electronics and communication engineering - Siddaganga

Academic Year : 2020-21 Batch : 2018-19

Department of Electronics & Communication Engg., SIT, Tumkur. 1

SCHEME & SYLLABUS

OF

V & VI SEMESTERS B.E.

ELECTRONICS AND COMMUNICATION ENGINEERING

AY : 2020-21

(Applicable to 2018-19 Batch)



Vision

To create professionally competent and socially sensitive Electronics and Communication engineers capable of working in multicultural global environment.

Mission

To provide a congenial environment for superior learning experience and offer high quality education relevant to the current and future needs of the society and careers of students

in the field of Electronics and Communication Engineering. Programme Educational Objectives :

The graduates of Electronics and Communication engineering programme are able to : a) Design and build systems for providing solutions to real

life problems in the area of Electronics and Communication.

b) Be a successful entrepreneur, build careers in Industry,

government, public sector undertakings, pursue higher education and research.

c) Work individually, within multidisciplinary teams and

lead the team following sound professional and ethical practices.



Graduate attributes: Program Outcomes (POs)

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.

2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering

sciences.

3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.

4. Conduct investigations of complex problems: Use research-based knowledge and research methods

including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.

5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.

6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.

7. Environment and sustainability: Understand the impact



of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

9. Individual and team work: Function effectively as an

individual, and as a member or leader in diverse teams,

and in multidisciplinary settings. 10. Communication: Communicate effectively on complex

engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.

11. Project management and finance: Demonstrate

knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.

12. Life-long learning: Recognize the need for, and have the

preparation and ability to engage in independent and life-long learning in the broadest context of technological change.



Program Specific Outcomes (PSOs)

A graduate of the Electronics and Communication Engineering Program will demonstrate 1. The ability to analyse and design systems in the areas

related to microelectronics, Communication, Signal Processing and embedded systems for solving real world problems (Professional Skills).

2. The ability to identify problems in the areas of communication and embedded systems and provide efficient solutions using modern tools/algorithm individually or working in a team (Problem solving Skills).



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List of Electives (stream wise):

Microelectronics Signal

Processing Communication

Embedded Systems

Power Electronics RECE37

Digital Image Processing

RECE44

Linear Algebra and Applications

RECE09

Advanced Computer Architecture

RECE26

Machine Learning

RECE19

Random Process RECE17

DSP Algorithm and Architecture

RECE13

Speech Processing

RECE11

Error Control Coding RECE07

Embedded System Design

RECE43

Advanced Signal Processing RECE12

Wavelet Transforms RECE14

Artificial Neural Networks RECE16



MANAGEMENT AND ENTREPRENEURSHIP Contact Hours/Week : 3+0+0 Credits : 3.0

Total Lecture Hours : 39 CIE Marks : 50

Total Tutorial Hours : 00 SEE Marks : 50

Course Code : HSS06

Course Objectives: This course will enable students to:

1. Understand the principles and functions of management.

2. Analyze the importance of planning, organizing, staffing, leading and controlling

organization.

3. Understand and manage engineering design and quality

4. Inculcate entrepreneurial qualities and understand the need of rural

entrepreneurship

5. Acquire knowledge about funding agencies, understand procedure in applying for

funds and analyze the cases of successful entrepreneurs

Unit I

Introduction to Management: Definition of management, management skills, productivity and effectiveness, efficiency, functions and principles of management.

Planning: Nature of planning, types of plans- purpose of vision, mission, goals, objectives strategies, policies; steps in planning, MBO, Strategic planning

Organizing: Formal and informal organization, span of management, the structure and Process of organizing, Organizational structure: line and staff organization, Functional organization, matrix organization. 8 Hours

Unit II

Staffing: Definition, systems approach to HRM, factors affecting staffing, recruitment and selection, job design, skill and characteristics of a manager, selection process and techniques. Leading: Human factors in managing, motivation, Theory X and Y, the hierarchy of needs theory, leadership behaviour and styles. Controlling: Basic control process, critical control points and standards, Benchmarking requirements for effective control. 9 Hours

UNIT – III

Managing Engineering Design and Development: Product and Technology Life Cycles, Nature of Research and Development, Research Strategy and organization, Nature of Engineering Design, Systems Engineering / New Product Development. Managing Production/Operations: Types of production processes, Forecasting, Work measurement, Maintenance and Facilities (Plant) Engineering, Total Quality Management, Lean Manufacturing Engineers in Marketing and service activities: Marketing and the Engineer, Engineers in Service organizations. 8 Hours



UNIT – IV Entrepreneur & Entrepreneurship: Introduction, concept of Entrepreneur, characteristics of an entrepreneur, and qualities of an entrepreneur, functions of an entrepreneur, characteristics of entrepreneurship, factors affecting entrepreneurial growth. Entrepreneurship and economic development-rural, woman and social entrepreneurship Financing and Institutional Support for Entrepreneurship: Startups, business plans, venture capitalists, angel investors, funding agencies -commercial banks, development banks, NBFCS and incubation centres. Innovations and project trends.

8 Hours

UNIT – V Taxation benefits: Depreciation allowances, rehabilitation allowance, investment allowance and other tax concession benefits to an entrepreneur.

2 Case Studies from Stay Hungry and Stay Foolish – Rashmi Bansal, IIM Ahmedabad:

1) Success story of naukri.com by Sanjeev Bikhchandani

2) Success story of Make My Trip by Deep Kalra. 6 Hours

TEXT BOOKS:

1. Harold Koontz,

Heinz Weihrich

Essentials of Management, McGraw Hill

Education, 10th Edition, 2015

2. Lucy C. Morse Managing Engineering and Technology, Pearson Education, 6th Edition, 2015.

3. S.S. Khanka Entrepreneurial Development, S. Chand Publishing, 4th Edition, Reprint 2020.

ISBN 978-81-219-1801-5

REFERENCE BOOKS:

1. James A.F. Stoner, R.

Edward Freeman, Daniel R. Gilbert

Management, Pearson Education, 6th Edition, 2018.

2. Rashmi Bansal Stay Hungry Stay Foolish, IIM Ahmedabad,

1st Edition, 2008.

3. Rashmi Bansal Connect the Dots, Bushfire Publishers,

1st Edition, 2019.

Course Outcomes: After the completion of this course, students will be able to:

1. Explain various functions of management (L2).

2. Apply the knowledge of management principles and strategies in various functional areas. (L3).

3. Manage engineering design and product development (L3).

4. Describe entrepreneurship, its characteristics, and benefits and identify various funding sources for starting a business venture (L3).

5. Explain various taxation benefits enjoyed by an entrepreneur and analyze the characteristics and strategies adopted by successful entrepreneurs. (L2 & L3)



MICROWAVE ENGINEERING

Contact Hours/ Week : 3+1+0 Credits: 3.5



Course Code : 5REC01


Understand lumped and distributed circuit concepts. 1. Discuss different impedance matching techniques for transmission lines 2. Learn the behavior of planar transmission lines and working principle of

vacuum tubes and solid state devices. 3. Understand the working principles of various microwave passive devices

using S-parameters.

UNIT 1 RF Transmission Lines: Concept of lumped elements and distributed elements, Parameters, (Line equations, Lossless line, Distortion less line,

Input impedance- qualitative analysis only), reflection coefficient, transmission coefficient, SWR, standing wave patterns, mismatch losses. Smith charts: Solve basic transmission line problems by graphical approach. 8+3 hours

UNIT 2 Impedance transformations for matching: (i) QWT: Input impedance, applications. (ii) Single stub impedance matching (Smith Chart Approach).

Waveguides: The TEm,n and TMm,n waves in rectangular waveguides-(qualitative analysis only), Excitation of waveguides, Guide terminations, Rectangular resonant cavity. 8+3 hours

UNIT 3

Microwave network theory: S-parameter properties(No proof), S matrix representation of Two-port & multi-port network. Microwave passive devices: Phase shifters, Attenuators, Wave guide Tees (Derivation of S-matrix for E-plane, H-plane & Magic Tee), Isolators,

Circulators, Directional couplers. 8+2 hours

UNIT 4

Planar transmission lines: Microstrip line (geometry, design equations), strip line, coplanar waveguide, slot lines - geometry only. Microwave Filters: Design of Low Pass Filter(Butterworth & Chebyshev filter). Microwave measurements: Introduction, slotted line, spectrum analyzer,

network analyzer, VSWR meter & measurements. 8+3 hours



UNIT 5 Microwave vacuum tube devices: Two cavity Klystron Amplifier, Reflex

klystron, Magnetron, Travelling Wave Tube Amplifier (construction & working principle, No design equations) Microwave solid state devices: PIN diode as a switch, GUNN diode operation,

MESFETs: construction and principle of working. 7+2 hours

Text Books:

1 Matthew N. O.

Sadiku and S.V.

Kulkarni

“Principles of Electromagnetics”, 6th Edition

Oxford Univ. Press, 2015.

2 John D Ryder “Networks, Lines and Fields”, 2nd Edition, PHI,

2015

3 Annapurna Das,

Sisir K Das

“Microwave Engineering”, 3rd Edition,

TMH, 2009.

Reference Book:

1 David M. Pozar “Microwave Engineering”, 4th Edition, John

Wiley & Sons Inc., 2012.

Course Outcomes: After the completion of this course, students will be able to: 1. Analyse the behavior of open wire lines and

waveguides. 2. Apply impedance matching techniques for maximum power

transmission. 3. Analyse waveguide Tees and passive devices using S-parameters. 4: Design micro strip lines and microwave filters. 5. Explain the working of microwave vacuum tube devices, solid

state devices and measurement devices.



DIGITAL SIGNAL PROCESSING

Contact Hours/ Week : 3+1 Credits: 3.5





1. Represent and analyze information from analog world to digital domain.

2. Discuss algorithms to reduce computations involved in converting the

signal from time to frequency domain and vice-versa. 3. Prepare the students to design and realize digital filters for

applications in real world.

UNIT 1 Introduction to Digital Signal Processing: Basic elements of a Digital Signal Processing system, advantages of DSP over analog signal processing,

Concept of frequency in continuous time and discrete-time domain (Section 1.3), Importance of Sampling (Section 1.4), Frequency ranges of natural signals, (Section 4.2.10).

Discrete Fourier Transform: Introduction, Fourier representations of finite- duration sequences, Properties of DFT, Linear convolution using DFT, Computation of Circular convolution and correlation, Relationship of DFT to other transforms, Spectral analysis using DFT.

Filtering of long sequences: Overlap-save method and overlap-add method. 9+3 hours

UNIT 2 Efficient Computation of DFT – Fast Fourier Transform Algorithms:

Decimation-in-time and decimation-in-frequency radix-2 FFT and IFFT algorithms, signal flow graphs, Efficient computation: 2, N point DFT from one N-point DFT, Increasing the resolution (2N point DFT from N point DFT),

Linear filtering approach to computation of the DFT:- Goertzel algorithm and Chirp-z transform algorithm. Dual tone Multi frequency (DTMF) signal detection Discrete Cosine Transform (DCT): Type-II DCT Pair, Properties of DCT and

applications of DCT. (Qualitative analysis) 7+2 hours

UNIT 3 Design and Realization of FIR Filters: Issues in filter design, importance of

linear phase, Frequency response of linear phase FIR filters, Locations of zeros of FIR filters, Design techniques of FIR filters- Windowing method and



Frequency sampling method Applications of FIR filters: Design of Hilbert transformer and Ideal

differentiators Basic structures for FIR systems: Direct, Cascade, Linear Phase and Frequency sampling structures. 7+3 hours

UNIT 4

Design of IIR filters: Elementary properties of IIR filters, Techniques for determining IIR filter coefficients, Frequency transformations in analog domain. Digital filter design from continuous time filters- Impulse invariant technique and Bilinear transformation methods. Comparison of FIR and IIR

filters. (Butterworth and Chebyshev Filter tables can be used) 9+3 hours

UNIT 5

Basic structures for IIR systems: Direct, Cascade, and Parallel structures. Applications: Linear-time invariant systems as frequency selective filters: digital resonators, notch filters, comb filters, all-pass filters, Minimum phase systems, Maximum-phase and Mixed phase systems Musical sound

processing: Single echo and multiple echo filters. 7+2 hours

Text Books:

1 J. G. Proakis and D. G.Manolakis

Digital Signal Processing: Principles, Algorithms

and Applications, Fourth Edition, PHI, 2014.

Reference Book:

1 S. Sanjit K. Mitra Digital Signal Processing: A computer-Based

Approach. TMH. 4/E, 2013.

2 LoLonnie C. Ludeman Fundamentals of digital signal processing,

John Wiley & Sons, 2009.

3 3 V Oppenheim and R. W. W. Shafer

Discrete-Time Signal Processing, PHI, 3/E,2014.


1. Represent and process information in digital domain as a function of time or frequency.

2. Compute the representation efficiently using FFT algorithms and linear filtering approaches.

3. Design a digital FIR filter for a given specification and realize using a given structure.

4. Design a digital IIR filter for a given specification and realize using a given structure.

5. Implement basic signal processing algorithms for applications in Communication and Signal Processing.



COMMUNICATION SYSTEMS-II

Contact Hours/ Week : 4 Credits : 4 Total Lecture Hours : 52 CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50 Course Code : 5REC03


1. Discuss waveform and line coding techniques.

2. Learn baseband pulse shaping techniques.

3. Understand different digital modulation techniques and its

applications.

4. Learn source and channel encoding and decoding techniques.

Unit I Waveform Coding: Introduction to Digital Communication System, Pulse Code Modulation, Quantization Noise and Signal-to-Noise Ratio, Robust

Quantization, Differential Pulse Code Modulation, Delta Modulation, Adaptive Delta Modulation. Line Coding: Discrete PAM signals, Power spectra of discrete PAM signals

(derivation of power spectra for NRZ polar and bipolar formats). 10 Hrs Unit II

Baseband Shaping for Data Transmission: Inter Symbol Interference, Nyquist Criteria for distortion less baseband binary transmission,

correlative coding, Eye Pattern, base-band M-ary PAM systems for data transmission. Detection of Signals in Noise: Geometric Interpretation of signals, Gram-

Schmidt orthogonalization Procedure, Receiver for additive white Gaussian noise channel, Correlation receiver, Matched Filter Receiver. 10 Hrs

Unit III Digital Modulation Techniques: Digital Modulation formats, Coherent binary modulation techniques, Coherent Quadrature Modulation Techniques, Non coherent binary modulation techniques (Non coherent

binary FSK and Differential PSK), Comparison of binary and quaternary modulation techniques. 10 Hrs

Unit IV

Digital Modulation Techniques (Contd). M-ary modulation techniques (M-ary PSK, M-ary QAM, M-ary FSK), Power spectra and bandwidth efficiency, Synchronization. Fundamental Limits on Performance: Uncertainty, Information and

Entropy, Source Coding Theorem, Huffman Coding, Discrete memoryless



Channels, Mutual Information, Channel Capacity, Channel Coding Theorem, Channel Capacity Theorem. 12 Hrs

Unit V Error Control Coding: Types of Codes, Linear Block Codes, Cyclic Codes – Generator polynomial, Parity Check Polynomial, Encoder, Syndrome Calculation, Algebraic Structure, Encoding using shift registers, Syndrome

Calculation, Convolution Coding, Convolution encoder in time and transform domain, Viterbi decoding. 10 Hrs Text Books

1 Simon Haykin Digital Communications, John Wiley, 2012.

Reference Books

1 Sklar B. Digital Communication, Pearson Education, 2007.

2 Lathi B.P. Modern digital and analog communication systems, Tata Mc. Graw-Hill, Ed 5, 2010.


1. Compute signal to quantization noise ratio and compare the

performance of different coding techniques for digital communication. 2. Analyze different base band pulse shaping techniques for error free

reception in a digital communication system. 3. Represent and detect the presence of information signals in the noise

at the receiver. 4. Analyze the performance of different digital modulation techniques in

terms of error rate and spectral efficiency. 5. Apply the knowledge of probability to analyze the information

characteristics of a discrete source. 6. Apply different error control coding techniques to design and develop

error control circuit for a communication channel



DIGITAL SIGNAL PROCESSING LAB

Contact Hours/Week : 3 Credits : 1.5

Total Lecture Hours : CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50 Course Code : 5RECL1


1. Learn signal representations in time and frequency domain using any

open source software. 2. Understand design of digital filter and implement basic signal

processing algorithms by coding using a high level programming

language. 1. Experiments using Octave/Scilab/R:

1. Generation of signals 2. DTFT of discrete-time signals 3. Discrete Fourier transform 4. Linear filtering in time and frequency domains

5. Spectral analysis using DFT 6. Goertzel algorithm: DTMF generation and detection 7. Design of IIR Butterworth low pass filters

8. Design of IIR Chebyshev low pass filters 9. Design of low pass FIR filters using windowing 10. Application of FIR filters: Design of Differentiator and Hilbert transformer.

11. Application of IIR Butterworth filter 12. Application of IIR Chebyshev filter 13. Design of Notch Filter and its application 14. Design of a Resonator

15. Design of comb filter and its application 2. C Programs for :

1. Computation of N –point DFT 2. Linear Convolution 3. Noise Suppression

Course outcomes: After the completion of this course, students will be able to:

1. Determine the response of a system for an arbitrary input



2. Perform spectral analysis of a signal and determine the frequency response of a system

3. Design a digital FIR filter for a given specification and use it in implementing special systems like Hilbert transformer and differentiator.

4. Design a digital IIR filter for a given specification and use it to

suppress unwanted signals. 5. Implement basic signal processing algorithms for applications in

communication, signal processing.

COMMUNICATION SYSTEMS-II LAB

Contact Hours/Week : 3 Credits : 1.5

Total Lecture Hours : CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50 Course Code : 5RECL2


1. Learn determination of various parameters for directional coupler,

power divider and ring resonator using Microwave Integrated Circuit kit

2. Understand use of microwave bench for measurement of unknown impedance and to plot the mode curves of reflex klystron.

3. Understand the process of sampling and digital modulation and demodulation techniques.

4. Learn simulation of source and channel encoder and decoder using

open source software. I. Rig Up Experiments:

A.Experiments Related to Communication Channels and Microwave Engineering:

1. Directional Coupler- To determine coupling factor & isolation Loss for

Parallel-line & Branch-line couplers. 2. (i)Power Divider- To measure S-parameters. (ii)Ring Resonator- To determine Effective Dielectric constant.

3. Measurement of Unknown impedance 4. To plot Mode curves of Reflex Klystron



5. To determine Reflection coefficient & VSWR for different loads using Microwave Office

B. Experiments Related to Communication Systems – II: 6. Flat Top Sampling 7. Binary Phase Shift Keying– Generation and Detection

8. Delta Modulator II. Simulations using open source software (Scilab/Octave):

9. Source Coding – Shannon Coding, Huffman Coding 10. Channel Coding – Linear Block Code, Cyclic Codes

Open Ended Experiments 1. Design a microwave filter for the given specifications using Microwave

Office.

2. Generate a QPSK waveform for a given message signal using two BPSK modulators.

Course Outcomes: After the completion of this course, students will be able to: 1. Analyze the performance of different microwave passive devices and

mode curve pattern of reflex klystron.

2. Measure unknown impedance for different loads using microwave bench.

3. Simulate and analyze different transmission lines. 4. Design a circuit to reconstruct the message signal from flat top

samples and verify sampling theorem. 5. Design and analyze delta modulator and binary phase shift key

modulator and demodulator.

6. Apply an appropriate modern tool to analyze source and channel coding techniques.



PROJECT MANAGEMENT

Contact Hours/ Week : 3 Credits : 3


Sub. Code : HSS07B SEE Marks: 50


CO1 Outline the procedure for analyzing a project CO2 Define the rationale of work breakdown structure CO3 Illustrate the use of network techniques for successful project

implementation

CO4 Design the procedures for overall financial analysis of the project alongside the resource requirement and ideal quality

CO5 Identify the sources and process for communication, risk management

and procurement CO6 Build a comprehensive plan for stakeholder management

Unit 1

Introduction: Project, Program, and portfolio, Operations management,

Product life cycle, Project life cycle, Project management life cycle, Ten

Knowledge areas, Role of project manager and PMO, Ten Project Knowledge

areas with their associated processes

Project Integration Management: Develop project charter, Develop project

management plan, Direct & manage project work, Monitor control project,

Perform integrated change control, Close project / phase.

7 Hrs.

Unit 2

Project scope management: Plan scope management, Collect

requirements, Define scope, Create WBS, Validate Scope, Control scope.

Project Schedule management: Plan schedule management, Define

activities, Sequence activities, Estimate activity durations, Develop

schedule, and Control schedule. 8 Hrs.

Unit 3

Project cost management: Plan cost management, Estimate cost, Determine budget, and Control costs.

Project quality management: Plan quality management, Manage quality and Control quality



Project resource management: Plan resource management, Estimate activity resources, Acquire resources, Develop team, Manage team and

Control resources. 8Hrs. Unit 4

Project communication management: Plan communication management plan, Manage communications and Monitor communications

Project risk management: Plan risk management, Identify risks, Perform qualitative risk analysis, Perform quantitative risk analysis, Plan risk responses, Implement risk responses and Monitor risks.

Project Procurement management: Plan procurement management,

Conduct procurement, Control procurements. 8 Hrs.

Unit 5 Project stake holder management: Identify stake holders, Plan stake holder

management, Manage stake holder engagement, and Monitor stake holder engagement A case study relevant to the domain knowledge of the department is taken up to explain the principles of the project management as brought out

above. 8 Hrs.

Text book:

1 Project Management Book of Knowledge (PMBOK), 6th Edition, PMI, USA

Reference book:

1 Prasanna Chandra

Project Planning: Analysis, Selection, Implementation and

Review, MC- Graw Hill Education, Edition VIII, 2017.



IOT NETWORK TECHNOLOGY

Contact Hours/ Week : 4 Credits: 4





1. Build an understanding of the fundamental concepts of computer

networking.

2. Describe the data communication aspects in computer networks.

3. Discuss protocols related to data link layer and network layer

4. Understand and build IoT framework using Raspberry Pi.

UNIT – I Introduction: OSI model, TCP/IP model, comparison. Physical layer: Topology, Line configuration, Data flow, Manchester encoding, TDM.

Data link layer: Framing, Flow control protocols: stop and wait, sliding window (go back n and selective repeat). (Text-1) 10 hours

UNIT – II

UNIT – III Network Layer- Logical addressing: Ipv4 addresses, Ipv6 addresses, Internet Protocol: Ipv4, Ipv6, and Transition from Ipv4 to Ipv6. Routing Algorithms: Shortest Path Routing, Distance Vector Routing, Link

State Routing. (Text-1). Transport Layer: Process to process delivery, UDP, TCP. 12 hours

UNIT – IV Wired LAN: Ethernet, IEEE standards, Standard Ethernet, IEEE 802.3 Changes in the standards. Wireless LAN: IEEE 802.11, Bluetooth, Connecting LANs, Backbone

networks and virtual LANs. (Text-1) Internet of Things: Introduction, Definition & Characteristics of IoT, Physical design of IoT, Logical design of IoT. (Text-2) 10 hours

Data Link Control: Error detecting code: CRC, HDLC – Information frame. Multiple access protocols; Slotted ALOHA, CSMA/CD, Reservation, Polling, Token passing, CDMA. (Text-1) 10 hours



Unit V IoT enabling Technology: WSN, cloud computing, communication

protocols, embedded systems, IoT levels and deployment templates. IoT Physical device and Endpoints: Introduction to Raspberry Pi, board architecture, OS on Raspberry Pi, Raspberry Pi interfaces, Programming

Raspberry Pi with python. (Text-2). 10 hours TEXT BOOK:

1 Behrouz

A Forouzan

Data Communications and Networking,

5th Edition, McGraw Hill, 2012.

2 Arshdeep Bahga, Vijay Madisetti

Internet of Things- a Hands on Approach,

1st edition, VPT, 2014

REFERENCE BOOKS:


1. Describe OSI reference model, TCP/IP suite and analyze framing and flow control techniques.

2. Demonstrate error control mechanisms and evaluate multiple access protocols

3. Analyse routing algorithms and evaluate the network performance.

4. Compare frame formats of IEEE standards for wired and wireless LANs

5. Apply the knowledge of computer network onto IoT paradigm and analyse IoT physical devices.

1 William Stallings Data and Computer Communication,

8th Edition, Pearson Education PHI, 2011.

2 Andrew

S Tanenbaum

Computer Networks – 5th Edition,

Pearson Education PHI, 2011.



WIRELESS COMMUNICATION

Contact Hours/ Week : 4 Credits : 4 Total Lecture Hours : 52 CIE Marks : 50 Lab work : 0 SEE Marks : 50



1. Learn the cellular concept and traffic theory.

2. Understand challenges in wireless communication and fading in

wireless channel.

Unit I Introduction to 3G/4G/5G Wireless Communication: Introduction, 2G,

3G, 4G, 5G wireless standards, Overview of cellular communication The Cellular Concept - System Design Fundamentals: Introduction, Frequency Reuse, Channel Assignment Strategies, Handoff Strategies,

Interference and System Capacity, Trunking and Grade of Service, Improving Coverage & Capacity in Cellular Systems. 8 Hrs

Unit II

Principles of Wireless Communications: The wireless communication environment, Modelling of wireless channel, System model for narrowband signals, Rayleigh fading wireless channel, BER performance of wireless systems for various modulations, Intuition for BER in a fading channel,

Channel estimation in wireless systems Diversity in Wireless Communications: Multiple Receive Antenna system model, Symbol detection in multiple antenna systems, BER in multi-

antenna wireless systems, Diversity Order. 14 Hrs

Unit III The wireless Channel: Basics of wireless channel modelling, 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 wireless channel, Coherence time of the wireless channel, Jakes model for wireless channel

correlation, Implications of channel time. 12 Hrs Unit IV

Code Division Multiple Access (CDMA): Introduction to CDMA, Basic

CDMA mechanism, Spreading codes based on pseudo-noise sequences, Advantages of CDMA, CDMA near-far problem and power control. 10 Hrs



Unit V Orthogonal Frequency Division Multiplexing (OFDM): Introduction to

OFDM, Subcarrier concept, OFDM signal generation, IFFT/FFT operations in OFDM, Addition of cyclic prefix, End-to-End system modelfor OFDM. Multiple-Input Multiple-Output (MIMO): Introduction to MIMO,MIMO system model. 8 Hrs

Text Books

1 Aditya K Jagannatham Principles of Modern Wireless Communication Systems, Mc. Graw-Hill Education (India), 2019

2 Theodore S.

Rappaport

Wireless Communications-Principles and Practice, 2e, Pearson Education, 2002.

Reference Books

1 Andreas, F. Molisch Wireless Communications, John Wiley &

Sons, 2011.

2 Andrea Goldsmith Wireless Communications, Cambridge University Press, 2005.


1. Apply cellular concepts and traffic theory to evaluate the signal reception performance in a cellular network

2. Model of wireless fading channel and characterize the bit error rate performance in wireless communication system

3. Design of 3G/4G wireless communication system 4. Analyze OFDM based wireless networks 5. Compare 3G, 4G and 5G technologies



WIRELESS COMMUNICATION LAB

Contact Hours/Week : 3 Credits : 1.5 Total Lecture Hours : CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50

Course Code : 6RECL1


1. Appreciate the theoretical concepts in wireless communication through

hands-on experiments using GNU radio software combine with

universal software defined peripheral.

Warm Up Experiments:

1. Two tone loop back

2. Generation and detection of ASK and FSK

Experiments:

1. FM Transmitter and Receiver

2. BER analysis for M-ary PSK

3. Eye Diagram plot

4. M-ary PSK Generation with channel model and reception with frequency

offset, timing offset and phase recovery

5. Digital Broadcasting of audio and video signals

6. Simulation of Frequency selective fading channel

7. Measurement of Doppler spread and delay in multipath channel

8. Orthogonal Frequency Division Modulator and Demodulator

9. CDMA Transmitter and Receiver

10. MIMO wireless communication system

Open Ended Experiments:

1. Comparison of bit error rates for M-ary FSK, M-ary PSK and M-ary QAM

2. Performance Comparison of AWGN, Flat Fading and Frequency Selective

Fading Channel for Wireless Communication System using 4-QPSK




1. Analyse and demonstrate various advanced modulation techniques for given specifications and interpret the results.

2. Use moderate tool to design and demonstrate FM transmitter and FM receiver.

3. Analyse, design and verify the performance of quadrature and M-ary PSK modulation techniques with channel model and signal recovery.

4. Measure multipath propagation parameters. 5. Demonstrate different wireless technologies.

4. Demonstrate the ability to provide efficient solutions for complex engineering problems in the area of wireless advanced communication

individually and working in a team.

IOT NETWORK TECHNOLOGY LAB

Contact Hours/Week : 3 Credits : 1.5 Total Lecture Hours : CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50 Course Code : 6RECL2


1. Implement the Error correction and detection techniques of Data link layer using C programming.

2. Implement the routing protocols of network layer using C programming. 3. Setup and simulate the wired and wireless LAN scenarios using network

simulator and C programming.

4. Integrate the Raspberry Pi controller to Internet of Things (IoT) for sensor data acquisition.

PART A (using C language)

1. Develop a program to find the transmitted data with CRC code for the

given bit stream and generator polynomial.

2. Develop a program for detecting an error in the received data using

CRC for the given generator polynomial.

3. Develop a program to detect and correct the error in the received data

using Hamming code.

4. Develop a program to implement a link state routing algorithm for a

given network graph and build a routing table for the given node.



PART B (using NS-2 simulator) 1. Simulate an Ethernet LAN using N nodes (6-10), change error rate

and data rate and compare throughput. 2. Simulate an Ethernet LAN using N nodes and set multiple traffic

nodes and determine collisions across different nodes. 3. Simulate a simple BSS with transmitting nodes in wireless LAN by

simulation and determine the performance with respect to transmission of packets.

Part C: IoT for sensors data acquisition using IoT Evaluation board

1. Develop a program on Raspberry pi to upload temperature and humidity data to thingspeak cloud.

2. Develop a program on Raspberry pi to publish temperature data to

MQTT broker. 3. Develop a program on Raspberry pi to subscribe to MQTT broker for

temperature data and print it. 4. Develop a program to create TCP server on Raspberry pi and respond

with humidity data to TCP client when requested. Part D: open- ended experiments:

1. Develop a program to perform byte stuffing and bit stuffing for a given

data. 2. Develop a program to encode the given bits using Hamming code. 3. Develop a program to find the loop less path with ‘N’ nodes in a

network using Spanning tree algorithm. 4. Develop a program on Raspberry pi to retrieve temperature and

humidity data from thingspeak cloud. 5. Develop a program to create UDP server on Raspberry pi and respond

with humidity data to UDP client when requested Course Outcomes: After the completion of this course, students will be able to:

1. Apply the knowledge of engineering fundamentals to design an algorithm for a given problem.

2. Create, implement and test the algorithms using C Programming.

3. Simulate and evaluate wired and wireless LAN using Network simulator.

4. Integrate sensors with the Raspberry Pi controller to IoT using various IoT Protocols.

5. Demonstrate the ability to provide efficient solutions for complex engineering problems in the area of embedded systems individually



PROFESSIONAL ELECTIVES

LINEAR ALGEBRA AND APPLICATIONS Contact Hours/Week : 3 Credits : 3 Total Lecture Hours : 39 CIE Marks : 50

Course Code : RECE09 SEE Marks : 50


1. Understanding basic concepts of systems of linear equations, characterize a linear system in terms of consistent or in-consistent. If consistent weather it has unique solution of multiple solutions. Determine linear dependence among vectors.

2. Express vectors in terms of linear combinations of other vectors, their span, and how they are related to each other geometrically.

3. Construct and interpret linear transformations. Characterize linear transforms using the concepts of existence and uniqueness.

4. Determine whether a vector is in a subspace spanned by a set of vectors. Construct a basis for a subspace. Characterize a matrix using the concept of rank, column-space, null-space.

5. Diagonalize a matrix using eigen vectors and eigen values. Construct an orthogonal basis for a subspace using Gram-Schmidt Process.

Determine the best possible solution to an inconsistent system. Develop a regression model for a set of data points

6. Develop an algorithm to transform a number of (possibly) correlated variables into a smaller number of uncorrelated variables and thereby achieve dimensional reduction. Apply principle component analysis for

feature extraction

Unit-1 System of Linear Equations, Row Reduction and Echelon Forms, Vector

Equations, Linear combinations, Matrix equation Ax=b, Solution sets of linear systems, Linear Independence. 7 Hrs

Unit-2

Linear Transformation, Matrix of linear transformation, Existence and Uniqueness, Isomorphism, Inverse of a matrix, Invertible matrix theorem, Determinant, determinant as area & volume. 8 Hrs

Unit-3

Vector Spaces and subspaces, null spaces, column spaces and linear

transformations, linearly independent sets: Bases, Spanning Set, Dimension of a vector space, rank. 8 Hrs



Unit-4 Eigen values & Eigenvectors, The Characteristic Equation, Diagonalization,

Inner product, outer product, length and orthogonality, orthogonal sets, orthogonal projections, Gram-Schmidt Process, Least square problems, Least-square lines. 8 Hrs

Unit-5

Diagonalization of symmetric matrices, Quadratic forms, Constrained optimization, Singular value decomposition, Principal Component Analysis: Application to image processing. 8 Hrs Text Books:

1. David C. Lay Linear Algebra and its Application, Pearson

Education, Fifth Edition. 2016

Reference Books:

1. Strang, G. Linear Algebra and its Applications, Third Edition, Thomson Learning.

2. Seymour

Lipschutz

Schaum's Outline of Linear Algebra,

Sixth Edition. 2017.

3. Kenneth Hoffman and

Ray Kunze

“Linear Algebra”, 2nd edition, Pearson Education (Asia) Pte. Ltd.Prentice Hall of India, 2004.


1. Interpret existence and uniqueness of solutions to linear equations 2. Evaluate basis and dimension of image and kernel of transformation 3. Construct a spanning set/basis for a vector space or subspace.

4. Apply Gram-Schmidt Process to construct an orthogonal basis for a subspace.

5. Evaluate the best possible solution for an inconsistent system with least square error.

6. Develop a suitable regression model for the given set of data. 7. Apply mathematical tools such as PCA and SVD for dimensional

reduction (feature extraction) and image processing applications.



RANDOM PROCESSES



Sub. Code : RECE17 SEE Marks : 50

Course objectives: To introduce students to the basic tools required to build and analyze probabilistic models

Unit 1

Review of Probability Theory-Experiments, sample space, Events, Axioms,

Joint and conditional probabilities,. Baye’s Theorem, Independence, Discrete Random Variables, Cumulative distribution function (CDF), Probability density function (PDF), Gaussian random variable, Uniform RV, Exponential RV. 8 Hrs

Unit 2

Operations on a Single R V: Expected value, Expected value of functions of Random variables, Moments, Central Moments, Conditional expected

values. Transformation of Random variables. 8 hrs

Unit 3

Pairs of Random variables, Joint Cumulative distribution function, Joint Probability density function, Joint probability mass functions, Conditional Distribution, density and mass functions, Expected values involving pairs of Random variables, Independent Random variables, Jointly Gaussian

Random variables. 8 Hrs

Unit 4 Multiple Random Variables: Joint and conditional probability mass

functions, CDF, PDF, Expected value involving multiple Random variables, Gaussian Random variable in multiple dimensions. 7 Hrs

Unit 5

Random Process: Definition and characterization, Mathematical tools for studying Random Processes, Stationary and Ergodic Random processes, Properties of Autocorrelation function. Example Processes: Markov processes, Gaussian Processes, Poisson Processes. 8 Hrs

TEXT BOOK:

1. S L Miller and D C Childers

Probability and Random processes: with applications to Signal processing and communication Academic Press/ Elsevier, Second Edition, 2014



REFERENCE BOOKS:

1. A. Papoullis and S U Pillai

Probability, Random variables and stochastic processes McGraw Hill, 4th Edition, 2002.

2. Peyton Z Peebles Probability, Random variables and Random signal principles TMH 4th Edition 2007

3. H Stark and Woods

Probability, statistics and random processes for engineers, Pearson 2012.

Course outcomes: After the completion of this course, students will be able to

1. Apply the knowledge of basics of probability and random variable theory to solve problems in communication systems.

2. Identify various parameters that describe important features of a random variable and use them to analyze randomness in data

3. Perform computations and model random phenomena using pairs of

random variables

4. Develop matrix notation to represent and analyze multidimensional random variables

5. Identify mathematical tool to characterize various random processes

such as Markov, Gaussian, Poisson etc.



ADVANCED COMPUTER ARCHITECTURE



Sub. Code : RECE26 SEE Marks: 50

Course Objectives: This course will enable students to: 1. Learn multiprocessors and multicomputer.

2. Compare the performance issues related to parallel processing.

Unit 1 Parallel computer models: The state of computing, Multiprocessors and

multi computers, Multi-vector and SIMD computers. 7 Hrs

Unit 2 Program and network properties: Conditions of parallelism, Data and resource Dependences, Hardware and software parallelism, Program partitioning and scheduling, Grain Size and latency, Program flow

mechanisms, Control flow versus data flow, Data flow Architecture, Demand driven mechanisms, Comparisons of flow mechanisms. 8 Hrs

Unit 3

Principles of Scalable Performance: Performance Metrics and Measures, Parallel Processing Applications, Speedup Performance Laws, Scalability Analysis and Approaches. 7 Hrs

Unit 4 Advanced processors: Advanced processor technology, Instruction-set Architectures, CISC Scalar Processors (VAX 8600, Motorola MC 68040) RISC Scalar Processors (SPARC, Intel i860) Superscalar Processors (IBM

RS/6000), VLIW Architectures, Vector and Symbolic processors. 9 Hrs

Unit 5 Pipelining: Linear pipeline processor, nonlinear pipeline processor, Instruction pipeline Design, Mechanisms for instruction pipelining,

Dynamic instruction scheduling, Branch Handling techniques, branch prediction. 8 Hrs

TEXT BOOK :

1 Kai Hwang Advanced computer architecture; Edition 3, TMH, 1993.

REFERENCE BOOKS:

1 Kai Hwang

and Zu

Scalable Parallel Computers Architecture,

MGH. 1998.

2 M.J Flynn Computer Architecture, Pipelined and



Parallel Processor Design, Narosa Publishing. 1995, Edition I

3

D.A.Patterson

And

J.L.Hennessy

Computer Architecture: A quantitative Approach, Morgan Kauffmann Feb., 2002.

Edition V, 2011

Course Outcomes: After the completion of this course, students will be able to: 1. Analyze the various parallel computing models like multiprocessors

and multicomputers, multi-vector and SIMD computers.

2. Identify the program and network properties. 3. Analyze the principles of scalable performance such as performance

metrics and measures, parallel processing applications, speedup performance laws.

4. Analyze the advanced processor technology, linear and nonlinear pipeline processor and instruction pipeline design.

DIGITAL IMAGE PROCESSING

Contact Hours/Week : 3 Credits : 3 Total Lecture Hours : 39 CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50

Course Code : RECE44


1. Understand fundamentals of digital image processing and image

processing algorithms Unit-1

Digital Image Fundamentals: Fundamental Steps in Digital Image Processing, Components of an Image Processing System, Image Sampling

and Quantization, Some Basic Relationships between Pixels, connected component analysis. Two-dimensional orthogonal & unitary transforms, Two dimensional

Discrete Fourier transform, Discrete cosine transform, Hadamard transform, Haar transform, KL transform. 8 Hrs

Unit -2

Enhancement in Spatial and Frequency Domain: Some Basic Intensity Transformation Functions, Histogram Processing, Fundamentals of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters.

Image Smoothing Using Frequency Domain Filters, Image Sharpening Using Frequency Domain Filters. 8 Hrs



Unit -3 Image Restoration: A Model of the Image Degradation/Restoration Process,

Restoration in the Presence of Noise Only-Spatial Filtering, Linear, Position-Invariant Degradations, Estimating the Degradation Function, Inverse Filtering, Minimum Mean Square Error (Wiener) Filtering, Geometric Mean Filter. 8 Hrs

Unit-4 Image Segmentation: Fundamentals, Point, Line and Edge Detection, Hough transform, Thresholding, Region-Based Segmentation. 7 Hrs

Unit -5 Morphological Image Processing: Preliminaries, Erosion and Dilation, Opening and Closing, The Hit-or-Miss Transformation, Some Basic

Morphological Algorithms. 8 Hrs

TEXT BOOKS:

1 Rafael C. Gonzalez and Richard E. Woods

Digital Image Processing, IV edition, Pearson

Education, 2018.

2 Anil K. Jain Fundamentals of Digital Image Processing, PHI,2011.

REFERENCE BOOKS:

1 Jayaraman, Esakkirajan, Veerakumar

Digital Image Processing and Analysis, Mc

Graw Hill India, 2009.


1. Identify various steps and components in a digital image processing system, analyze digital images in spatial and transform domain

2. Choose a suitable technique in spatial or frequency domain to enhance a given image

3. Develop a suitable model for image degradation and perform image restoration

4. Apply various image segmentation techniques to partition image into regions or objects

5. Apply suitable morphological operations to extract image components that are useful in the representation and description of region shapes

6. Use modern engineering tools to develop image processing systems working in a team



MACHINE LEARNING





1. Learn a spectrum of machine learning algorithms with a sound

mathematical background along with the technical know-how of

applying these algorithms for different real-world applications.

Unit -1

Introduction :

Review of Probability Theory and Linear Algebra, Probability densities,

Expectations and covariance, Bayesian probabilities, Curve fitting, Bayesian

curve fitting, Model selection, The Curse of Dimensionality, Decision Theory,

Minimizing the misclassification rate, Minimizing the expected loss, The

reject option, Inference and decision, Loss functions for regression 8 Hrs.

Unit -2

Linear Models for Regression:

Linear Basis Function Models, The Bias-Variance Decomposition: Maximum

likelihood and least squares, Geometry of least squares, Bayesian Linear

Regression, Bayesian Model Comparison, Limitations of Fixed Basis

Functions 8 Hrs.

Unit -3

Linear Models for Classification :

Discriminant Functions, Two classes and multiple classes, Fischer linear

discriminant, Probabilistic Generative Models, Maximum likelihood

solution, Discrete features, Probabilistic Discriminative Models, Fixed basis

functions, Logistic regression, Multiclass logistic regression, Perceptron,

Support Vector Machines 8 Hrs.

Unit -4

Unsupervised Learning :

Clustering: Agglomerative clustering, Batchelor and Wilkins algorithm,

Graph-based clustering, k-means, adaptive hierarchical clustering,

Gaussian mixture model 7 Hrs.



Unit -5 Neural Networks :

Neural Networks - Introduction, Early Models, Perceptron Learning, Feed-forward Network Functions, Network Training, Parameter optimization, Local quadratic approximation, Gradient descent optimization Error Backpropagation, Backpropagation 8 Hrs.

TEXT BOOKS:

1. Christopher Bishop Pattern Recognition and Machine Learning, Springer, 2011

2. Richard O Duda, Peter E Hart, David G Stock

Pattern Classification, John Wiley and Sons, Authorised reprint by Wiley India, 2012.

REFERENCE BOOKS:

1 S.Theodoridis and K.Koutroumbas

Pattern Recognition, 4th Ed.,

Academic Press, 2009.

2 Earl Gose, Richard Johnsonbaugh and Steve Jost,

Pattern Recognition and Image

Analysis, Prentice-Hall of India, 2016.


1. Explain the fundamental issues and challenges of machine learning: data collection, pre-processing, feature extraction, training, evaluation, model selection, model complexity, Error criterion (L2)

2. Use Bayesian decision theory to determine the discriminant function for a two-class problem (L3)

3. Apply algorithms to learn linear regression models to predict the value of a continuous valued output given a training data consisting of univariate/multivariate input features (L4)

4. Apply learning algorithms based on logistic regression, Support Vector Machines to predict discrete valued output given a training data comprising of features and corresponding class labels (L4)

5. Apply algorithms based on neural networks to perform simple learning tasks like speech recognition, digit recognition, optical

character recognition and similar cognitive applications (L4)

6. Apply unsupervised learning algorithms to learn patterns from given training set of unlabeled data points (L4)

(Knowledge levels: L1: Remembering, L2: Understand, L3: Apply, L4: Analysis, L5: Evaluate, L6: Create)

Academic Year : 2020-21 Program : ECE


SPEECH PROCESSING


1. Understand the characteristics of speech signal

2. Apply signal processing concepts to speech signal

3. Get an insight into a few applications of speech processing.

Unit-I

Production and Classification of Speech Sounds: Anatomy and physiology of speech production, spectrographic analysis of speech, categorization of speech sounds Digital models for the speech signal: The acoustic theory of speech production. 6 Hrs

Unit-2 Time domain models for speech processing: Short-time energy, average

magnitude, average zero-crossing rate, speech vs. silence discrimination using energy and zero-crossings, pitch period estimation using a parallel processing approach, short-time autocorrelation function, average magnitude difference function, pitch period estimation using autocorrelation

Short-time Fourier analysis: Fourier transform interpretation, Linear filtering interpretation, Sampling rates of STFT in time and frequency, Filter bank summation method of short-time synthesis, Overlap addition method

of short-time synthesis. 10 Hrs Unit -3

Homomorphic Speech Processing: Homomorphic systems for convolution, complex cepstrum of speech, pitch detection, formant estimation. 7 Hrs

Unit -4

Linear prediction analysis of speech: Principles of linear prediction, Computation of the gain for the model, Solution of the LPC equations,

Comparison between autocorrelation and covariance methods, Frequency domain interpretation of mean squared prediction error, synthesis of speech from LP parameters, pitch detection and formant analysis using LPC

parameters. 8 Hrs Unit -5

Applications: Speaker recognition systems, speech recognition systems, isolated word recognition, connected word recognition and large vocabulary

word recognition, hidden Markov models, Three basic problems of HMM, Types of HMM. 8 Hrs






Text Book:

1

Lawrence R. Rabiner and

Ronald W. Schafer

Digital processing of speech signals, Second Indian Reprint, Pearson Education 2005

Reference Books:

1 Thomas F. Quatieri Discrete-time speech signal processing

Principles and Practice, First Indian Reprint, Pearson Education 2004

2 Lawrence R.

Rabiner,

Biing-Hwang Juang, B. Yegnanarayana

Fundamentals of speech recognition”,

Pearson Education, 2009

Course Outcomes: After the completion of this course, students will be able

to:

1. Classify speech sounds based on source of excitation in English and Hindi languages (L2)

2 Use time domain features like short-time energy and zero crossing rate (ZCR), autocorrelation function, average magnitude difference

equation for making speech/non speech distinction (L3)

3. Use short time Fourier transform for analysis and synthesis of speech (L2)

4. Compare acoustic model, linear prediction and homomorphic filtering approaches for modelling speech (L3)

5. Distinguish between template matching, vector quantization and probabilistic model, HMM for use in speech recognition (L2)

6. Demonstrate communication skills and capacity for self learning through technical paper reading and report writing (L3)



ADVANCED SIGNAL PROCESSING





1. To understand the fundamentals of multirate signal processing and its applications in communication systems and signal processing

Unit-1

Review of Signals and Systems – Discrete time processing of continuous signals - Frequency domain analysis of a digital filter; Quantization error;

Fourier Analysis – DFT, DTFT, DFT as an estimate of the DTFT for Spectral estimation. DFT for convolution, DFT/DCT for compression, FFT. Ideal Vs non ideal filters,Digital Filters – State Space realization, Robust implementation of Digital Filters, Robust implementation of equi – ripple

FIR digital filters. 8 Hrs

Unit-2

Multirate Systems and Signal Processing. Fundamentals – Problems and definitions; Up sampling and down sampling; Sampling rate conversion by a rational factor; Multistage implementation of digital filters; Efficient implementation of

multirate systems. 8 Hrs Unit -3

DFT filter banks and Transmultiplexers – DFT filter banks, Maximally Decimated DFT filter banks and Transmultiplexers. Application of

transmultiplexers in communications Modulation. 8 Hrs

Unit -4

Maximally Decimated Filter banks – Vector spaces, Two Channel Perfect Reconstruction conditions; Design of PR filters Lattice Implementations of Orthonormal Filter Banks, Applications of Maximally Decimated filter banks

to an audio signal. 8 Hrs Unit -5

Introduction to Time Frequency Expansion; The STFT; The Gabor Transform, The Wavelet Transform; The Wavelet transform; Recursive Multi

resolution Decomposition. 7 Hrs Text Books:

1 Roberto Cristi

Modern Digital Signal Processing, Cengage Publishers, India, (erstwhile Thompson Publications), 2003.



Reference Books:

1 S.K. Mitra Digital Signal Processing: A Computer Based Approach, III Ed, Tata McGraw Hill, India, 2007.

2 E.C. Ifeachor and

B W Jarvis

Digital Signal Processing,

a practitioners approach, II Edition, Pearson Education, India, 2002 Reprint.

3 Proakis and

Manolakis

Digital Signal Processing, Prentice Hall 1996 (third

edition).

Course Outcomes : After the completion of this course, students will be

able to:

1. Design and Analyze discrete time systems and implement

2. Derive an efficient implementation of discrete time system using multirate operations and polyphase decomposition

3. Design and analyze filter banks and transmultiplexers using DFT concept

4. Analyze perfect reconstruction filter banks using orthogonal basis functions and time frequency representation of signals

5. Demonstrate the capacity of self-learning and communication skills through simulation of discrete time systems using

Matlab/Scilab/Simulink



WAVELET TRANSFORMS





1. To establish the theory necessary to understand and use wavelets in

signal processing.

Unit-1 Introduction: Review of Fourier theory, why wavelets, filter banks, multi-resolution analysis? Continuous time bases and wavelets: Introduction, C-T wavelets, definition

of CWT, CWT as a correlation, Constant Q-Factor filtering interpolation and time-frequency resolution, CWT as an operator, inverse CWT. 10 Hrs

Unit-2 Discrete-time bases and wavelets: Approximation of vectors in nested linear vector spaces, (i) example of approximating vectors in nested subspaces of a finite dimensional linear vector space: (ii) example of approximating vectors in nested subspaces of an infinite dimensional of vectors in linear vector

spaces. 8 Hrs Unit -3

Multi-resolution analysis: Formal definition of MRA, construction of a

general orthonormal MRA (i) scaling function and subspaces, (ii) implication of dilation equation and orthogonality, a wavelet basis for MRA (i) two scale relations for (t), (ii) basis for the detail subspace (iii) direct sum decomposition, digital filtering interpolation (i) decomposition filters, (ii)

reconstruction of the signal, Example MRA (i) bases for the approximations subspaces and Harr scaling function, (ii) bases for detail subspaces and Harr wavelet. 10 Hrs

Unit -4

Examples of wavelets: Examples of orthogonal basis generating wavelets, (i) Daubechies D4 scaling function and wavelet (ii) band limited wavelets, interpreting orthogonal MRAs for discrete time MRA (iii) basis functions for

DWT. 6 Hrs Unit -5

Applications: Speech, audio, image and video compression, denoising, feature extraction, inverse problems. 5 Hrs

Text Book:

1 Raghuveer M. Rao and Ajit S. Bopardikar

Wavelet transforms-Introduction to theory and

applications, Pearson Education 2000



Reference Books:

1 Prasad and Iyengar Wavelet transforms, Wiley Eastern, 2001

2 Gilbert Strang and Nguyen Yegnanarayana

Wavelet and filter banks, Wellesley Cambridge press, 1996

POWER ELECTRONICS





1. Understand fundamentals of power semiconductor devices and its

applications Unit - 1

Power Semiconductor Devices: Introduction, power semiconductor

devices, control characteristics of power devices, types of power electronic

circuits, thyristor characteristics, two transistor model of thyristor,

Thyristor turn on, gate triggering circuits, Resistance firing circuit,

resistance capacitance firing circuit, resistance capacitor full wave firing

circuit, UJT relaxation oscillator, PUT triggering, natural commutation and

forced commutation (qualitative analysis) 9 Hrs.

Unit – 2

AC Voltage controllers : Introduction, principle of on-off control, principle

of phase control, single phase bidirectional controller with resistive load,

single phase controllers with inductive load . 7 Hrs.

Unit – 3

Controlled Rectifiers : Introduction, principle of phase controlled converter

operation, single phase semi converter, single phase full converter, single

phase dual converter, three phase semi converter, three phase full

converter. 7 Hrs.

Unit - 4

DC Choppers : Introduction, basic chopper classification, basic chopper

operation, control strategies, chopper configuration, Jones and Morgan

chopper 8 Hrs.



Unit - 5

Inverters : Introduction, classification of inverters, performance parameters

of inverters, single phase half bridge voltage source inverter, single phase

full bridge inverters, Three phase inverters 1200 conduction mode and 1800

conduction mode, self commutated inverters, parallel inverter. 8 Hrs.

TEXT BOOKS:

1 Muhammad

H.Rashid

Power Electronics: Circuits, Devices and

Applications, Pearson Education India

Publisher, 3rd Edition, 2014.

2 M. D. Singh

&K.B.Khanchandani

Power Electronics, Tata McGraw-Hill Education

Publisher, 2nd Edition, 2011.

REFERENCE BOOKS:

1 Daniel W. Hart Power Electronics, Tata McGraw-Hill Education

Publisher, 2011.


1. Explain the characteristics of power semiconductor devices, design

and compare different triggering methods.

2. Contrast different ac voltage converters

3. Analyse and design single phase and three phase controlled rectifiers.

4. Compare different configurations of choppers for power control.

5. Illustrate the principle of operation of inverters and distinguish

between different inverters.

6. Demonstrate capability of self learning, team work and

communication skills through micro project.



DSP ALGORITHMS AND ARCHITECTURE





1. Learn the architecture of digital signal processors and implementation

aspects of DSP algorithms.

Unit-1

Introduction to Digital Signal Processing Introduction, A Digital Signal-Processing System, The Sampling Process,

Discrete Time Sequences, Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT), Linear Time-Invariant Systems, Digital Filters, Decimation and Interpolation. Architectures for Programmable Digital Signal-Processing Devices: Introduction, Basic Architectural Features,

DSP Computational Building Blocks. 8 Hrs

Unit-2 Architectures for Programmable Digital Signal-Processing

Devices(Contd…): Bus Architecture and Memory, Data Addressing Capabilities, Address Generation Unit, Programmability an Program Execution, Speed Issues, Features for External Interfacing. Programmable Digital Signal Processors: Introduction, Commercial Digital Signal-

processing Devices, Architecture of TMS320C54xx Digital Signal Processors, Data Addressing Modes of TMS320C54xx Processors. 8 Hrs

Unit -3

Programmable Digital Signal Processors (Contd…): Memory Space of TMS320C54xx Processors, Program Control, TMS320C54xx Instructions and Programming, On-Chip peripherals, Interrupts of TMS320C54xx Processors, Pipeline Operation of TMS320C54xx Processors. 8 Hrs

Unit -4

Implementations of Basic DSP Algorithms Introduction, The Q-notation, FIR Filters, IIR Filters, Implementation of FFT

Algorithms, Introduction, An FFT Algorithm for DFT Computation, A Butterfly Computation, Overflow and Scaling, Bit-Reversed Index Generation, FFT Implementation on the TMS320C54xx, Computation of the Signal Spectrum. 8 Hrs



Unit -5 Interfacing Memory and Parallel I/O Peripherals to Programmable DSP

Devices Introduction, Memory Space Organization, External Bus Interfacing Signals, Memory Interface, Parallel I/O Interface, Programmed I/O, Interrupts and I/O, Direct Memory Access (DMA). 7 Hrs

TEXT BOOK:

1 Avatar Singh and S. Srinivasan

Digital signal processing Implementations using DSP

microprocessors with examples from TMS320C54xx, Tenth Indian Reprint, Cengage Learning, 2010

REFERENCE BOOKS:

1 Texas Instruments TMS320C54x DSP Reference Set Vol. 1: CPU and peripherals, 2001

2 Texas Instruments TMS320C54x DSP Reference Set Vol. 2: Mnemonic Instruction Set, 2001

3 Ifeachor E. C., Jervis B. W.

Digital signal processing: A practical approach 2e, Pearson Education, 2002

4 B. Venakataramani and M. Bhaskar

Digital signal processors, TMH, 2002


1. Analyse basic signal processing concepts and apply them for DSP

processor implementation.

2. Identify the need of basic DSP operations, formulate the logic and

provide hardware solutions to implement these operations

3. Identify and apply the architectural features of TMS320C54xx to

provide efficient design solutions

4. Develop ALP for TMS320C54xx DSP processors exploring different

functional units and addressing modes

5. Provide solutions for signal processing problems by implementing

FFT, FIR and IIR algorithms on TMS320C54xx processor.

6. Design an interfacing circuit to connect DSP processor to memory and

peripherals.



ERROR CONTROL CODING




Course objectives: To introduce various techniques of traditional and modern coding theory concepts

Unit 1

Introduction to Algebra: Groups, Fields, Binary Field Arithmetic,

Construction of Galois Field GF (2m) and its basic properties, Computation using Galois Field GF (2m) Arithmetic. 8 Hrs

Unit 2

Vector spaces : Properties, matrices, Construction of G and H matrix, Single parity check codes, repetition codes, self dual codes Reed – Muller codes. Systematic and Non systematic cyclic codes, Encoding using Multiplication

circuits, Encoder circuit using parity polynomial, Meggitt decoder, Error trapping decoding, Cyclic Hamming codes, (23, 12) Golay code, Shortened cyclic codes. 8 Hrs

Unit 3 BCH Codes: Binary primitive BCH codes, Decoding procedures, Implementation of Galois field Arithmetic, Implementation of Error correction. Non – binary BCH codes: q – ary Linear Block Codes, Primitive

BCH codes over GF (q), Reed – Solomon Codes, Decoding of Non – Binary BCH and RS codes: The Berlekamp - Massey Algorithm. Majority Logic Decodable Codes: One – Step Majority logic decoding, one –

step Majority logic decodable Codes, Two – step Majority logic decoding, Multiple – step Majority logic decoding. 8 Hrs

Unit 4

Convolutional Codes: Encoding of Convolutional codes, Structural properties, Distance properties, Viterbi Decoding Algorithm for decoding, Soft – output Viterbi Algorithm, Stack and Fano sequential decoding Algorithms, Majority logic decoding. 8 Hrs

Unit 5

Turbo coding: Introduction to Turbo coding and their distance properties,

Design of Turbo codes.



Burst – Error – Correcting Codes: Burst and Random error correcting codes, Concept of Inter – leaving, cyclic codes for Burst Error correction – Fire

codes, Convolutional codes for Burst Error correction. 7 Hrs Text Books:

1 Shu Lin & Daniel J. Costello, Jr

Error Control Coding, Pearson / Prentice Hall, Second Edition, 2011.

Reference Books:

1 Blahut, R.E. Algebraic Codes for Data Transmission

Cambridge University Press, 2012.

Course outcomes: After the completion of this course, students will be able to:

1. Construct Galois fields as per the requirement and perform computations using Galois Field arithmetic.

2. Design various linear block codes and cyclic codes as per the specifications and develop encoding/decoding circuits.

3. Design BCH codes as per the specifications and perform Decoding.

4. Perform encoding/decoding of convolution codes.

5. Design Turbo Codes, Burst and random error correcting codes.



EMBEDDED SYSTEM DESIGN




Couse Objectives: At the end of this course students will

1. Exhibit the knowledge of representing the hardware and software in unified way.

2. Formulate the problems and choose suitable design, processor technology and integrating the embedded system.

3. Develop a supplement to design a software architecture in real time digital systems.

Unit 1 Introduction: Overview, Optimizing the Metrics, Processor Technology, Design Technology Custom Single Purpose Processors: Custom Single Purpose Processors

design, optimizing Program, FSMD, data path & FSM. 8 hrs

Unit 2

General purpose processors and ASIP’s: Software and operation of general purpose processors, Programmer’s View, Development Environment, ASIP’s, Microcontrollers, DSP. Standard Peripherals: Timers and Applications, PWM’s & Application,

UART, Stepper Motor Controls, A/D Converters. 8 hrs

Unit 3 Memory: Different types of ROM’s & RAM’s, Cache System.

Interfacing: Introduction to Interfacing, Interrupts and DMA, Communication: serial Protocols, Parallel Protocols, Wireless Protocols. 10 hrs

Unit 4 Interrupts: Basics, Shared Data Problem, Interrupt latency, Introduction to Real Time Operating System: Tasks and states,scheduler, tasks and data, shared data problem, reentrancy,

Semaphores and shared data, semaphores problem, semaphore variants. 7 hrs

Unit 5 Real Time Operating System Services: Message Queues, Mail boxes, and

Pipes, Timer Functions, Events, Memory Management, Interrupt Routines in an RTOS environment. 6 hrs



TEXT BOOKS:

1 Frank Vahid and Tony Givargis

Embedded system Design, John Wiley, 2002.

2 David E,Simon An Embedded Software Primer, Pearson

Education, 1999.

REFERENCE BOOKS :

1 Tammy Noergaard

Embedded Systems Architecture – A Comprehensive Guide for Engineers and Programmers, Elsevier Publication, 2005.


1. Apply the knowledge of digital system fundamentals to describe the

importance of embedded computing systems and their unique

Characteristic features, processor and design technology.

2. Design custom single purpose processor, analyze the FSMD, FSM and

optimize the processor.

3. Identify and contrast the features of the general purpose processors

and ASIP’s processor design technologies, and illustrate the standard

peripherals used to improve the productivity of the embedded system.

4. Identify the type of memory and the communication protocols used in

building an embedded system.

5. Apply the knowledge of software architecture to describe the difference

between various embedded system architectures and the interrupt

mechanism for embedded software design.

6. Analyse the typical RTOS services for embedded system software and

apply the various intercommunication and scheduling strategies for

building the embedded system software.



ARTIFICIAL NEURAL NETWORKS

Contact Hours/Week : 3 Credits : 3 Total Lecture Hours : 39 CIE Marks : 50 Total Tutorial Hours : SEE Marks : 50 Course Code : RECE16


1. Learn basic differences between human and machine intelligence.

Understand the attractive features of the biological neural networks to realize some of features through parallel and distributed processing models.

2. Explain the biological and mathematical foundations of neural

network models. 3. Learn different learning models to train an artificial neural network. 4. Identify various pattern recognition tasks & select suitable neural

network architectures.

5. Design, build and train neural networks to solve various pattern recognition tasks.

Unit-I

Review of Linear algebra: Linear combination of vectors, linearly dependent and independent set of vectors, Vector space, subspace, basis, rank, Eigen vectors, orthogonal vectors, inner product, outer product.(No questions will appear in the end exam from these topics)

Basics of Artificial Neural Networks: Trends in computing, Pattern and Data, Pattern recognition tasks. Basic methods of pattern recognition, Basics of Artificial Neural Networks, Biological Neural Network, Models of neuron: McCulloch-Pitts Model, Perceptron, Adaline, topology, Supervised

and unsupervised learning, Basic learning laws, Realization of logic functions using MP neuron. 7 Hours

Unit-II

Functional units of ANN & Single layer perceptron: Basic ANN Models (architectures) for Pattern recognition task, Pattern recognition tasks by i) Feed-forward ii) Feed-back iii) competitive learning Neural networks. Feed-forward neural network: Linear associative network, Analysis of pattern

classification networks, Linear separability, Perceptron convergence theorem. 7 Hours

Unit-III Multi-Layer perceptron: Linear Inseparability: Hard problems, MLFFNN:

Back propagation learning, Draw backs of back propagation algorithm, Heuristics to improve the performance of Back propagation learning discussion on error back propagation, Convolution neural network (CNN).

8 Hours



Unit-IV Feedback Neural Networks: Analysis of pattern storage networks, The

Hopfield Model, Energy analysis of Hopfield model, State transition diagram, Pattern storage: Hard problems, Stochastic Networks and simulated annealing. Competitive learning network: Basic competitive learning, Analysis of

pattern clustering Networks. Analysis of Feature Mapping Network 9 Hours

Unit-V Architectures for complex pattern recognition tasks: Bidirectional

associative memory, Architecture of Radial basis function (RBF) networks, Theorems for function approximation, RBF networks for function approximation, Covers theorem on separability of patterns, The XOR

problem, RBF Networks for pattern Classification, comparison of RBF with MLP networks. 8 Hours Text Books:

1 B. Yegnanarayana Artificial neural networks, PHI, 2010.

Reference Books:

1 Simon Haykin Neural Networks for Pattern Recognition,

Pearson Education Limited

2 Robert J. Schalkoff

Artificial Neural Networks”, Mcgraw-Hill Inc.

3 Jacek M. Zurada Introduction to artificial neural systems, Jaico publishing house, 2003.

4 Christopher M. Bishop

Neural networks for pattern recognition, Oxford University Press (1995)


1. Distinguish between human and machine intelligence 2. Analyze various learning methods of neural networks. 3. Illustrate the use of feed-forward neural network for simple pattern

recognition tasks.

4. Illustrate use of feed-back neural network for pattern storage problems. 5. Apply Radial basis function networks for complex pattern recognition

tasks

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electronics and communication engineering - Siddaganga