ES98G-15 Signal Processing
Introductory description
Module aims
The module aims to introduce signal processing to MSc students. It aims to develop the student’s ability to: Select and apply appropriate mathematical methods for modelling and analysing signals and systems; Understand the scientific principles underlying the generation and classification of signals; Use practical skills to measure and analyse real-world signals; Select and apply appropriate computer based methods for modelling signals and systems; Design signal processing systems to meet a target specification.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Analogue Signals and Systems: Time domain and s-domain representation of continuous-time signals; Linear time-invariant systems; Laplace transform; Analogue system transfer functions; Analogue system stability; Fourier transform and analogue frequency response; Analogue filter design and specification; Fourier series for periodic analogue signals; Computational modelling of analogue signals and systems
Digital Signals and Systems: Time-domain and z-domain representation of discrete-time signals; Signal conversion between analogue and digital representations; Sampling and aliasing; Linear shift-invariant systems; Z-transform; Digital system transfer functions; Digital system stability; Discrete-time Fourier transform and digital frequency response; Finite impulse response and infinite impulse response filters; Digital filter design and specification; Discrete Fourier Transform and evaluation with the Fast Fourier Transform; Computational modelling of digital signals and systems
Random Signal Processing: Random variable properties and variable distributions; Random signals; Signal estimation; Correlation; Power spectral density
Image Processing: Multi-dimensional signals; Representing images as signals; Multi-dimensional convolution; Image filtering
Learning outcomes
By the end of the module, students should be able to:
- 1. Apply mathematics to analyse deterministic and random signals and to analyse processing systems [M1]
- 2. Apply signal processing systems to classify signals and extract information [M1]
- 3. Critique practical issues behind signal processing and information retrieval [M12]
- 4. Design signal processing systems to meet a specification [M1]
- 5. Model signals, filters and processes using computer packages [M3]
- 6. Measure and analyse real-world signals [M12]
Indicative reading list
“Essentials of Digital Signal Processing”, B.P. Lathi and R.A. Green, Cambridge University Press, 2014
“Essential MATLAB”, B. Hahn and D. Valentine, Academic Press, 6th Edition, 2017
“Discrete-Time Signal Processing”, Oppenheim and Schafer, Pearson, 3rd Edition, 2013
Subject specific skills
- Ability to conceive, make and realise a component, product, system or process
- Ability to be pragmatic, taking a systematic approach and the logical and practical steps necessary for, often complex, concepts to become reality
Transferable skills
- Numeracy: apply mathematical and computational methods to communicate parameters, model and optimize solutions
- Apply problem solving skills, information retrieval, and the effective use of general IT facilities
- Plan self-learning and improve performance, as the foundation for lifelong learning/CPD
Study time
| Type | Required |
|---|---|
| Lectures | 24 sessions of 1 hour (16%) |
| Practical classes | 3 sessions of 2 hours (4%) |
| Other activity | 4 hours (3%) |
| Private study | 116 hours (77%) |
| Total | 150 hours |
Private study description
46 hours Guided independent learning
30 hours coursework submission
40 hours final exam study
Other activity description
2 x 1hr examples class
2 x 1hr revision class
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group D
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Lab Assignment | 30% | Yes (extension) | |
|
Assignment submission supported by timetabled laboratories. Submission consists of a written report (maximum length of 5 pages) in addition to written code files and code output. |
|||
| Online Examination | 70% | No | |
|
QMP Online Examination 2hr ~Platforms - QMP
|
|||
Feedback on assessment
- Model solutions to past papers.
- Individual and cohort-level feedback on assignments.
- Support through advice and feedback hours.
- Cohort-level feedback on final exam.
Anti-requisite modules
If you take this module, you cannot also take:
- ES3C5-15 Signal Processing
Courses
This module is Optional for:
- Year 1 of TESA-H800 Postgraduate Taught Biomedical Engineering
- Year 1 of TESA-H641 Postgraduate Taught Communications and Information Engineering
This module is Option list A for:
- Year 1 of TESA-H643 Postgraduate Taught Electrical Power Engineering