
1. Distributed computing systems fundamentals: Introduction to Distributed
computing systems, Models, Popularity. Distributed computing system.Design issues of Distributed operating system. Distributed computing environment.
2. Message Passing: Features of a good Message Passing System. Issues in IPC
by Message Passing Synchronization, Bullring, Multidatagram Messages,Encoding and Decoding of Message Data, Process Addressing, Failure handling, Group Communication.
3. Remote Procedure Calls: RPC Model, Implementing RPC Mechanism. Stub Generation. RPC Messages, Marshaling Arguments and Results. Server Management, Parameter-Passing semantics, call semantics, Communication protocols for RPCs, Client-Server Building, Exception handling, Security RPC in Heterogeneous Environments, Lightweight RPC.
4. Distributed Shared Memory: General Architecture of DSM systems. Design
and implementation Issues of DSM, Granularity, Structure of Shared Memory
Space. Consistency models, Replacement strategy, Thrashing.
Synchronization: Clock Synchronization. Event Ordering, Mutual Exclusion,
Deadlock, Election Algorithms.
5. Resource Management: Features of global scheduling algorithm. Task
assignment approach, Load-Balancing and Load approach.
6. Process Management: Introduction, Process Migration, Treads.
7. Distributed File Systems: Features of good DFS, File models, File Accessing
models. File-Sharing Semantics, File-Caching schemes, File Replication, Fault
Tolerance, Automatic Transactions, Design Principles, Case study: DCE
Distributed File Service.
8. Security: Potential Attacks to Computer systems, Cryptography,
Authentication, Access Control, Design Principles, Case study : DCE Security
service.
9. Case Study: Case study of Chorus.
Text book:
1. Distributed Operating Systems concepts and design- .K. Sinha (PHI).
2. Modern Operating System-Singhal
Reference Books:
1. Distributed Systems concepts and design-G.Coulouris, J.Dollimore & T.Kindberg
2. Modern Operating System-A.S. Tanenbaum(PHI).
| | Author: Arun Jadhav 17 Dec 2007 | Member Level: Diamond Points : 1 | Paper-III- Design and Analysis of Algorithms
Theory: 100 Marks,
Term Work: 25Marks
Section-I
1. Introduction: Algorithm definition and specification, Performance analysis randomized algorithms, Divide and Conquer method, Binary search, Merge sort Quick sort and convex hull.
2. Greedy method and Dynamic Programming : General methods ,Job sequencing with deadlines ,Minimum cost spanning trees, Optimal merge patterns, All pairs shortest paths, Optimal binary search trees, Reliability design, Traveling salesman problem and flow shop scheduling.
3. Lower bound Theory: Comparison trees, Oracles and adversary arguments, lower bounds through reductions.
Section-II
4. NP-Hard and NP- complete problems: Basic concepts, cook’s theorem.
NP –hard graph problems, NP-hard scheduling problems. NP-Hard code generation’s problems.
5. PARAM Algorithms: Introduction, computational model, Fundamental techniques and algorithms, Merging, lower bounds.
6. Mesh Algorithms: computational model packet routing fundamental algorithms, merging, computing the convex hull.
7. Hypercube Algorithms: Computational model, PPR routing fundamental algorithms, merging, computing the convex hull.
Books:
a. Fundamentals of computer algorithms –Ellis horowitz, sartaj sahani and Sanguthewar Rajasekaran
b. Design and analysis of algorithms- Aho Hoperraft &Ullman
c. Introduction to algorithms- Thomas H. cormen, charles S.Leiserson
d. Randomized algorithms-Rajeev Motwan and Prabhakar Raghwan
| | Author: Arun Jadhav 17 Dec 2007 | Member Level: Diamond Points : 1 | Elective – I and II
Paper-IV- DATA MINING
Theory: 100 Marks,
Term Work:25 Marks
1. Data Warehousing and Introduction to data mining basic elements of data
warehousing, Data warehousing and OLAP
2. Data model development for Data, Warehousing: business model, selection of
the data of interest, creation and maintaining keys, modeling transaction, data
warehousing optimization.
3. Data warehousing methodologies, type and comparisons.
4. Data Mining techniques, data mining algorithms, classification, Decision- Tree
based Classifiers clustering, association Association-Rule Mining Information
Extraction using Neural Networks.
5. Knowledge discovery, KDD environment,.
6. Visualization: data generalization and summarization-based characterization,
Analytical characterization: analysis of attribute relevance, mining class
Comparison, Discriminating between classes, mining descriptive statistical
measures in large database.
7. Data mining primitives, languages & system architectures: data mining
primitives, Query language, designing GUI based on a data mining query
language, architectures of data mining systems.
8. Advanced topics: spatial mining, temporal mining.
9. Web mining: web content mining, web structure mining, web usage mining
10. Application and trends in data mining : Applications, systems products and
research prototypes, multimedia data mining, indexing of multimedia
material, compression, space modeling.
Text books:
1. Paulraj ponniah, “Web warehousing fundamentals” – John Wiley.
2. M. H. Dunham, “Data mining introductory and advanced topics” – Pearson education
3. Han, Kamber, “Data mining concepts and techniques”, Morgan Kaufmann
4. Imhoff, Galemmo, Geiger, “Mastering data warehouse design”, Wiley dreamtech
| | Author: Arun Jadhav 17 Dec 2007 | Member Level: Diamond Points : 1 | Elective –I and II
Paper-V- Artificial Neural Networks
Theory: 100 Marks,
Term Work: 25Marks
1. Introduction: Inspiration from Neuroscience, History, Issues.
2. Hopfield model: Associative memory problem, model, stochastic networks
capacity of stochastic n/w.
3. Optimization problems: Weighed matching problem, Traveling salesman
problem, Graph bipartioning, optimization problems in image processing.
4. Simple perceptions: feed forward n/w, Threshold units, linear units, nonlinear
units stochastic units, capacity of simple perception.
5. Multi-layer n/w:Back propagation, examples and applications performance of
multilayer feed forward n/w Kohoanen self organizing n/w cognition &
neocognutron.
6. Recurrent n/w: Boltzmann n/w, Recurrent Back propagation, Learning time
sequence, Reinforcement learning.
7. Learning: Supervised, Unsupervised (Hebbian competitive), adaptive
resonance theory, Traveling salesman problem.
8. Application of artificial Neural Network.
Reference Books
1. Introduction to the theory of neural Computation-Hertz Keogh, Palmer
2. Artificial Neural Networks- B. Yegnanarayana (PHI)
3. Genetic Algorithms-David E. Goldberg (Addison Wesley)
| | Author: Arun Jadhav 17 Dec 2007 | Member Level: Diamond Points : 1 | Elective – I and II
Paper - COMPUTER VISION AND IMAGE PROCESSING
Theory: 100 Marks,
Term Work: 25Marks
UNIT – I
Digital Image Fundamentals: - Digital image Representation – Functional Units
of an Image processing system. Visual perception – Image Model _ Image
sampling and Quantization – grayscale resolution – pixel relationship – image
geometry. Image Transforms – Unitary Transform, Discrete Fourier Transform,
Cosine Transform, Sine Transform, Hadamard Transform, Slant and KL
Transform.
UNIT – II
Image Enhancement – Histogram processing – Spatial operations – Image
smoothing –Image Sharpening – Color Image Processing methods- Color Image
Models
UNIT –III
Image restoration and compression Degradation Model – Discrete Formulation –
Circulant matrices – Constrained and Unconstrained restoration geometric
transformations fundamentals – Compression Models – Error Free Compression
– Lossy Compression – International Image Compression Standards.
UNIT – IV
Image Analysis and Computer Vision: Spatial feature Extraction – Transform
feature –Edge detection-Boundary Representation-Region Representation-
Moment Representation-Structure-Shape Features-Texture-Scene Matching and
Detection-Image Segmentation-Classification techniques-Morphology-
Interpolation.
UNIT –V
Sensing 3D shape: how the 3rd dimension changes the problem. Stereo 3D
description, 3Dmodel, matching, TINA. Direct 3D sensing-structured light, range
finders, range image segmentation.
Emerging IT applications: Recognition of characters, Fingerprints and faces-
Image databases.
Reference Books
1. Fundamentals of Digital Image Processing-A.K.Jain
2. Image Processing and machine vision-Milan Sonka,Vaclav Hlavae
3. Pattern Recognition Principles-J.T. Tou and R.C.Gonzalez
4. Syntactic Pattern Recognition and applications.-King Sun Fun
5. Computer vision-Fairhurst (PHI).
| | Author: Arun Jadhav 17 Dec 2007 | Member Level: Diamond Points : 1 | Elective –I and II
Paper - Real Time Operating Systems
Theory: 100 Marks,
Term Work: 25 Marks
Unit 1
Basic Real Time Concepts: Terminology, Real time design issues, Example
Real-time systems, Brief history, Language issues: Language features, commonly
used programming languages, Software life cycle: Phases of the software
life cycle, non temporal transition in the software life cycle, spiral model.
Unit2
Real time specification and design techniques: Natural languages , Mathematical
specification , flow chart, structure chart, pseudo code, programming designing
languages, finite state automata , data flow diagrams, Petri nets, warnier-orr
notations, state charts, Sanity in using graphical techniques
Unit 3
Real time kernels: Polled loop system, phase state driven code, co routine
interrupt driven systems, foreground/background systems, full feature real time
operating system
Unit 4
Inter-Task Communicating and Synchronization: Buffering Data, Mailboxes,
Critical Regions, Semaphores, Event flags and signals, Deadlock.
Unit 5
Real time Memory Management: Process Stack Management, Dynamic
Allocation, Static Schemes.
Unit 6
System performance Analysis and optimization: Response Time calculation,
Interrupt Latency, Time- Loading and its Measurement, Scheduling is NPComplete,
Reducing Response Times and Time-loading, Analysis of memory
Requirements, Reducing Memory loading I/O performance.
Unit 7
Queuing Models: Probability functions, discrete, Basic Buffer size calculation,
Classical Queuing theory, Little’s law, Erlang’s Formula.
Unit 8
Reliability, Testing and Fault tolerance: Faults, Failures, Bugs and effects,
Reliability, testing fault tolerance.
Unit 9
Multiprocessing System: Classification of architecture, Distributed systems Non-
Von Neumann Architectures.
Unit 10
Hardware, Software Integration: Goals of real time system integration tools,
Methodology, The software Heisenberg Uncertainty Principle.
Unit 11
Real time Applications: Real time systems as complex system, first Real time
application, Real time databases Real time Image processing , Real time Unix
Building Real time Applications with real time programming languages.
Books:
1. Real Time Systems Design and Analysis : An Engineer’s HandbookPhillp A. Laplante, 2nd Edition, PHI
Reference Books:
1. Real Time system Design – Levi Shem Tov and Ashok K. Agrawala
(New York McGraw Hill)
2. Proceedings of IEEE Special Issue on Real Time Systems (Jan 1994)
3. Real Time Systems and their Programming Language Burns, Alan and Andy
Welling (New York, Addison Wesley)
4. The design of Real time Applications: M. Blackman (New York John Wiley & Sons).
5. Real time systems: C.M. Krishna, K.G. Shin (TMGh)
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