M.tech(Neural Networks)

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

M.TECH (NEURAL NETWORKS)

COURSE STRUCTURE AND SYLLABUS

I YEAR I SEMESTER

Code

Group

Subject

L

P

Credits

Advanced Problem Solving

3

0

3

Computer Systems Design

3

0

3

Artificial Intelligence

3

0

3

Neural Networks

3

0

3 Elective –I

Pervasive Computing Machine Learning Speech Processing

3

0

3 Elective -II

Wireless Networks and Mobile Computing Storage Area Networks

Cloud Computing

3

0

3 Lab

Advance Problem Solving Lab

0

3

2 Seminar

-

2

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ADVANCED PROBLEM SOLVING Unit I

OOP Using Java - Class and Objects, Variables, Operators, Expressions, Methods, Decision statements,Loops,Arrays,OOP concepts- Encapsulation, Inheritance, Polymorphism, Abstraction, Modularity, Exception handling, Input and Output,Java and Pointers,Interfaces,Packages, Abstract classes,Casting in Inheritance hierarchy,Casting with Interfaces,Vectors in java.util,Data Structures and OOP,Writing a java program-Design,coding,testing and debugging.

Basic concepts(Review)- Abstact Data Types, Data structures, Algorithms- Characteristics

of Algorithms, Performance analysis- Time complexity and Space complexity,Asymptotic Analysis-Big O, Omega and Theta notations.

Unit II

Linear data structures- The List ADT, Array and Linked Implementations,Singly Linked Lists- Operations-Insertion,Deletion,Traversals,DoublyLinkedLists-Operations-Insertion,Deletion,SkipLists-implementation, StackADT,definitions,operations, Array and Linked implementations,applications-infix to postfix conversion, recursion implementation,tail recursion,nontail recursion,indirect recursion, QueueADT, definitions and operations ,Array and Linked Implementations,Priority Queue ADT,Deque ADT,Implementation using doubly linked lists,Stacks and Queues in java.util.

Unit III

Non Linear data structures-Trees-Basic Terminology, Binary tree ADT,array and linked representations,iterative traversals,threaded binary trees,Applications-Disjoint-Sets,Union and Find algorithms,Huffman coding,General tree to binary tree conversion, Realizing a Priority Queue using Heap.

Search Trees- Binary Search Tree ADT, Implementation, Operations- Searching, Insertion and Deletion, Balanced Search trees-AVL Trees, Operations – Insertion and Searching,B-Trees, B-Tree of order m,Operations- Insertion,Deletion and Searching,Introduction to Red-BlackTrees, Splay Trees,B*-Trees,B+-Trees(Elementary treatement), Comparison of Search Trees,Trees in java.util.

Unit IV

Searching- Linear Search,Binary Search, Hashing-Hash functions,Collision-Handling schemes,Hashing in java.util,Dictionary ADT,Linear list representation,Skip list representation,Hash table

representation,Comparison of Searching methods.

Sorting- Bubble Sort,Insertion Sort,Shell sort,Heap Sort,Radix Sort,Quick sort,Merge sort, Comparison of Sorting methods,Sorting in java.util.

Unit V

Graphs–Basic Terminology, Graph Representations- Adjacency matrix,Adjacency lists,Adjacency multilists,Graph traversals- DFS and BFS, Spanning trees-Minimum cost spanning trees,Kruskal’s Algorithm for Minimum cost Spanning trees, Shortest paths- Single Source Shortest Path Problem,All Pairs Shortest Path Problem.

Text Processing - Pattern matching algorithms- The Knuth-Morris-Pratt algorithm,The Boyer-Moore algorithm,Tries- Standard Tries, Compressed Tries, Suffix tries.

TEXT BOOKS :

1. Data structures and Algorithms in Java,Adam Drozdek,Cengage Learning.

2. Data structures and Algorithms in Java,Michael T.Goodrich and R.Tomassia , Wiley India edition.

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REFERENCE BOOKS :

1. Data structures and algorithms in Java,Robert Lafore,Pearson Education. 2. Data structures with Java,W.H.Ford and W.R.Topp,Pearson Education. 3. Classic Data structures in Java,T.Budd,Pearson Education.

4. Data Structures using Java,D.S. Malik and P.S.Nair, Cengage Learning, 5.An Introduction to Data structures and Algorithms,J.A.Storer,Springer. 6.Data structures and Java Collections Frame Work,W.J.Collins,Mc Graw Hill. 7.Data structures with Java,J.R.Hubbard and A.Huray,PHI.

8.Data Structures using Java,Y.Langsam,M.Augenstein,A.Tanenbaum,Pearson Education. 9.Data structures with Java,J.R.Hubbard,Schaum’s Outlines,TMH.

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COMPUTER SYSTEMS DESIGN

UNIT I

Computer structure – hardware, software, system software, Von-neumann architecture – case

study. IA -32 Pentium: registers and addressing, instructions, assembly language, program flow

control, logic and shift/rotate instructions, multiply, divide MMX,SIMD instructions, I/O

operations, subroutines.

Input/Output organizaton, interrupts, DMA, Buses, Interface circuits, I/O interfaces, device

drivers in windows, interrupt handlers

UNIT II

Processing Unit: Execution of a complete instruction, multiple bus organization, hardwired

control, micro programmed control.

Pipelining: data hazards, instruction hazards, influence on instruction sets, data path & control

consideration,RISC architecture introduction.

UNIT – III

Memory: types and hierarchy, model level organization, cache memory, performance

considerations, mapping, virtual memory, swapping, paging, segmentation, replacement

policies.

UNIT – IV

Processes and Threads: processes, threads, inter process communication, classical IPC

problems, Deadlocks.

UNIT – V

File system: Files, directories, Implementation, Unix file system

Security: Threats, intruders, accident data loss, basics of cryptography, user authentication.

TEXT BOOKS:

1. Computer Organization – Car Hamacher, Zvonks Vranesic, SafeaZaky, Vth Edition,

McGraw Hill.

2. Modern Operating Systems, Andrew S Tanenbaum 2

nd

_{edition Pearson/PHI}

REFERENCE BOOKS:

1. Computer Organization and Architecture – William Stallings Sixth Edition,

pearson/PHI

2. Morris Mano -Computer System Architecture –3

rd

_{Edition-Pearson Education .}

3. Operating System Principles- Abraham Silberchatz, Peter B. Galvin, Greg Gagne 7

th

Edition, John Wiley

4. Operating Systems – Internals and Design Principles Stallings, Fifth Edition–2005,

Pearson Education/PHI

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

M.Tech (NEURAL NETWORKS)

I SEMESTER

ARTIFICIAL INTELLIGENCE UNIT-I

Introduction : AI problems, foundation of AI and history of AI intelligent agents:

Agents and Environments, the concept of rationality, the nature of environments, structure of agents, problem solving agents, problem formulation.

Searching: Searching for solutions, uniformed search strategies – Breadth first search, depth first search, Depth limited search, Iterative deepening depth first search bi-direction search - comparison. Search with partial information (Heuristic search) Greedy best first search, A* search, Memory bounded heuristic search, Heuristic functions.

UNIT-II

Local search Algorithms, Hill climbing, simulated, annealing search, local beam search, genetical algorithms. Constrain satisfaction problems: Backtracking search for CSPs local search for constraint satisfaction problems. Game Playing: Adversial search, Games, minimax, algorithm, optimal decisions in multiplayer games, Alpha-Beta pruning, Evaluation functions, cutting of search.

UNIT-III

Knowledge Representation & Reasons logical Agents, Knowledge – Based Agents, the Wumpus world, logic, propositional logic, Resolution patterns in propos ional logic, Resolution, Forward & Backward. Chaining.

First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts forward chaining, Backward chaining, Resolution.

UNIT-IV

Planning – Classical planning problem, Language of planning problems,

Expressiveness and extension, planning with state – space search, Forward states spare search, Backward states space search, Heuristics for stats space search. Planning search, planning with state space search, partial order planning Graphs.

UNIT-V

Learning – Forms of learning, Induction learning, Learning Decision Tree, Statistical learning methods, learning with complex data, learning with Hidden variables – The EM Algorithm, Instance Based learning, Neural Networks.

TEXT BOOKS:

1. Artificial Intelligence – A Modern Approach. Second Edition, Stuart Russel, Peter Norvig, PHI/Pearson Education.

2. Artificial Intelligence, 3rd Edition, Patrick Henry Winston., Pearson Edition,

Reference:

1. Artificial Intelligence , 2nd Edition, E.Rich and K.Knight (TMH). 2. Artificial Intelligence and Expert Systems – Patterson PHI

3. Expert Systems: Principles and Programming- Fourth Edn, Giarrantana/ Riley, Thomson 4. PROLOG Programming for Artificial Intelligence. Ivan Bratka- Third Edition

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NEURAL NETWORKS

UNIT I

INTRODUCTION - what is a neural network? Human Brain, Models of a Neuron, Neural

networks viewed as Directed Graphs, Network Architectures, Knowledge Representation,

Artificial Intelligence and Neural Networks (p. no’s 1 –49)

LEARNING PROCESS 1 – Error Correction learning, Memory based learning, Hebbian

learing,(50-55)

UNIT II

LEARNING PROCESS 2: Competitive, Boltzmann learning, Credit Asssignment Problem,

Memory, Adaption, Statistical nature of the learning process, (p. no’s 50 –116)

SINGLE LAYER PERCEPTRONS – Adaptive filtering problem, Unconstrained

Organization Techniques, Linear least square filters, least mean square algorithm, learning

curves, Learning rate annealing techniques, perceptron –convergence theorem, Relation

between perceptron and Bayes classifier for a Gaussian Environment (p. no’s 117 –155)

UNIT III

MULTILAYER PERCEPTRON – Back propagation algorithm XOR problem, Heuristics,

Output representation and decision rule, Comuter experiment, feature detection, (p. no’s 156 –

201)

BACK PROPAGATION - back propagation and differentiation, Hessian matrix,

Generalization, Cross validation, Network pruning Techniques, Virtues and limitations of back

propagation learning, Accelerated convergence, supervised learning. (p. no’s 202 –234)

UNIT IV

SELF ORGANIZATION MAPS – Two basic feature mapping models, Self organization

map, SOM algorithm, properties of feature map, computer simulations, learning vector

quantization, Adaptive patter classification, Hierechel Vector quantilizer, contexmel Maps

(p. no’s 443 –469, 9.1 –9.8 )

UNIT V

NEURO DYNAMICS – Dynamical systems, stavility of equilibrium states, attractors,

neurodynamical models , manipulation of attarctors as a recurrent network paradigm (p. no’s

664 –680, 14.1 –14.6 )

HOPFIELD MODELS – Hopfield models, computer experiment I (p. no’s 680-701, 14.7 –

14.8 )

TEXT BOOKS:

1. Neural networks A comprehensive foundations, Simon Hhaykin, Pearson Education 2

nd

Edition 2004

REFERENCE BOOKS

1. Artifical neural networks - B.Vegnanarayana Prentice Halll of India P Ltd 2005

2. Neural networks in Computer intelligence, Li Min Fu TMH 2003

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

M.Tech (NEURAL NETWORKS)

I SEMESTER

PERVASIVE COMPUTING ELECTIVE – I

Unit I:

Pervasive Computing Application Pervasive Computing devices and Interfaces

-Device technology trends, Connecting issues and protocols

Unit II:

Pervasive Computing and web based Applications - XML and its role in Pervasive

Computing - Wireless Application Protocol (WAP) Architecture and Security - Wireless

Mark-Up language (WML) – Introduction

Unit III:

Voice Enabling Pervasive Computing - Voice Standards - Speech Applications in

Pervasive Computing and security

Unit IV:

PDA in Pervasive Computing – Introduction - PDA software Components, Standards,

emerging trends - PDA Device characteristics - PDA Based Access Architecture

Unit V:

User Interface Issues in Pervasive Computing, Architecture - Smart Card- based

Authentication Mechanisms - Wearable computing Architecture

Text Books:

1. Jochen Burkhardt, Horst Henn, Stefan Hepper, Thomas Schaec & Klaus Rindtorff. ---

Pervasive Computing Technology and Architecture of Mobile Internet Applications, Addision

Wesley, Reading, 2002.

2. Uwe Ha nsman, Lothat Merk, Martin S Nicklous & Thomas Stober: Principles of Mobile

Computing, Second Edition, Springer- Verlag, New Delhi, 2003.

Reference Books:

1. Rahul Banerjee: Internetworking Technologies: An Engineering Perspective,

Prentice –Hall of India, New Delhi, 2003. (ISBN 81-203-2185-5)

2. Rahul Banerjee: Lecture Notes in Pervasive Computing, Outline Notes,

BITS-Pilani, 2003.

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MACHINE LEARNING ELECTIVE – I UNIT I

INTRODUCTION - Well-posed learning problems, Designing a learning system, Perspectives and

issues in machine learning

Concept learning and the general to specific ordering – Introduction, A concept learning task,

Concept learning as search, Find-S: finding a maximally specific hypothesis, Version spaces and the candidate elimination algorithm, Remarks on version spaces and candidate elimination, Inductive bias

UNIT II

Decision Tree learning – Introduction, Decision tree representation, Appropriate problems for decision

tree learning, The basic decision tree learning algorithm, Hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning

Artificial Neural Networks – Introduction, Neural network representation, Appropriate problems for

neural network learning, Perceptions, Multilayer networks and the back propagation algorithm, Remarks on the back propagation algorithm, An illustrative example face recognition

Advanced topics in artificial neural networks

Evaluation Hypotheses – Motivation, Estimation hypothesis accuracy, Basics of sampling theory, A

general approach for deriving confidence intervals, Difference in error of two hypotheses, Comparing learning algorithms

UNIT III

Bayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum

likelihood and least squared error hypotheses, Maximum likelihood hypotheses for predicting probabilities, Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve bayes classifier, An example learning to classify text, Bayesian belief networks The EM algorithm

Computational learning theory – Introduction, Probability learning an approximately correct

hypothesis, Sample complexity for Finite Hypothesis Space, Sample Complexity for infinite Hypothesis Spaces, The mistake bound model of learning - Instance-Based Learning- Introduction, k -Nearest Neighbor Learning, Locally Weighted Regression, Radial Basis Functions, Case-Based Reasoning, Remarks on Lazy and Eager Learning

Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Hypothesis Space

Search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms

UNIT IV

Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets:

Summary, Learning First Order Rules, Learning Sets of First Order Rules: FOIL, Induction as Inverted Deduction, Inverting Resolution

Analytical Learning - Introduction, Learning with Perfect Domain Theories: Prolog-EBG Remarks on

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UNIT V

Combining Inductive and Analytical Learning – Motivation, Inductive-Analytical Approaches to

Learning, Using Prior Knowledge to Initialize the Hypothesis, Using Prior Knowledge to Alter the Search Objective, Using Prior Knowledge to Augment Search Operators,

Reinforcement Learning – Introduction, The Learning Task, Q Learning, Non-Deterministic, Rewards

and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming

TEXT BOOKS:

1. Machine Learning – Tom M. Mitchell, - MGH

2.

Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor &

Francis(CRC)

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SPEECH PROCESSING

ELECTIVE-I

UNIT I

INTRODUCTION Production of speech,sound perception, speech Analysis, speech coding,

speech Enhancement, speech Synthesis, speech and speaker Recognition.

Signals and Linear Systems: Simple signal, Filtering and convolution, Frequency Analysis :

Fourier Transform, spectra and Correlation, Laplace Transform: Poles and Zeros, Discrete –

Time Signal and Systems: Sampling, Frequency Transforms of Discrete-Time Signals,

Decimation and Interpolation Filter: Band pass Filter, Digital Filters, Difference Equations and

Interpolation

SPEECH ANALYSIS

Introduction, Short-Time speech Analysis: Windowing, Spectra of Windows: Wide-and

Narrow –Band Spectrograms, Time-domain Parameters: Signal Analysis in the Time Domain,

Short –Time Average Energy and Magnitude, Short –Time Average Zero-Crossing Rate

( ZCR), short-Time Autocorrelation Function , Frequency–Domain (Spectral) Parameters:

Filter–Bank Analysis, Short-Time Fourier Transform Analysis, Spectral Displays, Formant

Estimation and Tracking .

UNIT II

SPEECH PRODUCTION AND ACOUSTIC PHONETICS :

Anatomy and Physiology of the speech Organs: the Lungs and the Thorax, Larynx and Vocal

Folds(cords), Vocal Tract, Articulatory phonetics: Manner of Atriculatory, Structure of the

Syllable, Voicing, Place of the Articulation, Phonemes in Other Language, Articulatory

Models, Acoustic Phonetics : Spectrograms, Vowels, Diphthongs, glides and Liquids, Nasals,

Fricatives, stops (Plosives), Variants of Normal Speech.

UNIT III

LINEAR PREDICTIVE CODING (LPC) ANALYSIS

Basic Principles of LPC, Least –Squares Autocorrelation Method, Least –Squares Covariance

Method, Computation Considerations, Spectral Estimation Via LPC, Updating the LPC Model

Sample by Sample, Window Considerations.

Cepstral Analysis: Mathematical details of Cepstral analysis, Applications for the spectrum,

Mel-Scale Cepstrum, F0 Pitch estimation:Time domain F0 estimation methods, short-time

Spectral methods

UNIT IV

Speech synthesis: Introduction, Principles of speech synthsis: Types of strored speech units to

concatenate, Memory size, Synthesis method, Limited text voice response system,

unrestricted-text TTS systems. Synthesizer methods: Articulatory synthesis, Formant synthesis, LPC

synthesis.

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UNIT V

Introduction: VaN CCriability in speech signals, segmenting speech into smaller units,

Performance evaluation, Database for speech recognition,pattern recognition methods,

pre=processing, parametric representation: parameters used in speech recognition, feature

extraction, Evaluation of similarity of speech patterns: frame-based distance measures, Making

ASR decisions, HMMs

Speaker recognition: Introduction, Verification Vs. Recognition, Recognition techniques:

Model evaluation, text dependence, statical Vs. dynamic features, stochastic models, vector

quantization, similarity and distance measures, cepstral analysis, Features that distinguish the

speakers: measures of the effectiveness of features, techniques to choose features, spectral

features, prosodic features

Text Books:

1. Speech Communication Douglas O’ Shaughnessy, Universities Press

Reference Books:

1. Fundamentals of Speech Recognition, Lawrence Rabiner, Biing-Hwang Juang, Pearson

Education

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WIRELESS NETWORKS AND MOBILE COMPUTING ELECTIVE –II

UNIT I : INTRODUCTION TO MOBILE AND WIRELESS LANDSCAPE

Definition of Mobile and Wireless, Components of Wireless Environment, Challenges Overview of Wireless Networks, Categories of Wireless Networks

Wireless LAN : Infra red Vs radio transmission, Infrastructure and Ad-hoc Network, IEEE 802.11, HIPERLAN, Bluetooth

GLOBAL SYSTEM FOR MOBILE COMMUNICATIONS(GSM)

GSM Architecture, GSM Entities, Call Routing in GSM, PLMN Interfaces, GSM Addresses and Identifiers, Network Aspects in GSM, GSM Frequency Allocation, Authentication and Security

UNIT II: MOBILE NETWORK LAYER

Mobile IP (Goals, assumptions, entities and terminology, IP packet delivery, agent advertisement and discovery, registration, tunneling and encapsulation, optimizations), Dynamic Host Configuration Protocol (DHCP), Mobile Ad-hoc networks : Routing, destination Sequence Distance Vector, Dynamic Source Routing.

MOBILE TRANSPORT LAYER

Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, Fast retransmit/fast recovery, Transmission /time-out freezing, Selective retransmission, Transaction oriented TCP.

UNIT III: BROADCAST SYSTEMS

Overview, Cyclical repetition of data, Digital audio broadcasting: Multimedia object transfer protocol, Digital video broadcasting: DVB data broadcasting, DVB for high-speed internet access, Convergence of broadcasting and mobile communications.

UNIT IV : PROTOCOLS AND TOOLS:

Wireless Application Protocol-WAP. (Introduction, protocol architecture, and treatment of protocols of all layers), Bluetooth (User scenarios, physical layer, MAC layer, networking, security, link management) and J2ME.

WIRELESS LANGUAGE AND CONTENT – GENERATION TECHNOLOGIES

Wireless Content Types, Markup Languages: HDML, WML, HTML, cHTML, XHTML, VoiceXML. Content- Generation Technologies: CGI with Perl, Java Servlets, Java Server Pages, Active Server Pages, XML with XSL Stylesheets, XML Document, XSL Stylesheet

UNIT V: MOBILE AND WIRELESS SECURITY

Creating a Secure Environment, Security Threats, Security Technologies, Other Security Measures, WAP Security, Smart Client Security

TEXT BOOKS:

1. Jochen Schiller, “Mobile Communications”, Pearson Education, Second Edition, 2008. 2. Martyn Mallick, “Mobile and Wireless Design Essentials”, Wiley, 2008.

3. Asoke K Talukder, et al, “Mobile Computing”, Tata McGraw Hill, 2008.

REFERENCE BOOKS:

1.Mobile Computing,Raj Kamal,Oxford University Press.

2.William Stallings, “ Wireless Communications & Networks”, Person, Second Edition, 2007.

3.Frank Adelstein et al, “Fundamentals of Mobile and Pervasive Computing”, TMH, 2005. 4.Jim Geier, “Wireless Networks first-step”, Pearson, 2005.

5.Sumit Kasera et al, “2.5G Mobile Networks: GPRS and EDGE”, TMH, 2008. 6.Matthew S.Gast, “802.11 Wireless Networks”, O’Reilly, Second Edition, 2006.

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

M.Tech (NEURAL NETWORKS)

I SEMESTER

STORAGE AREA NETWORKS

ELECTIVE II

UNIT I: Introduction to Storage Technology

Review data creation and the amount of data being created and understand the value of data to a business, challenges in data storage and data management, Solutions available for data storage, Core elements of a data center infrastructure, role of each element in supporting business activities

UNIT II: Storage Systems Architecture

Hardware and software components of the host environment, Key protocols and concepts used by each component ,Physical and logical components of a connectivity environment ,Major physical components of a disk drive and their function, logical constructs of a physical disk, access characteristics, and performance Implications, Concept of RAID and its components , Different RAID levels and their suitability for different application environments: RAID 0, RAID 1, RAID 3, RAID 4, RAID 5, RAID 0+1, RAID 1+0, RAID 6, Compare and contrast integrated and modular storage systems ,High-level architecture and working of an intelligent storage system

UNIT III: Introduction to Networked Storage

Evolution of networked storage, Architecture, components, and topologies of FC-SAN, NAS, and IP-SAN , Benefits of the different networked storage options, Understand the need for long-term archiving solutions and describe how CAS fulfills the need , Understand the appropriateness of the different networked storage options for different application environments

UNIT IV: Information Availability & Monitoring & Managing Datacenter

List reasons for planned/unplanned outages and the impact of downtime, Impact of downtime, Differentiate between business continuity (BC) and disaster recovery (DR) ,RTO and RPO, Identify single points of failure in a storage infrastructure and list solutions to mitigate these failures , Architecture of backup/recovery and the different backup/recovery topologies , replication technologies and their role in ensuring information availability and business continuity, Remote replication technologies and their role in providing disaster recovery and business continuity capabilities

Identify key areas to monitor in a data center, Industry standards for data center monitoring and management, Key metrics to monitor for different components in a storage infrastructure, Key management tasks in a data center

UNIT V: Securing Storage and Storage Virtualization

Information security, Critical security attributes for information systems, Storage security domains, List and analyzes the common threats in each domain, Virtualization technologies, block-level and file-level virtualization technologies and processes

Case Studies

The technologies described in the course are reinforced with EMC examples of actual solutions.

Realistic case studies enable the participant to design the most appropriate solution for given sets of criteria.

TEXT BOOKS :

1. EMC Corporation, Information Storage and Management, Wiley.

2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill , Osborne, 2003.

3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001. 4. Meeta Gupta, Storage Area Network Fundamentals, Pearson Education Limited, 2002.

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2. Robert Spalding, “Storage Networks: The Complete Reference“, Tata McGraw Hill , Osborne, 2003.

3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill ,Osborne, 2001. 4. Meeta Gupta, Storage Area Network Fundamentals, Pearson Education Limited, 2002.

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