Digital Signal Processing (DSP) - EL871

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Module delivery information

This module is not currently running in 2020 to 2021.


Introduction to signals and signal analysis. Frequency and time domain representations of signals. A review of the Fourier Series, Fourier Transform and Laplace Transforms. Noise: definitions and sources of noise in signal analysis.

Digital Signal Processing:
The sampling theorem, Aliasing, Anti-Aliasing and Anti-Imaging Filters, ADCs and DACs. The Fourier Transform (FT). The Discrete Fourier Transform (DFT) and The Fast Fourier Transform (FFT).The Z-transform. Pole-Zero placement methods for signal analysis. Transfer functions in S and Z domains. Theory, design and performance of Finite Impulse-Response (FIR) and Infinite-Impulse-Response (IIR) Filters. Multirate DSP. Architectures and devices for digital signal processing. Effects of Finite Precision.

Applications of DSP:
Processing and filtering of signals for Instrumentation and measurement, Processing and filtering of images: DSP in modern communication systems.


This module appears in the following module collections.

Contact hours

Total contact hours: 62
Private study hours: 88
Total study hours: 150

Method of assessment

Exam 60%
Coursework 40%

Indicative reading

See the library reading list for this module (Canterbury)

Learning outcomes

1 Understand the principles of Digital Signals in both the time and frequency domains and use the Fourier Transform, the Fast Fourier Transform and the Z-Transform to analyse such signals.
2 Understand and critically appraise the effects of noise on digital systems;
3 Employ standard methods to design filters for use in processing digital signals.
4 Comprehensively understand how DSP techniques can be used in Instrumentation and Measurement, image processing (and image compression) and modern communication systems.


  1. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  2. The named convenor is the convenor for the current academic session.
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