Download JNTU Kakinada (Jawaharlal Nehru Technological University, Kakinada) M.Tech (Master of Technology) R19 ECE SSP, DIP Syllabus
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADA
KAKINADA ? 533 003, Andhra Pradesh, India
DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING
COURSE STRUCTURE & SYLLABUS M.Tech ECE Common for
Systems & Signal Processing (SSP)
Digital Image Processing (DIP)
Programme
(Applicable for batches admitted from 2019-2020)
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA
I Semester
Course
Teaching
Credits
S. No.
Type/
Course Name
Scheme
Code
L
T
P
1
Core 1
Advanced Digital Signal Processing
3
0
0
3
2
Core 2
Digital Image and Video Processing
3
0
0
3
Prog.
Elective I
Specific
a. DSP Architectures
3
Elective
b. Statistical Signal Processing
3
0
0
3
c. Cognitive Radio
Prog.
Elective II
Specific
a. Adaptive Signal Processing
4
Elective
b. Computer Vision
3
0
0 3
c. Coding Theory & Applications
5
Lab 1
Advanced Digital Signal Processing Lab
0
0
4
2
6
Lab2
Digital Image and Video Processing Lab
0
0
4
2
7
MC
Research Methodology and IPR
2
0
0
2
8
Aud 1
Audit Course 1
2
0
0
0
Total
16
0
8
18
II Semester
S. No.
Course
Name of the Subject
Credits
Type/
Teaching
Code
Scheme
L
T
P
1
Core 3
Pattern Recognition and Machine Learning
3
0
0
3
2
Core 4
Detection and Estimation Theory
3
0
0
3
Prog.
Elective III
Specific
3
a. IOT and Applications
Elective
3
0
0
b. Wireless Sensor Networks
3
c. Soft Computing Techniques
Prog.
Elective IV
Specific
a. Audio/ Video coding and compression
4
3
0
0
Elective
b. Biomedical Signal Processing
3
c. Optical Networks
5
Lab 1
Pattern Recognition and Machine Learning Lab
0
0
4
2
6
Lab2
Detection and Estimation Theory Lab
0
0
4
2
7
MP
Mini Project (Seminar)
0
0
4
2
8
Aud 2
Audit Course 2
2
0
0
0
Total
14
0 12
18
III Semester
S. No.
Course
Subject
Teaching
Credits
Type/Code
Scheme
L
T
P
Prog.
Elective-V
3
0
0
3
1
Specific
a. Optimization Techniques
Elective
b. Modeling and Simulation Techniques
c. Artificial Intelligence
2
Open
a. Business Analytics
3
0
0
3
Elective
b. Industrial Safety
c. Operations Research
d. Cost Management of Engineering Projects
e. Composite Materials
f. Waste to Energy
3
Dissertation
Dissertation Phase ? I
0
0 20
10
Total
6
0 20
16
IV Semester
S. No.
Course
Subject
Teaching
Credits
Code
Scheme
L
T
P
1
Dissertation
Dissertation Phase ? II
--
--
32 16
Total
--
--
32 16
Audit course 1 & 2
1. English for Research Paper Writing
2. Disaster Management
3. Sanskrit for Technical Knowledge
4. Value Education
5. Constitution of India
6. Pedagogy Studies
7. Stress Management by Yoga
8. Personality Development through Life Enlightenment Skills.
L
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I Year I Semester
3
0
0
3
ADVANCED DIGITAL SIGNAL PROCESSING
Course Objectives
1. At the completion of this course, the student should have in depth knowledge of processing digital signals.
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
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
structures: Basic FIR/IIR filter design &structures, design techniques of linear phase FIR filters, IIR
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 &
interpolator, poly phase filters, QMF, digital filter banks, Applications in sub band coding.
Unit 3
Linear prediction & optimum linear filters, stationary random process, forward-backward linear
prediction filters, solution of normal equations, AR Lattice and ARMA Lattice-Ladder Filters, Wiener
Filters for Filtering and Prediction.
Unit 4
Adaptive Filters, Applications, Gradient Adaptive Lattice, Minimum mean square criterion, LMS
algorithm, Recursive Least Square algorithm. Estimation of Spectra from Finite-Duration Observations
of Signals . Nonparametric Methods for Power Spectrum Estimation, Parametric Methods for Power
Spectrum Estimation, Minimum- Variance Spectral Estimation, Eigen analysis Algorithms for Spectrum
Estimation.
Unit 5
Application of DSP & Multi rate DSP, Application to Radar, introduction to wavelets, application to
image processing, design of phase shifters, DSP in speech processing & other applications
TEXT BOOKS:
1. J.G.Proakis and D.G.Manolakis "Digital signal processing: Principles, Algorithm and
Applications", 4th Edition, Prentice Hall,2007.
2. N. J. Fliege, "Multirate Digital Signal Processing: Multirate Systems -Filter Banks ? Wavelets",
1st Edition, John Wiley and Sons Ltd,1999
REFERENCES:
1. Bruce W. Suter, "MultirateandWavelet Signal Processing",1stEdition, Academic Press,1997.
2. M. H. Hayes, "Statistical Digital Signal Processing and Modeling", John Wiley & Sons
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", McGraw Hill,2000
Course Outcomes:
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
3. To understand theory of prediction and solution of normalequations
4. To know applications of DSP at block level
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I Year I Semester
3
0
0
3
DIGITAL IMAGE and VIDEO PROCESSING
Course Objectives
1. To study the image fundamentals and mathematical transforms necessary for image Processing.
2. To study the image enhancement techniques
3. To study image restoration procedures.
4. To study the image compression procedures.
UNIT 1:
Fundamentals of Image Processing and Image Transforms:
Introduction, Image sampling, 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 Discrete Fourier transform and its transforms, Importance of phase,
Walsh transform, Hadamard transform, Haar transform, slant transform Discrete cosine transform, KL
transform, singular value decomposition, Radon transform, comparison of different image transforms.
UNIT 2:
Image Enhancement:
Spatial domain methods: Histogram processing, Fundamentals of Spatial filtering, Smoothingspatial
filters, Sharpening spatial filters. Frequency domain methods: Basics of filtering in frequency domain,
image smoothing, image sharpening, Selective filtering.
Image Restoration:
Introduction to Image restoration, Image degradation, Types of image blur, 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 based
segmentation., Classification of segmentation techniques, Region approach to image segmentation,
clustering techniques, Image segmentation based on thresholding, Edge based segmentation, Edge
detection and linking, Hough transform, Active contour
Image Compression:Introduction, Need for image compression, Redundancy in images, Classification of
redundancy
in images, image compression scheme, Classification of image compression schemes, Fundamentals of
information theory, Run length coding, Shannon ? Fano coding, Huffman coding, 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:
Three-Dimensional Motion Models, Geometric Image Formation, Photometric Image Formation,
Sampling of Videosignals, Filtering operations.
UNIT 5:
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
Estimation, Multi resolution motion estimation, Waveform based coding, Block based transform coding,
Predictive coding, Application of motion estimation in Video coding.
TEXT BOOKS
1. Digital Image Processing ? Gonzaleze and Woods, 3rdEd., Pearson.
2. Video Processing and Communication ? Yao Wang, JoemOstermann and Ya?quin Zhang.1st
Ed., PH Int.
3. S.Jayaraman, S.Esakkirajan and T.VeeraKumar, "Digital Image processing, TataMcGraw Hill
publishers, 2009
REFRENCE BOOKS
1.Digital Image Processing and Analysis-Human and Computer Vision Application with CVIP
Tools ? ScotteUmbaugh, 2nd Ed, CRC Press, 2011.
2.Digital Video Processing ? M. Tekalp, Prentice Hall International.
3.Digital Image Processing ? S.Jayaraman, S.Esakkirajan, T.Veera Kumar ? TMH, 2009.
4.Multidimentional Signal, Image and Video Processing and Coding ? John Woods, 2ndEd,
Elsevier.
5.Digital Image Processing with MATLAB and Labview ? Vipula Singh, Elsevier.
6.Video Demystified ? A Hand Book for the Digital Engineer ? Keith Jack, 5tEd., Elsevier
Course Outcomes:
1. Defining the digital image, representation of digital image, importance of image resolution,
applications in image processing.
2. Know the advantages of representation of digital images in transform domain, application of
various image transforms.
3. Know how an image can be enhanced by using histogram techniques, filtering techniques etc
4. Understand image degradation, image restoration techniques using spatial filters and frequency
domain
5. Know the detection of point, line and edges in images, edge linking through local
processing, global processing.
6. Understand the redundancy in images, various image compression techniques.
7. Know the video technology from analog color TV systems to digital video systems, how
video signal is sampled and filtering operations in video processing.
8. Know the general methodologies for 2D motion estimation, various coding used in video
processing.
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I Year I Semester
3
0
0
3
DSP ARCHITECTURES
(Elective - I)
Unit 1
Programmable DSP Hardware: Processing Architectures (von Neumann, Harvard), DSP core
algorithms (FIR, IIR, Convolution, Correlation, FFT), IEEE standard for Fixed and Floating Point
Computations, Special Architectures Modules used in Digital Signal Processors (like MAC unit, Barrel
shifters), On-Chip peripherals, DSP benchmarking.
Unit 2
Structural and Architectural Considerations: Parallelism in DSP processing, Texas Instruments TMS320
Digital Signal Processor Families, Fixed Point TI DSP Processors: TMS320C1X and TMS320C2X
Family,TMS320C25 ?Internal Architecture, Arithmetic and Logic Unit, Auxiliary Registers, Addressing
Modes (Immediate, Direct and Indirect, Bit-reverse Addressing), Basics of TMS320C54x and C55x
Families in respect of Architecture improvements and new applications fields, TMS320C5416 DSP
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,
TMS320C6X Family, Addressing Modes, Replacement of MAC unit by ILP, Detailed study of ISA,
Assembly Language Programming, Code Composer Studio, Mixed Cand Assembly Language
programming, On-chip peripherals, Simple applications developments as an embedded environment.
Unit 4
Multi-core DSPs: Introduction to Multi-core computing and applicability for DSP hardware, Concept of
threads, introduction to P-thread, mutex and similar concepts, heterogeneous and homogenous multi-
core systems, Shared Memory parallel programming ?OpenMP approach of parallel programming,
PRAGMA directives, OpenMP Constructs for work sharing like for loop, sections, TI TMS320C6678
(Eight Core subsystem).
Unit 5
FPGA based DSP Systems: Limitations of P-DSPs, Requirements of Signal processing for Cognitive
Radio (SDR), FPGA based signal processing design-case study of a complete design of DSP processor.
TEXT BOOKS
1. M. Sasikumar, D. Shikhare, Ravi Prakash, "Introduction to Parallel Processing", 1stEdition,
PHI,2006.
2. FayezGebali,"AlgorithmsandParallelComputing",1stEdition, JohnWiley&Sons,2011
REFERENCES
1. Rohit Chandra,RameshMenon, Leo Dagum, David Kohr, DrorMaydan, Jeff McDonald,"Parallel
Programming in OpenMP", 1st Edition, MorganKaufman,2000.
2. Ann Melnichuk,Long Talk, "Multicore Embedded systems", 1st Edition, CRCPress,2010.
3. Wayne Wolf, "High Performance Embedded Computing: Architectures, Applications and
Methodologies", 1stEdition, Morgan Kaufman,2006.
4. E.S.Gopi, "Algorithmic Collections for Digital Signal Processing Applications Using
MATLAB", 1st Edition, SpringerNetherlands,2007
Course Outcomes:
At the end of this course, students will be able to
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
environment
3. Deployment of DSP hardware for Control, Audio and Video Signal processing applications
4. Understanding of major areas and challenges in DSP based embedded systems.
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I Year I Semester
3
0
0
3
STATISTICAL SIGNAL PROCESSING
(Elective - I)
UNIT 1
Signal models and characterization: Types and properties of statistical models for signals and howthey
relate to signal processing, Common second-order methods of characterizing signals including
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
modeling, shanks method, all-pole modeling, FIR least squares inverse filters, Iterative pre filtering,
Finite data records, Autocorrelation method, Covariance method,
UNIT 3
Levinson recursion: Levinson durbin recursion, the step up and step down recursions, The Inverse
Levinson durbin recursion, Theschur recursion, The cholesky decomposition, The autocorrelation
extension problem, The levinson recursion, The split levinson recursion.
Statistical parameter estimation: Maximum likehood estimation, maximum a posterior estimation,
Cramer-Rao bound.
UNIT 4
Eigen structure based frequency estimation: Pisarenko, MUSIC, ESPRIT their application sensorarray
direction finding.
Spectrum estimation: Moving average (MA), Auto Regressive (AR), Auto Regressive Moving Average
(ARMA), Various non-parametirc approaches.
UNIT5
Wiener filtering: The finite impulse case, causal and non-causal infinite impulse responses cases,Least
mean squares adaptation, recursive least squares adaptation, Kalman filtering.
TEXT BOOKS:
1. Steven M.Kay, fundamentals of statistical signal processing: estimation Theory,Pretice-Hall,1993.
2. Monsoon H. Hayes, Statistical digital signal processing and modeling, USA, Wiley,1996.
REFERENCE BOOKS:
1. DimitrisG.Manolakis, Vinay K. Ingle, and Stephen M. Kogon, Statistical and adaptive signal
processing, Artech House, Inc,2005, ISBN 1580536107
Course Outcomes:
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
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
different constraints
4. Develop efficient methods for spectrum and frequency estimation suiting the requirements derived
from practical problems
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I Year I Semester
3
0
0
3
COGNITIVE RADIO
(Elective ?I)
Unit 1
Introduction to Cognitive Radios: Digital dividend, cognitive radio (CR) architecture, functions of
cognitive radio, dynamic spectrum access (DSA), components of cognitive radio, spectrum sensing,
spectrum analysis and decision, potential applications of cognitive radio.
Unit 2
Spectrum Sensing: Spectrum sensing, detection of spectrum holes (TVWS), collaborative sensing, geo-
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
programming, non-linear programming, integer programming, dynamic programming, stochastic
programming.
Unit 4
Dynamic Spectrum Access and Management: Spectrum broker, cognitive radio architectures,
centralized dynamic spectrum access, distributed dynamic spectrum access, learning algorithms and
protocols.
Unit 5
Spectrum Trading: Introduction to spectrum trading, classification to spectrum trading, radio resource
pricing, brief discussion on economics theories in DSA (utility, auction theory), classification of
auctions (single auctions, double auctions, concurrent, sequential).Research Challenges in Cognitive
Radio: Network layer and transport layer issues, cross- layer design for cognitive radio networks
TEXT BOOKS:
1. EkramHossain, DusitNiyato, Zhu Han, "Dynamic Spectrum Access and Management in
Cognitive Radio Networks", Cambridge University Press,2009.
2. Kwang-Cheng Chen, Ramjee Prasad, "Cognitive radio networks", John Wiley & Sons
Ltd.,2009.
REFERENCE BOOKS
1. Bruce Fette, "Cognitive radio technology", Elsevier, 2nd edition,2009.
2. HuseyinArslan, "Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems",
Springer,2007.
3. Francisco Rodrigo Porto Cavalcanti, Soren Andersson, "Optimizing Wireless
Communication Systems" Springer,2009.
4. Linda Doyle, "Essentials of Cognitive Radio", Cambridge University Press,2009
Course Outcomes: At the end of this course, students will be able to
1. Understand the fundamental concepts of cognitive radio networks.
2. Develop the cognitive radio, as well as techniques for spectrum holes detection that cognitive
radio takes advantages in order to exploit it.
3. Understand technologies to allow an efficient use of TVWS for radio communications based on
two spectrum sharing business models/policies.
4. Understand fundamental issues regarding dynamic spectrum access, the radio-resource
management and trading, as well as a number of optimization techniques for better
Spectrum exploitation
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I Year I Semester
3
0
0
3
ADAPTIVE SIGNAL PROCESSING
(Elective ?II)
UNIT ?1
Introduction to Adaptive Systems: Adaptive Systems:Definitions, Characteristics, Applications,
Example of an Adaptive System. The Adaptive Linear Combiner - Description, Weight Vectors,
Desired Response, Performance function - Gradient & Mean Square Error.
UNIT ?2
Development of Adaptive Filter Theory & Searching the Performance surface:
Introduction to Filtering - Smoothing and Prediction ? Linear Optimum Filtering, Problem statement,
Principle of Orthogonality - Minimum Mean Square Error, Wiener- Hopf equations, Error
Performance surface
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,
Comparison of Learning Curves.
UNIT ?4
LMS Algorithm & Applications: Overview - LMS Adaptation algorithms, Stability & Performance
analysis of LMS Algorithms - LMS Gradient & Stochastic algorithms - Convergence of LMS
algorithm.
Applications: Noise cancellation ? Cancellation of Echoes in long distance telephone circuits,
Adaptive Beam forming.
UNIT ?5
RLS & Kalman Filtering: Introduction to RLS Algorithm, Statement of Kalman filtering problem,
The Innovation Process, Estimation of State using the Innovation Process- Expression of Kalman
Gain, Filtering Examples using Kalman filtering.
TEXT BOOKS
1. Aaptive Signal Processing - Bernard Widrow, Samuel D.Strearns, 2005, PE.
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,
McGraw-Hill, New York
2. Adaptive signal processing-Theory and Applications - S.Thomas Alexander, 1986,
Springer ?Verlag.
3. Signal analysis ? Candy, McGraw Hill Int. Student Edition
4. James V. Candy - Signal Processing: A Modern Approach, McGraw-Hill, International
Edition, 1988.
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I Year I Semester
3
0
0
3
COMPUTER VISION
Unit 1
Image Formation Model:Monocular imaging system, Orthographic & Perspective Projection, Camera
model and Camera calibration, Binocular imaging systems, Perspective, Binocular Stereopsis: Camera
and Epipolar Geometry; Homography, Rectification, DLT, RANSAC, 3-D reconstruction framework;
Auto-calibration. Apparel, Binocular Stereopsis: Camera and Epipolar Geometry; Homography,
Rectification, DLT, RANSAC, 3-D reconstruction framework; Auto-calibration. Apparel, Stereo vision
Unit 2
Feature Extraction:Image representations (continuous and discrete), Edge detection, Edge linking,
corner detection, texture, binary shape analysis, boundary pattern analysis, circle and ellipse detection,
Light at Surfaces; Phong Model; Reflectance Map; Albedo estimation; Photometric Stereo; Use of
Surface Smoothness Constraint; Shape from Texture, color, motion andedges.
Unit 3
Shape Representation and Segmentation:Deformable curves and surfaces, Snakes and active
contours, Level set representations, Fourier and wavelet descriptors, Medial representations, Multi-
resolution analysis, Region Growing, Edge Based approaches to segmentation, Graph-Cut, Mean-Shift,
MRFs, Texture Segmentation
Unit 4
Motion Detection and Estimation : Regularization theory , Optical computation,Stereo VisionMotion
estimation, Background Subtraction and Modelling, Optical Flow, KLT, Spatio- Temporal Analysis,
Dynamic Stereo; Motion parameter estimation, Structure from motion, Motion Tracking inVideo
Unit 5
Object recognition: Hough transforms and other simple object recognition methods, Shape
correspondence and shape matching, Principal component analysis, Shape priors for recognition
Applications of Computer Vision:Automated Visual Inspection, Inspection of Cereal Grains,
Surveillance, In-Vehicle Vision Systems, CBIR, CBVR, Activity Recognition, computational
photography, Biometrics, stitching and document processing
TEXT BOOKS
1. D. Forsyth and J. Ponce,"Computer Vision - A modern approach", 2nd Edition, Pearson Prentice
Hall,2012
2. Szeliski, Richard, "Computer Vision: Algorithms and Applications", 1st Edition, Springer-
Verlag London Limited,2011.
REFERENCE BOOKS
1. Richard Hartley and Andrew Zisserman, "Multiple View Geometry in Computer Vision", 2nd
Edition, Cambridge University Press,2004.
2. K. Fukunaga,"Introduction to Statistical Pattern Recognition",2ndEdition, Morgan
Kaufmann,1990.
3. Rafael C. Gonzalez and Richard E. Woods," Digital Image Processing", 3rd Edition, Prentice
Hall,2008.
4. B. K. P. Horn, "Robot Vision", 1st Edition, McGraw-Hill,1986.
5. E. R. Davies"Computer and Machine Vision: Theory, Algorithms, Practicalities", 4th Edition,
ElsevierInc,2012.
Course Outcomes:
At the end of this course, students will be able to
1. Study the image formation models and feature extraction for computer vision
2. Identify the segmentation and motion detection and estimation techniques
3. Develop small applications and detect the objects in various applications
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I Year I Semester
3
0
0
3
CODING THEORY AND APPLICATIONS
(Elective ?II)
UNIT ?1
Coding for Reliable Digital Transmission and Storage: Mathematical model of Information, A
Logarithmic Measure of Information, Average and Mutual Information and Entropy, Types of Errors,
Error Control Strategies.
Linear Block Codes:Introduction to Linear Block Codes, Syndrome and Error Detection, Minimum
Distance of a Block code, Error-Detecting and Error-correcting Capabilities of a Block code, Standard
array and Syndrome Decoding, Probability of an undetected error for Linear Codes over a BSC,
Hamming Codes. Applications of Block codes for Error control in data storage system
UNIT ?2
Cyclic Codes:Description, Generator and Parity-check Matrices, Encoding, Syndrome Computation and
Error Detection, Decoding ,Cyclic Hamming Codes, Shortened cyclic codes, Error-trapping decoding for
cyclic codes, Majority logic decoding for cyclic codes.
UNIT ?3
Convolutional Codes:Encoding of Convolutional Codes, Structural and Distance Properties, maximum
likelihood decoding, Sequential decoding, Majority- logic decoding of Convolution codes. Application
of Viterbi Decoding and Sequential Decoding, Applications of Convolutional codes in ARQ system.
UNIT ?4
Burst ?Error-Correcting Codes:Decoding of Single-Burst error Correcting Cyclic codes, Single-Burst-
Error-Correcting Cyclic codes, Burst-Error-Correcting Convolutional Codes, Bounds on Burst Error-
Correcting Capability, Interleaved Cyclic and Convolutional Codes, Phased-Burst ?Error-Correcting
Cyclic and Convolutional codes.
UNIT -5
BCH ? Codes:BCH code- Definition, Minimum distance and BCH Bounds, Decoding Procedure for
BCH Codes- Syndrome Computation and Iterative Algorithms, Error Location Polynomials and
Numbers for single and double error correction
TEXT BOOKS
1. Error Control Coding- Fundamentals and Applications ?Shu Lin, Daniel J.Costello,Jr, Prentice
Hall, Inc.
2. Error Correcting Coding Theory-Man Young Rhee- 1989, McGraw-Hill Publishing.
REFERENCE BOOKS
1. Digital Communications-Fundamental and Application - Bernard Sklar, PE.
2. Digital Communications- John G. Proakis, 5th Ed., 2008, TMH.
3. Introduction to Error Control Codes-Salvatore Gravano-oxford
4. Error Correction Coding ? Mathematical Methods and Algorithms ? Todd K.Moon, 2006,
Wiley India.
5. Information Theory, Coding and Cryptography ? Ranjan Bose, 2nd Ed, 2009, TMH.
Course Outcomes:
On completion of this course student will be able to
1. Learning the measurement of information and errors.
2. Obtain knowledge in designing Linear Block Codes and Cyclic codes.
3. Construct tree and trellies diagrams for convolution codes
4. Design the Turbo codes and Space time codes and also their applications
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I Year I Semester
0
0
4
2
ADVANCED DIGITAL SIGNAL PROCESSING LAB
List of Assignments:
1. Basic Signal Representation
2. Correlation Auto and Cross
3. Stability Using Hurwitz Routh Criteria
4. Sampling FFT Of Input Sequence
5. Butterworth Low pass And High pass Filter Design
6. Chebychev Type I, II Filter
7. State Space Matrix from Differential Equation
8. Normal Equation Using Levinson Durbin
9. Decimation And Interpolation Using Rationale Factors
10. Maximally Decimated Analysis DFT Filter
11. Cascade Digital IIR Filter Realization
12. Convolution And M Fold Decimation &PSD Estimator
13. Estimation of PSD
14. Inverse Z Transform
15. Group Delay Calculation
16. Separation of T/F
17. Parallel Realization of IIR filter
Course Outcomes:
At the end of this course, students will be able to
1. Design different digital filters in software
2. Apply various transforms in time and frequency
3. Perform decimation and interpolation
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I Year I Semester
0
0
4
2
DIGITAL IMAGE AND VIDEO PROCESSING LAB
List of Assignments:
1. Perform basic operations on images like addition, subtraction etc.
2. Plot the histogram of an image and perform histogramequalization
3. Implement segmentationalgorithms
4. Perform videoenhancement
5. Perform videosegmentation
6. Perform image compression using lossy technique
7. Perform image compression using losslesstechnique
8. Perform imagerestoration
9. Convert a colour model into another
10. Calculate boundary features of animage
11. Calculate regional features of animage
12. Detect an object in an image/video using template matching/Bayes classifier
Course Outcomes:
At the end of this course, students will be able to
1. Perform image and video enhancement
2. Perform image and video segmentation
3. Detect an object in an image/video
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I Year I Semester
2
0
0
2
RESEARCH METHODOLOGY AND IPR
Unit 1
Meaningof research problem, Sources of research problem, Criteria Characteristics of a good research
problem, Errors in selecting a research problem, Scope and objectives of researchproblem.Approaches of
investigation of solutions for research problem, data collection, analysis, interpretation, Necessary
instrumentations
Unit 2
Effective literature studies approaches, analysis Plagiarism , Research ethics,
Unit 3
Effective technical writing, how to write report, PaperDeveloping a Research Proposal, Format of
research proposal, a presentation and assessment by a review committee
Unit 4
Nature of Intellectual Property: Patents, Designs, Trademarks and Copyright. Process of Patenting and
Development: technological research, innovation, patenting, development.International Scenario:
International cooperation on Intellectual Property. Procedure for grants of patents, Patenting under PCT.
Unit 5
Patent Rights: Scope of Patent Rights, Licensing and transfer of technology, Patent information and
databases, Geographical Indications.
Unit 6
New Developments in IPR: Administration of Patent System. New developments in IPR; IPR of
Biological Systems,Computer Software etc. Traditional knowledge Case Studies, IPR and IITs.
TEXT BOOKS
1. Stuart Melville and Wayne Goddard, "Research methodology: an introduction for science&
engineeringstudents'".
2. Wayne Goddard and Stuart Melville, "Research Methodology: An Introduction"
REFERENCE BOOKS
1. RanjitKumar, 2nd Edition , "Research Methodology: A Step by Step Guide for beginners"
2. Halbert, "Resisting Intellectual Property", Taylor & Francis Ltd,2007.
3. Mayall , "Industrial Design", McGraw Hill,1992.
4. Niebel , "Product Design", McGraw Hill,1974.
5. Asimov , "Introduction to Design", Prentice Hall,1962.
6. Robert P. Merges, Peter S. Menell, Mark A. Lemley, " Intellectual Property in New Technological
Age",2016.
7. T. Ramappa, "Intellectual Property Rights Under WTO", S. Chand,2008
Course Outcomes:
At the end of this course, students will be able to
1. Understand research problem formulation.
2. Analyze research related information
3. Follow research ethics
4. Understand that today's world is controlled by Computer, Information Technology, but
tomorrow world will be ruled by ideas, concept, and creativity.
5. Understanding that when IPR would take such important place in growth of individuals &
nation, it is needless to emphasis the need of information about Intellectual Property Right to be
promoted among students in general & engineering in particular.
6. Understand that IPR protection provides an incentive to inventors for further research work and
investment in R & D, which leads to creation of new and better products, and in turn brings
about, economic growth and social benefits.
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I Year II Semester
3
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3
PATTERN RECOGNITION AND MACHINE LEARNING
Course Objectives
1. To equip students with basic mathematical and statistical techniques commonly used in
pattern recognition.
2. To introduce students to a variety of pattern recognition algorithms.
3. Enable students to apply machine learning concepts in real life problems.
Unit 1
Introduction to Pattern Recognition: Problems, applications, design cycle, learning and adaptation,
examples, Probability Distributions, Parametric Learning - Maximum likelihood and Bayesian Decision
Theory- Bayes rule, discriminant functions, loss functions and Bayesian error analysis
Unit 2
Linear models: Linear Models for Regression, linear regression, logistic regression Linear Models
forClassification
Unit 3
Neural Network: perceptron, multi-layer perceptron, backpropagation algorithm, error surfaces, practical
techniques for improving backpropagation, additional networks and training methods, Adaboost, Deep
Learning
Unit 4
Linear discriminant functions - decision surfaces, two-category, multi-category, minimum- squared
error procedures, the Ho-Kashyap procedures, linear programming algorithms, Support vector machine
Unit 5
Algorithm independent machine learning ? lack of inherent superiority of any classifier, bias and
variance, re-sampling for classifier design, combining classifiers
Unsupervised learning and clustering ? k-means clustering, fuzzy k-means clustering, hierarchical
clustering
TEXT BOOKS:
1. Richard O. Duda, Peter E. Hart, David G. Stork, "Pattern Classification", 2nd Edition John Wiley
& Sons,2001.
2. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, "The Elements of Statistical Learning",
2nd Edition, Springer,2009.
REFERENCE BOOKS:
1. C. Bishop, "Pattern Recognition and Machine Learning", Springer,2006
Course Outcomes:
At the end of this course, students will be able to
1. Study the parametric and linear models for classification
2. Design neural network and SVM for classification
3. Develop machine independent and unsupervised learning techniques
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I Year II Semester
3
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3
DETECTION AND ESTIMATION THEORY
Course Objectives
1. To enable the students to acquire the fundamental concepts of Signal Detection and
Estimation
2. To get familiarize with different Hypotheses in detection and estimation problems
3. To introduce the methods of Detection and estimation of signals in white and non-white
Gaussian noise.
4. To familiarize with the detection of random signals.
5. To enable the students to understand the time varying waveform detection and its estimation
Unit 1
Review of Vector Spaces: Vectors and matrices: notation and properties, orthogonality and linear
independence, bases, distance properties, matrix operations, Eigen values and eigenvectors.
Unit 2
Properties of Symmetric Matrices: Diagonalizationof symmetric matrices, symmetric positive definite and
semi definite matrices, principal component analysis (PCA), singular value decomposition.
Unit 3
Stochastic Processes: Time average and moments, ergodicity, power spectral density, covariance
matrices, response of LTI system to random process, cyclostationary process, and spectral factorization.
Unit 4
Detection Theory: Detection in white Gaussian noise, correlator and matched filter interpretation, Bayes`
criterion of signal detection, MAP, LMS, entropy detectors, detection in colored Gaussian noise,
Karhunen-Loeve expansions and whitening filters.
Unit 5
Estimation Theory: Minimum variance estimators, Cramer-Rao lower bound, examples of linear models,
system identification, Markov classification, clustering algorithms. Topics in Kalman and Weiner
Filtering: Discrete time Wiener-Hopf equation, error variance computation, causal discrete time Wiener
filter, discrete Kalman filter, extended Kalman filter, examples. Specialized Topics in Estimation:
Spectral estimation methods like MUSIC, ESPIRIT, DOA Estimation.
TEXT BOOKS:
1. Steven M. Kay, "Fundamentals of Statistical Signal Processing, Volume I: Estimation
Theory",Prentice Hall,1993
2. Steven M. Kay, "Fundamentals of Statistical Signal Processing, Volume II: Detection Theory",
1st Edition, Prentice Hall,1998
REFERENCE BOOKS:
1. Thomas Kailath, BabakHassibi, Ali H. Sayed, "Linear Estimation", Prentice Hall,2000.
2. H. Vincent Poor, "An Introduction to Signal Detection and Estimation", 2nd Edition,
Springer,1998.
Course Outcomes:
At the end of this course, students will be able to
1. Understand the mathematical background of signal detection andestimation
2. Use classical and Bayesian approaches to formulate and solve problems for signal detection and
parameter estimation from noisy signals.
3. Derive and apply filtering methods for parameter estimation
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I Year II Semester
3
0
0
3
IOT AND APPLICATIONS
(Elective-III)
Unit1
IoT& Web Technology The Internet of Things Today, Time for Convergence, Towards the IoT
Universe, Internet of Things Vision, IoT Strategic Research and Innovation Directions, IoT
Applications, Future Internet Technologies, Infrastructure, Networks and Communication, Processes,
Data Management, Security, Privacy & Trust, Device Level Energy Issues, IoT Related
Standardization, Recommendations on Research Topics.
Unit 2
M2M to IoT ? A Basic Perspective? Introduction, Some Definitions, M2M Value Chains, IoT chain
and global information monopolies. M2M to IoT-An Architectural Overview? Building an architecture,
Main design principles and needed capabilities, An IoT architecture outline, standards considerations.
Unit 3
IoT Architecture -State of the Art ? Introduction, State of the art, Architecture Reference Model-
Introduction, Reference Model and architecture, IoT reference Model, IoT Reference Architecture-
Introduction, Functional View, Information View, Deployment and Operational View, Other Relevant
architectural views.
Unit 4
IoT Applications for Value Creations Introduction, IoT applications for industry: Future Factory
Concepts, Brownfield IoT, Smart Objects, Smart Applications, Four Aspects in your Business to Master
IoT, Value Creation from Big Data and Serialization, IoT for Retailing Industry, IoT For Oil and Gas
Industry, Opinions on IoT Application and Value for Industry, Home Management, e Health.
Unit 5
Internet of Things Privacy, Security and Governance Introduction, Overview of Governance, Privacy
and Security Issues,Contribution from FP7 Projects, Security, Privacy and Trust in IoT-Data-Platforms
for Smart Cities, First Steps Towards a Secure Platform, Smartie Approach. Data Aggregation for the
IoT inSmart Cities, Security
TEXT BOOKS
1. Vijay Madisetti and ArshdeepBahga, "Internet of Things (A Hands-on-Approach)", 1st Edition,
VPT,2014.
2. Francis daCosta, "Rethinking the Internet of Things: A Scalable Approach to Connecting
Everything", 1stEdition, Apress Publications,2013.
REFERENCE BOOKS:
1. CunoPfister, "Getting Started with the Internet of Things", O Reilly Media,2011.
Course Outcomes:
At the end of this course, students will be able to
1. Understand the concept of IoT andM2M
2. Study IoT architecture and applications in variousfields
3. Study the security and privacy issues in IoT.
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I Year II Semester
3
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0
3
WIRELESS SENSOR NETWORKS
(Elective-III)
Unit 1
Introduction and overview of sensor network architecture and its applications, sensor network
comparison with Ad Hoc Networks, Sensor node architecture with hardware and software details.
Unit 2
Hardware: Examples like mica2, micaZ, telosB, cricket, Imote2, tmote, btnode, and Sun SPOT,
Software (Operating Systems): tinyOS, MANTIS, Contiki, and RetOS.
Unit 3
Programming tools: C, nesC. Performance comparison of wireless sensor networks simulation and
experimental platforms like open source (ns-2) and commercial (QualNet, Opnet)
Unit 4
Overview of sensor network protocols (details of atleast 2 important protocol per layer): Physical,
MAC and routing/ Network layer protocols, node discovery protocols, multi-hop and cluster based
protocols, Fundamentals of 802.15.4, Bluetooth, BLE (Bluetooth low energy), UWB.
Unit 5
Data dissemination and processing; differences compared with other database management systems,
data storage; query processing.Specialized features: Energy preservation and efficiency; security
challenges; fault- tolerance, Issues related to Localization, connectivity and topology, Sensor
deployment mechanisms; coverage issues; sensor Web; sensor Grid, Open issues for future research,
and Enabling technologies in wireless sensor network.
TEXT BOOKS:
1. H. Karl and A. Willig, "Protocols and Architectures for Wireless Sensor Networks", John Wiley
& Sons, India,2012.
2. C. S. Raghavendra, K. M. Sivalingam, and T. Znati, Editors, "Wireless Sensor Networks",
Springer Verlag, 1stIndian reprint,2010.
REFERENCES BOOKS:
1. F. Zhao and L. Guibas, "Wireless Sensor Networks: An Information Processing Approach",
Morgan Kaufmann, 1st Indian reprint,2013.
2. YingshuLi, MyT. Thai, Weili Wu, "Wireless sensor Network and Applications", Springer series
on signals and communication technology,2008.
Course Outcomes:
At the end of this course, students will be able to
1. Design wireless sensor network system for different applications under consideration.
2. Understand the hardware details of different types of sensors and select right type of sensor for
various applications.
3. Understand radio standards and communication protocols to be used for wireless sensor network
based systems and application.
4. Use operating systems and programming languages for wireless sensor nodes, performance of
wireless sensor networks systems and platforms.
5. Handle special issues related to sensors like energy conservation and security challenges
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I Year II Semester
3
0
0
3
SOFT COMPUTING TECHNIQUES
(Elective-III)
UNIT ?I:
Introduction: Approaches to intelligent control, Architecture for intelligent control, Symbolic reasoning
system, Rule-based systems, the AI approach, Knowledge representation - Expert systems.
UNIT ?II:
Artificial Neural Networks: Concept of Artificial Neural Networks and its basic mathematical model,
McCulloch-Pitts neuron model, simple perceptron, Adaline and Madaline, Feed-forward Multilayer
Perceptron, Learning and Training the neural network, Data Processing: Scaling, Fourier transformation,
principal-component analysis and wavelet transformations, Hopfield network, Self-organizing network
and Recurrent network, Neural Network based controller.
UNIT ?III:
Fuzzy Logic System:
Introduction to crisp sets and fuzzy sets, basic fuzzy set operation and approximate reasoning,
Introduction to fuzzy logic modeling and control, Fuzzification, inferencing and defuzzification, Fuzzy
knowledge and rule bases, Fuzzy modeling and control schemes for nonlinear systems,Self-organizing
fuzzy logic control, Fuzzy logic control for nonlinear time delay system.
UNIT ?IV:
Genetic Algorithm:
Basic concept of Genetic algorithm and detail algorithmic steps, Adjustment of free parameters, Solution
of typical control problems using genetic algorithm, Concept on some other search techniques like Tabu
search and ant D-colony search techniques for solving optimization problems.
UNIT ?V:
Applications:
GA application to power system optimization problem, Case studies: Identification and control of linear
and nonlinear dynamic systems using MATLAB-Neural Network toolbox, Stability analysis of Neural-
Network interconnection systems, Implementation of fuzzy logic controller using MATLAB fuzzy-logic
toolbox, Stability analysis of fuzzy control systems.
TEXT BOOKS:
1. Introduction to Artificial Neural Systems - Jacek.M.Zurada, Jaico Publishing House, 1999.
2. Neural Networks and Fuzzy Systems - Kosko, B., Prentice-Hall of India Pvt. Ltd., 1994.
REFERENCE BOOKS:
1. Fuzzy Sets, Uncertainty and Information - Klir G.J. &Folger T.A., Prentice-Hall of India
Pvt. Ltd., 1993.
2. Fuzzy Set Theory and Its Applications - Zimmerman H.J. Kluwer Academic Publishers, 1994.
3. Introduction to Fuzzy Control - Driankov, Hellendroon, Narosa Publishers.
4. Artificial Neural Networks - Dr. B. Yagananarayana, 1999, PHI, New Delhi.
5. Elements of Artificial Neural Networks - KishanMehrotra, Chelkuri K. Mohan, Sanjay
Ranka, Penram International.
6. Artificial Neural Network ?Simon Haykin, 2nd Ed., Pearson Education.
7. Introduction Neural Networks Using MATLAB 6.0 - S.N. Shivanandam, S. Sumati, S. N.
Deepa,1/e, TMH, New Delhi.
Course Outcomes
At the end of this course the student can able to:
1. Understand the basic concepts of Artificial neural network systems.
2. Understand the McCulloch-Pitts neuron model, simple and multilayer Perception, Adeline and
Madeline concepts.
3. Data processing, Hopfield and self-organizing network.
4. Difference between crisp sets to fuzzy sets, fuzzy models, fuzzification, inference,
membership functions, rule based approaches and defuzzification.
5. Self ? organizing fuzzy logic control, non linear time delay systems.
6. Understand the concept of Genetic Algorithm steps. Tabu, anD-colony search techniques for
solving optimization problems.
7. GA applications to power system optimization problems, identification and control of linear and
nonlinear dynamic systems using MATLAB-Neural network toolbox.
8. Know the application and importance stability analysis
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I Year II Semester
3
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3
AUDIO / VIDEO CODING & COMPRESSION
(Elective-V))
Unit 1
Introduction to Multimedia Systems and Processing, Lossless Image Compression Systems Image
Compression Systems, Huffman Coding, Arithmetic and Lempel-Ziv Coding, Other Coding Techniques
Unit 2
Lossy Image Compression Systems, Theory of Quantization, Delta Modulation and DPCM, Transform
Coding & K-L Transforms, Discrete Cosine Transforms, Multi-Resolution Analysis, Theory of
Wavelets, Discrete Wavelet Transforms, Still Image Compression Standards: JBIG and JPEG
Unit 3
Video Coding and Motion Estimation: Basic Building Blocks & Temporal Redundancy, Block based
motion estimation algorithms, Other fast search motion estimation algorithms.Video Coding Standards
MPEG-1 standards, MPEG-2 Standard, MPEG-4 Standard, H.261,
H.263 Standards, H.264 standard
Unit 4
Audio Coding, Basic of Audio Coding, Audio Coding, Transform and Filter banks, Polyphase filter
implementation, Audio Coding, Format and encoding, Psychoacoustic Models
Unit 5
Multimedia Synchronization, Basic definitions and requirements, References Model and Specification,
Time stamping and pack architecture, Packet architectures and audio-video interleaving, Multimedia
Synchronization, Playback continuity, Video Indexing and Retrieval: Basics of content based image
retrieval, Video Content Representation, Video Sequence Query Processing
TEXTBOOKS
1. Iain E.G. Richardson, "H.264 and MPEG-4 Video Compression", Wiley,2003.
2. Khalid Sayood, "Introduction to Data Compression", 4th Edition, Morgan Kaufmann, 2012
REFERENCE BOOKS:
1. Mohammed Ghanbari, "Standard Codecs: Image Compression to Advanced Video Coding", 3rd
Edition, The Institution of Engineering and Technology,2011.
2. Julius O. Smith III, "Spectral Audio Signal Processing", W3K Publishing,2011.
3. Nicolas Moreau, "Tools for Signal Compression: Applications to Speech and Audio Coding",
Wiley,2011.
Course Outcomes
At the end of this course, students will be able to
1. Familiarity to lossy and lossless compression systems.
2. Study of Video coding techniques and standards.
3. Understand audio coding and multimedia synchronization techniques
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I Year II Semester
3
0
0
3
BIOMEDICAL SIGNAL PROCESSING
(Elective ?IV)
Unit 1
Acquisition, Generation of Bio-signals,Origin of bio-signals, Types of bio-signals,Study of diagnostically
significant bio-signal parameters
Unit 2
Electrodes for bio-physiological sensing and conditioning, Electrode-electrolyte interface, polarization,
electrode skin interface and motion artefact, biomaterial used for electrode, Types of electrodes (body
surface, internal, array of electrodes,microelectrodes), Practical aspects of using electrodes, Acquisition of
bio-signals (signal conditioning) and Signal conversion (ADC's DAC's) Processing, Digital filtering
Unit 3
Biomedical signal processing by Fourier analysis, Biomedical signal processing by wavelet (time-
frequency) analysis, Analysis (Computation of signal parameters that are diagnostically significant)
Unit 4
Classification of signals and noise, spectral analysis of deterministic, stationary random signals and non-
stationary signals, Coherent treatment of various biomedical signal processing methods and applications.
Unit 5
Principal component analysis, Correlation and regression, Analysis of chaotic signals Application areas of
Bio?Signals analysis Multi Resolution Analysis(MRA) and wavelets, Principal Component
Analysis(PCA), Independent Component Analysis(ICA). Pattern classification?supervised and
unsupervised classification, Neural networks, Support vector Machines, Hidden Markov models.
Examples of biomedical signal classification examples.
TEXT BOOKS:
1. W. J. Tompkins, "Biomedical Digital Signal Processing", Prentice Hall,1993.
2. Eugene N Bruce, "Biomedical Signal Processing and Signal Modeling", John Wiley & Son's
publication,2001.
REFERENCE BOOKS:
1. Myer Kutz, "Biomedical Engineering and Design Handbook, Volume I", McGraw Hill, 2009.
2. D C Reddy, "Biomedical Signal Processing", McGraw Hill,2005.Katarzyn J. Blinowska,
JaroslawZygierewicz, "Practical Biomedical Signal Analysis Using MATLAB", 1st Edition, CRC
Press,2011.
Course Outcomes
At the end of this course, students will be able to
1. Understand different types of biomedical signal.
2. Identify and analyze different biomedical signals.
3. Find applications related to biomedical signal processing
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I Year II Semester
3
0
0
3
OPTICAL NETWORKS
(Elective-IV)
Unit 1
SONET/SDH: optical transport network, IP, routing and forwarding, multiprotocol label switching.WDM
network elements: optical line terminals and amplifiers, optical add/drop multiplexers, OADM
architectures, reconfigurable OADM, optical cross connects.
Unit 2
Control and management: network management functions, optical layer services and interfacing,
performance and fault management, configuration management, optical safety.
Unit 3
Network Survivability: protection in SONET/SDH & client layer, optical layer protection schemes
Unit 4
WDM network design: LTD and RWA problems, dimensioning wavelength routing networks, statistical
dimensioning models.
Unit 5
Access networks: Optical time division multiplexing, synchronization, header processing, buffering, burst
switching, test beds, Introduction to PON, GPON, AON.
TEXT BOOKS:
1. Rajiv Ramaswami, Sivarajan, Sasaki, "Optical Networks: A Practical Perspective", MK, Elsevier,
3 rd edition, 2010.
REFERENCE BOOKS:
1. C. Siva Ram Murthy and Mohan Gurusamy, "WDM Optical Networks: Concepts Design, and
Algorithms", PHI, EEE, 2001.
Course Outcomes:
At the end of this course, students will be able to
1. Contribute in the areas of optical network and WDM network design.
2. Implement simple optical network and understand further technology developments for future
enhanced network
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I Year II Semester
0
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4
2
Pattern Recognition & Machine Learning Lab
List of Assignments:
1. Implement maximum likelihood algorithm
2. Implement Bayes classifier
3. Implement linear regression
4. Design a classifier using perceptron rule
5. Design a classifier using feedforward back-propagation and delta rule algorithms
6. Implement deep learning algorithm
7. Implement linear discriminant algorithm
8. Design a two class classifier using SVM
9. Design a multiclass classifier using SVM
10. Perform unsupervised learning
Course Outcomes:
At the end of this course, students will be able to
3. Contribute in the areas of optical network and WDM network design.
4. Implement simple optical network and understand further technology developments for future enhanced
network
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I Year II Semester
0
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4
2
DETECTION AND ESTIMATION THEORY LAB
List of Assignments:
1. Simulate signal and noise models models.
2. Simulate spatially separated target Signal in the presence of Additive Correlated White Noise
3. Simulate spatially
separated
target Signal in the presence of Additive Uncorrelated
White Noise
4. Simulate spatially separated target Signal in the presence of Additive Correlated Colored Noise
5. Detect Constant amplitude Signal in AWGN
6. Detect Time varying Known Signals in AWGN
7. Detect Unknown Signals in AWGN
8. Compare performance comparison of the Estimation techniques - MLE, MMSE, Bayes
Estimator, MAP Estimator, Expectation Maximization (EM) algorithm
9. Performance comparison of conventional Energy Detectors and Coherent Matched Filter
Techniques
Course Outcomes:
At the end of this course, students will be able to
1. Simulate signals and noise
2. Detect signals in the presence of noise
3. Compare various estimation techniques
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I Year II Semester
0
0
4
2
MINI PROJECT
Syllabus Contents
The students are required to search / gather the material / information on a specific a topic
comprehend it and present / discuss in the class.
Course Outcomes
At the end of this course, students will be able to
1. Understand of contemporary / emerging technology for various processes and systems.
2. Share knowledge effectively in oral and written form and formulate documents
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III Semester
3
0
0
3
OPTIMIZATION TECHNIQUES
(Elective-V)
Unit 1
Introduction to Classical Methods and Linear Programming Problems Terminology. Design Variables,
Constraints, Objective Function, Problem Formulation. Calculus method, Kuhn Tucker conditions,
Method ofMultipliers.
Unit 2
Linear Programming Problem,Simplex method, Two-phase method, Big-M method, duality, Integer
linear Programming, Dynamic Programming, Sensitivity analysis.
Unit 3
Single Variable Optimization Problems: Optimality Criterion, Bracketing Methods, Region Elimination
Methods, Interval Halving Method, Fibonacci Search Method, Golden Section Method. Gradient Based
Methods: Newton-Raphson Method, Bisection Method, Secant Method, Cubic search method.
Unit 4
Multi Variable and Constrained Optimization Technique, Optimality criteria , Direct search Method,
Simplex search methods, Hooke-Jeeve`s pattern search method, Powell`s conjugate direction method,
Gradient based Smethod, Cauchy`s Steepest descent method, Newton`s method , Conjugate gradient
method. Kuhn - Tucker conditions, Penalty Function, Concept of Lagrangian multiplier, Complex
search method, Random search method
Unit 5
Intelligent Optimization Techniques:Introduction to Intelligent Optimization, Soft Computing, Genetic
Algorithm: Types of reproduction operators, crossover & mutation, Simulated Annealing Algorithm,
Particle Swarm Optimization (PSO) - Graph Grammer Approach - Example Problems. Genetic
Programming (GP): Principles of genetic programming, terminal sets, functional sets, differences
between GA & GP, random population generation, solving differential equations usingGP.
TEXTBOOKS
1. S. S. Rao, "Engineering Optimization: Theory and Practice", Wiley,2008.
2. K. Deb, "Optimization for Engineering design algorithms and Examples", Prentice Hall,
2005.
REFERENCE BOOKS:
1. C.J. Ray, "Optimum Design of Mechanical Elements", Wiley,2007.
2. R. Saravanan, "Manufacturing Optimization through Intelligent Techniques, Taylor & Francis
Publications,2006.
3. D. E. Goldberg, "Genetic algorithms in Search, Optimization, and Machine learning", Addison-
Wesley Longman Publishing,1989.
Course Outcomes:
At the end of this course, students will be able to
1. Understand importance of optimization
2. Apply basic concepts of mathematics to formulate an optimization problem Analyze and
appreciate variety of performance measures for various optimization problems.
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III Semester
3
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3
MODELLING AND SIMULATION TECHNIQUES
(Elective-V)
Unit 1
Introduction Circuitsasdynamicsystems, Transfer functions, poles and zeroes, State space, Deterministic
Systems, Difference and Differential Equations, Solution of Linear Difference and Differential
Equations, Numerical Simulation Methods for ODEs, System Identification, Stability and Sensitivity
Analysis.
Unit 2
Statistical methods, Description of data, Data-fitting methods, Regression analysis, Least Squares
Method, Analysis of Variance, Goodness of fit.
Unit 3
Probability and Random Processes, Discrete and Continuous Distribution, Central Limit theorem,
Measure of Randomness, MonteCarloMethods.Stochastic Processes and Markov Chains, Time Series
Models.
Unit 4
Modeling and simulation concepts, Discrete-event simulation, Event scheduling/Time advance
algorithms, Verification and validation of simulation models.
Unit 5
Continuous simulation: Modeling with differential equations, Example models, Bond Graph Modeling,
Population Dynamics Modeling, System dynamics
TEXTBOOKS
1. R. L. Woods and K. L. Lawrence, "Modeling and Simulation of Dynamic Systems", Prentice-
Hall,1997.
REFERENCE BOOKS:
1. Z. Navalih, "VHDL Analysis and Modelling of Digital Systems", McGraw-Hill,1993.
2. J. Banks, JS. Carson and B. Nelson, "Discrete-Event System Simulation", 2ndEdition, Prentice-
Hall of India,1996
Course Outcomes:
At the end of this course, students will be able to
1. Identify and model discrete systems (deterministic and random)
2. Identify and model discrete signals (deterministic and random)
3. Understand modelling and simulation techniques to characterize systems/processes.
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III Semester
3
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3
ARTIFICIAL INTELLIGENCE
(Elective-V)
Unit 1
What is AI (Artificial Intelligence)? : The AI Problems, The Underlying Assumption, What are AI
Techniques, The Level Of The Model, Criteria For Success, Some General References, One Final
WordProblems, State Space Search & Heuristic Search Techniques: Defining The Problems As A State
Space Search, Production Systems, Production Characteristics, Production System Characteristics, And
Issues In The Design Of Search Programs, Additional Problems. Generate- And-Test, Hill Climbing,
Best-First Search, Problem Reduction, Constraint Satisfaction, Means- Ends Analysis.
Unit 2
Knowledge Representation Issues: Representations and Mappings, Approaches To Knowledge
Representation. Using Predicate Logic: Representation Simple Facts in Logic, Representing Instance
And Isa Relationships, Computable Functions And Predicates, Resolution. Representing Knowledge
Using Rules: Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus
backward reasoning.
Unit 3
Symbolic Reasoning Under Uncertainty: Introduction To No monotonic Reasoning, Logics For Non-
monotonic Reasoning. Statistical Reasoning: Probability and Bays' Theorem, Certainty Factors And
Rule-Base Systems, Bayesian Networks, Dempster Shafer Theory Fuzzy Logic. Weak Slot-and-Filler
Structures: Semantic Nets, Frames. Strong Slot-and-Filler Structures: Conceptual Dependency, Scripts,
CYC
Unit 4
Game Playing: Overview, And Example Domain: Overview, minimax, Alpha-Beta Cut-off,
Refinements, Iterative deepening, The Blocks World, Components Of A Planning System, Goal Stack
Planning, Nonlinear Planning Using Constraint Posting, Hierarchical Planning, Reactive Systems, Other
Planning Techniques. Understanding: What is understanding? What make it hard? As constraint
satisfaction
Unit 5
Natural Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Semantic
Analysis, Discourse And Pragmatic Processing, Spell Checking Connectionist Models: Introduction:
Hopfield Network, Learning In Neural Network, Application Of Neural Networks, Recurrent Networks,
Distributed Representations, Connectionist AI And Symbolic AI.
TEXTBOOKS:
1. Elaine Rich and Kevin Knight "Artificial Intelligence", 2nd Edition, Tata Mcgraw-Hill, 2005.
REFERENCES:
1. Stuart Russel and Peter Norvig, "Artificial Intelligence: A Modern Approach", 3rd Edition, Prentice
Hall, 2009.
Course Outcomes:
At the end of this course, students will be able to
1. Understand the concept of Artificial Intelligence, search techniques and knowledge
representation issues
2. Understanding reasoning and fuzzy logic for artificial intelligence
3. Understanding game playing and natural language processing.
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III Semester
0
0
20
10
(DISSERTATION) DISSERTATION PHASE ? I AND PHASE ? II
Syllabus Contents:
The dissertation / project topic should be selected / chosen to ensure the satisfaction of the urgent need
to establish a direct link between education, national development and productivity and thus reduce the
gap between the world of work and the world of study. The dissertation should have thefollowing
Relevance to social needs ofsociety
Relevance to value addition to existing facilities in theinstitute
Relevance to industryneed
Problems of nationalimportance
Research and development in various domain
The student should complete thefollowing:
Literature survey ProblemDefinition
Motivation for study and Objectives
Preliminary design / feasibility / modularapproaches
Implementation andVerification
Report andpresentation
The dissertation stage II is based on a report prepared by the students on dissertation allotted to them. It
may be based on:
Experimental verification / Proof ofconcept.
Design, fabrication, testing of CommunicationSystem.
The viva-voce examination will be based on the above report andwork.
Guidelines for Dissertation Phase ? I and II at M. Tech. (Electronics):
As per the AICTE directives, the dissertation is a yearlong activity, to be carried out and
evaluated in two phases i.e. Phase ? I: July to December and Phase ? II: January toJune.
The dissertation may be carried out preferably in-house i.e. department's laboratories and
centers OR in industry allotted through department's T & Pcoordinator.
After multiple interactions with guide and based on comprehensive literature survey, the student
shall identify the domain and define dissertation objectives. The referred literature should
preferably include IEEE/IET/IETE/Springer/Science Direct/ACM journals in the areas of
Computing and Processing (Hardware and Software), Circuits-Devices and Systems,
Communication-Networking and Security, Robotics and Control Systems, Signal Processing
and Analysis and any other related domain. In case of Industry sponsored projects, the relevant
application notes, while papers, product catalogues should be referred andreported.
Student is expected to detail out specifications, methodology, resources required, critical issues
involved in design and implementation and phase wise work distribution, and submit the
proposal within a month from the date ofregistration.
Phase ? I deliverables: A document report comprising of summary of literature survey, detailed
objectives, project specifications, paper and/or computer aided design, proof of
concept/functionality, part results, A record of continuousprogress.
Phase ? I evaluation: A committee comprising of guides of respective specialization shall assess
the progress/performance of the student based on report, presentation and Q &A. In case of
unsatisfactory performance, committee may recommend repeating the Phase-I work.
During phase ? II, student is expected to exert on design, development and testing of the
proposed work as per the schedule. Accomplished results/contributions/innovations should be
published in terms of research papers in reputed journals and reviewed focused conferences OR
IP/Patents.
Phase ? II deliverables: A dissertation report as per the specified format, developed system in
the form of hardware and/or software, a record of continuousprogress.
Phase ? II evaluation: Guide along with appointed external examiner shall assess the
progress/performance of the student based on report, presentation and Q &A. In caseof
unsatisfactory performance, committee may recommend for extension or repeating the work
Course Outcomes:
At the end of this course, students will be able to
Ability to synthesize knowledge and skills previously gained and applied to an in-depth study
and execution of new technicalproblem.
Capable to select from different methodologies, methods and forms of analysis to produce a
suitable research design, and justify theirdesign.
Ability to present the findings of their technical solution in a writtenreport.
Presenting the work in International/ National conference or reputedjournals.
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III Semester
3
0
0
3
OPEN ELECTIVES
BUSINESS ANALYTICS
Unit1:
Business analytics: Overview of Business analytics, Scope of Business analytics, Business Analytics
Process, Relationship of Business Analytics Process and organisation, competitive advantages of
BusinessAnalytics.
Statistical Tools: Statistical Notation, Descriptive Statistical methods,
Review of probability distribution and data modelling, sampling and estimation methods overview.
Unit 2:
Trendiness and Regression Analysis: Modelling Relationships and Trends in Data, simple Linear
Regression.Important Resources, Business Analytics Personnel, Data and modelsfor Business analytics,
problem solving, Visualizing and Exploring Data, Business Analytics Technology
Unit 3:
Organization Structures of Business analytics, Team management, Management Issues, Designing
Information Policy, Outsourcing, Ensuring Data Quality, Measuring contribution of Business analytics,
Managing
Changes.Descriptive
Analytics,
predictive
analytics,
predicative
Modelling,
Predictiveanalyticsanalysis,DataMining,Data Mining Methodologies, Prescriptive analytics and its step
in the business analytics Process, Prescriptive Modelling, nonlinear Optimization.
Unit 4:
Forecasting Techniques: Qualitative and Judgmental Forecasting, Statistical Forecasting Models,
Forecasting Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual Variables,
Selecting Appropriate Forecasting Models.
Monte Carlo Simulation and Risk Analysis: Monte CarleSimulation
Using Analytic Solver Platform, New-Product Development Model, Newsvendor Model, Overbooking
Model, Cash Budget Model.
Unit 5:
Decision Analysis: Formulating Decision Problems, DecisionStrategies with the without
Outcome Probabilities, Decision Trees, The Value of Information, Utility and Decision Making.
Recent Trends in : Embedded and collaborative business intelligence,Visual data recovery, Data
Storytelling and Data journalism
Reference:
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans, Dara G.
Schniederjans, Christopher M. Starkey, Pearson FTPress.
2. Business Analytics by James Evans, personsEducation.
COURSE OUTCOMES
1.
Students will demonstrate knowledge of dataanalytics.
2.
Students will demonstrate the ability of think critically in making decisions based on data and
deepanalytics.
3.
Students will demonstrate the ability to use technical skills in predicative and prescriptive
modeling to support businessdecision-making.
4.
Students will demonstrate the ability to translate data into clear, actionable insights
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III Semester
3
0
0
3
OPENELECTIVES
INDUSTRIALSAFETY
Unit-I:
Industrial safety: Accident, causes, types, results and control, mechanical and electrical hazards, types,
causes and preventive steps/procedure, describe salient points of factories act 1948 for health and safety,
wash rooms, drinking water layouts, light, cleanliness, fire, guarding, pressure vessels, etc, Safety color
codes. Fire prevention and firefighting, equipment and methods.
Unit-II:
Fundamentals of maintenance engineering: Definition and aim of maintenance engineering, Primary and
secondary functions and responsibility of maintenance department, Types of maintenance, Types and
applications of tools used for maintenance, Maintenance cost & its relation with replacement economy,
Service life of equipment.
Unit-III:
Wear and Corrosion and their prevention: Wear- types, causes, effects, wear reduction methods,
lubricants-types and applications, Lubrication methods, general sketch, working and applications, i.
Screw down grease cup, ii. Pressure grease gun, iii. Splash lubrication, iv. Gravity lubrication, v.
Wick feed lubrication vi. Side feed lubrication, vii. Ring lubrication, Definition, principle and factors
affecting the corrosion. Types of corrosion, corrosion prevention methods.
Unit-IV:
Fault tracing: Fault tracing-concept and importance, decision treeconcept, need and applications,
sequence of fault finding activities, show as decision tree, draw decision tree for problems in
machine tools, hydraulic, pneumatic,automotive, thermal and electrical equipment's like, I. Any one
machine tool, ii. Pump iii. Air compressor, iv. Internal combustion engine,v. Boiler,vi .Electrical
motors, Types of faults in machine tools and their generalcauses.
Unit-V:
Periodic and preventive maintenance: Periodic inspection-concept and need, degreasing, cleaning and
repairing schemes, overhauling of mechanical components, overhauling of electrical motor, common
troubles and remedies of electric motor, repair complexities and its use, definition, need, steps and
advantages of preventive maintenance. Steps/procedure for periodic and preventive maintenance of:
I. Machine tools, ii. Pumps, iii.Air compressors, iv. Diesel generating (DG) sets, Program and
schedule of preventive maintenance of mechanical and electrical equipment, advantages of
preventive maintenance. Repair cycle concept andimportance
Reference:
1. Maintenance Engineering Handbook, Higgins & Morrow, Da InformationServices.
2. Maintenance Engineering, H. P. Garg, S. Chand andCompany.
3. Pump-hydraulic Compressors, Audels, McgrewHillPublication.
4. Foundation Engineering Handbook, Winterkorn, Hans, Chapman & HallLondon
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III Semester
3
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3
OPENELECTIVES
OPERATIONSRESEARCH
Unit 1:
Optimization Techniques, Model Formulation, models, General L.R Formulation, Simplex Techniques,
Sensitivity Analysis, Inventory Control Models
Unit 2
Formulation of a LPP - Graphical solution revised simplex method - duality theory - dual simplex
method - sensitivity analysis - parametric programming
Unit 3:
Nonlinear programming problem - Kuhn-Tucker conditions min cost flow problem - max flow problem -
CPM/PERT
Unit 4
Scheduling and sequencing - single server and multiple server models - deterministic inventory models -
Probabilistic inventory control models - Geometric Programming.
Unit 5
Competitive Models,Single and Multi-channel Problems, Sequencing Models, Dynamic Programming,
Flow in Networks, Elementary Graph Theory, Game Theory Simulation
References:
1. H.A. Taha, Operations Research, An Introduction, PHI, 2008
2. H.M. Wagner, Principles of Operations Research, PHI, Delhi, 1982.
3. J.C. Pant, Introduction to Optimisation: Operations Research, Jain Brothers, Delhi, 2008
4. Hitler Libermann Operations Research: McGraw Hill Pub. 2009
5. Pannerselvam, Operations Research: Prentice Hall of India 2010
6. Harvey M Wagner, Principles of Operations Research: Prentice Hall of India 2010
Course Outcomes:
At the end of the course, the student should be able to
1. Students should able to apply the dynamic programming to solve problems of discreet and
continuous variables.
2. Students should able to apply the concept of non-linear programming
3. Students should able to carry out sensitivity analysis
4. Student should able to model the real world problem and simulate it.
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III Semester
3
0
0
3
OPEN ELECTIVE
COST MANAGEMENT OF ENGINEERING PROJECTS
Pre-requisite: MEFA & Management Science
Course Educational Objectives:
To learn cost concepts in decision making
To learn different stages and aspects of a project and execution
To learn resources planning, quality management.
To learn application of techniques such as linear programming, PERT/CPM
To learn profit planning and budgeting
Unit I: Introduction and Overview of the Strategic Cost Management Process
Unit II: Cost concepts in decision-making; Relevant cost, Differential cost, Incremental cost and Opportunity cost.
Objectives of a Costing System; Inventory valuation; Creation of a Database for operational control; Provision of
data for Decision-Making.
Unit III: Project: meaning, Different types, why to manage, cost overruns centres, various stages of project
execution: conception to commissioning. Project execution as conglomeration of technical and nontechnical
activities.Detailed Engineering activities. Pre project execution main clearances and documents Project team: Role
of each member. Importance Project site: Data required with significance. Project contracts.Types and contents.
Project execution Project cost control. Bar charts and Network diagram. Project commissioning: mechanical and
process
Unit IV: Cost Behavior and Profit Planning Marginal Costing; Distinction between Marginal Costing and
Absorption Costing; Break-even Analysis, Cost-Volume-Profit Analysis. Various decision-making
problems.Standard Costing and Variance Analysis. Pricing strategies: Pareto Analysis. Target costing, Life Cycle
Costing. Costing of service sector.Just-in-time approach, Material Requirement Planning, Enterprise Resource
Planning, Total Quality Management and Theory of constraints.Activity-Based Cost Management, Bench Marking;
Balanced Score Card and Value-Chain Analysis.Budgetary Control; Flexible Budgets; Performance budgets; Zero-
based budgets.Measurement of Divisional profitability pricing decisions including transfer pricing.
Unit V: Quantitative techniques for cost management, Linear Programming, PERT/CPM, Transportation problems,
Assignment problems, Simulation, Learning Curve Theory.
Course Outcomes: After completion of this course, the studentwill be able to
Understand the cost management process and various costs involved in a project
Analyze various aspects of a project like project site, project team, contracts, execution and commissioning
Perform various costing and cost management and cost management, profit planning
Apply linear programming PERT/CPM to cost management
Reference Books:
1. Cost Accounting A Managerial Emphasis, Prentice Hall of India, New Delhi
2. Charles T. Horngren and George Foster, Advanced Management Accounting
3. Robert S Kaplan Anthony A. Alkinson, Management & Cost Accounting
4. Ashish K. Bhattacharya, Principles & Practices of Cost Accounting A. H. Wheeler publisher
5. N.D. Vohra, Quantitative Techniques in Management, Tata McGraw Hill Book Co. Ltd.
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III Semester
3
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3
OPEN ELECTIVE
COMPOSITE MATERIALS
UNIT?I:
Introduction: Definition ? Classification and characteristics of Composite materials. Advantages and
application of composites.Functional requirements of reinforcement and matrix.Effect of
reinforcement (size, shape, distribution, volume fraction) on overall composite performance.
UNIT ? II:
Reinforcements: Preparation-layup, curing, properties and applications of glass fibers, carbon fibers,
Kevlar fibers and Boron fibers. Properties and applications of whiskers, particle reinforcements.
Mechanical Behavior of composites: Rule of mixtures, Inverse rule of mixtures. Isostrain and
Isostress conditions.
UNIT ? III:
Manufacturing of Metal Matrix Composites: Casting ? Solid State diffusion technique, Cladding ?
Hot isostaticpressing.Properties and applications. Manufacturing of Ceramic Matrix Composites:
Liquid Metal Infiltration ? Liquid phase sintering. Manufacturing of Carbon ? Carbon composites:
Knitting, Braiding, Weaving. Properties and applications.
UNIT?IV:
Manufacturing of Polymer Matrix Composites: Preparation of Moulding compounds and prepregs ?
hand layup method ? Autoclave method ? Filament winding method ? Compression moulding ?
Reaction injection moulding. Properties and applications.
UNIT ? V:
Strength: Laminar Failure Criteria-strength ratio, maximum stress criteria, maximum strain criteria,
interacting failure criteria, hygrothermal failure. Laminate first play failure-insight strength;
Laminate strength-ply discount truncated maximum strain criterion; strength design using caplet
plots; stress concentrations.
TEXT BOOKS:
1. Material Science and Technology ? Vol 13 ? Composites by R.W.Cahn ? VCH, West
Germany.
2. Materials Science and Engineering, An introduction. WD Callister, Jr., Adapted by R.
Balasubramaniam, John Wiley & Sons, NY, Indian edition,2007.
References:
1. Hand Book of CompositeMaterials-ed-Lubin.
2. Composite Materials ? K.K.Chawla.
3. Composite Materials Science and Applications ? Deborah D.L.Chung.
4. Composite Materials Design and Applications ? Danial Gay, Suong V. Hoa, and Stephen W.
Tasi.
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III Semester
3
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3
OPEN ELECTIVE
WASTE TO ENERGY
Unit-I:
Introduction to Energy from Waste: Classification of waste as fuel ? Agro based, Forest
residue, Industrial waste - MSW ? Conversion devices ? Incinerators, gasifiers, digestors
Unit-II:
Biomass Pyrolysis: Pyrolysis ? Types, slow fast ? Manufacture of charcoal ? Methods - Yields and
application ? Manufacture of pyrolytic oils and gases, yields and applications.
Unit-III:
Biomass Gasification: Gasifiers ? Fixed bed system ? Downdraft and updraft gasifiers ? Fluidized bed
gasifiers ? Design, construction and operation ? Gasifier burner arrangement for
thermal heating ? Gasifier engine arrangement and electrical power ? Equilibrium and kinetic
consideration in gasifier operation
Unit-IV:
Biomass Combustion: Biomass stoves ? Improved chullahs, types, some exotic designs, Fixed bed
combustors, Types, inclined grate combustors, Fluidized bed combustors, Design, construction and
operation - Operation of all the above biomass combustors.
Unit-V:
Biogas: Properties of biogas (Calorific value and composition) - Biogas plant technology and status -
Bio energy system - Design and constructional features - Biomass resources and their classification -
Biomass conversion processes - Thermo chemical conversion - Direct combustion - biomass gasification
- pyrolysis and liquefaction - biochemical conversion - anaerobic digestion - Types of biogas Plants ?
Applications - Alcohol production from biomass - Bio diesel production - Urban waste to energy
conversion - Biomass energy programme in India.
References:
1. Non Conventional Energy, Desai, Ashok V., Wiley Eastern Ltd., 1990.
2. Biogas Technology - A Practical Hand Book - Khandelwal, K. C. and Mahdi, S. S., Vol. I & II,
Tata McGraw Hill Publishing Co. Ltd., 1983.
3. Food, Feed and Fuel from Biomass, Challal, D. S., IBH Publishing Co. Pvt. Ltd., 1991.
Biomass Conversion and Technology, C. Y. WereKo-Brobby and E. B. Hagan, John Wiley &
Sons, 1996.
AUDIT 1 and 2: ENGLISH FOR RESEARCH PAPER WRITING
Course objectives:
Students will be able to:
1. Understand that how to improve your writing skills and level of readability
2. Learn about what to write in each section
3. Understand the skills needed when writing a Title Ensure the good quality of paper at very first-
time submission
Syllabus
Units
CONTENTS
Hours
1
Planning and Preparation, Word Order, Breaking up long sentences, 4
Structuring Paragraphs and Sentences, Being Concise
and Removing Redundancy, Avoiding Ambiguity and Vagueness
2
Clarifying Who Did What, Highlighting Your Findings, Hedging 4
and Criticizing, Paraphrasing and Plagiarism, Sections of a Paper,
Abstracts. Introduction
3
Review of the Literature, Methods, Results, Discussion,
4
Conclusions, The Final Check.
4
key skills are needed when writing a Title, key skills are needed 4
when writing an Abstract, key skills are needed when writing an
Introduction, skills needed when writing a Review of the Literature,
5
skills are needed when writing the Methods, skills needed when 4
writing the Results, skills are needed when writing the Discussion,
skills are needed when writing the Conclusions
6
useful phrases, how to ensure paper is as good as it could possibly
4
be the first- time submission
Suggested Studies:
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman'sbook .
4. Adrian Wallwork , English for Writing Research Papers, Springer New York Dordrecht
Heidelberg London, 2011
AUDIT 1 and 2: DISASTER MANAGEMENT
Course Objectives: -Students will be able to:
1. learn to demonstrate a critical understanding of key concepts in disaster risk reduction and
humanitarian response.
2. critically evaluate disaster risk reduction and humanitarian response policy and practice from
multiple perspectives.
3. develop an understanding of standards of humanitarian response and practical relevance in specific
types of disasters and conflict situations.
4. critically understand the strengths and weaknesses of disaster management approaches, planning
and programming in different countries, particularly their home country or the countries
they work in
Syllabus
Units
CONTENTS
Hours
1
Introduction
4
Disaster: Definition, Factors And Significance; Difference Between
Hazard And Disaster; Natural And Manmade Disasters: Difference,
Nature, Types And Magnitude.
2
Repercussions Of Disasters And Hazards: Economic Damage, Loss
4
Of Human And Animal Life, Destruction Of Ecosystem.
Natural Disasters: Earthquakes, Volcanisms, Cyclones, Tsunamis,
Floods, Droughts And Famines, Landslides And Avalanches, Man-
made disaster: Nuclear Reactor Meltdown, Industrial Accidents, Oil
Slicks And Spills, Outbreaks Of Disease And Epidemics, War And
Conflicts.
3
Disaster Prone Areas In India
4
Study Of Seismic Zones; Areas Prone To Floods And Droughts,
Landslides And Avalanches; Areas Prone To Cyclonic And Coastal
Hazards With Special Reference To Tsunami; Post-Disaster Diseases
And Epidemics
4
Disaster Preparedness And Management
4
Preparedness: Monitoring Of Phenomena Triggering A Disaster Or
Hazard; Evaluation Of Risk: Application Of Remote Sensing, Data
From Meteorological And Other Agencies, Media Reports:
Governmental And Community Preparedness.
5
Risk Assessment
4
Disaster Risk: Concept And Elements, Disaster Risk Reduction, Global
And National Disaster Risk Situation. Techniques Of Risk Assessment,
Global Co-Operation In Risk Assessment And Warning, People's
Participation In Risk Assessment. Strategies for Survival.
6
Disaster Mitigation
4
Meaning, Concept And Strategies Of Disaster Mitigation, Emerging
Trends In Mitigation. Structural Mitigation And Non-Structural
Mitigation, Programs Of Disaster Mitigation In India.
Suggested Readings:
1. R. Nishith, Singh AK, "Disaster Management in India: Perspectives, issues and strategies
"'New Royal book Company.
2. Sahni, PardeepEt.Al. (Eds.)," Disaster Mitigation Experiences And Reflections", Prentice Hall
Of India, New Delhi.
3. Goel S. L. , Disaster Administration And Management Text And Case Studies" ,Deep &Deep
Publication Pvt. Ltd., New Delhi.
AUDIT 1 and 2: SANSKRIT FOR TECHNICAL KNOWLEDGE
Course Objectives
1. To get a working knowledge in illustrious Sanskrit, the scientific language in the world
2. Learning of Sanskrit to improve brain functioning
3. Learning of Sanskrit to develop the logic in mathematics, science & other subjects
enhancing the memory power
4. The engineering scholars equipped with Sanskrit will be able to explore the huge
knowledge from ancient literature
Syllabus
Unit Content
Hours
1
Alphabets in Sanskrit,
8
Past/Present/Future Tense,
Simple Sentences
2
Order
8
Introduction of roots
Technical information about Sanskrit Literature
3
Technical concepts of Engineering-Electrical, Mechanical, Architecture, 8
Mathematics
Suggested reading
1. "Abhyaspustakam" ? Dr.Vishwas, Samskrita-Bharti Publication, New Delhi
2. "Teach Yourself Sanskrit" PrathamaDeeksha-VempatiKutumbshastri, Rashtriya Sanskrit
Sansthanam, New Delhi Publication
3. "India's Glorious Scientific Tradition" Suresh Soni, Ocean books (P) Ltd., New Delhi.
Course Output
Students will be able to
1. Understanding basic Sanskrit language
2. Ancient Sanskrit literature about science & technology can be understood
3. Being a logical language will help to develop logic in students
AUDIT 1 and 2: VALUE EDUCATION
Course Objectives
Students will be able to
1. Understand value of education and self- development
2. Imbibe good values in students
3. Let the should know about the importance of character
Syllabus
Unit
Content
Hours
1
Values and self-development ?Social values and individual attitudes. 4
Work ethics, Indian vision of humanism.
Moral and non- moral valuation. Standards and principles.
Value judgements
2
Importance of cultivation of values.
6
Sense of duty. Devotion, Self-reliance. Confidence, Concentration.
Truthfulness, Cleanliness.
Honesty, Humanity. Power of faith, National Unity.
Patriotism.Love for nature ,Discipline
3
Personality and Behavior Development - Soul and Scientific attitude. 6
Positive Thinking. Integrity and discipline.
Punctuality, Love and Kindness.
Avoid fault Thinking.
Free from anger, Dignity of labour.
Universal brotherhood and religious tolerance.
True friendship.
Happiness Vs suffering, love for truth.
Aware of self-destructive habits.
Association and Cooperation.
Doing best for saving nature
4
Character and Competence ?Holy books vs Blind faith.
6
Self-management and Good health.
Science of reincarnation.
Equality, Nonviolence ,Humility, Role of Women.
All religions and same message.
Mind your Mind, Self-control.
Honesty, Studying effectively
Suggested reading
1 Chakroborty, S.K. "Values and Ethics for organizations Theory and practice", Oxford
University Press, New Delhi
Course outcomes
Students will be able to 1.Knowledge of self-development
2.Learn the importance of Human values 3.Developing the overall personality
AUDIT 1 and 2: CONSTITUTION OF INDIA
Course Objectives:
Students will be able to:
1. Understand the premises informing the twin themes of liberty and freedom from a civil rights
perspective.
2. To address the growth of Indian opinion regarding modern Indian intellectuals' constitutional
role and entitlement to civil and economic rights as well as the emergence of nationhood in
the early years of Indian nationalism.
3. To address the role of socialism in India after the commencement of the Bolshevik Revolution
in 1917 and its impact on the initial drafting of the Indian Constitution.
Syllabus
Units
Content
Hour
s
History of Making of the Indian Constitution:
1
History
4
Drafting Committee, ( Composition & Working)
Philosophy of the Indian Constitution:
2
Preamble Salient Features
4
Contours of Constitutional Rights & Duties:
Fundamental Rights
Right to Equality
Right to Freedom
3
Right against Exploitation
4
Right to Freedom of Religion
Cultural and Educational Rights
Right to Constitutional Remedies
Directive Principles of State Policy
Fundamental Duties.
Organs of Governance:
Parliament
Composition
Qualifications and Disqualifications
4
Powers and Functions
Executive
4
President
Governor
Council of Ministers
Judiciary, Appointment and Transfer of Judges, Qualifications
Powers and Functions
Local Administration:
District's Administration head: Role and Importance,
5
Municipalities: Introduction, Mayor and role of Elected Representative, CE O
of Municipal Corporation.
Pachayati raj: Introduction, PRI: ZilaPachayat.
4
Elected officials and their roles, CEO ZilaPachayat: Position and role.
Block level: Organizational Hierarchy (Different departments),
Village level: Role of Elected and Appointed officials,
Importance of grass root democracy
Election Commission:
Election Commission: Role and Functioning.
6
Chief Election Commissioner and Election Commissioners.
4
State Election Commission: Role and Functioning.
Institute and Bodies for the welfare of SC/ST/OBC and women.
Suggested reading
1. The Constitution of India, 1950 (Bare Act), Government Publication.
2. Dr. S. N. Busi, Dr. B. R. Ambedkar framing of Indian Constitution, 1st Edition, 2015.
3. M. P. Jain, Indian Constitution Law, 7th Edn., Lexis Nexis, 2014.
4. D.D. Basu, Introduction to the Constitution of India, Lexis Nexis, 2015.
Course Outcomes:
Students will be able to:
1. Discuss the growth of the demand for civil rights in India for the bulk of Indians before the
arrival of Gandhi in Indian politics.
2. Discuss the
intellectual
origins of
the
framework
of
argument
that
informed
the conceptualization of social reforms leading to revolution in
India.
3. Discuss the circumstances surrounding the foundation of the Congress Socialist Party
[CSP] under the leadership of Jawaharlal Nehru and the eventual failure of the proposal of
direct elections through adult suffrage in the Indian Constitution.
4. Discuss the passage of the Hindu Code Bill of 1956.
AUDIT 1 and 2: PEDAGOGY STUDIES
Course Objectives:
Students will be able to:
4. Review existing evidence on the review topic to inform programme design and policy
making undertaken by the DfID, other agencies and researchers.
5. Identify critical evidence gaps to guide the development.
Syllabus
Units Content
Hours
Introduction and Methodology:
Aims and rationale, Policy background, Conceptual framework and
1
terminology
4
Theories of learning, Curriculum, Teacher education.
Conceptual framework, Research questions.
Overview of methodology and Searching.
Thematic overview: Pedagogical practices are being used by teachers in
2
formal and informal classrooms in developing countries.
2
Curriculum, Teacher education.
Evidence on the effectiveness of pedagogical practices
Methodology for the in depth stage: quality assessment of included
studies.
How can teacher education (curriculum and practicum) and the school
3
curriculum and guidance materials best support effective pedagogy?
4
Theory of change.
Strength and nature of the body of evidence for effective pedagogical
practices.
Pedagogic theory and pedagogical approaches.
Teachers' attitudes and beliefs and Pedagogic strategies.
Professional development: alignment with classroom practices and
follow-up support
4
Peer support
4
Support from the head teacher and the community.
Curriculum and assessment
Barriers to learning: limited resources and large class sizes
Research gaps and future directions
Research design
Contexts
2
5
Pedagogy
Teacher education
Curriculum and assessment
Dissemination and research impact.
Suggested reading
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare,
31 (2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of
Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning
of basic maths and reading in Africa: Does teacher preparation count? International Journal
Educational Development, 33 (3): 272?282.
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary
education. Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, `learning to read' campaign.
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
Course Outcomes:
Students will be able to understand:
1. What pedagogical practices are being used by teachers in formal and informal classrooms
in developing countries?
2. What is the evidence on the effectiveness of these pedagogical practices, in what
conditions, and with what population of learners?
3. How can teacher education (curriculum and practicum) and the school curriculum and
guidance materials best support effective pedagogy?
AUDIT 1 and 2: STRESS MANAGEMENT BY YOGA
Course Objectives
1. To achieve overall health of body and mind
2. To overcome stress
Syllabus
Unit
Content
Hours
1
Definitions of Eight parts of yog. ( Ashtanga )
8
2
Yam and Niyam. Do`s and Don't's in life.
8
i) Ahinsa, satya, astheya, bramhacharya and aparigraha
ii) Shaucha, santosh, tapa, swadhyay, ishwarpranidhan
3
Asan and Pranayam
8
1. Various yog poses and their benefits for mind & body
2. Regularization of breathing techniques and its effects-Types of
pranayam
Suggested reading
1. `Yogic Asanas for Group Tarining-Part-I" : Janardan Swami YogabhyasiMandal, Nagpur
2. "Rajayoga or conquering the Internal Nature" by Swami Vivekananda, Advaita
Ashrama (Publication Department), Kolkata
Course Outcomes:
Students will be able to:
1. Develop healthy mind in a healthy body thus improving social health also
2. Improve efficiency
AUDIT 1 and 2: PERSONALITY DEVELOPMENT THROUGH LIFE
ENLIGHTENMENT SKILLS
Course Objectives
1. To learn to achieve the highest goal happily
2. To become a person with stable mind, pleasing personality and determination
3. To awaken wisdom in students
Syllabus
Unit
Content
Hours
1
Neetisatakam-Holistic development of personality
8
Verses- 19,20,21,22 (wisdom)
Verses- 29,31,32 (pride & heroism)
Verses- 26,28,63,65 (virtue)
Verses- 52,53,59 (dont's)
Verses- 71,73,75,78 (do's)
2
Approach to day to day work and duties.
8
ShrimadBhagwadGeeta : Chapter 2-Verses 41, 47,48,
Chapter 3-Verses 13, 21, 27, 35, Chapter 6-Verses 5,13,17, 23, 35,
Chapter 18-Verses 45, 46, 48.
3
Statements of basic knowledge.
8
ShrimadBhagwadGeeta: Chapter2-Verses 56, 62, 68
Chapter 12 -Verses 13, 14, 15, 16,17, 18
Personality of Role model. ShrimadBhagwadGeeta: Chapter2-
Verses 17, Chapter 3-Verses 36,37,42,
Chapter 4-Verses 18, 38,39
Chapter18 ? Verses 37,38,63
Suggested reading
1. "Srimad Bhagavad Gita" by Swami SwarupanandaAdvaita Ashram (Publication Department),
Kolkata
2. Bhartrihari's Three Satakam (Niti-sringar-vairagya) by P.Gopinath, Rashtriya Sanskrit
Sansthanam, New Delhi.
Course Outcomes
Students will be able to
1. Study of Shrimad-Bhagwad-Geeta will help the student in developing his personality and
achieve the highest goal in life
2. The person who has studied Geeta will lead the nation and mankind to peace and prosperity
3. Study of Neetishatakam will help in developing versatile personality of students
Document Outline
This post was last modified on 16 March 2021