IDCoM Research Projects

Research Projects at the Institute for Digital Communications (IDCoM). You can search keywords within Project Titles.

We also have a number of Digital Communications PhD opportunities for postgraduate students looking to join the School.

Search keywords within Research Project titles
Project Title Principal Supervisor Project Summary
Signal Processing for a Networked Battlespace

Professor Mike Davies

This research is carried out under the Unversity Defence Research Collaboration (UDRC) funded by the MOD and EPSRC.

The UDRC is a collaborative research project with the work being carried out by two Consortia. Edinburgh Consortium is made of the University of Edinburgh, Heriot-Watt University and The Queen's University of Belfast. LSSCN Consortium is made up of Loughborough University, University of Surrey, University of Strathclyde, Cardiff University and Newcastle University.

 

ADVANTAGE: Advanced Communications and Information processing in smart grid systems

Professor John Thompson

Smart grid engineers understand the power network that the smart grid is designed for and how to communicate and process data concerning the power grid, so that it can be controlled effectively.

The ITN (Initial Training Network) ADVANTAGE is a major inter-disciplinary and inter-sectoral project between power and communications engineering research and development communities. It will train the next generation of engineers and scientists, leading to the development of smart grid technology within Europe and internationally. This 4 year research programme is led and co-ordinated by the University of Edinburgh.

TNT: Tracking Network dynamics with Tensor factorisations. Application to the human Chronnectome in Alzheimer's disease

Dr Javier Escudero Rodriguez

Because of the ageing population, the number of people with dementia will increase dramatically in the next years. Alzheimer's disease is the most common cause of dementia and it is particularly difficult to diagnose. We need better ways to detect and monitor the changes that Alzheimer's disease causes in the brain. To achieve this, we will consider the electroencephalogram (EEG), an affordable piece of equipment that can be used outside hospitals to measure brain activity safely at several locations over the scalp (called "channels").

We will create new signal processing tools to analyse EEG brain networks based on tensor factorisations to inspect how the components of brain activity networks change with time.

UP-VLC: Ultra-parallel visible light communications

Professor Harald Haas

Running from October 2012 to September 2016, UP-VLC is an ambitious EPSRC-funded £4.6 million Programme Grant which will explore the transformative technology of communications in an imaginative and foresighted way.

A systematic study of physical layer network coding: From Information-Theoretic Understanding to Practical DSP Algorithm Design

Dr Tharmalingam Ratnarajah

High spectral efficiency is the holy grail of wireless networks due to the well-known scarcity of radio spectrum. While up to recently there seemed to be no way out of the apparent end of the road in spectral efficiency growth, the emerging approach of Network Coding has cast new light in the spectral efficiency prospects of wireless networks [1]. Initial results have demonstrated that the use of network coding increases the spectral efficiency up to 50% [2, 3]. Such a significant performance gain is crucial for many important bandwidth-hungry applications such as broadband cellular systems, wireless sensor networks, underwater communication scenarios, etc.

MacSeNet: Machine Sensing Training Network

Professor Mike Davies

The aim of this Innovative Training Network is to train a new generation of creative, entrepreneurial and innovative early stage researchers (ESRs) in the research area of measurement and estimation of signals using knowledge or data about the underlying structure.

SpaRTaN: Sparse Representations and Compressed Sensing Training Network

Professor Mike Davies

The aim of this Initial Training Network is to train a new generation of interdisciplinary researchers in sparse representations and compressed sensing, contributing to Europe’s leading role in scientific innovation.

Robust Repeatable Respiratory Monitoring in EIT

Professor Hugh McCann

The project aims at developing a new electrical impedance tomography (EIT) device for medical use. This device, called ReMEIT, should enable 3D absolute conductivity image reconstruction. To achieve this goal the project intends to capture the exact positions of the measuring electrodes and the exact thoracic shape using an optical shape capture device. These are absolutely novel approaches in EIT imaging that, if successful, could represent an immense progress in EIT research and a big step towards reliable clinical use of this technology. The project partners not only plan to develop the device but they also propose a strategy for its validation under invivo conditions. At first, healthy volunteers with no history of lung disease will be examined by ReMEIT and, later, the EIT device will be applied in critically ill patients suffering from various pulmonary diseases. In the former case, reference data will be obtained by magnetic resonance imaging (MRI), in the latter one, routine chest X-ray, computed tomography (CT)and MRI data will be utilised.

Tackling the looming spectrum crisis in Wireless Communication

Professor Harald Haas

The proposed work in this EPSRC Fellowship is aimed at providing radical new solutions to this fundamental and far reaching challenge. A key pillar of the proposed work is the extension of the RF spectrum to include the infrared as well as the visible light spectra. The recent advancements in light emitting diode (LED) device technology now seems to let the vision of using light for high speed wireless communications become a reality.

Optical Free-Space Backhaul and Power for Energy Autonomous Small Cells

Professor Harald Haas

The central aim of the project is the design of a novel simple structure for a communication base station. Its operation will be based on off-the-shelf optical components such as white LEDs, laser-diodes and photo-diodes.

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