This course builds on the basic signal processing course ELEC 421, as well as the ELEC 323/324 sequence in several important ways. First, the course applies signals and system concepts and filter design to applications found in communications, speech, image and radar signal processing. Second, this course thoroughly investigates the representation and modelling of signals as random phenomena as is required to model the transmission of unknown messages or to model the degradation of a signal by a physical medium or channel.

Course Learning Outcomes (CLOs)
  • Understand sampling, A/D and D/A conversion, including frequency domain behavior, quantization noise effects, and design tradeoffs (advantages and disadvantages) of different types of low-pass, bandpass, oversampled and sigma-delta A/D converters.
  • Using Matlab, perform Monte-Carlo simulations of quantization error effects and compare to statistical models of these errors. Be able to write and debug Matlab programs to perform these experiments.
  • To develop an understanding of how to model nonlinear and non-stationary effects as random phenomena applied locally to signals.
  • To develop an understanding of multi-rate signal processing concepts, (interpolation, decimation, polyphase representation, filter banks, Noble identities) in the Z-domain and Frequency domain as well as their application to digital filter design and communications systems.
  • Design digital filters for application to multi-rate systems.
  • Understand how to represent a (correlated) wide-sense stationary discrete-time random process as a digital filter with a white noise input. Understanding of linear prediction concepts. Be able to write and debug Matlab programs to perform experiments with random phenomena. Application to signal restoration, inverse filtering, minimum-phase systems, lossy signal compression and adaptive filtering.

Design of optimum finite impulse response (FIR) and infinite impulse response (IIR) minimum mean squared error (Wiener) filters, as well as analysis of filter performance and understand their degree of applicability to different problems.

Credit Breakdown

Lecture: 3
Lab: 0.5
Tutorial: 0

Academic Unit Breakdown

Mathematics 0
Natural Sciences 0
Complementary Studies 0
Engineering Science 15
Engineering Design 27