The Programme is organised into two themes. Research theme one will develop new computer vision algorithms to enable efficient search and description of vast image and video datasets - for example of the entire video archive of the BBC. Our vision is that anything visual should be searchable for, in the manner of a Google search of the web: by specifying a query, and having results returned immediately, irrespective of the size of the data. Such enabling capabilities will have widespread application both for general image/video search - consider how Google's web search has opened up new areas - and also for designing customized solutions for searching. A second aspect of theme 1 is to automatically extract detailed descriptions of the visual content. The aim here is to achieve human like performance and beyond, for example in recognizing configurations of parts and spatial layout, counting and delineating objects, or recognizing human actions and inter-actions in videos, significantly superseding the current limitations of computer vision systems, and enabling new and far reaching applications. The new algorithms will learn automatically, building on recent breakthroughs in large scale discriminative and deep machine learning. They will be capable of weakly-supervised learning, for example from images and videos downloaded from the internet, and require very little human supervision. The second theme addresses transfer and translation. This also has two aspects. The first is to apply the new computer vision methodologies to `non-natural' sensors and devices, such as ultrasound imaging and X-ray, which have different characteristics (noise, dimension, invariances) to the standard RGB channels of data captured by `natural' cameras (iphones, TV cameras). The second aspect of this theme is to seek impact in a variety of other disciplines and industry which today greatly under-utilise the power of the latest computer vision ideas. We will target these disciplines to enable them to leapfrog the divide between what they use (or do not use) today which is dominated by manual review and highly interactive analysis frame-by-frame, to a new era where automated efficient sorting, detection and mensuration of very large datasets becomes the norm. In short, our goal is to ensure that the newly developed methods are used by academic researchers in other areas, and turned into products for societal and economic benefit. To this end open source software, datasets, and demonstrators will be disseminated on the project website. The ubiquity of digital imaging means that every UK citizen may potentially benefit from the Programme research in different ways. One example is an enhanced iplayer that can search for where particular characters appear in a programme, or intelligently fast forward to the next `hugging' sequence. A second is wider deployment of lower cost imaging solutions in healthcare delivery. A third, also motivated by healthcare, is through the employment of new machine learning methods for validating targets for drug discovery based on microscopy images

The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.

Living standards in the UK are at significant risk from the rising costs of energy and the increasing gap between demand and the UK's generating capacity. Plugging this gap requires technological innovations which are affordable and can be implemented over reasonably short time-scales. An important area where efficiency gains can be achieved quickly is improving the management of heat released from industrial processes. All industrial and power generation processes produce heat which is often released into the environment in the form of high temperature exhaust products. New technologies are being developed to recover this otherwise wasted energy for use elsewhere, such as electricity, heating or cooling. If applied across the UK manufacturing sector, these technologies could save the energy output of around 20 power stations. Heat-recovery technologies are also used for renewable power from biomass, geothermal, solar-thermal sources and in de-centralized power generation. The development of heat recovery technology is therefore important in terms of cutting our carbon footprint as well as increasing UK energy security. Heat recovery systems work by transferring heat into a high-pressure working-fluid, using a heat exchanger. In order to produce electricity, the working fluid drives a turbine which is connected to an electrical generator. Heat recovery systems often use working fluids which are refrigerants or long-chain hydrocarbons. The properties of these working fluids differ greatly from those which have traditionally been used within turbines (such as air within aero-engines/gas-turbines or water vapour within steam turbines) and can be made up of several components including mixtures of gases and liquids. There is very little known about the behaviour of these unconventional working fluids within turbines largely due to a lack of experimental data with which to test current theories. This is important because turbine designers require accurate models in order to develop high performance machines, and uncertainties in the modelling can have a detrimental impact on both the development costs and the overall performance of a heat recovery system. There is also a potential to exploit the unusual behaviour of these working fluids, such as their ability to change from liquid to gas across the turbine, which can be exploited to increase system power to size ratios (power density) in ways not possible using normal working fluids like water. The project will explore how the behaviour of multi-component fluids can be used to increase turbine performance. In order to achieve this, the work will involve developing methods to simulate multi-component fluids within turbines. The project will use experiments and computational techniques to model these flows and use the results from this work to improve current computational methods. The project involves a collaboration with GE who are global leader in the design, manufacture and supply of heat recovery systems. GE will incorporate the results of this work into their design systems. In doing so, the results from this project will accelerate the development of heat-recovery technologies which will be used world-wide.

The market for photovoltaic (PV) solar modules is experiencing astonishing growth due to increasing energy demand, security of supply issues, increasing cost of fossil fuels and concerns over global warming. The world market for photovoltaics grew by 139% to 21GW in 2010. Although this extraordinary pace of growth is unlikely to be maintained in the short term it will advance rapidly again at the point where grid parity is achieved. It is important that the UK retains a strong research presence in this important technology. It is proposed that the SUPERSOLAR Hub of Universities be set up to co-ordinate research activities, establish a network of academic and industrial researchers, conduct cross-technology research and provide a focus for international co-operation. SUPERSOLAR is led by CREST at Loughborough University and supported by the Universities of Bath, Liverpool, Oxford, Sheffield and Southampton. This group is active in all of the PV technologies including new materials, thin film chalcopyrite, c-Si, thin film a-Si, dye sensitised solar cells, organic PV, concentrator PV, PV systems performance and testing. SUPERSOLAR will set up a solar cell efficiency measurement facility for the benefit of the PV community in the UK. The consortium contains a deliberate balance of expertise, with no bias towards any one technology.

Achieving UK and EU emissions targets requires a transformation in the power generation and manufacturing industries. In the UK we consume 350TWhr of electricity every year, but with modern power-stations which are typically around 50% efficient a large proportion of energy is wasted as rejected heat. Recovering just 10% of this heat would save the equivalent power output of 22 power stations. This is not to mention the heat which could be recovered from manufacturing industries where large quantities of energy are wasted through the heating and cooling during metal-forming processes. In order to make heat-recovery economically viable, low-temperature Organic Rankine Cycles (ORC) can be deployed using fluids with boiling-points close to ambient temperatures, such as many 'molecularly-complex' fluids. The power is extracted in an ORC across a turbine, where these 'molecularly-complex' fluids exist in a gaseous state, and pass through the turbine at high speeds. Increasing the power extracted from the turbine makes heat-recovery systems much more economically favourable and can be achieved by raising the pressure ratio across the turbine. In order to do this efficiently requires a better understanding of molecular-complex gas flows because there is very little known about these complex flows in turbines. The lack of an in-depth understanding of the molecular complex gas-dynamics in ORC turbines means that it is unlikely that optimum power levels are being achieved with present-day design methods. Therefore this proposal aims to determine methods of significantly increasing heat-recovery system power outputs by exploiting the effects of molecular complexity in Organic Rankine Cycle turbines. A target is set of doubling current turbine power levels. In order to determine methodologies to achieve this, a combination of experimental and computational tests are planned. Experiments of molecularly complex gas flows will be studied using a specially designed experimental test-rig which will be able to mimic the flow conditions found in the ORC turbine. The computational simulations will involve the use of a research flow-solver, which will be modified to account for molecular-complex gas properties. The experimental data will aid the development of an accurate computational model, which will then be used to determine novel turbine blade designs to operate at high pressure ratios. This research will directly benefit both the fluid-mechanics research community and the power-generation industry. The research will improve our fundamental understanding of the fluid mechanics of molecularly complex fluids, and will also aid the development of sustainable power generation technologies. An improved understanding of molecular-complex gas flows in turbines has the potential to substantially reduce the UK's fossil fuel dependence and improve our ability to recover currently otherwise 'wasted' heat from power stations and manufacturing processes as well as solar and geothermal radiation. This has a large societal benefit both in-terms of aiding the fight against climate-change and improving the UK's energy security. This work will help towards meeting the targets of the UK Climate Change Act 2008 to reduce by 34 percent our greenhouse gas emissions by 2020 and 80 percent by 2050, against the 1990 baseline.