EE 8104   Adaptive Signal Processing

 

 

Instructor

 

Sridhar (Sri) Krishnan, Ph.D., P.Eng.

T 247 Eric Palin Hall

T) 416.979.5000 X 6086

F) 416.979.5280

 

E) krishnan@ee.ryerson.ca

W) www.ee.ryerson.ca/~krishnan/ee8104.html

 

Lecture hours:  Mondays, 6pm to 9pm

Office hours:  Tuesdays, 2pm to 5pm

 

 

Calender Description

 

An introduction to adaptive signal processing is provided. The class begins with a brief review of linear signals and systems, and Wiener filter theory. Next, linear prediction and lattice filter structures are presented. Adaptive transversal filters are introduced and the least mean squares algorithm is discussed in detail. Recursive least squares based adaptive filters and their implementation are covered in the reminder of the class. Assignments are computer oriented.

 

 

Course Details

 

  1. Fundamentals of Digital Signal Processing

 

·        Discrete-time signals and systems

·        Z-transform

·        Discrete Fourier Transform (FFT)

·        FIR, IIR filters

 

  1. Adaptive Filter Algorithms

 

 

 

 

  1. Signal Modeling

 

 

  1. Spectral Estimation

 

·        Parametric Spectral Estimation

·        Non-parametric Spectral Estimation

 

  1. Advanced Topics

 

·        Array Processing

·        Adaptive Signal Decompositions

·        Automatic Signal Denoising

 

 

Course Evaluation

 

·        Two Month End Tests: 20%

      (Feb 3, March 3)

 

·        Final Exam (will be determined later):   35%

 

·        Project:  25%

 

·        3 Computer Assignments:  15%

 

·        Class Participation: 5%

 

 

Recommended Books

 

·        Manolakis, Ingle, and Kogon, “Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing”, McGraw Hill, 2000.

 

·        Proakis and Manolakis, “Digital Signal Processing: Principles, Algorithms, and Applications”, 3e, Prentice Hall, 1996.

 

·        Haykin, “Adaptive Filter Theory”, 4e, Prentice Hall, 2002.

 

 

 

Recommended Journals

 

 

 

Policy on Report Writing

 

“Students agree that by taking this course all required papers may be subject to submission for textual similarity review to Turnitin.com for the detection of plagiarism. All submitted documents will be included as source documents in the Turnitin.com reference database solely for the purpose of detecting plagiarism of such papers. Use of the Turnitin.com service is subject to the terms of user agreement posted on the Tunritin.com site”