Module finder

It seems clear that people’s lives can go well or badly. But what is it for one’s life to go well? Does it consist in feeling good more often than feeling bad? Or getting most of what you want? Or does it consist in achievement, friendship, knowledge and a variety of other disparate things? It is highly tempting to think that your happiness matters for how well your life goes. But this raises further questions: what is happiness? Can it be measured? Is it a sensible goal for public policy? This module aims to explore questions such as these.

This module explores the use and programmability of field-programmable gate arrays (FPGAs) for accelerating data-intensive applications. They are usually found in the datacenter, high-performance computing (HPC) facilities, as well as high-end edge computing including in the automotive industry. Traditionally, FPGAs have mainly been a prototyping platform. The recent advances in semiconductor technology have enabled their use as a competitive accelerator platform in datacenters and on the edge. Their high flexibility can surpass the performance and efficiency of general-purpose processors, but this comes at a cost.

When a task is able to be solved computationally, general-purpose computing is the easiest route due to the maturity of software and hardware stacks, but it may be too slow or inefficient. Would an acceleration platform like an FPGA or even a GPU be able to target the task effectively? Which algorithm would an FPGA solution use, and how could it benefit from its strengths such as the internal parallelism? Are there any programming models and programming languages for such data-intensive designs?

This module covers the programmability aspects of FPGAs in the context of HPC and high-end edge devices. It covers the computer architecture of highly-heterogeneous SoCs and HPC systems, soft and hard interconnects relating to FPGA acceleration, and the steps required to develop and finally deploy data-intensive hardware accelerators.

This module explores the use and programmability of field-programmable gate arrays (FPGAs) for accelerating data-intensive applications. They are usually found in the datacenter, high-performance computing (HPC) facilities, as well as high-end edge computing including in the automotive industry. Traditionally, FPGAs have mainly been a prototyping platform. The recent advances in semiconductor technology have enabled their use as a competitive accelerator platform in datacenters and on the edge. Their high flexibility can surpass the performance and efficiency of general-purpose processors, but this comes at a cost.

When a task is able to be solved computationally, general-purpose computing is the easiest route due to the maturity of software and hardware stacks, but it may be too slow or inefficient. Would an acceleration platform like an FPGA or even a GPU be able to target the task effectively? Which algorithm would an FPGA solution use, and how could it benefit from its strengths such as the internal parallelism? Are there any programming models and programming languages for such data-intensive designs?

This module covers the programmability aspects of FPGAs in the context of HPC and high-end edge devices. It covers the computer architecture of highly-heterogeneous SoCs and HPC systems, soft and hard interconnects relating to FPGA acceleration, and the steps required to develop and finally deploy data-intensive hardware accelerators.

Conventional laboratory experiments are useful mainly to assist understanding or analysis. Because they are of necessity stereotyped, they are of limited usefulness when a circuit or system must be designed to meet a given specification. The majority of engineering tasks fall into this latter category, and therefore require design or synthesis skills, in addition to the understanding of underlying engineering principles.

Students on all Biomedical Engineering pathways will work together on the main design exercises but with a particular focus or task to complete depending on their pathway; either Electronic Systems/Mechatronics for Health or Artificial Intelligence/Digital Health. In this way they will work together to produce a prototype system

This module includes individual and team design exercises devised to provide a bridge between 'conventional' experiments and the project work in the third and fourth years, (which in turn provide a bridge to 'real' projects in industry). The exercise has real deadlines and concrete deliverables and students are encouraged to be creative, develop imaginative solutions and to make mistakes.

Exercises share common characteristics:
• Customer orientated rather than proscriptive specifications are given
• Design work carried out, bringing academic knowledge to bear on practical problems
• Laboratory sessions are used for development/ construction/ verification of designs
• Allow students to demonstrate their communication skills in writing individual and group reports/presentations.

In support of these design exercises, those on the Electronic Systems/Mechatronics for Health pathway will be introduced to some advanced programming, simulation, and design modelling frameworks and tools.

They will explore the analogue relationship between mechanical and electrical systems, enabling circuit problems and mechanical systems to be treated in the same framework. Combining this with modelling and analysis will develop a better understanding of vibration problems in continuous mechanical systems and allow simulation and visualisation of any mechanical implementation within the design project.

Conventional laboratory experiments are useful mainly to assist understanding or analysis. Because they are of necessity stereotyped, they are of limited usefulness when a circuit or system must be designed to meet a given specification. The majority of engineering tasks fall into this latter category, and therefore require design or synthesis skills, in addition to the understanding of underlying engineering principles.

Students on all Biomedical Engineering pathways will work together on the main design exercises but with a particular focus or task to complete depending on their pathway; either Electronic Systems/Mechatronics for Health or Artificial Intelligence/Digital Health. In this way they will work together to produce a prototype system

This module includes individual and team design exercises devised to provide a bridge between 'conventional' experiments and the project work in the third and fourth years, (which in turn provide a bridge to 'real' projects in industry). The exercise has real deadlines and concrete deliverables and students are encouraged to be creative, develop imaginative solutions and to make mistakes.

Exercises share common characteristics:
• Customer orientated rather than proscriptive specifications are given
• Design work carried out, bringing academic knowledge to bear on practical problems
• Laboratory sessions are used for development/ construction/ verification of designs
• Allow students to demonstrate their communication skills in writing individual and group reports/presentations.

In support of these design exercises, those on the Electronic Systems/Mechatronics for Health pathway will be introduced to some advanced programming, simulation, and design modelling frameworks and tools.

They will explore the analogue relationship between mechanical and electrical systems, enabling circuit problems and mechanical systems to be treated in the same framework. Combining this with modelling and analysis will develop a better understanding of vibration problems in continuous mechanical systems and allow simulation and visualisation of any mechanical implementation within the design project.

Harmonic analysis extends key ideas of Fourier analysis from Euclidean spaces to general topological groups. A fundamental goal is understanding algebras of functions on a group in terms of elementary functions. These correspond t the idea representing signals in terms of standing waves. Harmonic analysis is now a key part of modern mathematics with important applications in physics and engineering.

Harmonic analysis extends key ideas of Fourier analysis from Euclidean spaces to general topological groups. A fundamental goal is understanding algebras of functions on a group in terms of elementary functions. These correspond to the idea representing signals in terms of standing waves. The Quantum Fourier Transform is a fundamental ingredient in quantum computing algorithms and the module will also give an introduction to key ideas relating to quantum information.

Harmonic analysis extends key ideas of Fourier analysis from Euclidean spaces to general topological groups. A fundamental goal is understanding algebras of functions on a group in terms of elementary functions. These correspond to the idea representing signals in terms of standing waves. The Quantum Fourier Transform is a fundamental ingredient in quantum computing algorithms and the module will also give an introduction to key ideas relating to quantum information.

In this module you will build on your previous learning so that you can prioritise and respond to the changing levels of support that people require when health status changes. You will develop your ability to manage and evaluate care across healthcare settings to promote, restore and stablise health status.

In this module you will build on your previous learning so that you can prioritise and respond to the changing levels of support that people require when health status changes. You will develop your ability to manage and evaluate care across healthcare settings to promote, restore and stablise health status.