Download JNTUK M.Tech R19 ECE CE And SP, CSP Syllabus

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA

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KAKINADA – 533 003, Andhra Pradesh, India

DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING

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COURSE STRUCTURE & SYLLABUS M.Tech ECE Common for

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Communication Engineering & Signal Processing (CE&SP)


Communication & Signal Processing (CSP)

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Programme


(Applicable for batches admitted from 2019-2020)

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA

I Semester

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II Semester

III Semester

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IV Semester

Audit course 1 & 2

1. English for Research Paper Writing

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2. Disaster Management


3. Sanskrit for Technical Knowledge



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4. Value Education


5. Constitution of India


6. Pedagogy Studies

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7. Stress Management by Yoga


8. Personality Development through Life Enlightenment Skills.



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I Year I Semester

ADVANCED DIGITAL SIGNAL PROCESSING

Course Objectives

1. At the completion of this course, the student should have in depth knowledge of processing digital signals.



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2. To study about discrete time systems and to learn about FFT algorithms.


3. To study the design techniques for FIR and IIR digital filters


4. To study the finite word length effects in signal processing

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5. To study the properties of random signal, Multirate digital signal processing and about QMF filters

Unit 1


Overview of DSP, Characterization in time and frequency, FFT Algorithms, Digital filter design and

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structures: Basic FIR/IIR filter design &structures, design techniques of linear phase FIR filters, IIR

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filters by impulse invariance, bilinear transformation, FIR/IIR Cascaded lattice structures, and Parallel


all pass realization of IIR.

Unit 2


Multi rate DSP, Decimators and Interpolators, Sampling rate conversion, multistage decimator &

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interpolator, poly phase filters, QMF, digital filter banks, Applications in sub band coding.

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Unit 3


Linear prediction & optimum linear filters, stationary random process, forward-backward linear

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prediction filters, solution of normal equations, AR Lattice and ARMA Lattice-Ladder Filters, Wiener


Filters for Filtering and Prediction.

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Unit 4

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Adaptive Filters, Applications, Gradient Adaptive Lattice, Minimum mean square criterion, LMS


algorithm, Recursive Least Square algorithm. Estimation of Spectra from Finite-Duration Observations

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of Signals. Nonparametric Methods for Power Spectrum Estimation, Parametric Methods for Power


Spectrum Estimation, Minimum- Variance Spectral Estimation, Eigen analysis Algorithms for Spectrum

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Estimation.

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Unit 5


Application of DSP & Multi rate DSP, Application to Radar, introduction to wavelets, application to

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image processing, design of phase shifters, DSP in speech processing & other applications

TEXT BOOKS:

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1. J.G.Proakis and D.G.Manolakis “Digital signal processing: Principles, Algorithm and
   
   
   Applications", 4th Edition, Prentice Hall,2007.

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2. N. J. Fliege, “Multirate Digital Signal Processing: Multirate Systems -Filter Banks – Wavelets”,
   
   
   1st Edition, John Wiley and Sons Ltd, 1999

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REFERENCES:

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1. Bruce W. Suter, “Multirateand Wavelet Signal Processing”,1stEdition, Academic Press,1997.


2. M. H. Hayes, “Statistical Digital Signal Processing and Modeling", John Wiley & Sons
   
   
   

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   Inc.,2002.


3. S.Haykin, "Adaptive Filter Theory”, 4th Edition, Prentice Hall,2001.


4. D.G. Manolakis, V.K. Ingle and S.M.Kogon, “Statistical and Adaptive Signal Processing",
   

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   McGraw Hill, 2000.

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Course Outcomes:

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At the end of this course, students will be able to

1. To understand theory of different filters and algorithms


2. To understand theory of multirate DSP, solve numerical problems and write algorithms

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3. To understand theory of prediction and solution of normal equations


4. To know applications of DSP at block level



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I Year I Semester

DIGITAL IMAGE and VIDEO PROCESSING

Course Objectives

1. To study the image fundamentals and mathematical transforms necessary for image Processing.



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2. To study the image enhancement techniques


3. To study image restoration procedures.


4. To study the image compression procedures.

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UNIT 1:

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Fundamentals of Image Processing and Image Transforms: Introduction, Image sampling,

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Quantization, Resolution, Image file formats, Elements of image processing system, Applications of


Digital image processing. Introduction, Need for transform, image transforms, Fourier transform, 2 D

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Discrete Fourier transform and its transforms, Importance of phase, Walsh transform, Hadamard


transform, Haar transform, slant transform Discrete cosine transform, KL transform, singular value

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decomposition, Radon transform, comparison of different image transforms.

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UNIT 2:


Image Enhancement: Spatial domain methods: Histogram processing, Fundamentals of Spatial filtering,

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Smoothing spatial filters, Sharpening spatial filters. Frequency domain methods: Basics of filtering in


frequency domain, image smoothing, image sharpening, Selective filtering.

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ImageRestoration: Introduction to Image restoration, Image degradation, Types of image blur,

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Classification of image restoration techniques, Image restoration model, Linear and Nonlinear image


restoration techniques, Blind deconvolution.

UNIT 3:


Image Segmentation: Introduction to image segmentation, Point, Line and Edge Detection, Region

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based segmentation., Classification of segmentation techniques, Region approach to image segmentation,

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clustering techniques, Image segmentation based on thresholding, Edge based segmentation, Edge


detection and linking, Hough transform, Active contour

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Image Compression: Introduction, Need for image compression, Redundancy in images, Classification


of redundancy in images, image compression scheme, Classification of image compression schemes,

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Fundamentals of information theory, Run length coding, Shannon Fano coding, Huffman coding,

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Arithmetic coding, Predictive coding, Transformed based compression, Image compression standard,


Wavelet-based image compression, JPEG Standards.

UNIT 4:


Basic Steps of Video Processing: Analog Video, Digital Video. Time-Varying Image Formation models:

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Three-Dimensional Motion Models, Geometric Image Formation, Photometric Image Formation,

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Sampling of Video signals, filtering operations.

UNIT 5:

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2-D Motion Estimation: Optical flow, General Methodologies, Pixel Based Motion Estimation, Block-


Matching Algorithm, Mesh based Motion Estimation, Global Motion Estimation, Region based Motion

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Estimation, Multi resolution motion estimation, Waveform based coding, Block based transform coding,


Predictive coding, Application of motion estimation in Video coding.

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TEXT BOOKS

1. Digital Image Processing – Gonzaleze and Woods, 3rdEd., Pearson.

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2. Video Processing and Communication – Yao Wang, JoemOstermann and Ya–quin Zhang.1stEd., PH
   
   
   Int.

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3. S.Jayaraman, S.Esakkirajan and T.VeeraKumar,“Digital Image processing, TataMcGraw Hill
   
   
   publishers, 2009

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REFRENCE BOOKS

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1. Digital Image Processing and Analysis-Human and Computer Vision Application with CVIPTools-
   
   
   ScotteUmbaugh, 2nd Ed, CRC Press, 2011.

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2. Digital Video Processing – M. Tekalp, Prentice Hall International.


3. Multi-dimentional Signal, Image and Video Processing and Coding – John Woods, 2ndEd,Elsevier.



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4. Digital Image Processing with MATLAB and Labview – Vipula Singh, Elsevier.


5. Video Demystified – A Hand Book for the Digital Engineer – Keith Jack, 5tEd., Elsevier

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Course Outcomes:

1. Defining the digital image, representation of digital image, importance of image resolution,
   

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   applications in image processing.


2. Know the advantages of representation of digital images in transform domain, application of
   

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   various image transforms.


3. Know how an image can be enhanced by using histogram techniques, filtering techniques etc

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4. Understand image degradation, image restoration techniques using spatial filters and frequency
   
   
   domain

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5. Know the detection of point, line and edges in images, edge linking through local processing,
   
   
   global processing.

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6. Understand the redundancy in images, various image compression techniques.


7. Know the video technology from analog color TV systems to digital video systems, howvideo
   
   

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   signal is sampled and filtering operations in video processing.


8. Know the general methodologies for 2D motion estimation, various coding used in video
   
   

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   processing

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I Year I Semester

DSP ARCHITECTURES


(Elective - I)

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Unit 1

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Programmable DSP Hardware: Processing Architectures (von Neumann, Harvard), DSP core


algorithms (FIR, IIR, Convolution, Correlation, FFT), IEEE standard for Fixed and Floating Point

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Computations, Special Architectures Modules used in Digital Signal Processors (like MAC unit, Barrel


shifters), On-Chip peripherals, DSP benchmarking.

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Unit 2

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Structural and Architectural Considerations: Parallelism in DSP processing, Texas Instruments TMS320


Digital Signal Processor Families, Fixed Point TI DSP Processors: TMS320C1X and TMS320C2X

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Family, TMS320C25 –Internal Architecture, Arithmetic and Logic Unit, Auxiliary Registers, Addressing


Modes (Immediate, Direct and Indirect, Bit-reverse Addressing), Basics of TMS320C54x and C55x

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Families in respect of Architecture improvements and new applications fields, TMS320C5416 DSP

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Architecture, Memory Map, Interrupt System, Peripheral Devices, Illustrative Examples for assembly


coding.

Unit 3


VLIW Architecture: Current DSP Architectures, GPUs as an alternative to DSP Processors,

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TMS320C6X Family, Addressing Modes, Replacement of MAC unit by ILP, Detailed study of ISA,

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Assembly Language Programming, Code Composer Studio, Mixed Cand Assembly Language


programming, On-chip peripherals, Simple application developments as an embedded environment.

Unit 4


Multi-core DSPs: Introduction to Multi-core computing and applicability for DSP hardware, Concept of

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threads, introduction to P-thread, mutex and similar concepts, heterogeneous and homogenous multi-

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core systems, Shared Memory parallel programming –OpenMP approach of parallel programming,


PRAGMA directives, OpenMP Constructs for work sharing like for loop, sections, TI TMS320C6678

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(Eight Core subsystem).

Unit 5

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FPGA based DSP Systems: Limitations of P-DSPs, Requirements of Signal processing for Cognitive

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Radio (SDR), FPGA based signal processing design-case study of a complete design of DSP processor.

TEXT BOOKS

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1. M. Sasikumar, D. Shikhare, Ravi Prakash, “Introduction to Parallel Processing", 1stEdition,
   
   
   PHI,2006.



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2. FayezGebali,“AlgorithmsandParallelComputing”,1Edition, JohnWiley&Sons,2011

REFERENCES

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1. Rohit Chandra,RameshMenon, Leo Dagum, David Kohr, DrorMaydan, Jeff McDonald, “Parallel
   
   
   Programming in OpenMP”, 1st Edition, MorganKaufman,2000.

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2. Ann Melnichuk,Long Talk, “Multicore Embedded systems”, 1st Edition, CRCPress,2010.


3. Wayne Wolf, “High Performance Embedded Computing: Architectures, Applications and
   
   

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   Methodologies”, 1stEdition, Morgan Kaufman,2006.


4. E.S.Gopi, “Algorithmic Collections for Digital Signal Processing Applications Using
   
   

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   MATLAB”, 1st Edition, SpringerNetherlands,2007

Course Outcomes:


At the end of this course, students will be able to

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1. Identify and formalize architectural level characterization of P-DSP hardware


2. Ability to design, programming (assembly and C), and testing code using Code Composer Studio
   
   

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   environment


3. Deployment of DSP hardware for Control, Audio and Video Signal processing applications



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4. Understanding of major areas and challenges in DSP based embedded systems.

I Year I Semester

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STATISTICAL SIGNAL PROCESSING


(Elective - I)

UNIT 1


Signal models and characterization: Types and properties of statistical models for signals and how they

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relate to signal processing, Common second-order methods of characterizing signals including

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autocorrelation, partial correlation, cross-correlation, power spectral density and cross power spectral


density.

UNIT 2


Signal Modelling : The least squares method, The pade approximation, Pronys method, pole zero

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modeling, shanks method, all-pole modeling, FIR least squares inverse filters, Iterative pre filtering,

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Finite data records, Autocorrelation method, Covariance method,

UNIT 3

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Levinson recursion: Levinson durbin recursion, the step up and step down recursions, The Inverse


Levinson durbinrecursion, Theschur recursion, The cholesky decomposition, The autocorrelation

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extension problem, The levinson recursion, The split levinson recursion.

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Statistical parameter estimation: Maximum likehood estimation, maximum a posterior estimation,


Cramer-Rao bound.

UNIT4


Eigen structure based frequency estimation: Pisarenko, MUSIC, ESPRIT their application sensor array

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direction finding.

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Spectrum estimation: Moving average (MA), Auto Regressive (AR), Auto Regressive Moving Average


(ARMA), Various non-parametirc approaches.

UNIT 5


Wiener filtering: The finite impulse case, causal and non-causal infinite impulse responses cases,Least

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mean squares adaptation, recursive least squares adaptation, Kalman filtering.

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TEXT BOOKS:

1. Steven M.Kay, fundamentals of statistical signal processing: estimation Theory,Pretice-Hall,1993.


2. Monsoon H. Hayes, Stastical digital signal processing and modeling, USA, Wiley, 1996.

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REFERENCE BOOKS:

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1. Dimitris G.Manolakis, Vinay K. Ingle, and Stephen M. Kogon, Statistical and adaptive
   
   
   signalprocessing, Artech House, Inc,2005, ISBN 1580536107

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Course Outcomes:

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1. Analyze signals and develop their statistical models for efficient processing


2. Formulate filtering problems from real life applications and design filtering solutions to estimate a
   
   

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   desired signal from a given mixture by minimizing a cost function


3. Design and analyze efficient algorithms for estimation of various parameters of signals with
   
   

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   different constraints


4. Develop efficient methods for spectrum and frequency estimation suiting the requirements derived
   
   

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   from practical problems

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I Year I Semester

COGNITIVE RADIO


(Elective -I)

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Unit 1

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Introduction to Cognitive Radios: Digital dividend, cognitive radio (CR) architecture, functions of


cognitive radio, dynamic spectrum access (DSA), components of cognitive radio, spectrum sensing,

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spectrum analysis and decision, potential applications of cognitive radio.

Unit 2

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Spectrum Sensing: Spectrum sensing, detection of spectrum holes (TVWS), collaborative sensing, geo-

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location database and spectrum sharing business models (spectrum of commons, real time secondary


spectrum market).

Unit 3


Optimization Techniques of Dynamic Spectrum Allocation: Linear programming, convex

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programming, non-linear programming, integer programming, dynamic programming, stochastic

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programming.

Unit 4

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Dynamic Spectrum Access and Management: Spectrum broker, cognitive radio architectures,


centralized dynamic spectrum access, distributed dynamic spectrum access, learning algorithms and

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protocols.

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Unit 5


Spectrum Trading: Introduction to spectrum trading, classification to spectrum trading, radio resource

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pricing, brief discussion on economics theories in DSA (utility, auction theory), classification of


auctions (single auctions, double auctions, concurrent, sequential).Research Challenges in Cognitive

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Radio: Network layer and transport layer issues, cross- layer design for cognitive radio networks

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TEXT BOOKS:

1. Ekram Hossain, DusitNiyato, Zhu Han, “Dynamic Spectrum Access and Management in
   
   
   

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   Cognitive Radio Networks”, Cambridge University Press, 2009.


2. Kwang-Cheng Chen, Ramjee Prasad, “Cognitive radio networks", John Wiley & Sons
   
   
   

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   Ltd., 2009.

REFERENCE BOOKS

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1. Bruce Fette, “Cognitive radio technology”, Elsevier, 2nd edition,2009.


2. Huseyin Arslan, “Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems”,
   
   

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   Springer,2007.


3. Francisco Rodrigo Porto Cavalcanti, Soren Andersson, “Optimizing Wireless
   
   

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   Communication Systems” Springer,2009.


4. Linda Doyle, “Essentials of Cognitive Radio”, Cambridge University Press,2009



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Course Outcomes

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At the end of this course, students will be able to

1. Understand the fundamental concepts of cognitive radio networks.



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2. Develop the cognitive radio, as well as techniques for spectrum holes detection that cognitive
   
   
   radio takes advantages in order to exploit it.



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3. Understand technologies to allow an efficient use of TVWS for radio communications based on
   
   
   two spectrum sharing business models/policies.



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4. Understand fundamental issues regarding dynamic spectrum access, the radio-resource
   
   
   management and trading, as well as a number of optimisation techniques for better spectrum
   
   

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   exploitation.

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I Year I Semester

ADAPTIVE SIGNAL PROCESSING


(Elective -II)

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UNIT-1

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Introduction to Adaptive Systems: Adaptive Systems: Definitions, Characteristics, Applications,


Example of an Adaptive System. The Adaptive Linear Combiner - Description, Weight Vectors,

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Desired Response, Performance function - Gradient & Mean Square Error.

UNIT-2

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Development of Adaptive Filter Theory & Searching the Performance surface:

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Introduction to Filtering - Smoothing and Prediction – Linear Optimum Filtering, Problem statement,


Principle of Orthogonality
Minimum Mean Square Error, Wiener- Hopf equations, Error

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Performance surface

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Searching the performance surface Methods & Ideas of Gradient Search methods, Gradient


Searching Algorithm & its Solution, Stability& Rate of convergence, Learning Curve.

UNIT -3


Steepest Descent Algorithms: Gradient Search by Newton's Method, Method of Steepest Descent,

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Comparison of Learning Curves.

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UNIT -4


LMS Algorithm & Applications: Overview - LMS Adaptation algorithms, Stability & Performance

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analysis of LMS Algorithms
LMS Gradient & algorithms - Convergence of LMS

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algorithm.


Applications: Noise cancellation
Cancellation of Echoes in long distance telephone circuits,

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Adaptive Beam forming.

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UNIT-5


RLS &Kalman Filtering: Introduction to RLS Algorithm, Statement of Kalman filtering problem,

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The Innovation Process, Estimation of State using the Innovation Process- Expression of Kalman


Gain, Filtering Examples using Kalman filtering.

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TEXT BOOKS

1. Aaptive Signal Processing - Bernard Widrow, SamuelD.Strearns, 2005, PE.

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2. Adaptive Filter Theory - Simon Haykin-, 4th Ed., 2002,PE Asia.

REFERENCE BOOKS

1. Optimum signal processing: An introduction - Sophocles.J.Orfamadis, 2nd Ed., 1988,
   
   

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   McGraw-Hill, New York


2. Adaptive signal processing-Theory and Applications - S.Thomas Alexander, 1986,
   
   

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   Springer-Verlag.


3. Signal analysis – Candy, McGraw Hill Int. Student Edition



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4. James V. Candy - Signal Processing: A Modern Approach, McGraw-Hill, International
   
   
   Edition, 1988



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I Year I Semester

DIGITAL DATA COMMUNICATIONS

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(Elective –II)

Course objectives

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The main objectives of this subject are:

1. Different modulation techniques to improve the bandwidth and their properties.

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2. Networking and different protocol systems.


3. Error estimation and correction, asynchronous and synchronous protocols.



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4. Multiplexing techniques, different networking connections and interfacing devices.


5. Multiple access techniques and analysis.

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UNIT -1

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Digital Modulation Schemes: BPSK, QPSK, 8PSK, 16PSK, 8QAM, 16QAM, DPSK – Methods, Band


Width Efficiency, Carrier Recovery, Clock Recovery.

UNIT -2


Basic Concepts of Data Communications, Interfaces and Modems:

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Data Communication Networks, Protocols and Standards, UART, USB, Line Configuration, Topology,

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Transmission Modes, Digital Data Transmission, DTE-DCE interface, Categories of


Networks – TCP/IP Protocol suite and Comparison with OSI model.

UNIT -3


Error Correction: Types of Errors, Vertical Redundancy Check (VRC), LRC, CRC, Checksum, Error

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Correction using Hamming codeData Link Control: Line Discipline, Flow Control, Error Control

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Data Link Protocols: Asynchronous Protocols, Synchronous Protocols, Character Oriented Protocols,


Bit-Oriented Protocol, Link Access Procedure

UNIT -4


Multiplexing: Frequency Division Multiplexing (FDM), Time Division Multiplexing (TDM),

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Multiplexing Application, DSL.

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Local Area Networks: Ethernet, Other Ether Networks, Token Bus, Token Ring, FDDI.


Metropolitan Area Networks: IEEE 802.6, SMDS

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Switching: Circuit Switching, Packet Switching, Message Switching.


Networking and Inter

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