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Signal processing is an essential part of human life and of modern industrial systems. As humans we see and hear and process signals. This is the same in electronic systems: we sense and then process signals. We need to be able to understand these signals, sometimes to interpret them, sometimes to filter them and sometimes to develop systems to process them automatically. That is what this module is about, and we shall apply the processes to images and to music in continuous and discrete
systems.

Signal processing is an essential part of human life and of modern industrial systems. As humans we see and hear and process signals. This is the same in electronic systems: we sense and then process signals. We need to be able to understand these signals, sometimes to interpret them, sometimes to filter them and sometimes to develop systems to process them automatically. That is what this module is about, and we shall apply the processes to images and to music in continuous and discrete
systems.

Signal processing is an essential part of human life and of modern industrial systems. As humans we see and hear and process signals. This is the same in electronic systems: we sense and then process signals. We need to be able to understand these signals, sometimes to interpret them, sometimes to filter them and sometimes to develop systems to process them automatically. That is what this module is about, and we shall apply the processes to images and to music in continuous and discrete
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.

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.

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.

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,

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.