Module overview
The module provides the students with a theoretical and practical understanding of signals, including concepts of sampling, filtering, information theory, uncertainty and data compression. Practical aspects of these topics will also be covered using data from diverse sources such as IoT sensors, acoustic and imaging.
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Explain the different approaches to filter signal data
- Analyse linear systems using time and frequency domain methods
- Identify the advantages and problems arising from processing signals in quantised time and space
- Understand the use of information theory in signal compression
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Identify real-world applications of optimal filters
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Classify real-world data into different types of signals
- Use Fourier analysis to design a filter
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Implement signal processing using digital filters
- Implement image compression
Syllabus
Signals theory:
- Classification of signals
- Mathematical systems
- Linear systems
- Classical filters (intro)
- Fourier transform
Digital signals:
- Sampling (Nyquist) and ADCs.
- Signal encoding
- Discrete Fourier methods
Analysis:
- Window functions
- Optimal filtering, Kalman, Weiner and particle
- Noise cancellation
- Filters on data with varying spatio-temporal resolutions (IoT sensors, acoustic, imaging)
- Heisenberg uncertainty principle
Information theory and coding:
- Uncertainty and information
- Entropy and measure of information
- Kolmogorov complexity and minimal description length
- Data compression
Learning and Teaching
Teaching and learning methods
- Lectures
- Guided self-study
- Labs which will cover practical aspects of the module
| Type | Hours |
|---|---|
| Revision | 12 |
| Specialist Laboratory | 24 |
| Lecture | 24 |
| Guided independent study | 92 |
| Total study time | 152 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Class Test | 10% |
| Computing assignment | 20% |
| Exam | 50% |
| Computing assignment | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |