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Signals such as audio, music, sonar, images and video carry information about physical quantities that vary over time and space. They can describe anything from acoustic vibrations to radio waves, which makes them fundamental across the whole of engineering. To record, process, transmit and make sense of this information, engineers rely on a powerful set of computational and mathematical tools, and this module introduces them.

You will study the principles used to analyse signals and to understand how they are affected by systems, building up the fundamental concepts of frequency analysis, spectral analysis and digital systems theory. In frequency analysis, a signal is decomposed into its component frequencies. Because many systems affect each frequency independently, this lets you study a system through its effect on different frequencies: a loudspeaker, for instance, can be designed so that it does not unduly boost or attenuate particular frequencies and colour the sound. Spectral analysis also reveals frequency content, but accounts for the significant random variation most real signals show, allowing you to study physical systems and to relate different signals, such as the electrical signal driving a speaker and the acoustic signal reaching your ears. Because we now analyse and process signals on digital computers, you will also study how analogue signals are converted to digital form, when this can be done faithfully, and what errors arise if the requirements are not met, leading naturally to the analysis of digital systems using tools related to those for continuous systems.

By the end of the module, you will be able to analyse signals in both the time and frequency domains, characterise how systems act on them, and understand the principles and pitfalls of working with signals digitally. These are core skills across acoustics, audio, communications and control, and they provide the essential foundation for the more advanced signal and image processing modules later in the programme and for professional engineering practice.

Signals such as audio, music, sonar, image and video convey information about physical quantities that vary over time and space. Signals can, for example, describe acoustic vibrations or radio waves, and thus play an important role throughout engineering. To help engineers to record, process, transmit and understand this information, computational and mathematical tools are used.

In this module, you will study different principles used to analyse signals and will learn how signals are affected by certain systems. To do this, you will learn about fundamental concepts such as frequency analysis, spectral analysis and digital systems theory.

In frequency analysis, a signal is decomposed into different frequencies. As many systems affect different frequencies independently, such a description allows us to study a system by analysing how it affects different frequencies. For example, a loudspeaker can be described as a system and an engineer might be interested in designing this system so that it does not unduly boost or attenuate different frequencies, which would colour the sound of the speaker.

Spectral analysis also reveals the frequency content in a signal, but also takes account of the fact that most signals show significant random variation. These techniques are used, for example, to study physical systems and can be used to compare and relate different signals, such as, for example, the electrical signal driving a speaker and the acoustic signal reaching you ears.

Nowadays, when we analyse or process signals, we tend to use digital computers. Analogue signals thus need to be converted first to a digital representation. To understand when this is possible and to appreciate the errors that can occur if the correct requirements are not met, a more detailed understanding of this conversion process is required. This also leads to the related problem of analysing digital systems, using tools similar to those used for analogue, continuous systems.

Signals such as audio, music, sonar, images and video carry information about physical quantities that vary over time and space. They can describe anything from acoustic vibrations to radio waves, which makes them fundamental across the whole of engineering. To record, process, transmit and make sense of this information, engineers rely on a powerful set of computational and mathematical tools, and this module introduces them.

You will study the principles used to analyse signals and to understand how they are affected by systems, building up the fundamental concepts of frequency analysis, spectral analysis and digital systems theory. In frequency analysis, a signal is decomposed into its component frequencies. Because many systems affect each frequency independently, this lets you study a system through its effect on different frequencies: a loudspeaker, for instance, can be designed so that it does not unduly boost or attenuate particular frequencies and colour the sound. Spectral analysis also reveals frequency content, but accounts for the significant random variation most real signals show, allowing you to study physical systems and to relate different signals, such as the electrical signal driving a speaker and the acoustic signal reaching your ears. Because we now analyse and process signals on digital computers, you will also study how analogue signals are converted to digital form, when this can be done faithfully, and what errors arise if the requirements are not met, leading naturally to the analysis of digital systems using tools related to those for continuous systems.

By the end of the module, you will be able to analyse signals in both the time and frequency domains, characterise how systems act on them, and understand the principles and pitfalls of working with signals digitally. These are core skills across acoustics, audio, communications and control, and they provide the essential foundation for the more advanced signal and image processing modules later in the programme and for professional engineering practice.

To develop knowledge of the fundamentals of Signals and Systems.
To introduce the concepts of signal transforms, system convolution and linear operations.
To introduce the concepts of randomness in signals and systems.
To provide a comprehensive foundation for the Control and Communications modules and Level 6 and 7 signal and image processing,

To develop knowledge of the fundamentals of Signals and Systems.
To introduce the concepts of signal transforms, system convolution and linear operations.
To introduce the concepts of randomness in signals and systems.
To provide a comprehensive foundation for the Control and Communications modules and Level 6 and 7 signal and image processing,

This module aims to introduce to the students signal processing techniques, including analogue and digital filter design and systems design theories. The module also introduces the concepts of statistical signal processing including estimation and detection theories, with illustrative case studies to demonstrate how these techniques can be used in communications systems.

The module uses the specialist computation/simulation tool Matlab.

This module will introduce the student to a toolkit of techniques for signal processing for use in photonics. Many of the topics students will study in Photonics will rely on an understanding of how optical signals are acquired and processed – the connection is clear within optical communications, but signal processing is also important in other areas such as optical sensors and image processing. Recently, advances in machine learning, and in particular techniques using neural networks, has taken signal processing to a new level, and has found applications across all of science and engineering.
Modern digital signal processing relies on computational techniques, and so this module will teach the basics of Python, language of choice for much scientific computing and almost all machine learning applications. As well as lecture-based teaching, the students will be introduced to practical techniques of signal processing via exercises in computer labs, aimed at equipping the student with skills necessary for their future work in photonics, in particular their final MSc project.
In part 1 of the module, students will be introduced to the theory and practice of digital signal processing, with a focus on key applications for optical communications.
In part 2 of the module, the mathematical and programmatic techniques required for creating, training and testing neural networks will be covered, with a particular focus on the practical implementation of convolutional neural networks for solving real-world photonics challenges.

This module will introduce the student to a toolkit of techniques for signal processing for use in photonics. Many of the topics students will study in Photonics will rely on an understanding of how optical signals are acquired and processed – the connection is clear within optical communications, but signal processing is also important in other areas such as optical sensors and image processing. Recently, advances in machine learning, and in particular techniques using neural networks, has taken signal processing to a new level, and has found applications across all of science and engineering.
Modern digital signal processing relies on computational techniques, and so this module will teach the basics of Python, language of choice for much scientific computing and almost all machine learning applications. As well as lecture-based teaching, the students will be introduced to practical techniques of signal processing via exercises in computer labs, aimed at equipping the student with skills necessary for their future work in photonics, in particular their final MSc project.
In part 1 of the module, students will be introduced to the theory and practice of digital signal processing, with a focus on key applications for optical communications.
In part 2 of the module, the mathematical and programmatic techniques required for creating, training and testing neural networks will be covered, with a particular focus on the practical implementation of convolutional neural networks for solving real-world photonics challenges.

This module will introduce the student to a toolkit of techniques for signal processing for use in photonics. Many of the topics students will study in Photonics will rely on an understanding of how optical signals are acquired and processed – the connection is clear within optical communications, but signal processing is also important in other areas such as optical sensors and image processing. Recently, advances in machine learning, and in particular techniques using neural networks, has taken signal processing to a new level, and has found applications across all of science and engineering.
Modern digital signal processing relies on computational techniques, and so this module will teach the basics of Python, language of choice for much scientific computing and almost all machine learning applications. As well as lecture-based teaching, the students will be introduced to practical techniques of signal processing via exercises in computer labs, aimed at equipping the student with skills necessary for their future work in photonics, in particular their final MSc project.
In part 1 of the module, students will be introduced to the theory and practice of digital signal processing, with a focus on key applications for optical communications.
In part 2 of the module, the mathematical and programmatic techniques required for creating, training and testing neural networks will be covered, with a particular focus on the practical implementation of convolutional neural networks for solving real-world photonics challenges.

This module introduces the idea of signal analysis, and the mathematical concepts and methods used to classify, transform, and analyse signals. These fundamentals are then applied within then context of control, which is intended to give the students skills an knowledge to apply in many control scenarios - for example PID controllers. The course provides a route into more complex control modules in later years, in terms of both the mathematical theory and the ideas.