MAHATMA GANDHI UNIVERSITY

SCHEME AND SYLLABI

FOR

M. Tech. DEGREE PROGRAMME

IN

APPLIED ELECTRONICS &INSTRUMENTATION

with Specialization in

SIGNAL PROCESSING

(2011ADMISSION ONWARDS)

1

SCHEME AND SYLLABI FOR M. Tech. DEGREE PROGRAMME IN

APPLIED ELECTRONICS &INSTRUMENTATION

With Specialization in SIGNAL PROCESSING

SEMESTER – I

Sl.

No.

Course

No. Subject

Hrs / Week Evaluation Scheme (Marks)

Credits

(C) L T P

Sessional

ESE Total

TA CT Sub

Total

1 MAESP 101 Linear Algebra for Signal Processing 3 1 0 25 25 50 100 150 4

2 MAESP 102 Random Processes 3 1 0 25 25 50 100 150 4

3 MAESP103 Multirate Signal Processing 3 1 0 25 25 50 100 150 4

4 MAESP 104 DSP Algorithms And Architecture 3 1 0 25 25 50 100 150 4

5 MAESP 105 Elective – I 3 0 0 25 25 50 100 150 3

6 MAESP 106 Elective – II 3 0 0 25 25 50 100 150 3

7 MAESP 107 Seminar-I 0 0 2 50 0 50 0 50 1

8 MAESP 108 Signal Processing Lab-I 0 0 3 25 25 50 100 150 2

Total 18 4 5 225 175 400 700 1100 25

Elective – I (MAESP 105) Elective – II (MAESP 106)

MAESP 105 - 1 Signal Compression-Theory and Methods MAESP 106 - 1 Wavelet theory

MAESP 105 - 2 Coding Theory MAESP 106 - 2 Digital Communication Techniques

MAESP 105 - 3 Speech and Audio Processing MAESP 106 - 3 Optical Signal Processing

MAESP 105 - 4 Linear and Nonlinear Optimization MAESP 106 - 4 Neural Networks

L – Lecture, T – Tutorial, P – Practical

TA – Teacher’s Assessment (Assignments, attendance, group discussion, tutorials, seminars,

etc.)

CT – Class Test (Minimum of two tests to be conducted by the Institute)

ESE – End Semester Examination to be conducted by the University

Electives: New Electives may be added by the department according to the needs of emerging

fields of technology. The name of the elective and its syllabus should be submitted to

the University before the course is offered.

2

SEMESTER - II

Sl.

No.

Course

No. Subject

Hrs / Week Evaluation Scheme (Marks)

Credits

(C) L T P

Sessional

ESE Total

TA CT Sub

Total

1 MAESP 201 Advanced Digital Signal Processing 3 1 0 25 25 50 100 150 4

2 MAESP 202 Detection And Estimation Theory 3 1 0 25 25 50 100 150 4

3 MAESP 203 Multidimensional Signal Processing 3 1 0 25 25 50 100 150 4

4 MAESP 204 Digital Image and Video Processing 3 1 0 25 25 50 100 150 4

5 MAESP 205 Elective – III 3 0 0 25 25 50 100 150 3

6 MAESP 206 Elective – IV 3 0 0 25 25 50 100 150 3

7 MAESP 207 Seminar – II 0 0 2 50 0 50 0 50 1

8 MAESP 208 Signal Processing Lab-II 0 0 3 25 25 50 100 150 2

Total 18 4 5 225 175 400 700 1100 25

Elective – III (MAESP 205) Elective – IV (MAESP 206)

MAESP 205 - 1 Theory of Transforms MAESP 206 - 1 Array Signal Processing

MAESP 205 - 2 Biomedical Signal Processing MAESP 206 - 2 Spectrum Analysis

MAESP 205 - 3 Information Hiding and Data Encryption MAESP 206 - 3 Spread Spectrum and CDMA Systems

MAESP 205 - 4 Wireless Communication MAESP 206 - 4 Pattern Recognition And Analysis

L – Lecture, T – Tutorial, P – Practical

TA – Teacher’s Assessment (Assignments, attendance, group discussion, tutorials, seminars,

etc.)

CT – Class Test (Minimum of two tests to be conducted by the Institute)

ESE – End Semester Examination to be conducted by the University

Electives: New Electives may be added by the department according to the needs of emerging

fields of technology. The name of the elective and its syllabus should be submitted to

the University before the course is offered.

3

SEMESTER - III

Sl.

No. Course No. Subject

Hrs / Week Evaluation Scheme (Marks)

Credits

(C) L T P

Sessional ESE**

(Oral) Total

TA* CT Sub

Total

1 MAESP 301

Industrial Training or

Industrial Training and Mini

Project

0 0 20 50 0 50 100 150 10

2 MAESP 302 Master’s Thesis Phase – I 0 0 10 100*** 0 100 0 100 5

Total 0 0 30 150 0 150 100 250 15

* TA based on a Technical Report submitted together with presentation at the end of the

Industrial Training and Mini Project

** Evaluation of the Industrial Training and Mini Project will be conducted at the end of the third

semester by a panel of examiners, with at least one external examiner, constituted by the

University.

*** The marks will be awarded by a panel of examiners constituted by the concerned institute

SEMESTER - IV

Sl.

No. Course No. Subject

Hrs / Week Evaluation Scheme (Marks)

Credits

(C) L T P

Sessional ESE**

(Oral

&

Viva)

Total

TA* CT Sub

Total

1 MAESP 401 Master’s Thesis 0 0 30 100 0 100 100 200 15

2 MAESP 402 Master’s Comprehensive Viva 100 100

Total 30 100 0 100 200 300 15

Grand Total of all Semesters 2750 80

* 50% of the marks to be awarded by the Project Guide and the remaining 50% to be awarded

by a panel of examiners, including the Project Guide, constituted by the Department

** Thesis evaluation and Viva-voce will be conducted at the end of the fourth semester by a panel

of examiners, with at least one external examiner, constituted by the University.

4

MAESP 101 LINEAR ALGEBRA FOR SIGNAL PROCESSING L T P C

3 1 0 4

Module 1:

Vector Spaces:- Complex numbers, Definition of Vector Spaces, Properties of Vector

Spaces, Subspaces, Sums and Direct Sums, Span and Linear Independence, Bases,

Dimension. Inner Product Spaces:- Inner Products, Norms, Orthogonal Bases,

Orthogonal Projections and Minimization Problem, Linear Functionals and Adjoints.

Some Important bases:- Standard Ordered Bases, DFT Bases, DCT Bases.

Module 2:

Linear Maps:- Definitions and Examples, Null Spaces and Ranges, The matrix of a

Linear Map, Invertibilty. Eigen Values and Eigen Vectors:- Invariant Subspaces,

Polynomials Applied to Operators, Upper Triangular Matrices, Diagonal Matrices,

Invariant Subspaces on Real Vector Spaces.

Module 3:

Operators on Inner Product Spaces:- Self Adjoint and Normal Operators, Spectral

Theorem, Normal Operators on Real Inner Product Spaces, Positive Operators, Isometries,

Polar and Singular Value Decompositions

Some Important Classes of Linear Systems:-Shift Invariant Systems and Topelitz

Matrices, Operators and Square Matrices, Self Adjoint Operators and Hermitian Matrices,

Projections and idempotent matrices. Rotations and unitary matrices.

Module 4:

Metric Spaces: - Definition, Convergence and Completeness.

Hilbert spaces: - Introduction [Ref 3, Appendix]. l2 and L2 spaces. Definition and some

properties.Orthogonal Complements, Orthonormal Sets, Fourier Expansion. Conjugate

Space, Adjoint of an Operator, Self Adjoint Operators, Normal and Unitary operators,

Projections.

5

References :

1. Sheldon Axler, Linear Algebra Done Right, Springer

2. G. F. Simmons, Introduction to Topology and Modern Analysis, Tata McGraw

Hill.

3. Paul R. Halmos, Finite-Dimensional Vector Spaces, Springer

4. Todd K. Moon and Wynn C. Stirling, Mathematical Methods and Algorithms for

Signal Processing, Pearson

5. Arch W. Naylor and George R. Sell, Linear Operator Theory in Engineering and

Science, Springer

6. Peter D. Lax, Linear Algebra, Wiley Students Edition.

7. Michael W. Frazier, An Introduction to Wavelets Through Linear Algebra,

Springer.

8. Gilbert Strang, Linear Algebra and Its Applications, Thomson Learning.

6

MAESP 102 RANDOM PROCESSES L T P C

3 1 0 4

Module 1:

Probability space: Introduction to probability, Sample space, field, σ-field, Borel set,

Probability space, joint, conditional and total probabilities, independence, Bayes’ theorem.

Random Variable :- Definition of random variable, Continuous and discrete random

variable. Probability mass function and Probability density function, Cumulative

distribution function, Basic distribution functions- binomial, uniform, exponential and

normal. Properties of these distribution functions.

Module 2:

Random Vector: - Definition of random vector, joint statistics. independent events and

conditional probability. Conditional distributions, Sum and transformation of random

variables.

Characteristic Function: - Expectation, variance, moments, characteristic function,

moment generating function, covariance and correlation, conditional expectation,

Fundamental Theorem of expectation.

Module 3:

Random process: - Definition of random process, IID process, Poisson process,

properties of Poisson process, Markov process, birth-death process, Wiener process.

Response of processes to LTI systems : Random processes as inputs to Linear Time

Invariant (LTI) systems; power spectral density, Gaussian processes as input to LTI

systems, white Gaussian noise.

Module 4:

Convergence: - Markov and Chebyshev inequalities, weak and strong law of large

numbers, Central Limit Theorem. Convergence of random sequences- almost sure

convergence, convergence in probability, convergence in mean

Stationarity: - Stationary and ergodic process – point-wise ergodic theorem, Ergodic

decomposition, Karhunen –Loeve expansion.

7

References:

1. Athanasios Papoulis , S. Unnikrishna Pillai, Probability, Random Variables and

Stochastic Processes, TMH

2. Henry Stark , John W Woods, Probability and Random Processes With Application to

Signal Processing, 3/e, Pearson Education India

3. Geoffrey Grimm , Probability and Random Processes, 3rd edition, 2001, Oxford

University Press

4. V. Krishnan, Probability and Random Processes, 2006, John Wiley & Sons 5. Albert

Leon Garcia, Probability and Random Processes for Electrical Engineering, 1993,

Prentice Hall

6. Dr. Kishor S. Trivedi, Probability and Statistics with Reliability, Queuing, and

Computer Science Applications, John Wiley and Sons, New York, 2001.

7. Kingsbury N., Random Processes [Connexions Web site]. January 22, 2004.

8. Gray, R. M. and Davisson L. D., An Introduction to Statistical Signal Processing.

8

MAESP 103 MULTIRATE SIGNAL PROCESSING L T P C

3 1 0 4

Pre requisites – Signals and Systems, DSP

Module 1: Fundamentals of Multirate Theory

The sampling theorem - sampling at sub nyquist rate - Basic Formulations and schemes.

Basic Multirate operations- Decimation and Interpolation - Digital Filter Banks- DFT

Filter Bank- Identities- Polyphase representation

Maximally decimated filter banks: Polyphase representation - Errors in the QMF bank-

Perfect reconstruction (PR) QMF Bank - Design of an alias free QMF Bank

Module 2: M-channel perfect reconstruction filter banks

Uniform band and non uniform filter bank - tree structured filter bank- Errors created by

filter bank system- Polyphase representation- perfect reconstruction systems

Module 3: Perfect reconstruction (PR) filter banks

Paraunitary PR Filter Banks- Filter Bank Properties induced by paraunitarity- Two

channel FIR paraunitary QMF Bank- Linear phase PR Filter banks- Necessary conditions

for Linear phase property- Quantization Effects: -Types of quantization effects in filter

banks. - coefficient sensitivity effects, dynamic range and scaling.

Module 4: Cosine Modulated filter banks

Cosine Modulated pseudo QMF Bank- Alas cancellation- phase - Phase distortion- Closed

form expression- Polyphase structure- PR Systems

References:

1. P.P. Vaidyanathan, “Multirate systems and filter banks.” Prentice Hall. PTR. 1993.

2. N.J. Fliege, “Multirate digital signal processing .” John Wiley.

9

3. Fredric J. Harris, Multirate Signal Processing for Communication Systems, Prentice

Hall, 2004

4. Ljiljana Milic, Multirate Filtering for Digital Signal Processing: MATLAB

Applications : Information Science Reference; 1/e, 2008

5. Sanjit K. Mitra, “ Digital Signal Processing: A computer based approach.” McGraw

Hill. 1998.

6. R.E. Crochiere. L. R. Rabiner, “Multirate Digital Signal Processing”, Prentice Hall.

Inc.1983.

7. J.G. Proakis. D.G. Manolakis, “Digital Signal Processing: Principles. Algorithms and

Applications”, 3rd Edn. Prentice Hall India, 1999

10

MAESP 104 DSP ALGORITHMS AND ARCHITECTURE L T P C

3 1 0 4

Pre requisite – Computer Organization and Architecture, Microprocessors and

microcontrollers

Module 1:

Need for special DSP processors, Von Newmann versus Harvard Architecture,

Architectures of superscalar and VLIW fixed and floating point processors. Review of

Pipelined RISC. Architecture and Instruction Set Design. Performance and Benchmarks-

SPEC CPU 2000, EEMBC DSP benchmarks. Basic Pipeline: Implementation Details-

Pipline Hazards (based on MIPS 4000 arch).

Module 2:

Instruction Level Parallelism (ILP): Concepts, Dynamic Scheduling - Reducing Data

hazards. Dynamic Hardware Prediction - Reducing Branch Hazards. Multiple Issue-

Hardware-based Speculation. Limitations of ILP. Review of Memory Hierarchy – Cache

design, Cache Performance Issues & Improving Techniques.

Module 3:

Computer arithmetic- Signed Digit Numbers(SD) - Multiplier Adder Graph –

Logarithmic and Residue Number System (LNS, RNS) - Index Multiplier – Pipelined

Adders - Modulo Adders - Distributed Arithmetic(DA) - CORDIC Algorithm.

Module 4:

Case studies: Introduction to architecture Details of (a) BlackFin processor (Analog

Devices) (b) TMS320C64X Digital Signal Processing Applications: FIR and IIR Digital

Filter Design, Filter Design Programs using MATLAB - Fourier Transform: DFT, FFT

programs using MATLAB - Real Time Implementation on DSP processors- Factors to be

considered for optimized implementation based on processor architecture: Implementation

of simple Real Time Digital Filters, FFT using DSP [Only familiarity with instruction set

is expected. It is not required to memorize all the instructions.]

11

References:

1. Rulph Chassaing, Digital Signal Processing and Applications with the C6713 and

C6416 , Wiley, 2005

2. Nasser Kehtarnawaz, Real Time Signal Processing Based on TMS320C6000,

Elsevier,2004

3. JL Hennesy, D.A. Patterson, Computer Architecture A Quantitative Approach; 3rd

Edition, Elsevier India

4. Uwe Mayer-BAeses , Digital Signal Processing with FPGAs, Springer, 2001.

5. Users manual for of various fixed and floating point DSPs, TMS320C6x Data Sheets

from TI.

6. Blackfin Processor Hardware Reference, Analog Devices, Version 3.0, 2004.

12

MAESP 105 - 1 SIGNAL COMPRESSION-THEORY AND

METHODS L T P C

3 0 0 3

Module 1:

Review of Information Theory: The discrete memoryless information source - Kraft

inequality; optimal codes Source coding theorem.

Compression Techniques - Lossless and Lossy Compression - Mathematical Preliminaries

for Lossless Compression -Huffman Coding - Optimality of Huffman codes - Extended

Huffman Coding - Adaptive Huffman Coding - Arithmetic Coding - Adaptive Arithmetic

coding, Run Length Coding, Dictionary Techniques - Lempel-Ziv coding, Applications -

Predictive Coding - Prediction with Partial Match - Burrows Wheeler Transform,

Dynamic Markov Compression

Module 2:

Rate distortion theory: Rate distortion function R(D),Properties of R(D); Calculation of

R(D) for the binary source and the Gaussian source, Rate distortion theorem, Converse of

the Rate distortion theorem, Quantization - Uniform & Non-uniform - optimal and

adaptive quantization, vector quantization and structures for VQ, Optimality conditions for

VQ, Predictive Coding - Differential Encoding Schemes

Module 3:

Mathematical Preliminaries for Transforms, Karhunen Loeve Transform, Discrete Cosine

and Sine Transforms, Discrete Walsh Hadamard Transform, Lapped transforms -

Transform coding - Subband coding - Wavelet Based Compression - Analysis/Synthesis

Schemes.

Module 4:

Comparison of data Compression standards: Zip and Gzip, Speech Compression

Standards: PCM-G.711, ADPCM G.726, SBC G.722, LD-CELP G.728, CS-ACELP (-A)

G.729, MPC-MLQ , G.723.1, GSM HR VSELP, IS-54 VSELP, IS-96 QCELP, Immarsat -

B APC, MELP, FS 1015, LPC10, FS1016, CELP, G721. Audio Compression standards:

MPEG, Philips PASC, Sony ATRAC, Dolby AC-3, Image Compression standards: JBIG,

13

GIF, JPEG & JPEG derived industry standards, CALIC, SPIHT, EZW, JPEG 2000. Video

Compression Standards: MPEG, H.261, H.263 & H264.

References :

1. Khalid Sayood, “Introduction to Data Compression”, Morgan Kaufmann

Publishers, Second Edn., 2005.

2. David Salomon, “Data Compression: The Complete Reference”, Springer

Publications, 4th Edn., 2006.

3. Thomas M. Cover, Joy A. Thomas, “Elements of Information Theory," John Wiley

& Sons, Inc., 1991.

4. Toby Berger, “Rate Distortion Theory: A Mathematical Basis for Data

Compression”, Prentice Hall, Inc., 1971

5. K.R.Rao, P.C.Yip, “The Transform and Data Compression Handbook”, CRC

Press., 2001.

6. R.G.Gallager, “Information Theory and Reliable Communication”, John Wiley &

Sons, Inc., 1968.

7. Ali N. Akansu, Richard A. Haddad, “Multiresolution Signal Decomposition:

Transforms, Subbands and Wavelets”, Academic Press., 1992

8. Martin Vetterli, Jelena Kovacevic, “Wavelets and Subband Coding”, Prentice Hall

Inc., 1988

14

MAESP 105 - 2 CODING THEORY L T P C

3 0 0 3

Module 1: Entropy and Loss less Source coding

Entropy- Memory less sources- Markov sources- Entropy of a discrete Random variable-

Joint, conditional and relative entropy- Mutual Information and conditional mutual

information- Chain relation for entropy, relative entropy and mutual Information- Lossless

source coding- Uniquely decodable codes- Instantaneous codes- Kraft's inequality -

Optimal codes- Huffman code- Shannon's Source Coding Theorem

Module 2: Channel Capacity and Coding Theorem

Asymptotic Equipartition Property (AEP)- High probability sets and typical sets- Method

of typical sequence as a combinatorial approach for bounding error probabilities. Channel

Capacity- Capacity computation for some simple channels- Arimoto-Blahut algorithm-

Fano's inequality-Proof of Shannon's Channel Coding Theorem and its converse- Channels

with feed back- Joint source channel coding Theorem.

Module 3: Continuous Sources and Channels

Differential Entropy- Joint, relative and conditional differential entropy- Mutual

information- Waveform channels- Gaussian channels- Mutual information and Capacity

calculation for Band limited Gaussian channels- Shannon limit- Parallel Gaussian

Channels-Capacity of channels with colored Gaussian noise- Water filling.

Module 4: Finite Field Arithmetic

Introduction, Groups- Rings- Fields- Arithmetic of Galois Field- Integer Ring- Polynomial

Rings- Polynomials and Euclidean algorithm, primitive elements, Construction and basic

properties of Finite Fields- Computations using Galois Field arithmetic- sub fields-

Minimal polynomial and conjugates- Vector space- Vector Subspace- Linear

independence

15

References:

1. T. Cover and J. Thomas, Elements of Information Theory, John Wiley & Sons 1991.

2. Taub & Schilling, Principles of communication systems, TMH.

3. Shulin & Daniel J. Costello, Error control coding – Fundamentals and Application,

Prentice Hall Ed.

4. Robert Gallager, “Information Theory and Reliable Communication”, John Wiley &

Sons.

5. R. J. McEliece, “The theory of information & coding”, Addison Wesley Publishing

Co., 1977.

6. T. Bergu, Rate Distortion Theory a Mathematical Basis for Data Compression, PH Inc.

1971.

7. Special Issue on Rate Distortion Theory, IEEE Signal Processing Magazine,

November 1998.

8. Bernard Sklar, Digital Communication, 2/e, Pearson Education, 2001.

16

MAESP 105 - 3 SPEECH AND AUDIO PROCESSING L T P C

3 0 0 3

Module 1:

Mechanism of speech production - Acoustic theory of speech production (Excitation,

Vocal tract model for speech analysis, Formant structure, Pitch)- digital models –

linear prediction of speech - AR Model, ARMA model -auto correlation - formulation of

LPC equation - solution of LPC equations - Levinson Durbin algorithm – Levinson

recursion - Schur algorithm – lattice formulations and solutions - PARCOR coefficients -

Spectral analysis of speech - Short Time Fourier analysis - filter bank design. Auditory

Perception: Psychoacoustics- Frequency Analysis and Critical Bands – Masking

properties of human ear

Module 2:

Speech coding -subband coding of speech - transform coding - channel vocoder - formant

vocoder – cepstral vocoder - vector quantizer coder- Linear predictive Coder. Speech

synthesis - pitch extraction algorithms - gold rabiner pitch trackers - autocorrelation pitch

trackers - voice/unvoiced detection - homomorphic speech processing - homomorphic

systems for convolution - complex cepstrums – pitch extraction using homomorphic

speech processing. Sound Mixtures and Separation - CASA, ICA & Model based

separation.

Module 3:

Speech Transformations - Time Scale Modification - Voice Morphing. Automatic speech

recognition systems - isolated word recognition - connected word recognition -large

vocabulary word recognition systems - pattern classification - DTW, HMM - speaker

recognition systems - speaker verification systems – speaker identification Systems.

17

Module 4:

Audio Processing : Non speech and Music Signals - Modeling -Differential, transform

and subband coding of audio signals & standards - High Quality Audio coding using

Psychoacoustic models - MPEG Audio coding standard. Music Production - sequence of

steps in a bowed string instrument - Frequency response measurement of the bridge of a

violin. Audio Data bases and applications - Content based retrieval.

References:

1. Rabiner L.R. & Schafer R.W., Digital Processing of Speech Signals, Prentice Hall Inc.

2. O'Shaughnessy, D. Speech Communication, Human and Machine. Addison-Wesley.

3. Thomas F. Quatieri , Discrete-time Speech Signal Processing: Principles and Practice

Prentice Hall, Signal Processing Series.

4. Rabiner L.R. & Gold, Theory and Applications of Digital Signal Processing, Prentice

Hall of India

5. Jayant, N. S. and P. Noll. Digital Coding of Waveforms: Principles and

Applications to Speech and Video. Signal Processing Series, Englewood Cliffs: Prentice-

Hall

6. Deller, J., J. Proakis, and J. Hansen. Discrete-Time Processing of Speech Signals.

Macmillan.

7. Ben Gold & Nelson Morgan , Speech and Audio Signal Processing, John Wiley &

Sons, Inc.

8. Owens F.J., Signal Processing of Speech, Macmillan New Electronics

9. Saito S. & Nakata K., Fundamentals of Speech Signal Processing, Academic Press,Inc.

18

10. Papamichalis P.E., Practical Approaches to Speech Coding, Texas Instruments,

Prentice Hall

11. Thomas Parsons, Voice and Speech Processing, McGraw Hill Series

12. Chris Rowden, Speech Processing, McGraw-Hill International Limited

13. Moore. B, An Introduction to Psychology of hearing , Academic Press, London, 1997

14. E. Zwicker and L. Fastl, Psychoacoustics-facts and models, Springer-Verlag., 1990

19

MAESP 105 - 4 LINEAR AND NONLINEAR OPTIMIZATION L T P C

3 0 0 3

Module 1:

Mathematical Background: Sequences and Subsequences- Mapping and functions-

Continuous functions- Infimum and Supremum of functions- Minima and maxima of

functions- Differentiable functions. Vectors and vector spaces- Matrices- Linear

transformation- Quadratic forms- Definite quadratic forms- Gradient and Hessian- Linear

equations- Solution of a set of linear equations-Basic solution and degeneracy. Convex

sets and Convex cones- Introduction and preliminary definition- Convex sets and

properties- Convex Hulls- Extreme point- Separation and support of convex sets- Convex

Polytopes and Polyhedra- Convex cones- Convex and concave functions- Basic

properties- Differentiable convex functions- Generalization of convex functions.

.

Module 2:

Linear Programming: Introduction -Optimization model, formulation and applications-

Classical optimization techniques: Single and multi variable problems-Types of

constraints. Linear optimization algorithms: The simplex method -Basic solution and

extreme point -Degeneracy-The primal simplex method -Dual linear programs - Primal,

dual, and duality theory - The dual simplex method -The primal-dual algorithm-Duality

applications. Post optimization problems: Sensitivity analysis and parametric

programming

Module 3:

Nonlinear Programming: Minimization and maximization of convex functions- Local &

Global optimum- Convergence-Speed of convergence. Unconstrained optimization: One

dimensional minimization - Elimination methods: Fibonacci & Golden section search -

Gradient methods - Steepest descent method. Constrained optimization: Constrained

optimization with equality and inequality constraints. Kelley's convex cutting plane

algorithm - Gradient projection method - Penalty Function methods

20

Module 4:

Constrained optimization: Lagrangian method - Sufficiency conditions - Kuhn-Tucker

optimality conditions- Rate of convergence - Engineering applications Quadratic

programming problems-Convex programming problems.

References:

1. David G Luenberger, .Linear and Non Linear Programming., 2nd Ed, Addison-

Wesley, 1984

2. S.S.Rao, .Engineering Optimization.; Theory and Practice; Revised 3rd Edition, New

Age International Publishers, New Delhi

3. Fletcher R., Practical methods of optimization, John Wiley, 1980.

4. Hillier and Lieberman, Introduction to Operations Research, McGraw-Hill, 8th

edition, 2005.

5. Saul I Gass, Linear programming, McGraw-Hill, 5th edition, 2005.

6. Bazarra M.S., Sherali H.D. & Shetty C.M., Nonlinear Programming Theory and

Algorithms, John Wiley, New York, 1979.

7. Kalyanmoy Deb, Optimization for Engineering: Design-Algorithms and Examples,

Prentice Hall (India), 1998.

8. S. M. Sinha, Mathematical programming: Theory and Methods, Elsevier, 2006

21

MAESP 106 – 1 WAVELET THEORY L T P C

3 0 0 3

Module 1:

Fourier and Sampling Theory Generalized Fourier theory, Fourier transform, Short-

time(windowed) Fourier transform, Time-frequency analysis, Fundamental notions of the

theory of sampling. Theory of Frames: Bases, Resolution of unity, Definition of frames,

Geometrical considerations and the general notion of a frame, Frame projector, Example –

windowed Fourier frames.

Module 2:

Wavelets: The basic functions, Specifications, Admissibility conditions, Continuous

wavelet transform (CWT), Discrete wavelet transform (DWT). The multiresolution

analysis (MRA) of L2

(R): The MRA axioms, Construction of an MRA from scaling

functions - The dilation equation and the wavelet equation, Compactly supported

orthonormal wavelet bases - Necessary and sufficient conditions for orthonormality.

Module 3:

Regularity and selection of wavelets: Smoothness and approximation order - Analysis in

Soboleve space, Criteria for wavelet selection with examples.

Construction of wavelets : Splines, Cardinal B-spline MRA, Subband filtering schemes,

Compactly supported orthonormal wavelet bases.

Module 4:

Wavelet transform: Wavelet decomposition and reconstruction of functions in L2

(R).

Fast wavelet transform algorithms - Relation to filter banks, Wavelet packets –

Representation of functions, Selection of basis. Construction of wavelets:

Biorthogonality and biorthogonal basis, Biorthogonal system of wavelets - construction,

The Lifting scheme

22

References :

1. Stephen G. Mallat, “A wavelet tour of signal processing” 2nd Edition Academic

Press, 2000.

2. M. Vetterli, J. Kovacevic, “Wavelets and subband coding” Prentice Hall Inc, 1995

3. Gilbert Strang and Truong Q. Nguyen, “Wavelets and filter banks” 2nd Edition

Wellesley- Cambridge Press, 1998.

4. Gerald Kaiser, “A friendly guide to wavelets” Birkhauser/Springer International

Edition, 1994, Indian reprint 2005.

5. L. Prasad and S. S. Iyengar, “Wavelet analysis with applications to image

processing” CRC Press, 1997.

6. J. C. Goswami and A. K. Chan, “Fundamentals of wavelets: Theory, Algorithms

and Applications” Wiley-Interscience Publication, John Wiley & Sons Inc., 1999.

7. Mark A. Pinsky, “Introduction to Fourier Analysis and Wavelets” Brooks/Cole

Series in Advanced Mathematics, 2002

8. Christian Blatter, “Wavelets: A primer” A. K. Peters, Massachusetts,1998.

9. M. Holschneider, “Wavelets: An analysis tool” Oxford Science Publications, 1998.

10. R. M. Rao and A. Bopardikar, “Wavelet transforms: Introduction to theory and

applications” Addison-Wesley, 1998.

11. Ingrid Daubechies, “Ten lectures on wavelets” SIAM, 1990.

12. H. L. Resnikoff and R. O. Wells, Jr., “Wavelet analysis: The scalable structure of

information” Springer, 1998.

13. P. P. Vaidyanathan, “Multirate systems and filter banks” Prentice Hall P T R,

1993.

14. P. Wojtaszczyk, “A mathematical introduction to wavelets” Cambridge University

Press 1997.

15. Michael W. Frazier, “An introduction to wavelets through linear algebra”

Springer-Verlag, 1999.

16. Anthony N. Michel and Charles J. Herget, “Applied algebra and functional

analysis” Dover Publications Inc., 1993.

23

MAESP 106 - 2 DIGITAL COMMUNICATION TECHNIQUES L T P C

3 0 0 3

Module 1:

Random Variables and Processes, Review of Random variables: Moment generating

function, Chernoff bound, Markov’s inequality, Chebyshev’s inequality, Central limit

Theorem, Chi square, Rayleigh and Rician distributions, Correlation, Covariance matrix-

Stationary processes, wide sense stationary processes, ergodic process, cross correlation

and autocorrelation functions-Gaussian process

Module 2:

Pulse Modulation- Sampling process, Aliasing, Reconstruction, PAM, Quantization, PCM,

Noise in PCM system, Modifications of PCM – Delta modulation, DPCM, ADPCM,

ADM, Processing Gain.

Base band Pulse Transmission – Matched filter, properties, Error rate due to noise, ISI –

Nyquist criterion for distortion less transmission, Ideal solution, Raised cosine spectrum,

Correlative level coding - Duobinary coding, precoding, Modified duobinary coding,

Generalized Partial response signaling, Base band M-ary PAM transmission, eye pattern,

optimum linear receiver. Adaptive Equalization.

Module 3:

Communication over Additive Gaussian Noise Channels, Characterization of

Communication Signals and Systems- Signal space representation- Connecting Linear

Vector Space to Physical Waveform Space- Scalar and Vector Communication over

Memory less Channels. Optimum waveform receiver in additive white Gaussian noise

(AWGN) channels – Cross correlation receiver, Matched filter receiver and error

probabilities. Optimum Receiver for Signals with random phase in AWGN Channels-

Optimum receiver for Binary Signals- Optimum receiver for M-ary orthogonal signals-

Probability of error for envelope detection of M-ary Orthogonal signals. Optimum

waveform receiver for coloured Gaussian noise channels- Karhunen Loeve expansion

approach, whitening.

24

Module 4:

Spread spectrum communication - Pseudo–noise sequences, Properties of PN sequences.

Generation of PN Sequences, generator polynomials, Maximal length codes and Gold

Codes. Spread spectrum communication– Notion of spread spectrum, Direct sequence

spread spectrum with coherent binary phase shift keying, Signal space dimensionality and

processing gain, Probability of error, Anti-jam Characteristics, Frequency Hop spread

spectrum with MFSK, Slow and Fast frequency hopping. Multiple Access Techniques,

multi path channels, classification, Coherence time, Coherence bandwidth, Statistical

characterization of multi path channels, Binary signaling over a Rayleigh fading channel,

Diversity techniques - Diversity in time, frequency and space. TDMA and CDMA –

RAKE receiver. Source coding of speech.

References :

1. J.G. Proakis, Digital Communication, MGH

2. Simon Haykin, Communication systems, John-Wiley & sons.

3. Taub & Schilling, Principles of communication systems, TMH.

4. Edward. A. Lee and David. G. Messerschmitt, Digital Communication, Allied

Publishers (second edition).

5. Marvin. K. Simon, Sami. M. Hinedi and William. C. Lindsey, “Digital

Communication Techniques”, PHI.

6. William Feller, An introduction to Probability Theory and its applications, Vol 1& 2,

Wiley 2000.

7. Sheldon.M.Ross, Introduction to Probability Models, Academic Press, 7th edition.

8. Bernard Sklar, Digital Communication, 2/e, Pearson Education, 2001.

9. Harold Kolimbris, Digital Communication Systems, 1/e, Pearson Education, 2000.

10. Sam Shanmugham – Digital and Analog Communication systems, Wiley India.

25

MAESP 106 - 3 OPTICAL SIGNAL PROCESSING L T P C

3 0 0 3

Module 1:

Basics of signal processing and optics, Characterization of a General signal, examples

ofsignals, Spatial signal. Basic laws of geometrical optics, Refractions by mirrors, the lens

formulas, General Imaging conditions, the optical invariant, Optical Aberrations.

Module 2:

Physical Optics, The Fresnel Transforms, the Fourier transform, Examples of Fourier

transforms, the inverse Fourier transform, Extended Fourier transform analysis, Maximum

information capacity and optimum packing density, System coherence.

Module 3:

Spectrum Analysis and Spatial Filtering, Light sources, spatial light modulators, The

detection process in Fourier domain, System performance parameters, Dynamic range.

Some fundamentals of signal processing, Spatial Filters, Binary Spatial Filters, Magnitude

Spatial Filters, Phase Spatial Filters, Real valued Spatial Filters, Interferometric techniques

for constructing Spatial Filters. Optical signal processor and filter generator, Applications

for optical signal processing.

Module 4:

Acousto-optic cell spatial light modulators, Applications of acousto-optic devices. Basic

Acousto-optic power spectrum analyzer. Heterodyne systems: Interference between two

waves, the optical Radio.

References :

1. Anthony Vanderlugt, Optical signal processing: Wiley-Interscience

2. Dr. Hiroshi Ishikawa , Ultrafast All-Optical Signal Processing Devices: Wiley

3. Francis T. S. Yu, Suganda Jutamulia,Optical Signal Processing, Computing, and

Neural Networks: Krieger Publishing Company

4. D. Casasent, Optical data processing-Applications, Springer-Verlag, Berlin

26

5. H.J. Caulfield, Handbook of holography, Academic Press New York

6. P.M. Dufffieux, The Fourier Transform and its applications to Optics, John Wiley

and sons

7. J. Horner ,Optical Signal Processing Academic Press

8. Joseph W. Goodman, Introduction to Fourier Optics, second edition Mc Graw Hill.

27

MAESP 106 - 4 NEURAL NETWORKS L T P C

3 0 0 3

Module 1:

Introduction to neural networks. Artificial intelligence and neural networks. The human

brain and the nervous system. The biological neuron. Models of the single neuron. Neural

networks viewed as directed graphs. Network architectures. Knowledge representation

in neural networks. Applications of neural networks.

Module 2:

Learning in neural networks. Types of learning methods. Classification of learning

methods. Statistical nature of the learning processs. Statistical learning theory. The

Probably Approximately Correct (PAC) model.

Module 3:

Learning in a single layer perceptron. Adaptive filtering and the LMS algorithm. Learning

rate annealing techniques. Perceptron convergence theorem. Multilayer perceptron: the

error back-propagation learning method. Accelerated convergence in back-propagation

learning. Radial basis function network. The counter-propagation network.

.

Module 4:

Support vector machines. Optimal hyperplane for non-separable patterns. Building support

vector machines. Principal component analysis (PCA). Hebbian based and lateral

inhibition based adaptive PCA. Kernel based PCA. Self Organization Maps. Learning

vector quantization. Information theoretic models. Maximum Entropy Principle. Mutual

information and Kullback-Leibler divergence

References :

1. Simon Haykin, Neural Networks - A comprehensive foundation, Pearson

Education Asia, 2001.

28

2. Frederic M. Ham & Ivica Kostanic, Principles of Neuro-computing for Science

and Engineering, Tata Mc Graw hill, 2002.

3. Kumar S, Neural Networks : A Classroom Approach, TMH

4. J.S.R. Jjang, C.T. Sun and E. Mizutani, Neuro fuzzy and Soft Computing : A

computational approach to learning and machine intelligence, Prentice Hall of

India,2002

5. Yegna Narayana B – Artificial Neural Networks – PHI

6. Timothy J Ross – Fuzzy logic with Engineering Applications

7. Christopher Bishop, Neural Networks for Pattern Recognition, Oxford

University Press

8. J M Zurada, Introduction to Atificial Neural Networks, Jaico Publishing House

29

MAESP 107

SEMINAR – I L T P C

0 0 2 1

Each student shall present a seminar on any topic of interest related to the core / elective

courses offered in the first semester of the M. Tech. Programme. He / she shall select the

topic based on the references from international journals of repute, preferably IEEE

journals. They should get the paper approved by the Programme Co-ordinator / Faculty

member in charge of the seminar and shall present it in the class. Every student shall

participate in the seminar. The students should undertake a detailed study on the topic and

submit a report at the end of the semester. Marks will be awarded based on the topic,

presentation, participation in the seminar and the report submitted.

30

MAESP 108 SIGNAL PROCESSING LAB - I L T P C

0 0 3 2

Tools- Matlab, DSP Kits – TMS320C6X or AD or equivalent

Introduction to DSP

Generation of Test Signals

Operations on signals (Shifting, Scaling, Addition, Multiplication, Convolution,

Correlation of Two Sequences)

Analysis of Signal in Frequency Domain (DTFT, DFT, Z-transform, Circular

Convolution)

Properties of Discrete Time Systems (Impulse Response, Step response, Frequency

Response and Stability of Systems)

Digital Filter Structures.( IIR and FIR realization)

Digital Filter Design (FIR using Window)

Digital Filter Design (IIR filter Design)

Linear Algebra – Change of Basis, Gram-Schmidt Orthogonalization, Least square

solutions, Eigen Value Decomposition, Singular Value Decomposition.

Familiarization of TMS 320 DSK- Generation of Test Signals, Operations on signals

31

MAESP 201 ADVANCED DIGITAL SIGNAL PROCESSING L T P C

3 1 0 4

Module 1:

Review of discrete time Complex Gaussian processes, MA, AR, ARMA processes and

their properties, MMSE predictors, LMMSE predictor, orthogonality theorem (concept of

innovation processes), Weiner filter, Yule-walker equation, unconstrained Weiner filter (in

z domain), recursive Weiner filter (using innovation process). Kalman filter, recursions in

Kalman filter, Extended Kalman filter, comparison of Kalman and Weiner filters.

Module 2:

Filters with recursions based on the steepest descent and Newton's method, criteria for the

convergence, rate of convergence. LMS filter, mean and variance of LMS, the MSE of

LMS and misadjusment, Convergence of LMS.

Module 3:

RLS recursions, assumptions for RLS, convergence of RLS coefficients and MSE. Filter

based on innovations, generation of forward and backward innovations, forward and

reverse error recursions. Implementation of Weiner, LMS and RLS filters using lattice

filters, Linear Prediction, Levinson Durbin algorithm, reverse Levinson Durbin algorithm.

Module 4:

Non-linear signal processing: Non-linear filters, Non-gaussian models, Generalized

Gaussian and stable distributions, Median smoothers, Rank/order filters, Weighted median

smoother.

References:

1. S. Haykin. Adaptive Filters Theory. Prentice-Hall.

2. Dimitris G. Manolakis, Vinay K. Ingle, Stephan M Krgon, Statistical and Adaptive

Signal Processing, Mc Graw Hill (2000)

3. G. R. Arce – Non-linear signal processing: A statistical approach, Wiley 2004.

32

4. Monson Hayes, Statistical Signal Processing and Modelling, Wiley India Pvt. Ltd

5. J. Astola, P. Kuosmanen, Fundamentals of non-linear digital filtering, CRC Press,

1997.

6. Proakis & Manolakis, Digital Signal Processing . – PHI, New Delhi

7. S. J. Orfanidis, Optimum Signal Processing., Mc-Graw Hill..

8. Ifeacher- Digital Signal Processing- Addision –Wesley

9. Sanjit k. Mitra - Digital Signal Processing –– TMH

10. A. V. Oppenheim & Ronald W. Schafer - Discrete Time Signal processing –– PHI,

New Delhi

11. Jones D. Adaptive Filters [Connexions Web site]. May 12, 2005. Available at:

http://cnx.rice.edu/content/col10280/1.1/

33

1.

MAESP 202 DETECTION AND ESTIMATION THEORY L T P C

3 1 0 4

Module 1: Fundamentals of Detection Theory

Hypothesis Testing: Bayes’ Detection, MAP Detection, ML Detection, Minimum

Probability of Error Criterion, Min-Max Criterion, Neyman-Pearson Criterion, Multiple

Hypothesis, Composite Hypothesis Testing: Generalized likelihood ratio test (GLRT),

Receiver Operating Characteristic Curves.

Module 2: Fundamentals of Estimation Theory

Role of Estimation in Signal Processing, Unbiased Estimation, Minimum variance

unbiased(MVU) estimators, Finding MVU Estimators, Cramer-Rao Lower Bound, Linear

Modeling-Examples, Sufficient Statistics, Use of Sufficient Statistics to find the MVU

Estimator

Module 3: Estimation Techniques

Deterministic Parameter Estimation: Least Squares Estimation-Batch Processing,

Recursive Least Squares Estimation, Best Linear Unbiased Estimation, Likelihood and

Maximum Likelihood Estimation

Module 4: Estimation Techniques (contd)

Random Parameter Estimation: Bayesian Philosophy, Selection of a Prior PDF,

Bayesian linear model, Minimum Mean Square Error Estimator, Maximum a Posteriori

Estimation

State Estimation: Prediction, Single and Multistage Predictors, Filtering, The Kalman

Filter

References :

1. M D Srinath, P K Rajasekaran, R Viswanathan, Introduction to Statistical Signal

Processing with Applications, “Pearson”

34

2. Steven M. Kay, “Statistical Signal Processing: Vol. 1: Estimation Theory, Vol. 2:

Detection Theory," Prentice Hall Inc., 1998.

3. Jerry M. Mendel, “Lessons in Estimation Theory for Signal Processing,

Communication and Control," Prentice Hall Inc., 1995

4. Ralph D. Hippenstiel, “Detection Theory- Applications and Digital Signal

Processing”, CRC Press, 2002.

5. Bernard C. Levy, “Principles of Signal Detection and Parameter Estimation”,

Springer, New York, 2008.

6. Harry L. Van Trees, “Detection, Estimation and Modulation Theory, Part 1 and 2,"

John Wiley & Sons Inc. 1968.

7. Neel A. Macmillan and C. Douglas Creelman, “Detection Theory: A User's Guide

(Sec. Edn.)” Lawrence Erlbaum Associates Publishers, USA, 2004.

8. Monson H. Hayes, “Statistical Digital Signal Processing and Modelling," John Wiley

& Sons Inc., 1996.

35

MAESP 203 MULTIDIMENSIONAL SIGNAL PROCESSING

L T P C

3 1 0 4

Module 1: Multidimensional systems

Fundamental operations on Multidimensional signals, Linear Shift - Invariant systems-

cascade and parallel connection of systems- separable systems, stable systems- Frequency

responses of 2D LTI Systems- Impulse response- Multidimensional Fourier transforms- z

transform, properties of the Fourier and z transform.

Module 2: Sampling continuous 2D signals

Periodic sampling with rectangular geometry- sampling density, Aliasing effects created

by sampling - Periodic sampling with different sampling geometrics-hexagonal- Quincunx

etc.- comparison

Module 3: Multidimensional Discrete Fourier Transform

Multidimensional discrete Fourier transform- Properties of DFT, Circular convolution-

Calculation of DFT- DFT for periodically sampled signals - Fast Fourier transform for

periodically sampled signals- The Discrete Cosine Transform. .

Module 4: Multidimensional Digital Filter Design

Separable Filters- Linear phase filters- FIR Filters- Implementation of FIR filters - design

of FIR filters using windows- Two dimensional window functions, IIR Filters

References :

1. John Woods, Multidimensional signal, image, and video processing and coding,

Academic Press, 2006.

2. Dudgeon Dan E. , Multidimensional Digital Signal Processing, Prentice Hall,

Englewood Cliffs, New Jersey

3. P.P. Vaidyanathan. “Multirate systems and filter banks.” Prentice Hall. PTR. 1993.

4. Jae S. Lim, Two- Dimensional Signal and Image Processing , - Prentice Hall

Englewood Cliffs, New Jersey, 1990.

36

MAESP 204 DIGITAL IMAGE AND VIDEO PROCESSING

L T P C

3 1 0 4

Module 1:

Image representation: Gray scale and colour Images, image sampling and

quantization.Two dimensional orthogonal transforms: DFT, WHT, Haar transform, KLT,

DCT. Imageenhancement - filters in spatial and frequency domains, histogram-based

processing,homomorphic filtering. Edge detection - non parametric and model based

approaches,LOG filters, localisation problem.

Module 2:

Image Restoration: Degradation Models, PSF, circulant and block - circulant matrices,

deconvolution, restoration using inverse filtering, Wiener filtering and maximum entropy-

based methods. Image Segmentation: Pixel classification, Bi-level thresholding, Multi-

level thresholding, P-tile method, Adaptive thresholding, Spectral & spatial classification,

Edge detection, Hough transform, Region growing.

Module 3:

Fundamental concepts of image compression - Compression models – Information

theoretic perspective - Fundamental coding theorem - Lossless Compression: Huffman

Coding- Arithmetic coding - Bit plane coding - Run length coding . Lossy compression:

Transform coding - Image compression standards.

Module 4:

Video Processing: Representation of Digital Video, Spatio-temporal sampling; Motion

Estimation; Video Filtering; Video Compression, Video coding standards- H.264

References :

1. A. K. Jain, Fundamentals of digital image processing, Prentice Hall of India, 1989.

2. R. C. Gonzalez, R. E. Woods, Digital Image Processing, Pearson Education.

37

3. Iain E Richardson, H.264 and MPEG-4 Video Compression, John Wiley & Sons,

September 2003

4. A. M. Tekalp, Digital Video Processing , Prentice-Hall

5. A Bovik, Handbook of Image & Video Processing, Academic Press, 2000

6. W. K. Pratt, Digital image processing, Prentice Hall

7. A. Rosenfeld and A. C. Kak, Digital image processing, Vols. 1 and 2, Prentice

Hall, 1986.

8. H. C. Andrew and B. R. Hunt, Digital image restoration, Prentice Hall, 1977

9. R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, McGraw-Hill International

Edition, 1995

10. K.R.Rao, Zoran.S Bojkovic, Dragorad A Milovanovic, Multimedia

Communication Systems: Techniques ,standards and Networks , Prentice Hall

38

MAESP 205 - 1 THEORY OF TRANSFORMS L T P C

3 0 0 3

Module 1: Linear Operators on Finite-dimensional Vector Spaces

Eigenvalue problems, eigenvalues, eigenvectors and eigenspace of a linear operator,

Linear operators with an eigenbasis, decomposition of vector spaces, Similarity

transformations - Diagonalization, Primary decomposition theorem, Jordan Canonical

form/decomposition; Fredholm alternative theorem, Least squares solutions and pseudo-

inverses, LU decomposition, Orthogonal transformations, Singular value decomposition,

Householder transformation.

Module 2: Normed Linear Spaces

Functionals - Norm, Convergence - Cauchy sequence, Completeness of vector spaces;

Infinite dimensional vector spaces - Normed linear spaces; Banach Spaces, Inner product

spaces, Hilbert spaces; Continuous linear operators. Bounded Linear Operators and

Spectral Theory Bounded linear operators in finite dimensional inner product spaces -

Adjoint of an operator, Norm of an operator; Self-adjoint operators - Spectral analysis of

self-adjoint operators; Bessel’s inequality, Parseval’s identity; Reisz Representation

Theorem, Compact linear operators

Module 3: Theory of Distributions

Generalized functions and the Dirac’s delta; Differential operators - Green’s function and

the inverse linear operators.The Making of Integral Transforms The making of Laplace

transform and Fourier transform, Self-reciprocal functions and operators under Fourier

transform - The construction of Fractional Fourier transform; Construction of z-transform

- Discrete-time Fourier transform and discrete Fourier transform.

Lapped Transforms Karhunen-Loeve transform - Lapped orthogonal transforms and

biorthogonal transforms – Construction of discrete cosine and sine transforms.

39

Module 4: The Making of Continuous Wavelet Transform

Reisz basis, Resolution of unity, Definition of frames, Geometrical considerations and the

general notion of a frame, Frame projector, Example - windowed Fourier frames;

Continuous wavelet transform.

References :

1. Arch W. Naylor and George R. Sell, “Linear Operator Theory in Engineering and

Science,” 2nd Edition, Springer-Verlag, New York, 1982.

2. Larry Smith, “Linear Algebra,” 2nd Edition, Springer-Verlag, New York 1982

3. Lokenath Debnath and Piotr Mikusinski, “Hilbert Spaces with Applications,” 3rd

Edition, Academic Press, Indian reprint 2006.

4. A. David Wunsch, “Complex Variables with Applications,” 2nd Edition, Addison-

Wesley Publishing Company, New York, 1994.

5. Erwin Kreyszig, “Introductory Functional Analysis with Applications,” John

Wiley and Sons, 1989.

6. George Bachman and Lawrence Narici, “Functional Analysis,” Dover Publications

Inc., 2000.

7. Frederick W Byron, Jr and Robert W Fuller, “Mathematics of Classical and

Quantum Physics,” Dover Publications Inc., 1992.

8. Athanasios Papoulis, “Fourier Integral and its Applications,” McGraw-Hill

International, New York, 1962.

9. Athanasios Papoulis, “Systems and Transforms with Applications in Optics,”

McGraw-Hill International, New York, 1968.

10. Anthony N. Michel and Charles J. Herget, “Applied Algebra and Functional

Analysis,” Dover Publications Inc., 1993.

11. Stephen G. Mallat, “A Wavelet Tour of Signal Processing,” 2nd Edition, Academic

Press, 2000.

12. Gerald Kaiser, “A Friendly Guide to Wavelets,” Birkhauser/Springer International

Edition, 1994, Indian reprint 2005.

13. Ingrid Daubechies, “Ten Lectures on Wavelets,” SIAM, 1990.

40

MAESP 205 -2 BIOMEDICAL SIGNAL PROCESSING L T P C

3 0 0 3

Module 1:

Introduction to Biomedical Signals - Examples of Biomedical signals - ECG, EEG, EMG

etc - Tasks in Biomedical Signal Processing - Computer Aided Diagnosis. Origin of bio

potentials - Review of linear systems - Fourier Transform and Time Frequency Analysis

(Wavelet) of biomedical signals- Processing of Random & Stochastic signals - spectral

estimation - Properties and effects of noise in biomedical instruments - Filtering in

biomedical instruments

Module 2:

Concurrent, coupled and correlated processes - illustration with case studies - Adaptive

and optimal filtering - Modeling of Biomedical signals - Detection of biomedical signals

in noise - removal of artifacts of one signal embedded in another -Maternal-Fetal ECG -

Muscle-contraction interference. Event detection - case studies with ECG & EEG -

Independent component Analysis - Cocktail party problem applied to EEG signals -

Classification of biomedical signals.

Module 3:

Cardio vascular applications : Basic ECG - Electrical Activity of the heart- ECG data

acquisition - ECG parameters & their estimation - Use of multiscale analysis for ECG

parameters estimation - Noise & Artifacts- ECG Signal Processing: Baseline Wandering,

Power line interference, Muscle noise filtering - QRS detection - Arrhythmia analysis -

Data Compression: Lossless & Lossy- Heart Rate Variability - Time Domain measures -

Heart Rhythm representation - Spectral analysis of heart rate variability - interaction with

other physiological signals

Module4:

Neurological Applications : The electroencephalogram - EEG rhythms & waveform -

categorization of EEG activity - recording techniques - EEG applications- Epilepsy, sleep

disorders, brain computer interface. Modeling EEG- linear, stochastic models - Non linear

41

modeling of EEG - artifacts in EEG & their characteristics and processing - Model based

spectral analysis - EEG segmentation - Joint Time-Frequency analysis - correlation

analysis of EEG channels - coherence analysis of EEG channels.

References :

1. Rangayyan, “Biomedical Signal Analysis”, Wiley 2002.

2. D.C.Reddy, “Biomedical Signal Processing: Principles and techniques” , Tata

McGraw Hill, New Delhi, 2005

3. Willis J Tompkins, Biomedical Digital Signal Processing, Prentice Hall, 1993

4. Bruce, “Biomedical Signal Processing & Signal Modeling,” Wiley, 2001

5. Sörnmo, “Bioelectrical Signal Processing in Cardiac & Neurological

Applications”, Elsevier

6. Semmlow, “Biosignal and Biomedical Image Processing”, Marcel Dekker, 2004

7. Enderle, “Introduction to Biomedical Engineering,” 2/e, Elsevier, 2005

42

MAESP 205 - 3 INFORMATION HIDING AND DATA

ENCRYPTION L T P C

3 0 0 3

Module 1:

Introduction to Cryptography OSI Security Architecture, Classical Encryption

techniques, Cipher Principles, Data Encryption Standard, Block Cipher Design Principles

and Modes of Operation, Evaluation criteria for AES, AES Cipher, Triple DES,

Placement of Encryption Function , Traffic Confidentiality

Module 2:

Public Key Cryptography Key Management, Diffie-Hellman key Exchange, Elliptic

Curve Architecture and Cryptography, Introduction to Number Theory, Confidentiality

using Symmetric Encryption, Public Key Cryptography and RSA. Practical

implementation of Cryptography

Module 3:

Information Hiding: Principle and Objectives of Watermarking and Steganography.

Mathematical formulations, Public - Private Key Steganography, Information hiding in

noisy data (adaptive and nonadapive )and written texts.

Module 4:

Steganographic techniques: - substitution and bitplane tools - transform domain tools-

Spread Spectrum Techniques- Statistical methods- Distortion and Cover Generation

methods. Steganalysis: - of images and audio. Watermarking:- techniques, methods,

benchmarks for digital watermarking. Practical implementation of steganograpgy.

References :

1. Stefan Katzenbeisser, Fabien A. P. Petitcolas, Information Hiding Techniques for

Steganography and Digital Watermarking, Artech House Publishers, 2000.

43

2. Neal Koblitz, A Course in Number Theory and Cryptography, 2nd Edition,

Springer

3. William Stallings, Cryptography And Network Security – Principles and Practices,

Prentice Hall of India, Third Edition, 2003.

4. Bruce Schneier, Applied Cryptography, John Wiley & Sons Inc, 2001.

5. Charles B. Pfleeger, Shari Lawrence Pfleeger, Security in Computing, Third

Edition, Pearson Education, 2003.

6. H.S. Zuckerman , An Introduction to the theory of Numbers, 5th Edition, John

Wiley & Sons

7. A.J. Menezes etc al, Handbook of Applied Cryptography, CRC Press

8. Branislav Kisacanin , Mathematical Problems and Proofs, Combinatorics, Number

theory and Geometry

9. Atul Kahate, Cryptography and Network Security, Tata McGraw-Hill, 2003

44

MAESP 205 - 4 WIRELESS COMMUNICATION L T P C

3 0 0 3

Module 1:

Fading and Diversity :Wireless Channel Models- path loss and shadowing models

statistical fading models- Narrow band and wideband Fading models- Review of

performance of digital modulation schemes over wireless channels- Diversity- Repetition

coding and Time Diversity- Frequency and Space Diversity- Receive Diversity- Concept

of diversity branches and signal paths- Combining methods- Selective diversity combining

- Switched combining- maximal ratio combining- Equal gain combining performance

analysis for Rayleigh fading channels.

Module 2:

Cellular Communication : Cellular Networks- Multiple Access:

FDM/TDM/FDMA/TDMA- Spatial reuse- Co-channel interference Analysis- Hand over

Analysis- Erlang Capacity Analysis- Spectral efficiency and Grade of Service- Improving

capacity - Cell splitting and sectorization

Module 3:

Spread spectrum and CDMA: Motivation- Direct sequence spread spectrum- Frequency

Hopping systems- Time Hopping.- Anti-jamming- Pseudo Random (PN) sequence-

Maximal length sequences- Gold sequences- Generation of PN sequences.- Diversity in

DS-SS systems- Rake Receiver- Performance analysis. Spread Spectrum Multiple Access-

CDMA Systems- Interference Analysis for Broadcast and Multiple Access Channels-

Capacity of cellular CDMA networks- Reverse link power control- Hard and Soft hand off

strategies.

Module 4:

Fading Channel Capacity: Capacity of Wireless Channels- Capacity of flat and frequency

selective fading channels- Multiple Input Multiple output (MIMO) systems- Narrow band

multiple antenna system model- Parallel Decomposition of MIMO Channels- Capacity of

45

MIMO Channels. Cellular Wireless Communication Standards, Second generation cellular

systems: GSM specifications and Air Interface - specifications, IS 95 CDMA- 3G systems:

UMTS & CDMA 2000 standards and specifications

References:

1. Andrea Goldsmith, Wireless Communications, Cambridge University press.

2. Simon Haykin and Michael Moher, Modern Wireless Communications, Pearson

Education.

3. T.S. Rappaport, Wireless Communication, principles & practice, PHI, 2001.

4. G.L Stuber, Principles of Mobile Communications, 2nd edition, Kluwer Academic

Publishers.

5. Kamilo Feher, Wireless digital communication, PHI, 1995.

6. R.L Peterson, R.E. Ziemer and David E. Borth, Introduction to Spread Spectrum

Communication, Pearson Education.

7. A.J.Viterbi, CDMA- Principles of Spread Spectrum, Addison Wesley, 1995.

8. D. Tse & P. Viswanath, Fundamentals of Wireless Communication, Cambridge

University Press, 2005.

46

MAESP 206 - 1 ARRAY SIGNAL PROCESSING L T P C

3 0 0 3

Module 1:

Spatial Signals : Signals in space and time. Spatial frequency, Direction vs. frequency.

Wave fields. Far field and Near field signals.

Module 2:

Sensor Arrays : Spatial sampling, Nyquist criterion. Sensor arrays. Uniform linear

arrays, planar and random arrays. Array transfer (steering) vector. Array steering vector

for ULA. Broadband arrays

Module 3:

Spatial Frequency: Aliasing in spatial frequency domain. Spatial Frequency Transform,

Spatial spectrum. Spatial Domain Filtering. Beam Forming. Spatially white signal

Module 4:

Direction of Arrival Estimation: Non parametric methods - Beam forming and Capon

methods. Resolution of Beam forming method. Subspace methods - MUSIC, Minimum

Norm and ESPRIT techniques. Spatial Smoothing.

References :

1. Dan E. Dugeon and Don H. Johnson. (1993). Array Signal Processing: Concepts

and Techniques. Prentice Hall.

2. Petre Stoica and Randolph L. Moses. (2005, 1997) Spectral Analysis of Signals.

Prentice Hall.

3. Bass J, McPheeters C, Finnigan J, Rodriguez E. Array Signal Processing

[Connexions Web site]. February 8, 2005. Available at:

http://cnx.rice.edu/content/col10255/1.3/

47

MAESP 206 - 2 SPECTRUM ANALYSIS L T P C

3 0 0 3

Module1:

Power Spectral Density: Energy spectral density of deterministic signals, Power spectral

density of random signals, Properties of PSD.

Module 2:

PSD Estimation - Non-parametric methods Estimation of PSD from finite data, Non-

parametric methods : Periodogram properties, bias and variance analysis, Blackman-

Tuckey method, Window design considerations, time-bandwidth product and resolution -

variance trade-offs in window design, Refined periodogram methods : Bartlet method,

Welch method.

Module 3:

PSD Estimation - Parametric methods: Parametric method for rational spectra :-

Covariance structure of ARMA process, AR signals, Yule-Walker method, Least square

method, Levinson-Durbin Algorithm, MA signals, Modified Yule-Walker method, Two-

stage least square method, Burg method for AR parameter estimation. Parametric

method for line spectra :- Models of sinusoidal signals in noise, Non-linear least squares

method, Higher order Yule-Walker method, MUSIC and Pisarenko methods, Min-norm

method, ESPRIT method.

Module 4:

Filterbank methods: Filterbank interpretation of periodogram, Slepia base-band filters,

refined filterbank method for higher resolution spectral analysis, Capon method,

Introduction to higher order spectra.

References :

1. P. Stoica , R.L. Moses, Introduction to Spectral Analysis, Prentice Hall

2. Kay S, M, Modern Spectral Estimation Theory & Applications, , Prentice Hall

48

MAESP 206 - 3 SPREAD SPECTRUM AND CDMA SYSTEMS

Prerequisite: DCT L T P C

3 0 0 3

Module 1: Fundamentals of Spread Spectrum

Introduction to spread spectrum communication, direct sequence spread spectrum,

frequency-hop spread spectrum system. Spreading sequences- maximal-length sequences,

gold codes, Walsh orthogonal codes- properties and generation of sequences.

Synchronization and Tracking: delay lock and tau-dither loops, coarse synchronization-

principles of serial search and match filter techniques.

Module 2: Performance Analysis of SS system

Performance of spread spectrum system in jamming environments- Barrage noise

jamming, partial band jamming, pulsed noise jamming and single tone jamming. Error

probability of DS-CDMA system under AWGN and fading channels, RAKE receiver

Module 3: Capacity, Coverage and multiuser detection

Basics of spread spectrum multiple access in cellular environments, reverse Link power

control, multiple cell pilot tracking, soft and hard handoffs, cell coverage issues with hard

and soft handoff, spread spectrum multiple access outage, outage with imperfect power

control, Erlang capacity of forward and reverse links. Multi-user Detection -MF detector,

decorrelating detector, MMSE detector. Interference Cancellation: successive, Parallel

Interference Cancellation, performance analysis of multiuser detectors and interference

cancellers.

Module 4: CDMA Systems

General aspects of CDMA cellular systems, IS-95 standard, Downlink and uplink,

Evolution to Third Generation systems, WCDMA and CDMA-2000 standards, Principles

of Multicarrier communication, MCCDMA and MC-DS-CDMA.

References :

49

1. Valery P. Ipatov, Spread Spectrum and CDMA Principles and Applications,

Wiley, 2005

2. R. L. Peterson, R. Ziemer and D. Borth, “Introduction to Spread Spectrum

Communications,” Prentice Hall, 1995.

3. A. J. Viterbi, “CDMA - Principles of Spread Spectrum Communications,”

Addison-Wesley, 1997.

4. S. Verdu, “ Multiuser Detection” , Cambridge University Press- 1998

5. M. K. Simon, J. K. Omura, R. A. Scholts and B. K. Levitt, “ Spread Spectrum

Communications Handbook”, McGraw- Hill, Newyork-1994

6. Cooper and McGillem, “Modern Communications and Spread Spectrum”

McGraw- Hill, 1985

7. S. Glisic and B. Vucetic, “Spread Spectrum CDMA Systems for Wireless

Communications,” Artech House, 1997

50

MAESP 206-4 PATTERN RECOGNITION AND ANLAYSIS L T P C

3 0 0 3

Module 1:

Introduction - features, feature vectors and classifiers, Supervised versus unsupervised

pattern recognition. Classifiers based on Bayes Decision theory- introduction, discriminant

functions and decision surfaces, Bayesian classification for normal distributions,

Estimation of unknown probability density functions, the nearest neighbour rule. Linear

classifiers,- Linear discriminant functions and decision hyper planes, The perceptron

algorithm, MSE estimation, Logistic determination, Support Vector machines

Module 2:

Non-Linear classifiers- Two layer and three layer perceptrons, Back propagation

algorithm, Networks with Weight sharing, Polynomial classifiers, Radial Basis function

networks.

Module 3: Device Drivers

Non-Linear classifiers- Support Vector machines-nonlinear case, Decision trees,

combining classifiers, Feature selection, Receiver Operating Characteristics (ROC) curve,

Class separability measures, Optimal feature generation, The Bayesian information

criterion

Module 4: Automotive Networked Embedded Systems

Clustering- Cluster analysis, Proximity measures, Clustering Algorithms - Sequential

algorithms, Neural Network implementation. Hierarchical algorithms - Agglomerative

algorithms, Divisive algorithms. Schemes based on function optimization - Fuzzy

clustering algorithms, Probabilistic clustering, K - means algorithm. Clustering algorithms

based on graph theory , Competitive learning algorithms, Binary Morphology Clustering

Algorithms Boundary detection methods, Valley seeking clustering, Kernel clustering

methods. Clustering validity.

51

References :

1. Sergios Theodoridis, Konstantinos Koutroumbas, “Pattern Recognition”, Academic

Press, 2006.

2. Richard O. Duda and Hart P.E, and David G Stork, Pattern classification , 2nd Edn.,

John Wiley & Sons Inc., 2001

3. Earl Gose, Richard Johnsonbaugh, and Steve Jost; Pattern Recognition and Image

Analysis, PHI Pvte. Ltd., NewDelhi-1, 1999.

4. Fu K.S., Syntactic Pattern recognition and applications, Prentice Hall, Eaglewood

cliffs, N.J., 1982

5. Andrew R. Webb, Statistical Pattern Recognition, John Wiley & Sons, 2002.

6. Christopher M Bishop, Pattern Recognition and Machine Learning, Springer 2007.

52

MAESP 207 SEMINAR – II L T P C

0 0 2 1

Each student shall present a seminar on any topic of interest related to the core / elective

courses offered in the second semester of the M. Tech. Programme. He / she shall select

the topic based on the references from international journals of repute, preferably IEEE

journals. They should get the paper approved by the Programme Co-ordinator / Faculty

member in charge of the seminar and shall present it in the class. Every student shall

participate in the seminar. The students should undertake a detailed study on the topic and

submit a report at the end of the semester. Marks will be awarded based on the topic,

presentation, participation in the seminar and the report submitted.

53

MAESP 208 SIGNAL PROCESSING LAB -II

L T P C

0 0 3 2

Tools- Matlab, DSP Kits – TMS320C6X or AD or equivalent

Multirate Signal Processing – Decimation and Interpolation, Filter bank design.

Speech processing

Understanding various audio and speech file formats and conversion utilities. Reading and

writing audio files using Matlab. Implementation of various speech compression

algorithms. DPCM, Adaptive Quantization, ADPCM, Transform Coding.

Image Processing

Reading, display, and saving of different image file formats using Matlab

Implementation of 2-D transforms- (DFT/ DCT/ Walsh Transform/Wavelets)

Image enhancement Operations- Median filtering, neighbourhood averaging, edge

enhancement, Edge detection- Canny, Sobel, Laplacian Image segmentation- Region

Growing, thresholding, watershed Morphological Image Processing- Binary and Gray

level Morphology Weiner Filtering

Real time signal processing using TMS 320 DSK

54

MAESP 301 INDUSTRIAL TRAINING AND MINIPROJECT L T P C

0 0 20 10

The student shall undergo

i) An Industrial Training of 12 weeks duration in an industry / company / institution

approved by the institute under the guidance of a staff member in the concerned field.

At the end of the training he / she have to submit a report on the work being carried

out.

OR

ii) An Industrial Training of 1 month duration and Mini Project of 2 months duration

in an industry / company / institution approved by the institute under the guidance of a

staff member in the concerned field. At the end of the training he / she have to submit

a report on the work being carried out.

MAESP 302 MASTER’S THESIS PHASE - I L T P C

0 0 10 5

The thesis (Phase - I) shall consist of research work done by the candidate or a

comprehensive and critical review of any recent development in the subject or a detailed

report of project work consisting of experimentation / numerical work, design and or

development work that the candidate has executed.

In Phase - I of the thesis, it is expected that the student should decide a topic of thesis,

which is useful in the field or practical life. It is expected that students should refer

national & international journals and proceedings of national & international seminars.

Emphasis should be given to the introduction to the topic, literature survey, and scope of

the proposed work along with some preliminary work / experimentation carried out on the

thesis topic. Student should submit two copies of the Phase - I thesis report covering the

content discussed above and highlighting the features of work to be carried out in

Phase – II of the thesis. Student should follow standard practice of thesis writing. The

candidate will deliver a talk on the topic and the assessment will be made on the basis of

the work and talks there on by a panel of internal examiners one of which will be the

internal guide. These examiners should give suggestions in writing to the student to be

incorporated in the Phase – II of the thesis.

55

MAESP 401 MASTER’S THESIS L T P C

0 0 30 15

In the fourth semester, the student has to continue the thesis work and after successfully

finishing the work, he / she have to submit a detailed thesis report. The work carried out

should lead to a publication in a National / International Conference. They should have

submitted the paper before M. Tech. evaluation and specific weightage should be given to

accepted papers in reputed conferences.

MAESP 402 MASTER’S COMPREHENSIVE VIVA

A comprehensive viva-voce examination will be conducted at the end of the fourth

semester by an internal examiner and external examiners appointed by the university to

assess the candidate’s overall knowledge in the respective field of specialization.