The aim of the module is to acquaint students with the principles and practice of digital communications - from the fundamental basis of communication to how signals are represented and processed.
This module is only available to students in the second year of their degree and is not available as an unusual option to students in other years of study.
The aim of the module is to acquaint students with the principles and practice of digital communications - from the fundamental basis of communication to how signals are represented and processed.
The module develops an analytical approach to problems in communication design and operation, grounded in elements of communication theory sufficient to give students an understanding of the problems that affect its reliability and efficiency.
It introduces the theory and implementation of digital signal processing approaches, including the representation of signals in communication systems, filtering techniques and the applications of digital signal processing.
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.
Information Sources and Coding: Information theory, coding of information for efficiency and error protection;
Data transmission: Channel characteristics, signalling methods, interference and noise, synchronisation, data compression and encryption;
Signal Representation: Representation of discrete time signals in time and frequency; z transform and Fourier representations; discrete approximation of continuous signals; sampling and quantisation; stochastic signals and noise processes;
Filtering: Analysis and synthesis of discrete time filters; finite impulse response and infinite impulse response filters; frequency response of digital filters; poles and zeros; filters for correlation and detection; matched filters;
Digital Signal Processing applications: Processing of speech signals using digital techniques.
By the end of the module, students should be able to:
Please see Talis Aspire link for most up to darte list.
View reading list on Talis Aspire
At the end of the course students should be able to:
calculate the information content and entropy of a random variable from its probability distribution;
relate the entropies of variables in terms of their probabilities;
construct efficient codes for data on communication channels;
understand the concept of digital signals;
understand encoding and communication schemes in terms of the spectral properties of signals;
describe compression schemes, and efficient coding using Fourier Transform and other representations for data.
At the end of the course students should be able to:
Using MatLab to work on other problems related to mathematics
Have a better understanding of advanced mathematics;
Equipped with basic knowledge to work on other areas such as audio, video and in general big data processing;
Applications in other sciences: genomics; neuroscience; astrophysics; noisy signal classification; and pattern recognition including biometrics.
| Type | Required |
|---|---|
| Lectures | 30 sessions of 1 hour (20%) |
| Seminars | 10 sessions of 1 hour (7%) |
| Private study | 110 hours (73%) |
| Total | 150 hours |
There are many online materials useful for our module such as textbooks for machine learning and in general you should read:
In addition, students should:
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Programming assignment (Coursework) | 20% | Yes (extension) | |
| In-person Examination | 80% | No | |
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CS249 exam - A paper which examines the course content and ensures learning outcomes are achieved.
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| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| In-person Examination - Resit | 100% | No | |
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CS249 resit examination
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Feedback in seminars
This module is Optional for:
This module is Option list A for:
This module is Option list B for: