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A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.