Advanced
Signal Processing for Computer Scientists
CMPUT
675
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: No teaching assistant
Lectures: Thursday 14:10 to 17:10 in CSC
349
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;
Wavelet Transform; Wiener and Kalman Filters, and various applications. This course
qualifies as a breadth requirement in
theory.
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 the
University of Alberta Bookstore 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% |
|
LEC # |
TOPICS |
KEY DATES |
|
September 15 |
. |
|
|
September 23 |
Read Chapter 1 and 2 |
|
|
September 30 |
Assign 1 In Read Chapter 3 |
|
|
October 7 |
Assign 2 In Read Chapter 4 and 5 |
|
|
October 14-I |
Read Chapter 7 |
|
|
October 21-I |
Assign 3 In Project abstract due today Read Chapter 10 |
|
|
October 21-II |
Read Chapter 8 |
|
|
October 28-I |
Read Chapter 9 |
|
|
October 28-II |
. |
|
|
November 4 |
. |
|
|
November 11 |
No-class |
. |
|
November 18-I |
Read Chapter 6 |
|
|
November 18-II |
Read Chapter 14 |
|
|
November 18-III |
Read Chapter 11 Assign 4 In |
|
|
November 25-I |
. |
|
|
November 25-II |
. |
|
|
December 2 |
. |
|
|
December 9-I |
. |
|
|
December 9-II |
. |
|
|
December 10 |
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
Ø
B.
Champagne and F. Lebeau, Discrete Time Signal
Processing, Course note of ECSE-412, Winter 2004