Digital Signal Processing (DSP) has become over the years an important tool with applications in
Electrical and Mechanical Engineering fields. DSP has penetrated many domains of applications,
such as digital communications, medical imaging, audio & video systems, consumer electronics,
robotics, remote sensing, finance etc.

The Discrete-Time Signal Processing paradigm is a convenient setting to analyse the basic
principles of DSP. At the end of this course, the students should be able to understand these basic
principles, and apply fundamental algorithms and methods to analyse and design discrete-time
systems for modern DSP applications. Though the course will focus on the study of theoretical
concepts, methods and algorithms, the student will be confronted with application and imple-
mentation issues, through various examples and assignments requiring personal computer work
including processing of real-world signals.

Having successfully completed this course, students will:
1. Understand the properties of z-transforms and its relations to LTI systems.
2. Have the understanding and the ability to use sampling and related concepts such changing
sampling rate, pre- ltering to avoid aliasing, oversampling & noise shaping.
3. Understand the concepts of all pass and minimum phase systems.
4. Understand IIR/FIR filter structures and ability to design filters.
5. Understand the processing of real time signals using Fourier techniques and ability to plan
and construct systems for spectral estimation of real signals.
6. Understand implementational aspects of simple DSP algorithms.

Weekly problems; mid-semester quiz; Matlab project; final exam.

Lectures; Weekly tutorial; Computer laboratories.

ENGN2228

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