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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.

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, 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, 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, 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, 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, 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.

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,