This proposal seeks funding to create a Centre for Doctoral Training (CDT) in Connected Electronic and Photonic Systems (CEPS). Photonics has moved from a niche industry to being embedded in the majority of deployed systems, ranging from sensing, biophotonics and advanced manufacturing, through communications from the chip-to-chip to transcontinental scale, to display technologies, bringing higher resolution, lower power operation and enabling new ways of human-machine interaction. These advances have set the scene for a major change in commercialisation activity where electronics photonics and wireless converge in a wide range of information, sensing, communications, manufacturing and personal healthcare systems. Currently manufactured systems are realised by combining separately developed photonics, electronic and wireless components. This approach is labour intensive and requires many electrical interconnects as well as optical alignment on the micron scale. Devices are optimised separately and then brought together to meet systems specifications. Such an approach, although it has delivered remarkable results, not least the communications systems upon which the internet depends, limits the benefits that could come from systems-led design and the development of technologies for seamless integration of electronic photonics and wireless systems. To realise such connected systems requires researchers who have not only deep understanding of their specialist area, but also an excellent understanding across the fields of electronic photonics and wireless hardware and software. This proposal seeks to meet this important need, building upon the uniqueness and extent of the UCL and Cambridge research, where research activities are already focussing on higher levels of electronic, photonic and wireless integration; the convergence of wireless and optical communication systems; combined quantum and classical communication systems; the application of THz and optical low-latency connections in data centres; techniques for the low-cost roll-out of optical fibre to replace the copper network; the substitution of many conventional lighting products with photonic light sources and extensive application of photonics in medical diagnostics and personalised medicine. Many of these activities will increasingly rely on more advanced systems integration, and so the proposed CDT includes experts in electronic circuits, wireless systems and software. By drawing these complementary activities together, and building upon initial work towards this goal carried out within our previously funded CDT in Integrated Photonic and Electronic Systems, it is proposed to develop an advanced training programme to equip the next generation of very high calibre doctoral students with the required technical expertise, responsible innovation (RI), commercial and business skills to enable the £90 billion annual turnover UK electronics and photonics industry to create the closely integrated systems of the future. The CEPS CDT will provide a wide range of methods for learning for research students, well beyond that conventionally available, so that they can gain the required skills. In addition to conventional lectures and seminars, for example, there will be bespoke experimental coursework activities, reading clubs, roadmapping activities, responsible innovation (RI) studies, secondments to companies and other research laboratories and business planning courses. Connecting electronic and photonic systems is likely to expand the range of applications into which these technologies are deployed in other key sectors of the economy, such as industrial manufacturing, consumer electronics, data processing, defence, energy, engineering, security and medicine. As a result, a key feature of the CDT will be a developed awareness in its student cohorts of the breadth of opportunity available and the confidence that they can make strong impact thereon.

The CDT proposal 'Fuel Cells and their Fuels - Clean Power for the 21st Century' is a focused and structured programme to train >52 students within 9 years in basic principles of the subject and guide them in conducting their PhD theses. This initiative answers the need for developing the human resources well before the demand for trained and experienced engineering and scientific staff begins to strongly increase towards the end of this decade. Market introduction of fuel cell products is expected from 2015 and the requirement for effort in developing robust and cost effective products will grow in parallel with market entry. The consortium consists of the Universities of Birmingham (lead), Nottingham, Loughborough, Imperial College and University College of London. Ulster University is added as a partner in developing teaching modules. The six Centre directors and the 60+ supervisor group have an excellent background of scientific and teaching expertise and are well established in national and international projects and Fuel Cell, Hydrogen and other fuel processing research and development. The Centre programme consists of seven compulsory taught modules worth 70 credit points, covering the four basic introduction modules to Fuel Cell and Hydrogen technologies and one on Safety issues, plus two business-oriented modules which were designed according to suggestions from industry partners. Further - optional - modules worth 50 credits cover the more specialised aspects of Fuel Cell and fuel processing technologies, but also include socio-economic topics and further modules on business skills that are invaluable in preparing students for their careers in industry. The programme covers the following topics out of which the individual students will select their area of specialisation: - electrochemistry, modelling, catalysis; - materials and components for low temperature fuel cells (PEFC, 80 and 120 -130 degC), and for high temperature fuel cells (SOFC) operating at 500 to 800 degC; - design, components, optimisation and control for low and high temperature fuel cell systems; including direct use of hydrocarbons in fuel cells, fuel processing and handling of fuel impurities; integration of hydrogen systems including hybrid fuel-cell-battery and gas turbine systems; optimisation, control design and modelling; integration of renewable energies into energy systems using hydrogen as a stabilising vector; - hydrogen production from fossil fuels and carbon-neutral feedstock, biological processes, and by photochemistry; hydrogen storage, and purification; development of low and high temperature electrolysers; - analysis of degradation phenomena at various scales (nano-scale in functional layers up to systems level), including the development of accelerated testing procedures; - socio-economic and cross-cutting issues: public health, public acceptance, economics, market introduction; system studies on the benefits of FCH technologies to national and international energy supply. The training programme can build on the vast investments made by the participating universities in the past and facilitated by EPSRC, EU, industry and private funds. The laboratory infrastructure is up to date and fully enables the work of the student cohort. Industry funding is used to complement the EPSRC funding and add studentships on top of the envisaged 52 placements. The Centre will emphasise the importance of networking and exchange of information across the scientific and engineering field and thus interacts strongly with the EPSRC-SUPERGEN Hub in Fuel Cells and Hydrogen, thus integrating the other UK universities active in this research area, and also encourage exchanges with other European and international training initiatives. The modules will be accessible to professionals from the interacting industry in order to foster exchange of students with their peers in industry.

Sensors have for a long time played a vital role in battle awareness for all our armed forces, ranging from advanced imaging technologies, such as radar and sonar to acoustic and the electronic surveillance. Sensors are the "eyes and ears" of the military providing tactical information and assisting in the identification and assessment of threats. Integral in achieving these goals is signal processing. Indeed, through modern signal processing we have seen the basic radar transformed into a highly sophisticated sensing system with waveform agility and adaptive beam patterns, capable of high resolution imaging, and the detection and discrimination of multiple moving targets. Today, the modern defence world aspires to a network of interconnected sensors providing persistent and wide area surveillance of scenes of interest. This requires the collection, dissemination and fusion of data from a range of sensors of widely varying complexity and scale - from satellite imaging to mobile phones. In order to achieve such interconnected sensing, and to avoid the dangers of data overload, it is necessary to re-examine the full signal processing chain from sensor to final decision. The need to reconcile the use of more computationally demanding algorithms and the potential massive increase in data with fundamental resource limitations, both in terms of computation and bandwidth, provides new mathematical and computational challenges. This has led in recent years to the exploration of a number of new techniques, such as, compressed sensing, adaptive sensor management and distributed processing techniques to minimize the amount of data that is acquired or transmitted through the sensor network while maximizing its relevance. While there have been a number of targeted research programs to explore these new ideas, such as the USs "Integrated Sensing and Processing" program and their "Analog to Information" program, this field is still generally in its infancy. This project will study the processing of multi-sensor systems in a coherent programme of work, from efficient sampling, through distributed data processing and fusion, to efficient implementations. Underpinning all this work, we will investigate the significant issues with implementing complex algorithms on small, lighter and lower power computing platforms. Exemplar challenges will be used throughout the project covering all major sensing domains - Radar/radio frequency, Sonar/acoustics, and electro-optics/infrared - to demonstrate the performance of the innovations we develop.

Composite materials are becoming increasingly important for light-weight solutions in the transport and energy sectors. Reduced structural weight, with improved mechanical performance is essential to achieve aerospace and automotive's sustainability objectives, through reduced fuel-burn, as well as facilitating new technologies such as electric and hydrogen fuels. The nature of fibre reinforced composite materials however makes them highly susceptible to variation during the different stages of their manufacture. This can result in significant reductions in their mechanical performance and design tolerances not being met, reducing their weight saving advantages through requiring "over design". Modelling methods able to simulate the different processes involved in composite manufacture offer a powerful tool to help mitigate these issues early in the design stage. A major challenge in achieving good simulations is to consider the variability, inherent to both the material and the manufacturing processes, so that the statistical spread of possible outcomes is considered rather than a single deterministic result. To achieve this, a probabilistic modelling framework is required, which necessitates rapid numerical tools for modelling each step in the composite manufacturing process. Focussing specifically on textile composites, this project will develop a new bespoke solver, with methods to simulate preform creation, preform deposition and finally, preform compaction, three key steps of the composite manufacturing process. Aided by new and developing processor architectures, this bespoke solver will deliver a uniquely fast, yet accurate simulation capability. The methods developed for each process will be interrogated through systematic probabilistic sensitivity analyses to reduce their complexity while retaining their predictive capability. The aim being to find a balance between predictive capability and run-time efficiency. This will ultimately provide a tool that is numerically efficient enough to run sufficient iterations to capture the significant stochastic variation present in each of the textile composite manufacturing processes, even at large, component scale. The framework will then be applied to industrially relevant problems. Accounting for real-world variability, the tools will be used to optimise the processes for use in design and to further to explore the optimising of manufacturing processes. Close collaboration with the project's industrial partners and access to their demonstrator and production manufacturing data will ensure that the tools created are industry relevant and can be integrated within current design processes to achieve immediate impact. This will enable a step change in manufacturing engineers' ability to reach an acceptable solution with significantly fewer trials, less waste and faster time to market, contributing to the digital revolution that is now taking place in industry.

In space imaging, enhanced image quality is key to the detection and characterisation of difficult and transient targets. For example, accurate evaluation of the sea surface conditions can help with the detection and characterisation of ship wakes. These provide key information for tracking (illegal) vessels and are also useful in classifying the characteristics of the wake generating vessel. Until recently, one of the main factors hampering research into sea surface modelling was the lack of sufficient data of high enough quality, able to accurately describe the sea surface. Remote-sensing technologies have however shown remarkable progress in recent years and the availability of remotely sensed data of the Earth and sea surface is continuously growing. Several European missions (e.g., the Italian COSMO/SkyMed or the German TerraSAR-X) have developed a new generation of satellites exploiting synthetic aperture radar (SAR) to provide spatial resolutions previously unavailable from space-borne remote sensing. The UK is currently developing the first of a constellation of four satellites that will constitute the NovaSAR mission. This represents a milestone for Earth-observation capabilities but also requires the development of novel image modelling, analysis, and processing techniques, able to cope with this new generation of data and to optimally exploit them for information-extraction purposes. Indeed, the mathematical modelling and understanding of wakes and other sea surface signatures can be greatly enhanced through image analysis and information extraction from SAR imagery. Hence, this project is concerned not only with the development and validation of new sea surface models, but also with the design of very advanced methods for enhancing SAR image quality and for subsequent information extraction. The results of this project will be important in the detection and tracking of illegal vessels involved in smuggling goods or humans. They will also be indicative in terms of understanding and classifying the characteristics of the wake generating vessel. As a consequence, the work will directly benefit the design of stealthy vessels that can avoid such detections, reducing the risk to naval operations.