PROCESSING ENGINEERING AND SIGNAL COMMUNICATION … Engineering and Signal Processing Syllabus (2019...PC 19EC2102 Advanced Digital Signal Processing 3 0 3 PC 19EC2103 Digital Image

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M.Tech. Programme in

COMMUNICATION

ENGINEERING AND SIGNAL

PROCESSING

2019 Regulations

GAYATRI VIDYA PARISHAD COLLEGE OF ENGINEERING

(AUTONOMOUS)

AFFILIATED TO JNTU- KAKINADA

MADHURAWADA, VISAKHAPATNAM

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M.Tech. in Communication Engineering & Signal Processing

SEMESTER – I

Course Category

Course Code Theory / Lab L P C

PC 19EC2101 Information Theory and Coding

3 0 3

PC 19EC2102 Advanced Digital Signal Processing

3 0 3

PC 19EC2103 Digital Image and Video Processing

3 0 3

PE-I 19EC2150

19EC2151

19EC2152

19EC2250

1. Time Frequency Analysis

2. Optical Networks

3. Data Networks

4. Digital Design through Verilog

3 0 3

PE-II 19EC2153 19EC2154 19EC2254

1. Optimization techniques 2. RF & Microwave circuit design 3. VLSI Signal Processing

3 0 3

Core Lab 19EC2104 Advanced Digital Signal Processing Lab

0 3 1.5

Core Lab (Elective-I)

19EC2155

19EC2156

1. Digital Image and Video Processing Lab

2. Information Theory and Coding Lab

0 3 1.5

1

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19HM2101 Research Methodology &

IPR 2 0 2

TOTAL 17 6 20

SEMESTER – II

Course Category


PC 19EC2105 Modern Wireless Communications

3 0 3

PC 19EC2106 Pattern recognition and Machine Learning

3 0 3

PC 19EC2107 Antennas and Radiating Systems

3 0 3

PE-III 19EC2157

19EC2158

19EC2159

1. Adaptive Signal Processing

2. Detection and Estimation Theory

3. Biomedical Signal Processing

3 0 3

2

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PE-IV 19EC2160

19EC2161

19EC2261

1. DSP Architecture

2. Cognitive Radio

3. Internet of Things and applications

3 0 3

OE 19CH21P1

19ME21P1

19ME21P2

1.Waste as a Source of Energy

2.Operations Research

3.Composite Materials

2 0 2

Core Lab 19EC2108 Modern Wireless Communications Lab

0 3 1.5

Core Lab (Elective-II)

19EC2162

19EC2163

19EC2263

1. Pattern recognition and Machine Learning Lab

2. Antennas and Radiating Systems Lab

3. Internet of Things and Applications Lab

0 3 1.5

TOTAL 17 6 20

3

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SEMESTER – III

Course Category


Audit Course-1

19HE21A1 English for Research Paper Writing 3 0 0

Audit Course-2

19HM21A1 Constitution of India 3 0 0

IT/PT 19EC21IT/ 19EC21PT

Industrial Training / Pedagogy Training

2

Dissertation 19EC21T1 Dissertation (Phase-I) 0 20 10

TOTAL 4 20 12

SEMESTER – IV

Course Category


Dissertation 19EC21T2 Dissertation (Phase-II) 0 32 16

TOTAL 0 32 16

GRAND TOTAL 68

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INFORMATION THEORY AND CODING

Course Code: 19EC2101 L P C

3 0 3

Prerequisites: Linear algebra, Probability Theory, Digital communications Course Outcomes : At the end of this course,the student will be able to CO1: Analyze the channel performance using Information theory. CO2: Comprehend various error control code properties and linear block codes. CO3: Apply cyclic codes for error controlling. CO4: Apply convolution codes for error performance analysis. CO5: Apply TCM for error controlling using various channels. UNIT-I 10 Lectures Information Theory and Source Coding Introduction to information theory, Source coding: Entropy, Mutual Information, Shannon-Fano and Huffman coding, Arithmetic coding, Lempel-Ziv algorithm, Run Length Encoding and the PCX format, Rate distortion Function, Optimum Quantizer Design, Entropy rate of stochastic process, Image compression: The JPEG standard for Lossless and Lossy compression. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze different source coding techniques (L4) 2. Evaluate the efficiency of source coding techniques (L5) 3. Apply source coding for image compression (L3)

UNIT-II 10 Lectures Channel Capacity and Error Control Coding Channel models, Channel capacity, Channel coding, Information capacity theorem, Shannon Limit, Channel capacity for MIMO systems, random selection of codes, Matrix description of Linear block codes, Equivalent codes, parity check matrix, Decoding of Linear block code, syndrome decoding, probability of Error correction, Perfect codes, Hamming codes, LDPC codes, Optimal Linear codes, MDS codes, Bounds on Minimum distance, Space Time Block Codes. Learning outcomes: At the end of this unit, the student will be able to

1. Evaluate channel capacity and analyze trade-offs (L4)

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2. Analyze Linear Block Coding (L4) 3. Evaluate the performance of Linear Block codes for Error Controlling (L5)

UNIT-III 10 Lectures Cyclic Codes Method for Generating cyclic codes, Matrix Description of cyclic codes, Quasi-cyclic codes and Shortened cyclic codes, Burst error correction, Fire codes, Golay codes, CRC codes, Circuit implementation of cyclic codes. BCH and RS Codes Minimal Polynomials, Generator polynomials in terms of Minimal polynomials, Examples of BCH codes, Decoding of BCH codes, R S codes, Implementation of RS encoders and decoders, Performance of RS codes over Real channels, Nested codes. Learning outcomes: At the end of this unit, the student will be able to

1. Identify cyclic codes for error detection and correction. (L2) 2. Analyze the concept of cyclic codes and implement the circuit of cyclic codes. (L4) 3. Analyze BCH & RS codes for Channel performance improvement against burst

errors. (L4 )

UNIT-IV 10 Lectures Convolution codes Tree Codes and Trellis codes, Polynomial Description of convolutional codes, Distance Notions for convolutional codes, Generating function, Matrix description of convolutional codes, Viterbi decoding, Performance bounds, Turbo codes, Turbo decoding, Interleaver design for Turbo codes Learning outcomes: At the end of this unit, the student will be able to

1. Analyze and Evaluate the performance of convolutional codes (L4) 2. Illustrate the concept of Viterbi decoding. (L4) 3. Analyze and Evaluate the performance of Turbo codes. (L4)

UNIT-V 10 Lectures Trellis Coded Modulation Coded Modulation, Mapping of set partitioning, Ungerboeck’s TCM design rules, TCM decoder, Performance evolution of AWGN channel, TCM for Fading channels, STTC. Learning outcomes: At the end of this unit, the student will be able to

1. Explain the importance of coded modulation (L2)

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2. Analyze and Evaluate the performance of TCM (L4) 3. Evaluate the performance of TCM under various channel conditions (L4)

Textbooks 1. Bose, Ranjan. Information theory, coding and cryptography . Tata McGraw-Hill Education,

2008. 2. Cover, Thomas M., and Joy A. Thomas. Elements of information theory . John Wiley & Sons,

2012.

References Wicker, Stephen B. Error control systems for digital communication and storage . Vol. 1.

Englewood Cliffs: Prentice Hall, 1995.

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ADVANCED DIGITAL SIGNAL PROCESSING


3 0 3 Prerequisites: Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Apply different algorithms in FFT CO2: Comprehend multi-rate DSP and filter banks CO3 : Analyze AR and ARMA filters CO4: Analyze adaptive filters using MMSE, LMS and RLS algorithms CO5: Comprehend the estimation of a finite duration signal using parametric and non-parametric methods

UNIT-I 10 Lectures FIR and IIR FIlters 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, parallel realization of IIR. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze the performance of FFT-DIT and FFT-DIF algorithms (L4) 2. Design and analyze the performance of Impulse response Filers (L4) 3. Analyze and implement various structures of Impulse response filers (L4)

UNIT-II 10 Lectures Multirate DSP Multi rate DSP, Decimators and Interpolators, Sampling rate conversion, multi stage decimator & interpolator, poly phase filters, QMF, digital filter banks, Applications in sub-band coding. Learning outcomes: At the end of this unit, the student will be able to

1. Describe signal processing in digital domain (L2) 2. Describe sampling rate conversion by a rational factor (L2) 3. Analyze and design poly phase filers and digital filters (L4)

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UNIT-III 10 Lectures Linear prediction and 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. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze linear prediction filter (L4) 2. Analyze AR and ARMA filters (L4) 3. Analyze various Wiener filters (L4)

UNIT-IV 10 Lectures Adaptive Filters Applications of Adaptive filters, Gradient Adaptive Lattice, Minimum mean square criterion, LMS algorithm, Recursive Least Square algorithm. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze various Adaptive filters (L4) 2. Explain minimum mean square criterion (L2) 3. Apply MMSE, LMS and RLS algorithm (L3)

UNIT-V 10 Lectures Power Spectrum Estimation Estimation of Spectra from Finite-Duration Observations of Signals. Non-parametric Methods for Power Spectrum Estimation, Parametric Methods for Power Spectrum Estimation, Minimum-Variance Spectral Estimation, Eigen analysis Algorithms for Spectrum Estimation. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the estimation of spectrum (L2) 2. Analyze the parametric and non-parametric methods of Power Spectrum estimation

(L4) 3. Apply Power spectrum estimation algorithms (L3)

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Textbooks 1. Proakis, John G. Digital signal processing: principles algorithms and applications . Pearson

Education India, 2001. 2. Hayes, Monson H. Statistical digital signal processing and modeling . John Wiley & Sons,

2009.

References 1. Fliege, Norbert J. Multirate digital signal processing . Vol. 994. New York: John Wiley, 1994. 2. Suter, Bruce W. Multirate and wavelet signal processing . Vol. 8. Elsevier, 1997. 3. Haykin, Simon S. Adaptive filter theory . Pearson Education India, 2005. 4. Manolakis, Dimitris G., Vinay K. Ingle, and Stephen M. Kogon. Statistical and adaptive

signal processing: spectral estimation, signal modeling, adaptive filtering, and array processing . Boston: McGraw-Hill, 2000.

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DIGITAL IMAGE AND VIDEO PROCESSING

Course Code:19EC2103 L P C

3 0 3 Prerequisites: Signals and Systems, Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Comprehend the image and video processing fundamentals. CO2: Learn different techniques for image enhancement, video and image recovery CO3: Understand techniques for image and video segmentation. CO4: Summarize the techniques for image and video compression. CO5: Understand techniques for object recognition.

UNIT-I 10 Lectures Digital Image and Video Fundamentals Digital image and video fundamentals and formats, 2-D and 3-D sampling and aliasing, 2-D/3-D filtering, image decimation/interpolation, video sampling and interpolation, Basic image processing operations, Image Transforms, Need for image transforms, DFT, DCT, Walsh, Hadamard transform, Haar transform, Wavelet transform. Learning outcomes: At the end of this unit, the student will be able to

1. Understand different types of image and video file formats. (L2) 2. Describe the image and video sampling. (L2) 3. Understand the need for image transforms. (L2)

UNIT-II 10 Lectures Image and Video Enhancement and Restoration Histogram, Point processing, filtering, image restoration, algorithms for 2-D motion estimation, change detection, motion-compensated filtering, frame rate conversion, deinterlacing, video resolution enhancement, Image and Video restoration (recovery). Learning outcomes: At the end of this unit, the student will be able to

1. Understand different point processing techniques for image enhancement. (L2) 2. Analyze the various 2-D motion estimation algorithms. (L4) 3. Understand various image and video restoration techniques. (L2)

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UNIT-III 10 Lectures Image and Video Segmentation Discontinuity based segmentation- Line detection, edge detection, thresholding, Region based segmentation, Scene Change Detection, Spatio-temporal Change Detection, Motion Segmentation, Simultaneous Motion Estimation and Segmentation, Semantic Video Object Segmentation, Morphological image processing. Learning outcomes: At the end of this unit, the student will be able to

1. Discuss various image segmentation techniques. (L2) 2. Analyze the video object segmentation methods. (L4) 3. Describe the various morphological image processing techniques. (L2)

UNIT-IV 10 Lectures Image and Video Compression Lossless image compression including entropy coding, lossy image compression, video compression techniques, and international standards for image and video compression (JPEG, JPEG 2000, MPEG-2/4, H.264, SVC), Video Quality Assessment Learning outcomes: At the end of this unit, the student will be able to

1. Summarize various lossless and lossy compression techniques for image and video signals. (L2)

2. Analyze the international standards for image and video processing. (L4) 3. Describe the video quality assessment methods. (L2)

UNIT-V 10 Lectures Object recognition Image Feature representation and description-boundary representation, boundary descriptors, regional descriptors, feature selection techniques, introduction to classification, supervised and unsupervised learning, Template matching. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the various image feature and boundary representations. (L2) 2. Analyze the various descriptors. (L4) 3. Explain the difference between supervised and unsupervised learning. (L2)

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Textbooks 1. Bovik, Alan C. Handbook of image and video processing . Academic press, 2010. 2. Woods, John W. Multidimensional signal, image, and video processing and coding . Elsevier,

2011. 3. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing , 3rd Edition, Prentice Hall, 2008. References 1. M. Tekalp, Digital Video Processing , 2 nd Edition, Prentice Hall, 2015. 2. S. Shridhar, Digital Image Processing , 2 nd Edition, Oxford University Press, 2016.

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TIME FREQUENCY ANALYSIS (Elective-I)


3 0 3 Prerequisites: Signals and Systems, Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Study the various transform techniques in signal processing CO2: Study the time-frequency analysis . CO3: Study and understand the MRA techniques. CO4: Study and understand the various wavelet families. CO5: Understand the concepts of bio-orthogonal wavelets.

UNIT-I 10 Lectures Introduction Review of Fourier Transform, Parseval Theorem and need for joint time- frequency Analysis. Concept of non-stationary signals, Short-time Fourier transforms (STFT), Uncertainty Principle, and Localization/Isolation in time and frequency, Hilbert Spaces, Banach Spaces, and Fundamentals of Hilbert Transform. Learning outcomes: At the end of this unit, the student will be able to

1. Study and analyze the concepts of non-stationary signals. (L2) 2. Study and analyze the concepts of MRA systems. (L2) 3. Apply the Hilbert transform to find the various modes of a signal. (L2)

UNIT-II 10 Lectures Bases for Time-Frequency Analysis Wavelet Bases and filter Banks, Tilings of Wavelet Packet and Local Cosine Bases, Wavelet Transform, Real Wavelets, Analytic Wavelets, Discrete Wavelets, Instantaneous Frequency, Quadratic time-frequency energy, Wavelet Frames, Dyadic wavelet Transform, Construction of Haar and Roof scaling function using dilation equation and graphical method.

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Learning outcomes: At the end of this unit, the student will be able to 1. Study and analyze the concepts of various wavelet bases. (L2) 2. Distinguish continuous and discrete wavelet bases. (L2) 3. Construction of wavelet scaling functions using wavelet bases functions. (L2)

UNIT-III 10 Lecture Multi resolution Analysis Haar Multi resolution Analysis, MRA Axioms, Spanning Linear Subspaces, nested subspaces, Orthogonal Wavelets Bases, Scaling Functions, Conjugate Mirror Filters, Haar 2-band filter Banks, Study of up samplers and down samplers, Conditions for alias cancellation and perfect reconstruction, Discrete wavelet transform and relationship with filter Banks, Frequency analysis of Haar 2- band filter banks, scaling and wavelet dilation equations in time and frequency domains. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze the MRA signals. (L4) 2. Analyze the scaling Functions sub spaces. (L4) 3. Design & construct Haar wavelet filter banks. (L6)

UNIT-IV 10 Lectures Wavelets Daubechies Wavelet Bases, Daubechies compactly supported family of wavelets, Daubechies filter coefficient calculations, Case study of Daub-4 filter design, Connection between Haar and Daub-4, Concept of Regularity, Vanishing moments, Other classes of wavelets like Shannon, Meyer, and Battle-Lamarie. Learning outcomes: At the end of this unit, the student will be able to

1. Study the Daubechies family of wavelets. (L2) 2. Analyze the concept of vanishing moments. (L2) 3. Study and analyze other class of wavelets. (L2)

UNIT-V 10 Lectures Bi-orthogonal wavelets and Applications Construction and designs Wavelet Packet Trees, Time- frequency localization, compactly supported wavelet packets, case study of Walsh wavelet packet bases generated using Haar conjugate mirror filters till depth level 3, Lifting schemes for generating orthogonal bases of second generation wavelets.

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Learning outcomes: At the end of this unit, the student will be able to 1. Design wavelet packet tree. (L6) 2. Design Walsh wavelet packet using Haar conjugate mirror filter. (L6) 3. Study and analyze the orthogonal basis wavelets using lifting schemes. (L2)

Textbooks

1. K P Soman, KI Ramachandran, NG Resmi, Insight into Wavelets: From theory to practice , 3 rd Edition, PHI learning private limited, March, 2010.

2. S. Mallat, A Wavelet Tour of Signal Processing , 2 nd Edition, Academic Press, 1999. References

1. L. Cohen, Time-frequency analysis , 1 st Edition, Prentice Hall, 1995. 2. G. Strang and T. Q. Nguyen, Wavelets and Filter Banks, 2 nd Edition, Wellesley

Cambridge Press, 1998. 3. I. Daubechies, Ten Lectures on Wavelets , SIAM, 1992. 4. P.P. Vaidyanathan, Multirate Systems and Filter Banks , Prentice Hall, 1993. 5. M. Vetterli and J. Kovacevic, Wavelets and Subband Coding , Prentice Hall, 1995.

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OPTICAL NETWORKS (Elective-I)


3 0 3 Prerequisites: Optical Fiber Communications Course Outcomes: At the end of this course, the student will be able to CO1: Comprehend the implementation of SONET/SDH Network. CO2: Implementation and control of network management functions. CO3: Comprehending the concept of optical layer protections. CO4: Comprehending WDM Network design. CO5: Implementation of access networks. UNIT-I 10 Lectures 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. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the optical transport network. (L2) 2. Analyze the routing and switching techniques. (L4) 3. Understand the concept of OADM. (L2)

UNIT-II 10 Lectures Control and management network management functions, optical layer services and interfacing, performance and fault management, configuration management, optical safety. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the functions of network management. (L2) 2. Analyze the performance of optical layer. (L4) 3. Apply configuration management and optical safety of networks. (L3)

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UNIT-III 10 Lectures Network Survivability Protection in SONET/SDH & client layer, optical layer protection schemes Learning outcomes: At the end of this unit, the student will be able to

1. Understand protection in SONET. (L2) 2. Understand network survivability. (L2) 3. Analyze optical layer protection schemes. (L4)

UNIT-IV 10 Lectures WDM network design LTD and RWA problems, dimensioning wavelength routing networks, statistical dimensioning models. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze LTD and RWA problems. (L4) 2. Understand the dimensioning wavelength routing networks. (L2) 3. Analyze statistical dimensioning models. (L4)

UNIT-V 10 Lectures Access networks Optical time division multiplexing, synchronization, header processing, buffering, burst switching, test beds, Introduction to PON, GPON, AON. Learning outcomes: At the end of this unit, the student will be able to

1. Understand optical time division multiplexing. (L2) 2. Understand buffering and burst switching. (L2) 3. Understand PON, GPON, AON. (L2)

Textbooks

Rajiv Ramaswami, Sivarajan, Sasaki, Optical Networks: A Practical Perspective , MK, Elsevier, 3rd edition, 2010.

References C. Siva Ram Murthy and Mohan Gurusamy, WDM Optical Networks: Concepts Design, and Algorithms , PHI, EEE, 2001.

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DATA NETWORKS (Elective-I)


3 0 3

Prerequisites: Basic Computer Networks Course Outcomes: At the end of this course, the student will be able to

CO1: Understand advanced concepts in Communication Networking. CO2: Design and develop protocols for Communication Networks. CO3: Understand the mechanisms in Quality of Service in networking. CO4: Design and develop scheduling algorithm for communication networks CO5: Optimize the network Design.

UNIT-I 10 Lectures TCP/IP concepts Overview of-ATM, TCP/IP Congestion and Flow Control in Internet-Throughput analysis of TCP congestion control, TCP for high bandwidth delay networks, Fairness issues in TCP . Learning outcomes: At the end of this unit, the student will be able to

1. Understand and apply the concept of TCP/IP. (L2) 2. Analyze the TCP congestion and its control. (L4) 3. Understand applications of advance communication networks. (L2)

UNIT-II 10 Lectures Real Time Communications over Internet Adaptive applications, Latency and throughput issues, Integrated Services Model (intServ), Resource reservation in Internet, RSVP, Characterization of Traffic by Linearly Bounded Arrival Processes (LBAP), Leaky bucket algorithm and its properties. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of Real Time Communications over Internet (L2) 2. Understand Resource reservation in Internet (L2) 3. Understand Characterization of Traffic by different algorithms (L2 )

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UNIT-III 10 Lectures Packet Scheduling Algorithms Requirements and choices, Scheduling guaranteed service connections, GPS, WFQ and Rate proportional algorithms, High speed scheduler design, Theory of Latency Rate servers and delay bounds in packet switched networks for LBAP traffic, Active Queue Management - RED, WRED and Virtual clock, Control theoretic analysis of active queue management. Learning outcomes: At the end of this unit, the student will be able to

1. Understand basic concepts of Packet Scheduling (L2) 2. Apply different High speed scheduler design (L3) 3. Understand the advantages of Queue Management (L2)

UNIT-IV 10 Lectures Packet Classification Algorithms IP address lookup-challenges, Packet classification algorithms and Flow Identification-Grid of Tries, Cross-producting and controlled prefix expansion algorithms. Learning outcomes: At the end of this unit, the student will be able to

1. Apply the concept of IP address lookup (L3) 2. Apply the concept of Admission control in Internet (L3) 3. Understand controlled prefix algorithms (L2)

UNIT-V 10 Lectures Service Concepts in Internet Admission control in Internet, Concept of Effective bandwidth, Measurement based admission control, Differentiated Services in Internet (DiffServ), DiffServ architecture and framework, IPV4, IPV6, IP tunnelling, IP switching and MPLS, Overview of IP over ATM and its evolution to IP switching, MPLS architecture and framework, MPLS Protocols, Traffic engineering issues in MPLS. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concepts of IPV4, IPV6, IP tunneling (L2) 2. Understand the concepts of IP switching (L2) 3. Apply MPLS Protocols for traffic engineering issues (L3)

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Textbooks 1. Jean Wairand and Pravin Varaiya, High Performance Communications Networks , 2 nd

edition, 2000. 2. Jean Le Boudec and Patrick Thiran, Network Calculus A Theory of Deterministic

Queueing Systems for the Internet , Springer Veriag, 2001. 3. Forouzan, Data Communications Networking , 5th Edition,Mac Graw Hill

Education, 2013. 4. Prakash C.Gupta, Data Communications and Computer Networks, 7th Edition, PHI,2012.

References 1. Zhang Wang, Internet QoS, Morgan Kaufmann Publishers Inc. USA, 2001 . 2. Anurag Kumar, D. Manjunath and Joy Kuri, Communication Networking: An Analytical

Approach , Morgan Kaufman Publishers, 2004. 3. George Kesidis, ATM Network Performance , Kluwer Academic, Research Papers, 2005.

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DIGITAL DESIGN THROUGH VERILOG (Elective-I)


3 0 3

Prerequisites: Digital Logic Design.

Course outcomes: At the end of the course the student will be able to CO1: Outline the basic concepts of Verilog language. CO2: Design and develop different circuits in gate level modelling. CO3: Develop circuits in data flow level modelling and switch level modelling. CO4: Design different circuits in behavioral modelling using blocking and non-blocking statements. CO5: Design Finite state machines and comprehends concepts of functions, tasks, and user defined primitives. UNIT-I 10 Lectures INTRODUCTION TO VERILOG Verilog as HDL, Levels of Design Description, Concurrency, Simulation and Synthesis, Functional Verification, System Tasks, Programming Language Interface (PLI), Module, Simulation and Synthesis Tools, Test Benches. Language Constructs and Conventions: Introduction, Keywords, Identifiers, White Space Characters, Comments, Numbers, Strings, Logic Values, Strengths, Data Types, Scalars and Vectors, Parameters, Memory, Operators. System Tasks, Functions, and Compiler Directives: Parameters, Path Delays, Module Parameters, System Tasks and Functions, File-Based Tasks and Functions, Compiler Directives, Hierarchical Access, General Observations. Learning outcomes: At the end of this unit, the student will be able to

1 . Summarize the levels of design description (L2) 2. Describe the basic language constructs in VERILOG (L2) 3. Develop Test Bench and verify using simulation (L6)

UNIT-II 10 Lectures GATE LEVEL MODELING Introduction, AND Gate Primitive, Module Structure, Other Gate Primitives, Illustrative Examples, Tri-State Gates, Array of Instances of Primitives, Additional Examples, Design of Flip-flops with Gate Primitives, Delays, Strengths and Contention Resolution, Net Types, Design of Basic Circuits.

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Learning outcomes: At the end of this unit, the student will be able to 1. Build Gate primitives with different examples (L6) 2. Design and Development of Flip-Flops with gate primitives (L6) 3. Describe Strengths and Net Types (L2)

UNIT-III 10 Lectures DATA FLOW LEVEL and SWITCH LEVEL MODELING Introduction, Continuous Assignment Structures, Delays and Continuous Assignments, Assignment to Vectors, Operators. Switch Level Modeling Introduction, Basic Transistor Switches, CMOS Switch, Bidirectional Gates, Time Delays with Switch Primitives, Instantiations with Strengths and Delays, Strength Contention with Trireg Nets. Learning outcomes: At the end of this unit, the student will be able to

1. Describe Continuous Assignment Structures and data flow modeling(L2) 2. Design different circuits using Data flow and switch level modeling (L6) 3. Develop switch level circuits (L6)

UNIT-IV 12 Lectures BEHAVIORAL MODELING Introduction, Operations and Assignments, Functional Bifurcation, Initial Construct, Always Construct, Examples, Assignments with Delays, Wait construct, Multiple Always Blocks, Designs at Behavioral Level, Blocking and Non-blocking Assignments, The case statement, Simulation Flow. If and if-else constructs, assign-deassign construct, repeat construct, for loop, the disable construct, while loop, forever loop, parallel blocks, force-release construct, Event. Learning outcomes: At the end of this unit, the student will be able to

1. Design different circuits using behavioral modeling(L6) 2. Describe the concept of blocking and non-blocking (L2) 3. Develop different designs using loop constructs (L6)

UNIT-V 8 Lectures FUNCTIONS, TASKS AND USER-DEFINED PRIMITIVES Introduction, Function, recursive functions, Tasks, User Defined Primitives (UDP)- combinational UDPs, sequential UDPs, FSM Design -Moore and Mealy Machines. Learning outcomes: At the end of this unit, the student will be able to

1. Describe the language constructs like functions, tasks, UDP and FSM design (L2) 2. Design different circuits using functions and UDP (L6) 3. Develop Moore and Mealy machines using FSM Design (L6)

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Text Books 1. T. R. Padmanabhan and B. Bala Tripura Sundari, Design through Verilog HDL ,

WSE, IEEE Press, 2004 2. Bhasker, Jayaram. A Verilog HDL Primer , Star Galaxy Publishing, 1999.

References 1. Michael D. Ciletti, Advanced Digital Design with Verilog HDL , PHI, 2005. 2. John F. Wakerly, Digital Design Principles & Practices , PHI/Pearson Education

Asia, 3rd Ed., 2005.

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OPTIMIZATION TECHNIQUES (Elective-II)


3 0 3

Prerequisites: Mathematics Course Outcomes: At the end of this course, the student will be able to CO1: Comprehend the techniques and applications of Engineering optimization. CO2: Analyze characteristics of a general linear programming problem. CO3: Apply basic concepts of mathematics to formulate an optimization problem. CO4: Analyse various methods of solving the unconstrained minimization problem. CO5: Analyze and appreciate variety of performance measures for various optimization problems.

UNIT-I 10 Lectures Introduction to optimization Introduction to Classical Methods & Linear Programming Problems Terminology, Design Variables, Constraints, Objective Function, Problem Formulation, Calculus method, Kuhn Tucker conditions, Method of Multipliers. Learning outcomes: At the end of this unit, the student will be able to

1. Explain importance of optimization. (L2) 2. List out the design variables, constraints and objective function for optimization

techniques. (L2) 3. Analyze Kuhn Tucker conditions and method of multipliers. (L4)

UNIT-II 10 Lectures Linear Programming Problem Linear Programming Problem, Simplex method, Two-phase method, Big-M method, duality, Integer linear Programming, Dynamic Programming, Sensitivity analysis.

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Learning outcomes: At the end of this unit, the student will be able to 1. Analyze problems in which the objective function and the constraints appear as linear

functions of the decision variables. (L4) 2. Analyze the concept of simplex, two-phase and big-M method. (L4) 3. Explain integer linear and dynamic programming. (L2)

UNIT-III 10 Lectures 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. Learning outcomes: At the end of this unit, the student will be able to

1. Describe the concept of single variable optimization problems. (L2) 2. Analyze solution of nonlinear programming problems. (L4) 3. Analyze various optimization methodologies. (L4)

UNIT-IV 10 Lectures Multivariable and Constrained Optimization Techniques 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 method, 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. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze various methods of solving the unconstrained minimization problem. (L4) 2. Summarize the concept of multivariable optimization technique. (L2) 3. Analyze the optimality criteria for various optimization techniques. (L4)

UNIT-V 10 Lectures Intelligent Optimization Techniques Introduction to Intelligent Optimization, Genetic Algorithm: Types of reproduction operators, crossover & mutation, Simulated Annealing Algorithm, Particle Swarm Optimization (PSO), Genetic Programming (GP): Principles of genetic programming, terminal sets, functional sets, differences between GA & GP, random population generation, solving differential equations using GP.

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Learning outcomes: At the end of this unit, the student will be able to 1. Analyze optimization methods based on the behavior of biological and swarm of

insects. (L4) 2. Summarize the concepts of Genetic programming. (L2) 3. Analyze the differences between GA and GP. (L4)

Textbooks

1. S. S. Rao, Engineering Optimisation: Theory and Practice , Wiley, 2008. 2. K. Deb, Optimization for Engineering design algorithms and Examples , Prentice

Hall, 2 nd edition 2012. References

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.

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RF AND MICROWAVE CIRCUIT DESIGN (Elective-II)


3 0 3 Prerequisites: Microwave Engineering Course Outcomes: At the end of this course, the student will be able to CO1: Understand the behaviour of RF passive components and model active components. CO2: Perform transmission line analysis. CO3: Demonstrate use of Smith Chart for high frequency circuit design. CO4: Justify the choice/selection of components from the design aspects. CO5: Contribute in the areas of RF circuit design.

UNIT-I 10 Lectures Transmission Line Theory Lumped element circuit model for transmission line, field analysis, Smith chart, quarter wave transformer, generator and load mismatch, impedance matching and tuning. Learning outcomes: At the end of this unit, the student will be able to

1. Explain how transmission line theory bridges the gap between the field analysis and basic circuit theory. (L2)

2. Analyze the characteristics of quarter wave transformer. (L4) 3. Describe the design and performance of practical matching network. (L2)

UNIT-II 10 Lectures Microwave Network Analysis Impedance and equivalent voltage and current, Impedance and admittance matrix, The scattering matrix, transmission matrix, Signal flow graph. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze how circuit and network concepts can be extended to handle microwave design problems. (L4)

2. List out the characteristics of impedance and admittance matrix. (L2) 3. Analyze the concept of signal flow graph in microwave networks. (L4)

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UNIT-III 10 Lectures Microwave Components Microwave resonators, Microwave filters, power dividers and directional couplers, Ferromagnetic devices and components, Microwave Semiconductor Devices and Modeling: PIN diode, Tunnel diode, Varactor diode, Schottky diode, IMPATT and TRAPATT devices, transferred electron devices, Microwave BJTs, GaAs FETs, low noise and power GaAs FETs, MESFET, MOSFET, HEMT. Learning outcomes: At the end of this unit, the student will be able to

1. Evaluate nonlinear performance of microwave active devices. (L5) 2. Create resonators at microwave frequencies using distributed elements. (L6) 3. Analyze and design most common types of dividers and couplers.(L4)

UNIT-IV 10 Lectures Nonlinearity And Time Variance Inter Symbol Interference, random process & noise, definition of sensitivity and dynamic range, conversion gain and distortion. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze the effect of noise in assessment of performance of microwave system. (L4) 2. Analyze the sensitivity and dynamic range parameters. (L4) 3. Summarize the concept of conversion gain and distortion. (L2)

UNIT-V 10 Lectures Amplifiers Design Power gain equations, stability, impedance matching, constant gain and noise figure circles, small signal, low noise, high power and broadband amplifier, oscillators, Mixers design. Learning outcomes: At the end of this unit, the student will be able to

1. Apply S-parameter theory in amplifier design. (L3) 2. Describe the concept of low noise, high power and broadband amplifier. (L2) 3. Analyze the mixer design. (L4)

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Textbooks 1. Matthew M. Radmanesh, Advanced RF & Microwave Circuit Design: The Ultimate Guide to Superior Design , AuthorHouse, 2009. 2. D.M.Pozar, Microwave engineering ,Wiley, 4 th edition, 2011.

References 1. G.D. Vendelin, A.M. Pavoi, U. L. Rohde, Microwave Circuit Design Using Linear and Non Linear Techniques , John Wiley 1990. 2. S.Y. Liao, Microwave circuit Analysis and Amplifier Design , Prentice Hall 1987. 3. Radmanesh, RF and Microwave Electronics Illustrated , Pearson Education, 2004. 4. R.Ludwig and P.Bretchko, R. F. Circuit Design , Pearson Education Inc, 2009.

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VLSI SIGNAL PROCESSING (Elective-II)


3 0 3 Prerequisites: VLSI Design, Digital Signal Processing Course outcomes: At the end of the course the student will be able to CO1: Understand DSP algorithms, its DFG representation, pipelining and parallel processing approaches. CO2: Describe iteration bound, retiming techniques. CO3: Summarize the folding and unfolding algorithms. CO4: Outline the systolic architecture design. CO5: Understand different convolution techniques and features of DSP Processors. UNIT-I 10 Lectures DSP systems and algorithms Introduction to DSP systems: Introduction, Overview of typical DSP Algorithms, Representation of DSP Algorithms: Block Diagrams, Signal-Flow Graph, Data-Flow Graph, Dependence Graph. Pipelining and parallel processing: Introduction, Pipelining of FIR Digital Filters, Parallel Processing. Learning outcomes: At the end of this unit, the student will be able to

1. Summarize the different representations of DSP Algorithms (L2) 2. Describe the pipelining for Low Power (L2) 3. Illustrate the parallel processing for Low Power (L3)

UNIT-II 10 Lectures Iteration Bound and Retiming Introduction to Iteration bound, Data-Flow Graph Representations, Loop Bound and Iteration Bound, Algorithms for Computing Iteration Bound, Iteration Bound of Multirate Data-Flow Graphs. Introduction to retiming, Definitions and Properties, Solving Systems of Inequalities, Retiming Techniques. Learning outcomes: At the end of this unit, the student will be able to

1. Derive Loop Bound and Iteration Bound (L6) 2. Formulate the iteration bound of multirate data-flow graphs (L6) 3. Describe Retiming Techniques (L2)

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UNIT-III 10 Lectures Folding and Unfolding Unfolding: Introduction, an Algorithm for Unfolding, Properties of Unfolding, Critical Path, Unfolding and Retiming. Folding: Introduction, Folding transformation, Register Minimization Techniques, Register Minimization in Folded Architectures. Learning outcomes: At the end of this unit, the student will be able to

1. Describe unfolding (L2) 2. Summarize folding and folding transformation (L2) 3. Analyze unfolding and folding concepts for register minimization (L4)

UNIT-IV 10 Lectures Systolic architecture design Introduction, systolic Array Design Methodology, FIR Systolic Arrays, Selection of Scheduling Vector, Matrix-Matrix Multiplication and 2D Systolic Array Design, Systolic Design for Space Representations Containing Delays. Learning outcomes: At the end of this unit, the student will be able to

1. Discuss systolic array design methodology (L2) 2. Summarize FIR Systolic Arrays (L2) 3. Model 2D Systolic Array and Systolic design for space representations containing

delays (L3) UNIT-V 10 Lectures Convolution and Digital Signal Processors Fast Convolution: Cook-Toom Algorithm, Winograd Algorithm, Iterated Convolution and Cyclic Convolution. Programmable Digital Signal Processors: Introduction, Evolution of Programmable Digital Signal Processors, Features of DSP Processors. Learning outcomes: At the end of this unit, the student will be able to

1. Describe and apply fast convolution algorithms for signal processing applications (L2)

2. Summarize performance improvements and evolution of Programmable DSPs (L2)

3. Discuss features of DSP Processors (L2)

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Text Books Keshab K. Parhi, VLSI Digital signal processing systems, design and implementation ’, Wiley, Inter Science, 1999.

References

1. Mohammad Isamail and Terri Fiez, Analog VLSI signal and information processing , McGraw Hill, 1994

2. S.Y. Kung, H.J. White House, T. Kailath, VLSI and Modern Signal Processing , Prentice Hall, 1985.

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ADVANCED DIGITAL SIGNAL PROCESSING LAB

Course Code: 19EC2104 L P C 0 3 1.5

P erquisite: Advanced Digital Signal Processing

Course Outcomes: At the end of the course the student will be able to

CO1: Apply DIT and DFT algorithms in FFT CO2: Analyze IIR and FIR filters CO3: Analyze adaptive filters using LMS algorithms CO4: Analyze adaptive filters using RLS algorithms CO5: Analyze parametric and non-parametric methods for Power Spectrum Estimation

List of Experiments:

1. Implementation of N-point DFT (N = 8, 16 )

2. Compute the Discrete Cosine Transform of a signal

3. Implementation of N-point FFT (N = 8, 16 ) using DIT algorithms

4. Implementation of N-point FFT (N = 8, 16 ) using DIF algorithms

5. Implement IIR filter (Butterworth Low pass and High pass Filter)

6. Implement IIR filter (Chebyshev filter)

7. Design and implement Low pass, High pass and Band pass FIR filter

8. Implementation of Decimator and Interpolator

9. Implement I/D sampling rate converter

10. Design and implement Wiener filter for filtering and prediction

11. Design and implement adaptive filter using LMS algorithms

12. Design and implement adaptive filter using RLS algorithms

13. Estimation of PSD of a finite duration signal using Bartlette method

14. Estimation of PSD of a finite duration signal using Yule-Wallker method Note: Any TWELVE of the above experiments are to be conducted

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DIGITAL IMAGE AND VIDEO PROCESSING LAB

(Elective-1) Course Code: 19EC2155 L P C

0 3 1.5

Prerequisite: Digital Image and Video Processing


CO1 : Perform image and video enhancement. CO2 : Perform image and video segmentation. CO3 : Detect an object in an image/video. CO4: Perform image compression. CO5: Calculate boundary and regional features of an image.


1. Perform basic operations on images like addition, subtraction etc.

2. Plot the histogram of an image and perform histogram equalization

3. Implement segmentation algorithms

4. Perform image enhancement

5. Perform video enhancement

6. Perform image segmentation

7. Perform video segmentation

8. Perform image compression using lossy technique

9. Perform image compression using lossless technique

10. Perform image restoration

11. Convert a color model into another model

12. Calculate boundary features of an image

13. Calculate regional features of an image

14. Detect an object in an image/video using template matching/Bayes classifier

Note: Any TWELVE of the above experiments are to be conducted

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INFORMATION THEORY AND CODING LAB (Elective-I)


Perquisite: Information Theory and Coding

Course Outcomes: At the end of the course the student will be able to CO1: Evaluate the detection capability of parity generators. CO2: Evaluate single bit error correction capability of Hamming code. CO3: Understand the properties of linear block codes and error controlling capability of block codes. CO4: Understand encoding and decoding of convolution codes, cyclic codes and TCM codes. CO5: Evaluate the coding performance of Huffman coding and Shannon Fano coding.


1. Verify the error detection mechanism of even parity generator and odd parity generator.

2. Verify the error correction mechanism of (7,4) Hamming code.

3. Verify the error correction mechanism of (8,4) extended Hamming code.

4. Verify the error correction capability of Hamming code under multipath environment.

5. Verify the error correction mechanism of (7,4) block code for the following Generator

Matrix.

6. Verify linear block code properties for a code generated by the following Generator

Matrix.

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7. Verify the error correction mechanism of convolution code which has three flip-flops,

two modulo-2 adders and an output. The generator sequences of the encoder are

g 1 = (1 1 1), g 2 = (1 1 0), g 3 = (1 0 0)

8. Verify the error correction mechanism of (7,4) binary systematic cyclic encoder with

g(x)=1+x+x 2 and verify the operation using the message vector (1 1 0 1).

9. Verify the error correction mechanism of (7,4) binary asystematic cyclic encoder with

g(x)=1+x+x 2 and verify the operation using the message vector (1 1 0 1).

10. Verify Viterbi decoder algorithm.

11. Verify the generation and detection of Trellis Code Modulation (TCM).

12. Verify the linear cyclic properties for a code generated by the generator polynomial

g(x)=1+x+x 2 .

13. Verify Huffman coding.

14. Verify Shannon Fano coding.


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RESEARCH METHODOLOGY & IPR

Course Code: 19HM2101 L P C

2 0 2

Course Outcomes: At the end of the course the student will be able to CO1: Illustrate research problem formulation.

CO2: Analyse research related information and research ethics

CO3: Summarise the present day scenario controlled and monitored by Computer and Information Technology, where the future world will be ruled by dynamic ideas,concept, creativity and innovation. CO4: Explain how IPR would take such important place in growth of individuals & nation, to summarise the need of information about Intellectual Property Right to be promoted among student community in general & engineering in particular. CO5: Relatethat 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.

Unit I: 8 Lectures Research Methodology: An Introduction Meaning of research problem, Sources of research problem, Criteria and Characteristics of a good research problem, Errors in selecting a research problem, Scope and objectives of research problem. Approaches of investigation of solutions for research problem, data collection, analysis, interpretation, Necessary instrumentations.

Learning outcomes: At the end of this unit, the student will be able to

1. Explain the scope and objectives of a research problem (L2) 2. List out criteria and characteristics of a good research problem(L1) 3. Summarize the approaches of investigation of solutions for a research problem (L2)

Unit II: 6 Lectures Literature Survey and Ethics Effective literature studies approaches, analysis Plagiarism, Research ethics. Learning outcomes: At the end of this unit, the student will be able to

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1. Outline the Literature study approaches (L2) 2. Adapt Research ethics in professional life (L6) 3. Explain legal compliances of Plagiarism (L2)

Unit III: 6 Lectures Interpretation and Report Writing Effective technical writing, how to write a report, Paper Developing a Research Proposal, Format of research proposal, presentation and assessment by a review committee. Learning outcomes: At the end of this unit, the student will be able to

1. Demonstrate technical report writing (L2) 2. Develop research paper writing skills (L3) 3. Develop Power Point Presentation skills (L3)

Unit IV: 8 Lectures Intellectual Property Rights and Patents Nature of Intellectual Property: Patents, Designs, Trade and Copyrights. 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


1. Explain Intellectual Property Rights and differentiate amongPatents, Designs, Trade Marks and Copyrights (L2)

2. Outline the process of patenting and development (L2) 3. Explain the procedure for granting patent (L2)

Unit V: 6 Lectures Intellectual Patent Rights and Developments Scope of Patent Rights. Licensing and transfer of technology, Patent information and databases, Geographical Indications.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 / NITs/ IIITs.


1. Explain patent right and its scope (L2) 2. Make use ofPatent information and databases (L3) 3. Discover the new developments in IPR (L4)

Text Books

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1. C.R.Kothari, “Research Methodology” , 3 rd Edition, New Age International, 2017. 2. Ranjit Kumar, “Research Methodology – A Step by Step for Beginner’s” , 2 nd Edition,

Pearson, Education, 2016. 3. T. Ramappa, “ Intellectual Property Rights Under WTO” , 2 nd Edition, S Chand, 2015 4. Kompal Bansal &Par shit Bansal,“ Fundamentals of IPR for Beginner’s ”, 1 st Edition, BS

Publications, 2016.

References

1. Mark Saunders, Philip Levis, Adrain Thornbill, “ Research Methods for Business Students ”, 3 rd Edition (Reprint), Pearson Education, 2013.

2. KVS Sharma, “ Statistics made simple, Do it yourself ”, 2 nd Editi

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MODERN WIRELESS COMMUNICATIONS Course Code: 19EC2105 L P C

3 0 3 Prerequisites: Digital communications

Course Outcomes: At the end of the course the student will be able to CO1: Comprehend the characterization of Fading Channels. CO2: Model Wireless cellular communication system . CO3: Analyze the performance of CDMA. CO4: Configure MIMO scheme for channel performance improvement. CO5: Analyze the performance of OFDM and Model Wireless system Planning.

UNIT-I 10 Lectures Wireless Communication Modeling and Diversity The Wireless communication environment, Modeling of Wireless system, System model for Narrowband Signals, Raleigh Fading Wireless Channel, BER Performance of Wireless Systems, Intuition on BER for fading channel, Channel Estimation in Wireless system, Diversity in wireless communication, Multiple receive antenna system model, Symbol detection in multiple antenna systems, BER in multiple antenna systems, Diversity Order. Learning outcomes: At the end of this unit, the student will be able to

1. Understand different channel models (L2) 2. Analyze BER performance of different channels (L4) 3. Evaluate the BER performance of wireless systems with diversity (L5)

UNIT-II 10 Lectures Wireless Channel Modeling Basics of wireless channel modeling, Average delay spread in outdoor cellular channels, coherence bandwidth in wireless communications, Relation between ISI and coherence bandwidth, Doppler fading in wireless systems, Doppler impact on a channel, coherence time of the wireless channel, Jakes model for wireless channel correlation, implication of coherence time. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the parameters of wireless channel (L2) 2. Evaluate ISI effect in wireless communication (L2) 3. Analyze the Doppler effect in wireless communication (L4)

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UNIT-III 10 Lectures Code Division Multiple Access Introduction to IS-95 and CDMA, Basic CDMA mechanism, Fundamentals of CDMA codes, Spreading codes based on Pseudo-Noise (PN) sequences, correlation properties of random CDMA spreading sequences, Multi-user CDMA, advantages of CDMA, CDMA Near-Far problem and power control, Performance of CDMA downlink and uplink scenario with multi users, Asynchronous CDMA. Learning outcomes: At the end of this unit, the student will be able to

1. Understand CDMA technique (L2) 2. Analyze PN sequence (L4) 3. Evaluate the performance of multi user CDMA (L5)

UNIT-IV 10 Lectures Multiple Input Multiple Output System MIMO System model, MIMO zero-forcing (ZF) receiver, MIMO MMSE Receiver, Singular Value decomposition (SVD) of MIMO channel, MIMO Capacity, asymptotic MIMO Capacity, Alamouti and space-time codes, OSTBSC, Non-Linear MIMO Receiver: VBLAST, MIMO Beam forming. Learning outcomes: At the end of this unit, the student will be able to

1. Understand MIMO system model (L2) 2. Analyze MIMO zero forcing and MMSE Receiver (L4) 3. Apply Space time codes (L3)

UNIT-V 10 Lectures Orthogonal Frequency Division Multiplexing Multicarrier transmission, cyclic prefix in OFDM, Impact of cyclic prefix on data rate, Bit error rate for OFDM, MIMO –OFDM, Effect of frequency offset in OFDM, OFDM-Peak to average power ratio (PAPR), SC-FDMA Receiver, Sub Carrier mapping in SC-FDMA Learning outcomes: At the end of this unit, the student will be able to

1. Understand multi-carrier modulation technique (L2) 2. Analyze BER performance of OFDM and MIMO-OFDM (L4) 3. Evaluate PAPR performance of OFDM (L5)

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Text Books

Aditya K Jagannadham, Principles of Modern Communication Systems , Macgraw Hill Publication, 2016

References

1. V.K.Garg, J.E.Wilkes, Principle and Application of GSM , Pearson Education, 5 th edition, 2008.

2. V.K.Garg, IS-95 CDMA & CDMA 2000 , Pearson Education, 4 th edition, 2009. 3. T.S.Rappaport, Wireless Communications Principles and Practice , 2 nd edition, PHI, 2002. 4. William C.Y.Lee, Mobile Cellular Telecommunications Analog and Digital Systems ,

2 nd edition, TMH, 1995. 5. Asha Mehrotra, A GSM system Engineering Artech House Publishers Bosten, London,

1997. 6. https://nptel.ac.in/courses/117104099/

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PATTERN RECOGNITION AND MACHINE LEARNING


3 0 3 Prerequisites: Statics & Probability Theory

Course Outcomes: At the end of this course, the student will be able to CO1: Interpret different learning approaches along with analyzing theory for pattern classification. CO2: Study the parametric and linear models for classification. CO3: Design and develop MLP for classification. CO4: Design SVM for pattern classification. CO5: Study different clustering approaches and develop clustering algorithm for pattern classification.

UNIT-I 10 Lectures 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 Learning outcomes: At the end of this unit, the student will be able to

1. Understand basic knowledge of machine learning & pattern recognition applications. (L2)

2. Analyze parametric learning methods. (L4) 3. Design Bayesian classifier for pattern classification problem. (L6)

UNIT-II 10 Lectures Linear models Linear Models for Regression, linear regression, logistic regression Linear Models for Classification Learning outcomes: At the end of this unit, the student will be able to

1. Compare different regression models and its application. (L3) 2. Analyze various regression models for pattern recognition. (L4)

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3. Design and develop the model for prediction of time series data. (L6)

UNIT-III 10 Lectures Neural Network Perceptron, multi-layer perceptron, backpropagation algorithm, error surfaces, practical techniques for improving backpropagation, additional networks and training methods, Adaboost, Deep Learning. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of single layer and multi-layer perceptron. (L2) 2. Compare and analyze single layer and multi-layer perceptron. (L4) 3. Design MLP for pattern classification. (L6)

UNIT-IV 10 Lectures Linear discriminant functions Decision surfaces, two-category, multi-category, minimum squared error procedures, the Ho-Kashyap procedures, linear programming algorithms, Support vector machine Learning outcomes: At the end of this unit, the student will be able to

1. Understand the knowledge of decision boundaries. (L2) 2. Analyze various minimization techniques for different type of objective function.(L4) 3. Design SVM classifier for pattern classification. (L6)

UNIT-V 10 Lectures Unsupervised learning and clustering k-means clustering, fuzzy k-means clustering, hierarchical clustering. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the need of various clustering techniques. (L2) 2. Compare and analyze different clustering methods. (L4) 3. Design different clustering algorithm for pattern classification. (L6)

Textbooks

1. Symon Haykins Neural Networks A Comprehensive Foundation , Prentice Hall International, Inc

2. Vojislav Kecman, Learning and Soft Computing , MIT Press, March 19, 2002.

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3. Zurada, Jacek M. Introduction to artificial neural systems . Vol. 8. St. Paul: West publishing company, 1992.

References 1. C. Bishop, Pattern Recognition and Machine Learning , Springer, 2006. 2. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification , 2nd Edition John

Wiley & Sons, 2001. 3. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical

Learning , 2nd Edition, Springer, 2009 4. Fausett, Laurene. Fundamentals of neural networks: architectures, algorithms, and

applications . Prentice-Hall, Inc., 1994.

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ANTENNAS AND RADIATING SYSTEMS


3 0 3

Prerequisites: Electromagnetic Field Theory Course Outcomes: At the end of this course, the student will be able to CO1: Compute the far field distance, radiation pattern and gain of an antenna for given current distribution. CO2: Design antenna arrays for various desired radiation pattern characteristics. CO3: Design and analyze a compact Microstrip antenna. CO4: Understand the significance of Broadband antenna. CO5: Comprehend the importance of Reflector antenna.

UNIT-I 10 Lectures Antenna Fundamentals Fundamental Parameters of Antennas, Dipole antenna, Monopole antenna, Ground effects, Loop Antennas: Small Circular loop, Circular Loop of constant current, Circular loop with non- uniform current. Learning outcomes: At the end of this unit, the student will be able to

1. Explain the significance of antenna parameters. (L2) 2. Analyze the dipole and monopole antenna characteristics. (L4) 3. Analyze the characteristics of loop antennas. (L4)

UNIT-II 10 Lectures Linear Arrays Two element array, N Element array: Uniform Amplitude and spacing, Broadside and End fire array, Super directivity, Grating lobes, Planar array: Rectangular, hexagonal, Circular arrays, Design consideration. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze and Design array antenna systems from specifications (L4 & L6) 2. Analyze the characteristics of array antennas. (L4) 3. Apply the design considerations for array antennas. (L4)

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UNIT-III 10 Lectures Micro strip Antennas Basic Characteristics, Difference between Microstrip antenna and Microwave integrated circuit, Feeding mechanisms, Method of analysis, Rectangular Patch, Parametric Study of Rectangular microstrip patch antenna, Higher Order Modes of Rectangular microstrip patch antenna, Circular Patch. Learning outcomes: At the end of this unit, the student will be able to

1. Understand various theoretical methods for the analysis of Micro strip Antennas (L2 & L4)

2. Analyze the feeding mechanism in microstrip antenna. (L4) 3. Analyze the higher order modes of rectangular microstrip patch antenna. (L4)

UNIT-IV 10 Lectures Compact Broadband MSAs Compact Shorted RMSAs, Partially Shorted RMSAs, Effect of Dimensions of RMSAs with a Single Shorting Post, Effect of the Position of the Single Shorting Post Compact Shorted CMSA and its Variations, Broadband Circularly Polarized MSAs : Dual-Feed Circularly Polarized MSAs, Square MSA with Two Feeds Learning outcomes: At the end of this unit, the student will be able to

1. Evaluate the performance of Compact Microstrip antennas using broadband techniques. (L5)

2. Explain the effect of position of the single shorting post compact shorted CMSA. (L2) 3. Analyze dual-feed circularly polarized MSA. (L4)

UNIT-V 10 Lectures Horn Antennas E-Plane, H-plane Sectoral horns, Pyramidal and Conical horns-design, Reflector Antennas: Plane reflector, parabolic reflector, Cassegrain reflectors, Helical antenna and its design considerations. Learning outcomes: At the end of this unit, the student will be able to

1. Explain the necessity of horn antenna as a feed in reflector antennas. (L2) 2. Analyze the importance of plane and parabolic reflector. (L4) 3. Analyze the design considerations of Helical antenna. (L4)

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Textbooks 1. Constantine A. Balanis, Antenna Theory Analysis and Design , John Wiley & Sons, 4 th edition, 2016. 2. G. Kumar and K. P. Ray, Broadband Microstrip Antennas , Artech House, 2003. References 1. John D Kraus, Ronald J Marhefka, Ahmad S Khan, Antennas for All Applications , Tata McGraw-Hill, 2002. 2. R.C.Johnson and H.Jasik, Antenna Engineering hand book , Mc-Graw Hill, 1984. 3. I.J.Bhal and P.Bhartia, Micro-strip antennas , Artech house, 1980. 4. http://nptel.ac.in/courses/108101092/

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ADAPTIVE SIGNAL PROCESSING (Elective-III)

Course Code:19EC2157 L P C

3 0 3 Prerequisites: Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Understand the concepts of adaptive system modelling. CO2: Understand and analyze error performance by using various Gradient search methods. CO3: Apply mathematical modelling approach to use different adaptive algorithms. CO4: Apply adaptive modeling systems for real time applications. CO5: Design based on Kalman filtering and extended Kaman filtering.

UNIT-I 12 Lectures Adaptive Systems Characteristics, Areas of application, general properties, open and closed loop adaptation, applications of closed loop adaptation, Example of an Adaptive System, The Adaptive Linear Combiner: Description, Weight Vectors, Desired Response, Performance Function; Gradient and Minimum Mean-Square Error, Approaches to the Development of Adaptive Filter Theory: Introduction to Filtering Smoothing and Prediction-Linear Optimum Filtering, Problem Statement, Principle of Orthogonality, Minimum–Mean-Squared Error, Wiener –Hopf Equations, Error Performance, Normal Equation. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the general and specific properties of open and closed loop adaptive systems. (L2) 2. Understand the adaptive filter theory. (L2) 3. Understand the concepts of error minimization using Wiener-Hopf equation. (L2)

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UNIT-II 10 Lectures Gradient Searching and Estimation Searching the Performance Surface: Methods and Ideas of Gradient Search Methods, Gradient Searching Algorithm and its Solution, Stability and Rate of Convergence, Learning Curves, Gradient Search by Newton’s Method, Method of Steepest Descent, Comparison of Learning Curves, Gradient component estimation by derivative measurement, the performance penalty, derivative measurement and performance penalties with multiple weights, variance of the gradient estimate, effects on the weight vector solution.

Learning outcomes: At the end of this unit, the student will be able to 1. Understand the basic idea of Gradient search method. (L2) 2. Understand the concepts of Gradient search methods. (L2) 3. Understand and analyze the error variation performance by using various learning parameters. (L4)

UNIT-III 8 Lectures LMS & RLS Algorithms Overview, LMS Adaptation Algorithms, Stability and Performance Analysis of LMS Algorithms, LMS Gradient and Stochastic Algorithms, Convergence of LMS Algorithms, RLS algorithms. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the mathematical approach of LMS algorithm. (L2) 2. Understand the process of convergence of LMS algorithm. (L2) 3. Understand the mathematical approach of RLS algorithm. (L2)

UNIT-IV 10 Lectures Adaptive Modeling and System Identification General description, adaptive modeling of multipath communication channel, adaptive modeling in geophysical exploration, adaptive modeling in FIR digital filter synthesis, general description of inverse modeling, some theoretical examples. Learning outcomes: At the end of this unit, the student will be able to

1. Understand and analyze the multipath communication in adaptive nature. (L2) 2. Understand the process of identification by using Adaptive modelling. (L2) 3. Analyze the general description of Inverse modelling. (L4)

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UNIT-V 10 Lectures Kalman Filtering Theory Introduction, Recursive Mean Square Estimation for Scalar Random Variables, Statement of Kalman Filtering Problem, Innovation Process. Estimation of State using the Innovation Process, Filtering, Initial Conditions, Summary of Kalman Filters, Variants of the Kalman Filtering, the Extend Kalman Filtering, Identification as a Kalman Filtering Problem. Learning outcomes: At the end of this unit, the student will be able to

1. Apply Kalman filtering problem for random variable estimation. (L3) 2. Estimation of state using innovation process. (L5) 3. Understand the concepts of Extended Kalman filtering problem (L2)

Textbooks

1. Bernand Widrow, Samuel D. Stearns, Adaptive Signal Processing , Pearson Education, Asia, 2009.

2. Simon Haykins, Adaptive filter Theory , PHI, 2003. References

1. Sophocles J. Orfamidis, Optimum Signal Processing –An Introduction , 2/e, McGrawHill, 1990.

2. Alexander, Thomas S. Adaptive signal processing: theory and applications , Springer Science & Business Media, 2012.

3. TulayAdali, Simon Haykin, Adaptive Signal Processing –Next Generation Solutions , Wiley Publications, 2012.

***

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DETECTION AND ESTIMATION THEORY (Elective-III)


3 0 3 Prerequisites: Linear Algebra, Probability theory and Stochastic Processes, Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Understand basic matrix operations. CO2: Understand the concepts of orthogonal transformation techniques. CO3: Ability to understand the mathematical background of stochastic process. CO4: Use Bayes approach to formulate and solve signal detection problems. CO5: Apply filtering methods for parameter Estimation.

UNIT-I 8 Lectures Review of Vector Spaces Vectors and matrices: notation and properties, orthogonality and linear independence, bases, distance properties, matrix operations, Eigen values and eigenvectors. Learning outcomes: At the end of this unit, the student will be able to

1. Ability to understand vector notation for signal processing. (L2) 2. Analyze the orthogonality and linear independent concept. (L4) 3. Capacity to solve Eigen value decomposition of the signal. (L3)

UNIT-II 8 Lectures Properties of Symmetric Matrices Diagonalization of symmetric matrices, symmetric positive definite and semi definite matrices, principal component analysis (PCA), singular value decomposition. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concepts of symmetric matrices. (L2) 2. Understand the concepts of PCA. (L2) 3. Understand the concepts of SVD. (L2)

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UNIT-III 10 Lectures Stochastic Processes Time average and moments, ergodicity, power spectral density, covariance matrices, response of LTI system to random process, cyclostationary process, and spectral factorization. Learning outcomes: At the end of this unit, the student will be able to

1. Solve time average and moments and power spectral density problems. (L3) 2. Understand the concepts of LTI systems response to random process. (L2) 3. Understand the concepts of spectral factorization and cycle stationary process. (L2)

UNIT-IV 12 Lectures 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. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of Baye’s theorem. (L2) 2. Determine the signal using Baye’s approach. (L3) 3. Understand the concepts of KL Expansion and whitening filters. (L2)

UNIT-V 12 Lectures 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. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of Cramer-Rao lower bound. (L2) 2. Determine the parameters using Markov and clustering algorithms. (L3) 3. Understand and solve signal estimation problems using Kalman and Wiener filter

techniques. (L2)

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Textbooks

1. Gilbert Strang, Introduction to linear algebra , 5 th Edition, Wellesley, MA: Wellesley-Cambridge Press, 2016.

2. Hayes, Mouson. H, Statistical digital signal processing and modeling , John Wiley & Sons, 2009.

References

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. .

3. Thomas Kailath, BabakHassibi, Ali H. Sayed, Linear Estimation , Prentice Hall, 2000. 4. H. Vincent Poor, An Introduction to Signal Detection and Estimation , 2nd Edition,

Springer, 1998.

***

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BIOMEDICAL SIGNAL PROCESSING (Elective-III)


3 0 3 Prerequisites: Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Understand different types of biomedical signals. CO2: Understand the acquisition of various biomedical signal. CO3: Analyze the non-stationary biomedical signals in spectral domain using wavelet transform. CO4: Analyze the chaotic signals using various orthogonal transformation techniques. CO5: Understand biomedical signal classification using pattern recognition techniques.

UNIT-I 10 Lectures Introduction to Biomedical Signal Processing Acquisition, Generation of Bio-signals, Origin of bio-signals, Types of bio-signals, Study of diagnostically significant bio-signal parameters. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of action potential. (L2) 2. Understand the important epochs in various biomedical signals such as ECG, EEG.

(L2) 3. Describe the objectives of biomedical signal processing. (L2)

UNIT-II 10 Lectures 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. Learning outcomes: At the end of this unit, the student will be able to

1. Classify different electrodes used in signal acquisition. (L4) 2. Describe various artefacts and its causes. (L2) 3. Identify the effect of instrumentation or the procedure on the system. (L3)

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UNIT-III 10 Lectures Transform Techniques Biomedical signal processing by Fourier analysis, Biomedical signal processing by wavelet (time frequency) analysis, Analysis (Computation of signal parameters that are diagnostically significant), Classification of signals and noise, Spectral analysis of deterministic, stationary random signals and non-stationary signals, Learning outcomes: At the end of this unit, the student will be able to

1. Describe, apply and evaluate Fourier transform based method for signal processing. (L5) 2. Apply time-frequency domain techniques on non-stationary biomedical signals. (L4) 3. Describe and analyze stationary and non-stationary biosignals. (L4)

UNIT-IV 10 Lectures Dimensionality reduction techniques Principal component analysis, Correlation and regression, Analysis of chaotic signals Application areas of Bio–Signals analysis Multiresolution analysis(MRA) and wavelets, Principal component analysis(PCA), Independent component analysis(ICA) Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of correlation. (L2) 2. Understand the effect of noise correlated with desired biosignals. (L2) 3. Demonstrate the ability to apply various orthogonality transformation techniques such as PCA and ICA to separate correlated noise from the biomedical signals. (L3)

UNIT-V 10 Lectures Pattern classification supervised and unsupervised classification, Neural networks, Support vector Machines, Hidden Markov models. Examples of biomedical signal classification examples. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the significance of pattern recognition techniques. (L2) 2. Classify supervised and unsupervised learning methods. (L4) 3. Apply pattern recognition techniques to classify biomedical signals for diagnosis

purpose. (L3)

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Textbooks

1. R.M.Rangayyan Biomedical Signal analysis: A Case study approach , IEEE press, John Wiley & Sons. Inc, 2002.

2. C. Raja Rao, SK Guha, Principles of Medical Electronics and Biomedical instrumentation, Universities Press, 2001.

References

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. 3. D C Reddy, Biomedical Signal Processing , McGraw Hill, 2005.

4. Duda, R. O., Hart, P.E. and Stork, D.G., Pattern classification, JohnWiley & sons, 2012.

***

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DSP ARCHITECTURE (Elective-IV)


3 0 3 Prerequisites: Digital Signal Processing Course Outcomes: At the end of this course, the student will be able to CO1: Differentiate between DSP processor and General purpose microprocessor. CO2: Understand basic architectural features required in a Digital signal Processor CO3: Understand the use of very large instruction word architecture in achieving high performance through increased instruction parallelism. CO4: Design and implement various signal processing algorithms using 6X series processor. CO5: Design of various blocks of radio receiver using digital hardware.

UNIT-I 10 Lectures Introduction to DSP processors Difference between DSP Processor and General purpose microprocessor architecture. Computational accuracy in DSP implementations: fixed point format, floating point format, sources of error in DSP implementation, A/D conversion errors, DSP computational errors, D/A conversion errors, Basic TMS320 architectures. Learning outcomes: At the end of this unit, the student will be able to

1. Distinguish the difference between DSP processor and General purpose microprocessor (L2)

2. Analyze the issues that determine the computational accuracy of algorithms.(L4) 3. Analyze the difference between fixed point and floating point formats. (L4)

UNIT-II 10 Lectures Basic architectural features of DSP Devices MAC unit, Barrel shifters, Bus architecture and Memory, Data addressing capabilities, Address generation unit, Programmability and Program execution, Speed issues, Hardware looping, Interrupts, Stacks, Relative support, Pipelining and performance, Pipeline depth, Interlocking, Branching, effects, interrupt effects, Pipeline programming models.

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Learning outcomes: At the end of this unit, the student will be able to 1. Understand various building blocks that constitute a programmable digital signal processor from the point of view of implementation. (L2) 2. Analyze the methods to improve the speed of execution from the architectural point of view. (L4) 3. Analyze the performance of pipelining.(L4)

UNIT-III 10 Lectures VLIW architecture TMS320C6X architecture and Instruction set, Pipelining, Addressing modes, Timers, Interrupts, Multichannel Buffered Serial ports, Direct memory access, Code composer studio. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the difference between VLIW architecture and Conventional DSP architecture. (L2)

2. Apply various addressing modes to access memory. (L3) 3. Understand the various peripheral device features. (L2)

UNIT-IV 10 Lectures Implementation of Basic DSP algorithms FIR filter implementation: Band stop & Band pass, Effects of voice using three FIR low pass filters, FIR implementation with pseudorandom noise sequence as input to filter, IIR filter implementation using second order Difference equations, DFT of as a sequence of real numbers, FFT of a real time input signal. Learning outcomes: At the end of this unit, the student will be able to

1. Apply and evaluate TMS320C6X application codes. (L3 & L5) 2. Analyze the implementation of FIR, IIR algorithms. (L4) 3. Analyze the FFT of a real time input signal. (L4)

UNIT-V 10 Lectures FPGA based DSP systems Evolution of FPGA based DSP system design, Introduction to FPGA, Design flow for an FPGA based system design, CAD tools for FPGA based system design, Soft-core processors, FPGA based DSP system design. FPGA's in Telecommunication applications- Coordinate rotation

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Digital Computer (CORDIC) algorithms and its applications, Case study of an FPGA based Digital receiver. Learning outcomes: At the end of this unit, the student will be able to

1. List out some FPGA families and algorithms used for implementation of system on FPGAs. (L2) 2. Apply CORDIC algorithms in communication applications. (L3) 3. Analyze the case study of an FPGA based digital receiver. (L4)

Textbooks 1. Rulph Chassaing, Digital Signal Processing and Applications with the TMS320C6713

and TMS320C6416 DSK , Second edition, John Wiley & Sons, 2011. 2. B.Venkata Ramani and M. Bhaskar, Digital signal Processors, Architecture,

Programming and applications , Tata Mc Graw Hill, 2004.

References 1. Uwe Meyer-Baese, Digital Signal Processing with Field Programmable Gate Arrays , 4 th

edition, Springer Publications, 2014. 2. Avatar singh and S. Srinivasan, Digital Signal Processing , Thomson Publications,

2004.

***

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COGNITIVE RADIO (Elective-IV)


3 0 3 Prerequisites: Wireless Communications Course Outcomes : At the end of this course, the student will be able to CO1: Understand the fundamental concepts of cognitive radio networks. CO2: Develop the cognitive radio, as well as techniques for spectrum holes detection that cognitive radio takes advantages in order to exploit it. CO3: Understand technologies to allow an efficient use of TVWS for radio communications based on two spectrum sharing business models/policies. CO4: Understand fundamental issues regarding dynamic spectrum access. CO5: Comprehend radio-resource management and trading, as well as a number of optimisation techniques for better spectrum exploitation.

UNIT-I 10 Lectures Introduction to Cognitive Radio 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. Learning outcomes: At the end of this unit, the student will be able to

1. Understand cognitive radio architecture. (L2) 2. Analyze the functions of cognitive radio. (L4) 3. Understand the spectrum analysis and decision. (L2)

UNIT-II 10 Lectures 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). Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of spectrum sensing. (L2)

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2. Analyze collaborative sensing. (L4) 3. Understand geo-location database and spectrum sharing business models. (L2)

UNIT-III 10 Lectures Optimization Techniques of Dynamic Spectrum Allocation Linear programming, convex programming, nonlinear programming, integer programming, dynamic programming, stochastic programming. Learning outcomes: At the end of this unit, the student will be able to

1. Understand optimization techniques for dynamic spectrum allocation. (L2) 2. Analyze different programming mechanisms for dynamic spectrum allocation. (L4) 3. Understand stochastic programming. (L2)

UNIT-IV 10 Lectures Dynamic Spectrum Access and Management Spectrum broker, cognitive radio architectures, centralized dynamic spectrum access, distributed dynamic spectrum access, learning algorithms and protocols. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze cognitive radio architecture.(L4) 2. Understand the difference between centralized dynamic spectrum access and

distributed dynamic spectrum access.(L2) 3. Apply learning algorithms and protocols. (L3)

UNIT-V 10 Lectures 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). Learning outcomes: At the end of this unit, the student will be able to

1. Understand the concept of spectrum trading. (L2) 2. Understand about economics theories in DAS. (L2) 3. Understand the classification of auctions. (L2)

Textbooks

1. Ekram Hossain, Dusit Niyato, Zhu Han, Dynamic Spectrum Access and Management in

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Cognitive Radio Networks , Cambridge University Press, 2009. 2. Kwang-Cheng Chen, Ramjee Prasad, Cognitive radio networks , John Wiley & Sons

Ltd., 2009. 3. Bruce Fette, Cognitive radio technology , Elsevier, 2 nd edition, 2009. References 1. Huseyin Arslan, Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems , Springer, 2007. 2. Francisco Rodrigo Porto Cavalcanti, Soren Andersson, Optimizing Wireless Communication Systems Springer, 2009. 3. Linda Doyle, Essentials of Cognitive Radio , Cambridge University Press, 2009.

***

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INTERNET OF THINGS AND APPLICATIONS (Elective-IV)


3 0 3

Prerequisites: Microcontrollers and Interfacing, C Programming Course Outcomes: At the end of the course the student will be able to CO1: Explore the fundamentals and application areas of IoT. CO2: Illustrate the differences between IoT and M2M. CO3: Discuss file handling, date operations, classes. CO4: Build basic IoT applications using Raspberry Pi board. CO5: Develop IoT infrastructure for smart application UNIT-I 10 Lectures Introduction to IoT Introduction to Internet of Things, Physical Design of IoT, Logical Design of IoT, IoT Enabling Technologies, IoT Levels and deployment templates. Learning outcomes: At the end of this unit, the student will be able to

1. Analyze physical and logical design of IoT. (L4) 2. Understand the levels of IoT. (L2) 3. Analyze the development templates. (L4)

UNIT-II 10 Lectures M2M to IoT Introduction, M2M, difference between IoT and M2M, SDN and NFV for IoT. Sensors, Participatory sensing, RFIDs, and Wireless Sensor Networks: Sensor technology, participatory sensing, industrial IoT, automotive IoT, actuator, sensor data communication protocols, radio frequency identification technology, wireless sensor networks technology. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the difference between M2M and IoT. (L2) 2. Analyze SDN and NFV for IoT. (L4) 3. Analyze wireless sensor networks for IoT. (L4)

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UNIT-III 10 Lectures IoT platforms design methodology IoT design methodology, motivation for using python, python data types & data structures, control flow, functions, modules, packages, file handling, date/time operations, classes, python packages of interest for IoT. Learning outcomes: At the end of this unit, the student will be able to

1. Understand the steps for design methodology in IoT. (L2) 2. Analyze the basic programs in python for IoT applications. (L4) 3. Analyze the classes used in python programming. (L4)

UNIT-IV 10 Lectures IoT physical devices & endpoints Block diagram of basic IoT device, Raspberry pi, Linux on Raspberry pi, interfaces, programming Raspberry pi with python, other IoT devices. Learning outcomes: At the end of this unit, the student will be able to

1. understand the basic requirements for IoT device. (L2) 2. analyze the features of Raspberry pi board. (L4) 3. apply python programming for raspberry pi board-interfaces. (L3)

UNIT-V 10 Lectures IoT Physical Servers and Cloud Offerings Introduction to cloud storage models and communication APIs, WAMP – AutoBahn for IoT, Xively, cloud for IoT, Amazon Web services for IoT, case studies illustrating IoT design – home automation, smart cities, smart environment . Learning outcomes: At the end of this unit, the student will be able to

1. Understand the communication interfaces for IoT. (L2) 2. Analyze different types of clouds for IoT. (L4) 3. Create a home automation application using IoT. (L6)

Text Books 1. Arshdeep Bahga, Vijay Madisetti, Internet of Things – A hands-on approach ,

Universities Press, 2015.

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2. Jan Ho¨ ller, Vlasios Tsiatsis , Catherine Mulligan, Stamatis , Karnouskos, Stefan Avesand. David Boyle, From Machine-to-Machine to the Internet of Things- Introduction to a New Age of Intelligence , Elsevier, 2014.

3. Raj Kamal, Internet of Things architecture and design principles McGrawHill publications , 2017.

References 1. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Rob Barton and Jerome Henry,

IoT Fundamentals: Networking Technologies, Protocols and Use Cases for Internet of Things ,Cisco Press, 2017

2. Dieter Uckelmann, Mark Harrison, Michahelles, Florian (Eds), Architecting the Internet of Things , Springer, 2011.

***

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WASTE AS A SOURCE OF ENERGY

(Open Elective)

Course Code:19CH21P1 L P C

2 0 2

Course Outcomes: At the end of the course the student will be able to CO1: Differentiate and characterize different waste CO2: Recognize the various waste to energy conversion processes CO3: Explain the various biochemical conversion processes. CO4: Explain the various thermochemical conversion processes. CO5: Explain the various biomass process to energy conversion.

UNIT-I 6 Lectures Characterization and classification of waste as fuel: agro based, forest residues, industrial waste, domestic waste, Municipal solid waste. Learning outcomes: At the end of this unit, the student will be able to

1. Characterization of waste as fuel (L2) 2. Classify waste from different sources (L4) 3. Describe the characteristics of industrial waste (L2)

UNIT-II 7 Lectures Waste to energy options: combustion (unprocessed and processed fuel), gasification, anaerobic digestion, fermentation, pyrolysis.


1. Describe the process of converting waste to energy using combustion(L2) 2. Illustrate anaerobic digestion (L3) 3. Explain Gasification. (L2)

UNIT-III 7 Lectures Energy from waste- Bio-chemical Conversion: Anaerobic digestion of sewage and municipal wastes,direct combustion of MSW-refuse derived solid fuel, industrial waste, agro residues, anaerobic digestion, biogas production, land fill gas generation and utilization.

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1. Describe the process of converting waste to energy using Anaerobic digestion of sewage and municipal waste(L2).

2. Explain the process of bio-gas production from waste. (L2) 3. Describe direct combustion of Municipal Solid Waste(L2)

UNIT-IV 6 Lectures Energy from waste-thermo chemical conversion: Sources of energy generation, incineration, pyrolysis,gasification of waste using gasifiers, briquetting, utilization and advantages of briquetting, environmental and health impacts of incineration; strategies for reducing environmental impacts. Learning outcomes: At the end of this unit, the student will be able to

1. Describe different thermo-chemical conversion of waste to energy (L2) 2. Summarize the environmental and health impacts of incineration (L2) 3. Outline the strategies for reducing environmental impacts thermos-chemical conversion

(L3) UNIT-V 6 Lectures Biomass energy technologies: Biomass characterization (proximate and ultimate analysis); Biomass pyrolysis and gasification; Biofuels – biodiesel, bioethanol, Biobutanol; Algae and biofuels; Hydrolysis & hydrogenation; Solvent extraction of hydrocarbons; Pellets and bricks of biomass; Biomass based thermal power plants; Biomass as boiler fuel.


1. Describe different biomass technologies(L2). 2. Explain Biomass characterization(L2) 3. Describe the working of Biomass based thermal power plants (L2)

Text Books:

1. Desai Ashok V., Non Conventional Energy , Wiley Eastern Ltd., 1980. 2. Pichtel John, Waste Management Practices Municipal, Hazardous and Industrial , Taylor

& Francis , 2005.

***

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OPERATIONS RESEARCH

(Open Elective)

Course Code: 19ME21P1 L P C

2 0 2

Course Outcomes: At the end of the course, the student will be able to CO1: Formulate a linear programming problem for given problem and solve this problem by using Simplex techniques. CO2: Evaluate sensitivity analysis to the given input data in order to know sensitive of the output. CO3: Apply the concept of non-linear programming for solving the problems involving non-linear constraints and objectives. CO4: Solve deterministic and Probabilistic inventory control models for known and unknown demand of the items. CO5: Apply the dynamic programming to solve problems of discrete and continuous variables. UNIT-I 7 Lectures Optimization techniques, model formulation, models, simplex techniques, inventory control models Learning outcomes: At the end of this unit, the student will be able to

1. C lassify different optimization techniques. (L4) 2. Build a mathematical model for a given problem. (L6) 3. Identify inventory control models for solving given problem. (L1)

UNIT-II 8 Lectures Formulation of a LPP - graphical solution for LPP, revised simplex method - duality theory - dual simplex method - sensitivity analysis - parametric programming

Learning outcomes: At the end of this unit, the student will be able to 1. Formulate a linear programming problem for given problem. (L6) 2. Use simplex method to solve LPP problem. (L3) 3. Apply sensitivity analysis to the given input data in order to know sensitive of the

output. (L3)

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UNIT-III 6 Lectures Nonlinear programming problem - Kuhn-Tucker conditions, CPM/PERT

Learning outcomes: At the end of this unit, the student will be able to 1. Develop Kuhn tucker conditions for a solution of linear programming problems. (L6) 2. Choose a PERT technique for planning and control of time for the given project. (L5) 3. Select CPM technique for control of costs and time for the given project. (L5)

UNIT-IV 7 Lectures

single server and multiple server models - deterministic inventory models - probabilistic inventory control models - geometric Programming


1. List the order of activities in the operations problem. (L1) 2. Differentiate between single server and multi-server models. (L2) 3. Classify deterministic and probabilistic inventory models. (L4)

UNIT-V 7 Lectures

Single and multi-channel problems , sequencing models, dynamic programming, flow in networks, elementary graph theory, game theory simulation

Learning outcomes: At the end of this unit, the student will be able to 1. Differentiate between single and multi-channel problems. (L2) 2. Select the order of jobs to be processed on the machines. (L5) 3. Judge in taking decisions for conflicting objectives. (L5)

Text Books:

1. Kanthi Swarup, P.K. Gupta and Man Mohan, OperationsResearch , 14 th Edition, Sultan chand and son’s, New Delhi, 2008.

2. S. D. Sharma, Operations Research , Kedar Nath and Ram Nath, Meerut,2008.

Reference Books: 1. H.A. Taha, Operations Research, An Introduction , 7 th Edition, PHI, 2008. 2. J.C. Pant, Introduction to Optimisation: Operations Research ,7 th Edition, Jain Brothers,

Delhi, 2008. 3. Hitler Libermann, Operations Research , McGraw Hill Pub., 2009. 4. Pannerselvam, Operations Research , Prentice Hall of India, 2010.

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5. Harvey M Wagner, Principles of Operations Research , Prentice Hall of India, 2010

***

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COMPOSITE MATERIALS

(Open Elective)

Course Code: 19ME21P2 L P C

2 0 2

Course Outcomes: At the end of the course, the student will be able to CO1: Explain the advantages and applications of composite materials. CO2: Describe the properties of various reinforcements of composite materials. CO3: Summarize the manufacture of metal matrix, ceramic matrix and C-C composites. CO4: Describe the manufacture of polymer matrix composites. CO5: Formulate the failure theories of composite materials.

UNIT-I 7 Lectures

Introduction: Definition – Classification and characteristics of Composite materials. Applications of composites. Functional requirements of reinforcement and matrix. Effect of reinforcement (size, shape, distribution, volume fraction) on overall composite performance.

Learning outcomes: At the end of this unit, the student will be able to 1. C lassify various types of composite materials. (L4) 2. Describe the applications of composite materials. (L2) 3. Explain the roles of reinforcement and matrix in a composite material. (L2)

UNIT-II 7 Lectures

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. iso-strain and iso-stress conditions.

Learning outcomes: At the end of this unit, the student will be able to 1. Demonstrate the preparation, layup and curing of composites. (L3) 2. Compare characteristics of various reinforcements. (L5) 3. Formulate methods to compute properties of composites. (L6)

UNIT-III 7 Lectures

Manufacturing of Metal Matrix Composites: Casting – Solid State diffusion technique, Cladding – Hot isostatic pressing. Properties and applications. Manufacturing of Ceramic Matrix Composites: Liquid

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Metal Infiltration – Liquid phase sintering. Manufacturing of Carbon – Carbon composites: Knitting, Braiding, Weaving. Properties and applications.

Learning outcomes: At the end of this unit, the student will be able to 1. Choose manufacturing methods of metal matrix composites. (L5) 2. Recommend manufacturing methods of ceramic matrix composites. (L5) 3. Describe manufacturing methods of C-C composites. (L2)

UNIT-IV 7 Lectures

Manufacturing of Polymer Matrix Composites: Preparation of Molding compounds and prepregs – hand layup method – Autoclave method – Filament winding method – Compression molding – Reaction injection molding. Properties and applications.


1. Explain manufacturing methods of polymer matrix composites. (L2) 2. Choose appropriate manufacturing method to process polymer matrix composites. (L5) 3. Assess properties and applications of polymer matrix composites. (L5)

UNIT-V 7 Lectures

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.


1. Apply theories for failure of composites. (L3) 2. Evaluate the strength of composite. (L5) 3. Design a composite material for a particular application. (L6)

Text Books: 1. R.W.Cahn, Material Science and Technology – Vol 13 – Composites , West Germany,

1994. 2. WD Callister, Jr., Adapted by R. Balasubramaniam, Materials Science and Engineering ,

John Wiley & Sons, NY, Indian edition, 2007 .

Reference Books: 1. K.K.Chawla, Composite Materials , 3 rd Edition, springer, 2012.

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2. Deborah D.L. Chung, Composite Materials Science and Applications , 2 nd Edition, springer, 2010.

***

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MODERN WIRELESS COMMUNICATIONS LAB


Perquisites: Modern Wireless Communication Systems


CO1: Analyze BER characteristics and channel capacity of different modulation schemes CO2: Design and analyze BER characteristics and channel capacity of different modulation schemes with coding. CO3: Comprehend principles of different multiple access techniques. CO4: Analyze Compare the BER and Channel capacity of MIMO Systems. CO5: Evaluate the BER characteristics and PAPR of OFDM. List of Experiments:

1. BER Characteristics of Binary Modulation Schemes

2. BER Characteristics of M-ary Digital Schemes

3. BER Characteristics of Binary Modulation schemes with Channel Coding techniques

4. BER Characteristics of M-ary Modulation schemes with Channel Coding techniques

5. Performance comparison of FDMA, TDMA and CDMA under different channel

conditions

6. BER performance of SISO, SIMO and MIMO

7. Chanel Capacity of SISO, SIMO and MIMO

8. BER characteristics of STBC with MRC

9. BER Characteristics of OSTBC with MRC

10. BER performance of OFDM with different modulation schemes

11. PAPR calculation in OFDM with Hamming code

12. PAPR calculation in OFDM with convolution code

13. PAPR calculation in OFDM with cyclic code

14. BER performance of MIMO-OFDM with different modulation schemes


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PATTERN RECOGNITION AND MACHINE LEARNING LAB (Elective-II)


Perquisites: Pattern Recognition and Machine Learning.


CO1 : Implement maximum likelihood algorithm and Bayes classifier CO2: Execute Linear regression and linear discriminant algorithms

CO3: Demonstrate the process of designing a classifier using various algorithms . CO4: Outline the design process of logic gates using MLP CO5: Analyze SVM classifier for different real time applications

List of Experiments

1. Write a program to find the optimal linear separator in a binary classification problem using Bayesian classifier.

2. Study and write a program to fit a linear regression model for linear or quadratic data.

3. Apply a classifier using perceptron rule for linearly separable data set.

4. Apply MLP based neural network for X-OR classification.

5. Write a Back propagation based Artificial Neural Network learning program to classify Iris flower dataset.

6. Write a program to classify Sentiment of Tweets Using Deep Learning.

7. Write a program to present 2D points classification by AdaBoost-M1

8. Apply support vector machine to classify X-OR gate.

9. Write a program to classify Iris data set using SVM.

10. Write a program to implement k-Nearest Neighbor (KNN) algorithm to classify the iris data set. Print both correct and wrong predictions.

11. Write a program for the optimization of the Ho-Kashyap classification algorithm using appropriate learning samples.

12. Write a program for data classification using Decision Tree.

13. Develop a machine learning method to classifying your incoming mail.

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14. Write a program to segment any image using fuzzy k-mean clustering algorithm.


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ANTENNAS AND RADIATING SYSTEMS LAB (Elective-II)


Perquisites: Antennas and Radiating Systems


CO1: Understand the concepts involved in propagation of EM waves in waveguide. CO2: Determine specifications, design, construct, and test antenna CO3: Explore and use tools for designing, analyzing, and testing antennas. These tools include MATLAB, HFSS, spectrum analyzer, and network analyzer. CO4: Design compact antenna for Wi-Fi applications. CO5: Understand the importance of antenna array .


1. Design half wave dipole antenna

2. Calculation of transmission line parameters (R, L, C & G) and characteristic impedance (Zo)

of a flexible RG58 coaxial cable operating at a frequency of 1GHz.

3. Design of a microstrip feedline with characteristic impedance (Z 0 ) 50 ohm and calculate the

characteristic impedance (Z 0 ) of microstrip line for the obtained feed width.

4. Design of Microwave band reject filter using Split ring resonator.

5. Determine the dimensions (length & width) of a rectangular microstrip patch antenna

(RMSA).

6. Observe the variations of normalized input impedance with feed position of a rectangular

microstrip patch antenna (RMSA).

7. Design of Rectangular Microstrip Patch antenna (RMSA) with different feed techniques viz.,

edge, inset.

8. Design of Rectangular Microstrip Patch antenna (RMSA) using coaxial probe feed.

9. Design of a two element array using microstrip patch antenna

10. Design of a Frequency reconfigurable antenna

11. Analyze the field distribution in rectangular waveguide

12. Construct E-plane and H-plane Tee junctions and compare the field distributions.

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13. Analyze the field distributions in Magic Tee Junction.

14. Design of Truncated corner circularly polarized square Microstrip patch antenna.


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INTERNET OF THINGS AND APPLICATIONS LAB (Elective-II)


0 3 1.5 Prerequisite: Internet of Things and Applications Course Outcomes: At the end of the laboratory work, the student will be able to: CO1: Comprehend the concepts of python programming. CO2: Design Peripheral interfacing with the Raspberry Pi board. CO3: Interface actuators with the Raspberry Pi board. CO4: Generate text to speech conversion using python libraries. CO5: Understand the storage and retrieval of the data in cloud. List of Experiments: 1. Basics of python programming. (Creating variables, conditional operations, logical operations, data types, If-Else, for loop, while loop). 2. Python string operations (creating, concatenating, conversion numbers to strings and strings to numbers, length of string, position of string, slicing) 3. Python list operations (creating, adding, removing, enumerating a list, sorting a list, slicing). 4. Blinking LED using Python programming by interfacing LED to raspberry pi board.

5. Changing the state of LED by interfacing a push button switch and LED to raspberry pi board. 6. Implementation of traffic light system by interfacing 3 LEDs to raspberry pi board.

7. Interface a 7 segment display to raspberry pi board.

8. Interface a LDR to raspberry pi board to change the intensity of LED.

9. Interface a thermocouple to raspberry pi board to read the temperature of the room.

10. Converting text to speech.

11. Controlling servo motors by interfacing to raspberry pi board.

12. Changing direction of DC motors by interfacing to raspberry pi board.

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13. Controlling GPIO’S (changing state of LED) using telegram.

14. Bluetooth controlling GPIO’S in raspberry pi board.

Note: Any TWELVE of the above experiments are to be conducted.

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ENGLISH FOR RESEARCH PAPER WRITING

Course Code: 19HE21A1 L P C

3 0 0

Course Outcomes: At the end of course the s tudent will be able to CO1: Demonstrate writing meaningful sentences and coherent paragraphs CO2: Show conciseness, clarity and avoid redundancy in writing CO3: Summarize, evaluate literature, and write methodology, results and conclusion CO4: Describe how to develop title, write abstract and introduction CO5: Apply correct style of referencing and use punctuation appropriately

Unit-I: 08 Lectures Planning and preparation, word order & breaking up long sentences, structuring sentences and paragraphs

Learning outcomes: At the end of this unit, the student will be able to 1. explain planning and preparation required for research communication (L2) 2. use appropriate word order and write short sentences (L3) 3. demonstrate writing coherent paragraphs and sentences (L3)

Unit-II: 10 Lectures

Being concise, avoiding redundancy, ambiguity and vagueness, literature survey - highlighting your findings, hedging, paraphrasing and plagiarism Learning outcomes: At the end of this unit, the student will be able to

1. demonstrate conciseness, clarity and avoid redundancy (L3) 2. describe the process of literature survey (L2) 3. paraphrase and avoid plagiarism (L2)

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Unit-III: 12 Lectures Sections of a paper – abstract, introduction, etc.review of the literature, writing - methods, results, discussion, conclusions and final check


1. explain how to write abstract and introduction (L2) 2. describe how to summarize and evaluate literature (L2) 3. discuss how to write methodology, discussions, results and conclusion(L2)

Unit-IV: 12 Lectures

Writing – Title, Abstract and Introduction, Review of Literatureand Methods

Learning outcomes: At the end of this unit, the student will be able to 1. demonstrate how to develop title, write abstract and introduction(L3) 2. summarize and evaluate literature (L2) 3. show how to write methodology, discussions, results and conclusion(L3)

Unit V: 08 Lectures

Useful phrases and punctuation,in-text citation and bibliography – MLA/APA styles

Learning outcomes: At the end of this unit, the student will be able to 1. show how to use useful phrases (L3) 2. demonstrate how to use correct punctuation (L3) 3. apply correct style(s) of in-text citation and bibliography (L3

Text Books:

1. Adrian Wallwork, “ English for Writing Research Papers ”, Springer New York Dordrecht Heidelberg, London, 2011.

2. Day R. “ How to Write and Publish a Scientific Paper ” Cambridge University Press, 2006.

3. Goldbort R. “ Writing for Science ” Yale University Press, 2006. 4. Highman N. “ Handbook of Writing for the Mathematical Sciences ”, SIAM.

Highman’sbook, 1998.

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CONSTITUTION OF INDIA

Course Code:19HM21A1 L P C

3 0 0

Course Outcomes : At the end of the course, the student will be able to:

CO1: Describe historical background of the constitution making and its importance for building a democratic India. CO2: Explain the functioning of three wings of the government ie., executive, legislative and judiciary. CO3: Explain the value of the fundamental rights and duties for becoming good citizen of India. CO4: Analyse the decentralisation of power between central, state and local self-government. CO5: Apply the knowledge in strengthening of the constitutional institutions like CAG, Election Commission and UPSC for sustaining democracy.

UNIT-I 10 Lectures

Introduction to Indian Constitution: Constitution’ meaning of the term, Indian Constitution - Sources and constitutional history, Features - Citizenship, Preamble, Fundamental Rights and Duties, Directive Principles of State Policy.

Learning outcomes: At the end of this unit, the student will be able to 1. explain the concept of Indian constitution (L2) 2. apply the knowledge on directive principle of state policy (L3) 3. analyse the History, features of Indian constitution (L4)

UNIT-II 10 Lectures

Union Government and its Administration Structure of the Indian Union: Federalism, Centre- State relationship, President: Role, power and position, PM and Council of ministers, Cabinet and Central Secretariat, Lok Sabha, Rajya Sabha, The Supreme Court and High Court: Powers and Functions;

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Learning outcomes: At the end of this unit, the student will be able to 1. describe the structure of Indian government (L2) 2. differentiate between the state and central government (L5) 3. explain the role of President and Prime Minister (L1)

UNIT-III 10 Lectures

State Government and its Administration Governor - Role and Position - CM and Council of ministers, State Secretariat: Organisation, Structure and Functions

Learning outcomes: At the end of this unit, the student will be able to 1. describe the structure of state government (L2) 2. analyse the role Governor and Chief Minister (L4) 3. explain the role of state Secretariat (L2)

UNIT-IV 10 Lectures

Local Administration - District’s Administration Head - Role and Importance, Municipalities - Mayor and role of Elected Representative - CEO of Municipal Corporation PachayatiRaj: Functions PRI: Zilla Panchayat, Elected officials and their roles, CEO Zila Panchayat: Block level Organizational Hierarchy - (Different departments), Village level - Role of Elected and Appointed officials - Importance of grass-root democracy

Learning outcomes: At the end of this unit, the student will be able to 1. describe the local Administration (L2) 2. compare and contrast district administration role and importance (L5) 3. analyse the role of Myer and elected representatives of Municipalities (L4)

UNIT-V 10 Lectures

Election Commission: Role of Chief Election Commissioner and Election Commission; State Election Commission: Functions of Commissions for the welfare of SC/ST/OBC and women

Learning outcomes: At the end of this unit, the student will be able to 1. know the role of Election Commission apply knowledge (L1) 2. contrast and compare the role of Chief Election commissioner and Commissioner (L5) 3. analyse the role of state election commission (L4)

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Text Books:

1. Durga Das Basu, “ Introduction to the Constitution of India ”, Prentice – Hall of India Pvt.Ltd.. New Delhi

2. Subash Kashyap, “ Indian Constitution ”, National Book Trust 3. J.A. Siwach, “ Dynamics of Indian Government & Politics ” 4. D.C. Gupta, “ Indian Government and Politics ” 5. H.M.Sreevai, “ Constitutional Law of India ”, 4th edition in 3 volumes (Universal Law

Publication) 6. J.C. Johari,” Indian Government andPolitics Hans ” 7. J. Raj “ IndianGovernment and Politics ” 8. M.V. Pylee, “ Indian Constitution DurgaDasBasu, Human Rights in Constitutional Law ”,

Prentice – Hall of India Pvt.Ltd.. New Delhi 9. Noorani, A.G., (South Asia Human Rights Documentation Centre), “ Challenges to Civil

Right), Challenges to Civil Rights Guarantees in India ”, Oxford University Press 2012

E-Resources :

1. nptel.ac.in/courses/109104074/8 2. nptel.ac.in/courses/109104045/ 3. nptel.ac.in/courses/101104065/ 4. www.hss.iitb.ac.in/en/lecture-details 5. www.iitb.ac.in/en/event/2nd-lecture-institute-lecture-series-indian-constitution

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PROCESSING ENGINEERING AND SIGNAL COMMUNICATION … Engineering and Signal Processing Syllabus (2019...PC 19EC2102 Advanced Digital Signal Processing 3 0 3 PC 19EC2103 Digital Image