Advanced
Signal Processing for Computer Scientists
CMPUT
617
Instructor:
Pierre Boulanger
Tel: 780-492-3031
Email: pierreb@cs.ualberta.ca
URL: www.cs.ualberta.ca/~pierreb
Office: 411 Athabasca Hall
Office hours: By appointment only.
Teaching
Assistants: Idanis Diaz and Amir Sharifi
Lectures:
Every Friday 13h00 to 15h50 in Room CSC B-43
Course Description
This class addresses the representation, analysis, and
design of discrete time signals and systems. The major concepts covered
include: discrete-time processing of continuous-time signals; decimation,
interpolation, and sampling rate conversion; flow-graph structures for DT
systems; time-and frequency-domain design techniques for recursive (IIR) and
non-recursive (FIR) filters; linear prediction; discrete Fourier transform, FFT
algorithm; short-time Fourier analysis and filter banks; multivariate
techniques; Wavelet Transform; Cepstral analysis,
Wiener and Kalman Filters, and various applications.
Purpose
To
introduce Computer Scientists to advanced signal processing theory that can be
applied to various projects involving multi-dimensional datasets. The emphasis is
based on stochastic view of multi-dimensional signals and how to extract useful
and reliable information from those signals.
Basic statistical
analysis is preferred. It is also assumed that you already have some familiarity with
MATLAB.
Homework
will generally be handed out in lecture and be due in lecture on the following
week. Some parts of the homework may involve MATLAB exercises.
There
will be approximately 5 problem sets. Don't be misled by the relatively few
points assigned to homework grades in the final grade calculation. While the
grade that you get on your homework is at most a minor component of your final
grade, working the problems is a crucial part of the learning process and will
invariably have a major impact on your understanding of the material.
One
of the best ways of learning much of the material in this course is by
exploring many of the concepts with MATLAB. In addition to traditional homework
problems, this subject will have a computer exercise component based on the
MATLAB software package provided by AICT. MATLAB is widely used in academic and
industrial research laboratories in general, and is well-suited for work in
signal processing in particular. Many of you may probably have some experience
with MATLAB in undergraduate courses. For those of you who haven't, though,
you'll find that among the many attractive features of MATLAB are its ease of
use and very short learning curve.
You can buy MATLAB for students at AICT services
for ~$125.00 CAN.
Course
Project
There will be an individual semester project, culminating in
a final 8 pages report in IEEE format and a presentation at a day workshop.
Progress and check points before the final due date will count toward the final
grade.
The
final grade for the course is based on our best assessment of your
understanding of the material, as well as your commitment and participation.
The MATLAB project, problem sets, and Final projects are combined to give a
final grade:
|
ACTIVITIES |
Weight |
|
Final Project |
50% |
|
Problem Sets |
20% |
|
MATLAB Project(s) |
30% |
|
LECTURE
DATE |
TOPICS |
KEY DATES |
|
September
6 |
Read Chapter 1 and 2 |
|
|
September
13 |
Assign 1 In Read Chapter 3 |
|
|
September
20 |
Assign 2 In Read Chapter 4 and 5 |
|
|
September
27 |
Read Chapter 7 |
|
|
October 4 |
Assign 3 In Project abstract due today Read Chapter 10 |
|
|
October
11 |
Read Chapter 8 |
|
|
October
18 |
Read Chapter 9 |
|
|
October 25 |
The
Discrete Fourier Transform (DFT) |
Assign 4 In |
|
November
1 |
. |
|
|
November
8 |
||
|
November
15 |
Read Chapter 6 |
|
|
November
22 |
Read Chapter 14 |
|
|
November
29 |
|
Read Chapter 11 |
|
December
6 |
Projects presentation |
Send ppt slides to pierreb@cs.ualberta.ca |
|
December
18 |
Final
Project Report |
Send report to pierreb@ca.ualberta.ca |
Extra
Material for the Course
1.
B.
Champagne and F. Lebeau, Discrete Time Signal
Processing, Course note of ECSE-412, Winter 2004