Module code: ECS515U
Credits: 15
Semester: SEM2
This module aims to give you an understanding of basic signal and system concepts, e.g. average value, the difference between periodic, non-periodic and random signals, and orthogonality. It further aims to give a working understanding of the use of transform techniques, including Fourier, Laplace and Z, and an appreciation of the effects of noise on signals and signal processing.
Topics covered include:
- Concepts of signals and systems in continuous and discrete time.
- Ideas behind linear, time invariant systems and functions.
- Periodic and non-periodic, random, energy signal and power signal.
- Explain and use signal average values.
- Define signal symmetry, and to explain the concept and use of orthogonality.
- Introduce the Fourier trigonometric series.
- Introduce frequency domain representation and the Fourier transform.
- Show how the Fourier transform is applied to some simple aperiodic signals and how to interpret the results.
- Introduce discrete time signals and sampling, including aliasing and the unit sample sequence.
- Explain FIR and IRR discrete time systems.
- Show response of a discrete time system is convolution of the input sequence with the unit sample response.
- Introduce the Z-transform, and apply to discrete time signals and systems.
- Show that stability of discrete time systems can be examined by using Z-transform techniques.
- Introduce the Laplace transform and the complex frequency terms.
- Study certain simple applications of the Laplace transform, e.g. to the unit step function, the exponential function, the sinusoid and to combinations of these.
- Inverse Laplace transform.
Level: 5
