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UDRC: University Defence Research Collaboration in Signal Processing |
Prof Mike Davies
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Signal Processing is fundamental to the capability of all modern sensor weapon systems and the Defence Technology Strategy identified the development and application of signal processing techniques as high priority technical challenges within the MOD research agenda.
The UDRC is a leading partnership between industry, defence and is academia led and focuses on sensor signal processing for defence.
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Tackling the looming spectrum crisis in Wireless Communication |
Professor Harald Haas
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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.
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TASCC: Pervasive low-TeraHz and Video Sensing for Car Autonomy and Driver Assistance (PATH CAD) |
Prof Bernard Mulgrew
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This project combines novel low-THz (LTHz) sensor development with advanced video analysis, fusion and cross learning. Using the two streams integrated within the sensing, information and control systems of a modern automobile, we aim to map terrain and identify hazards such as potholes and surface texture changes in all weathers, and to detect and classify other road users (pedestrians, car, cyclists etc.).
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SpaRTaN: Sparse Representations and Compressed Sensing Training Network |
Professor Mike Davies
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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.
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Signal Processing in the Information Age |
Prof Michael E Davies
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The aim of the UDRC is to develop unprecedented research in signal processing with application to the defence industry and share knowledge, promote communications, guidance and training. The formation of consortia will bring together researchers from across the different aspects of signal processing to address the research challenges of operating in a networked battlespace. This will form part of a wider collaborative centre of excellence for signal processing that embraces academia, Research and Technology Organisations, defence manufacturing industries and the Defence Technology Centres. This collaboration will support a cutting edge signal and data processing capability in the UK, and lead to potentially greater research impact.
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Signal Processing for a Networked Battlespace |
Professor Mike Davies
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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.
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Sensor Signal Processing |
Professor Bernie Mulgrew
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The fundamental challenges for signal processing are: how best to sense; how to distribute the processing and communication of the data within the network to maximize performance and minimize cost; how to analyze it to extract the salient information.
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Rural and Remote Ubiquitous Broadband Wireless Access |
Dr Tharmalingam Ratnarajah
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This research network would bring together key research groups that are in the vanguard of developing novel technologies and algorithms for spectrally efficient generation wireless networks in the UK and India.
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Robust Repeatable Respiratory Monitoring in EIT |
Professor Hugh McCann
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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.
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RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories |
Dr Nicholas Polydorides
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Modern manufacturing involves highly controlled and automated processes meticulously designed to deliver products to specific needs within strict specifications and in a cost-efficient and sustainable way. Sensors capture continuous data streams about the state of the process, e.g., equipment and the product, to ensure performance in variable and often harsh conditions — however, the ability to analyse this data in real-time offers unique advantages currently out of reach. Learning to calibrate its operation from sensor data, monitor its health status and make accurate forecasts on product outcomes and maintenance requirements are process attributes of future autonomous factories.
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