DIGITAL SIGNAL PROCESSING (EL307)

ACADEMIC YEAR 2017-2018

T.E.(Electronics Engineering) Part-II Semester VI (Revised) January 2018

Course Schedule:

Lecture: 4 hrs/week

Practical: 2 Hrs/Week

Outside Study: 7 hrs/week.

 Course Assessment:

    Term Work: 25 Marks

     Examinations: Two  Unit Tests of 50 marks each and  final Theory examination of 100 Marks

     Design: MATLAB and TMS320C6713 and TMS320C6416 Processor based Mini-project and  Experimentation.  

    Term Work, Grading Policy: Based on Class Attendance, Assignments, Home work, Mini Projects and Laboratory performance.

Course Objective:

Students completing this course are expected to demonstrate the fundamentals and applications of discrete-time signals and systems, including sampling, convolution, discrete Fourier transforms and filtering. They are expected to be able perform spectral analysis on real signals using the discrete Fourier transform and to design digital filters. They will be practicing sampling, processing, and playing back audio and other signals using MATLAB.

Course Outcome:

By the end of the course, students will be able to:

•        Analyze digital and analog signals and systems;

•       Compute the Discrete-time Fourier transform (DTFT) of a simple sequence such as the impulse response of an FIR filter, and explain how the DTFT and z-transform are linked to each other.

•       Derive and define the Discrete Fourier Transform (DFT) and inverse DFT (IDFT)

•     Derive and apply the DFT properties

•      Design FIR and IIR filters by hand to meet specific magnitude and phase requirements.

•      Design FIR and IIR filter and implement it using MATLAB.

•     Recover information from signals and signal form Noise;

Overview of Learning Activities

   Learning activities include:

• Lecture Attendance;

• Completion of written assignments

• Completion of tutorial questions

• Completion of the Laboratory Work,

• Completion of  mini projects; and

• Self-directed study

Lecture Plan: Please Click here to view Lecture plan

Syllabus: Please Click here to see DSP syllabus

Textbooks:

1.  John G Prokis, Manolakis, “Digital SignalProcessing – Principles, Algorithms and Application”, 4th Edition,Pearson Education Publication. 

2.  Salivahanam, A Vallavaraj, C. Guanapriya, “Digital Signal Processing”, 1st   Edition, Tata McGraw Hill.

3.  Oppenheim, Schafer, "Discrete-Time Signal Processing" Second Edition, Prentice Hall.

4.   K. S. Mitra,DigitalSignal Processing: A Computer based Approach, BerkeleyMcGraw-Hill, 1998,  ISBN 0-941413-35-7.

5.  Ifeachor, Emmanuel C., and Barrie W. Jervis. "Digital Signal Processing: A Practical Approach." (2002).

6.  Raghuveer M. Rao , Ajit S. Bopardikar, “Wavelet Transforms : Introduction to theory and applications”, Pearson Education (1998).

7.   Lokenath Debnath, Firdous Ahmed Shah“Wavelet Transforms and Their Applications”, Birkhauser Publication 2001.

8.  P. P. Vaidyanathan, “Multirate Systems And Filter Banks” Pearson Education.

9.  Rulph Chassain, “Digital Signal Processing and Applications with theC6713 and C6416 DSK, Wiley Interscience, 2004.

10.  Rulph Chassaing, Donald S. Reay,"Digital Signal Processing and Applications with the TMS320C6713 and TMS320C6416 DSK", John Wiley.

    1.  Steven W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing, California technical Publishing,1997.

    2.  Richard G. Lyons, Understanding Digital Signal Processing, Pearson Education 2010.

Introduction:

 Digital Signal Processing is concerned with the digital representation of signals and the use of microprocessors and computers to analyze, modify, and extract information from signals. The digital signals found in most popular applications of DSP are derived from analog signals that have been sampled at regular intervals and converted into digital form.

DSP is used to:

·         Remove interference or noise from signals

·         Detect faint signals in noise, recognize known signals

·         Enhance recorded music

·         Obtain and analyze the spectrum of a signal

·         Compress a signal into fewer bits per sample

·         Model analog systems

Some common applications are speech synthesis and recognition, telephone echo cancellation, digital audio (CD)  technology, Digital Image Processing,  ECG, and  EEG analysis .

• What is Signal Processing?

Course Materials:

• Lecture Notes

              Unit I : Discrete Fourier Transform and FFT Algorithms

               Unit II : Wavelet Transform 

               Unit III : FIR Filter Design

               Unit IV : IIR Filter Design

               Unit V :  Realization of filter

               Unit VI :  Multi rate Digital Signal Processing

• Assignments

  • Question Bank and Assignment 1 Due Date: 28/01/2018 

  • Question Bank and Assignment 2 Due Date: 19/03/2018

• Project base learning

  •   List of the projects/topics   

• Lab  Manual

        Note: Please Print the experiments back to back (two sided)

• • Experiment 1

  • Experiment 2

  • Experiment 3

  • Experiment 4

  • Experiment 5

  • Experiment 6

  • Experiment 7

  • Experiment 8

  • Experiment 9

  • Experiment 10

  • Experiment 11

  • Experiment 12 Programs

 Other Resources

1. A Beginner's Guide to Digital Signal Processing (DSP)

2. http://www.dsprelated.com/

3. Inside DSP : A source of analysis, advice, and engineering for embedded processing technology and applications

4. Multirate Systems and Filter Banks

5.  Multirate Signal Processing

6. Fourier and Wavelet: FOUNDATIONS OF SIGNAL PROCESSING FOURIER AND WAVELET SIGNAL PROCESSING

7.  Wavelet and Subband Coding

8. DSP and Image Processing

9. ELECTRICAL, ELECTRONIC, AND INSTRUMENTATION

10. Michael J. Jacob Industrial Control Electronics Application And Design

11. John G. Webmaster, Medical Instrumentation Application and Design

SCOPUS INDEXED JOURNAL LIST: https://www.elsevier.com/__data/assets/excel_doc/0015/91122/title_list.xlsx

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