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TRTUK

THALES RESEARCH & TECHNOLOGY (UK) LIMITED
Country: United Kingdom
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46 Projects, page 1 of 10
  • Funder: UKRI Project Code: EP/R004757/1
    Funder Contribution: 2,050,760 GBP

    Hybrid autonomous systems are those where groups of people are in direct, ongoing interaction with groups of autonomous robots or autonomous software. One prominent current example involves rush-hour traffic made up of a mixture of cars driven by people and cars driven by smart algorithms. However, emerging technologies in robotics, AI and ICT mean that hybrid autonomous systems of this kind will become increasingly common in a much wider set of situations: Emerging technologies in robotics, AI and ICT mean that hybrid autonomous systems of this kind will become increasingly common in a much wider set of situations: - a mixture of autonomous and human-operated drones making deliveries or monitoring public spaces; - a mixture of human traders and autonomous trading agents buying and selling stocks; - a mixture of autonomous and human-operated trains and trams providing efficient, integrated public transport; - autonomous systems assisting with search and rescue missions in disaster areas that are difficult or dangerous to access; - robot carers assisting care workers with the provision of social care in the home In each of these cases smooth, reliable, safe interaction amongst machines and people will be key to success. But how can we guarantee that self-driving cars won't cause a crash or gridlock? How can we understand how autonomous systems will respond to new situations (both acute shocks and long-term gradual changes in their environment), or changes in the way that people interact with them? Consequently, as we enter this new design space, a crucial challenge for the engineers of hybrid autonomous systems across all of these settings is ensuring that the system behaviour is Robust and Resilient and that it meets Regulatory demands: the R3 Challenge. T-B PHASE directly addresses this R3 Challenge for Hybrid Autonomous Systems Engineering, by bringing together expertise in robotics, AI, and systems engineering at the University of Bristol and Thales in a five-year project that targets fundamental autonomous system design problems in the context of three real-world Thales use cases: Hybrid Low-Level Flight, Hybrid Rail Systems, and Hybrid Search & Rescue. Bristol and Thales have a long-standing track record of research collaboration, and by jointly pursuing fundamental research questions in the context of highly practical design problems, alongside a programme of engagement with industry, the public and regulatory bodies, T-B PHASE will significantly advance our capability to operate confidently in one of the most important emerging areas for modern engineering.

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  • Funder: UKRI Project Code: NE/I007148/1
    Funder Contribution: 881,353 GBP

    Our work brings together two important areas of science and engineering: wireless communications technology and glaciology. Using innovative techniques currently being developed for wireless communications to install a network of sensors, we will increase our understanding of how the world's large ice sheets will respond to climate change, while the knowledge gained by experimenting with wireless networks in an extreme environment will be of benefit to engineers developing the next generation of wireless networks such as mobile phone networks. Around the edge of the Greenland Ice Sheet are outlet glaciers, which allow ice to flow from the centre of the ice sheet into the sea. Where the ice meets the sea, icebergs are formed, and about half of the ice which leaves the ice sheet does so in this way. These glaciers are thought to be very sensitive to changes in air and ocean temperatures, but we do not yet know enough about them to be able to predict future changes, or understand those already observed. The processes leading to iceberg formation ('calving') are particularly important, but poorly understood. In particular, there is an urgent need to address the question of how changes in glacier flow ('dynamics') relate to changes in terminus position and calving rates. Does one drive the other, or is it more complex than that? To understand this, we need to know what the primary mechanisms are for calving in Greenland outlet glaciers, and we need characterise these mechanisms in a consistent, quantitative way across all such glaciers. Only then can the relevant processes be represented in computer models of the ice sheet and its outlet glaciers, allowing us to improve our predictions of how they will respond to climate change. To improve our understanding, it is vital to have detailed observations of iceberg calving events, but these are hard to obtain because of the difficulty of placing and maintaining instrumentation on the heavily-crevassed ice surface. To overcome the problem of getting the right observations, a network of expendable GPS receivers will be deployed on Helheim Glacier, an important calving glacier in south-east Greenland. Using special data processing techniques, GPS can be used to make measurements which are accurate to a few centimetres. The GPS receivers will be connected to each other and to a base station via a network of expendable, low-power wireless transceivers. The design of the network will mean that data can still be collected if parts of it are lost: it will be self-healing. The innovative nature of the network and its components make it economically and logistically possible to deploy a large number of sensors by helicopter in the calving region of the glacier. During the lifetime of the project, we expect to observe several calving events in detail. The data from the GPS receivers will be combined with other data sources, from aircraft, satellites and stereo photography, to obtain an unprecedented insight into iceberg formation. The data will be combined with computer models of ice flow, enabling various theories about iceberg formation to be explored and tested. This part of the project has the potential to deliver new science well beyond the end of the funded work.

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  • Funder: UKRI Project Code: EP/I000232/1
    Funder Contribution: 1,147,550 GBP

    Extensive work has been carried out on the technological, economic and societal potential for better management of energy demand. Huge potential of demand side management can only be exploited by exploring new ways to induce shifts of demand during peaks and hence reduce marginal costs. Digital communication technology can play a vital role in inducing this shift by enabling communication between the devices and the users. A holistic view of the interaction of all key-players - energy devices, energy supplier and energy users - is missing and this project aims at investigating this interaction using a multidisciplinary research team. The overall objective of this research is to evaluate the feasibility of using network technologies and sensor devices now being used in telecommunications, to create a Persuasive Energy-conscious Network (PEN) in a real life pilot setting and then study the potential impact on user behaviour leading to reductions in, and shifts in patterns of, loads of electricity. The project will aim to quantify the savings in carbon footprint (and operational energy cost) of the pilot test-bed when digital technologies (PEN) are deployed. It will also study the response of the users of the proposed monitoring and control system. As part of this research project, we will establish an autonomous self learning network of the sensors, energy consuming devices and users of energy. Self descriptive devices will be enabled to send meta-data describing relevant details of their energy consumption and context (time, task urgency etc.). The network will collect the data and create an energy consumption knowledge-base. The novel middleware will be incorporated that will run the modules of self learning and decision making to trigger actions that will shape the energy demand using specified goals. For this purpose, we will use the University of Surrey campus as initial test-bed.Technological interventions are more likely to achieve the intended energy savings if the interventions are designed with an understanding of how users view and interact with their energy systems. Within psychology, a wealth of research is available which shows that the type and format of information given to users can have a strong influence on their response. We know very little about how individuals may respond to flexible intelligent systems. We aim to examine the behavioural responses to the implementation of intelligent technologies that aim to reduce energy use in buildings.There are various ways of incentivising consumers to change load patterns. One of them is through financial models aimed at fostering the demand responsiveness of consumers. Those consumers who proactively engage in reducing or shifting their loads and significantly react to price signals should be rewarded by paying less for their electricity consumption. Part of this research will focus on the development of a financial model for an incentive/payment scheme and testing such financial models on the campus test-bed.As an outcome of the research we will deliver a pilot test bed for the autonomous and self learning Persuasive Energy-conscious Network. The psychological studies will be reported on likely expected behavioural responses of the users to the proposed technologies. A financial model will be implemented and its impact on energy demand transformation will be provided with quantified results of savings in terms of energy cost and CO2 emissions.The research will have targeted collaboration with the users of research such as the industrial researchers (e.g. Thales Research and Technologies), individual energy users (University students and staff) and corporate users of energy (University of Surrey's Estates and Facilities) and government bodies (Woking Borough Council) to highlight the potential of using the digital technology in meeting the requirements of these players in this research.

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  • Funder: UKRI Project Code: EP/R012288/1
    Funder Contribution: 1,024,320 GBP

    RISC-V is an Instruction Set Architecture (ISA) design. An ISA is essentially a specification for the instructions any compatible processor implementation should be able to execute, and the resources those instructions can access; it acts as the interface between the processor implementation (hardware) and programs that execute on it (software). In sharp contrast with proprietary analogues such as the x86 ISA from Intel, RISC-V is an open source design. This means it can be used freely by anyone for any purpose, which, in part, has meant rapid development of a rich support infrastructure around the project: this includes a) vibrant developer and user communities, built around an associated non-profit foundation, b) numerous implementations of the ISA, both in HDL (i.e., a soft core for use on an FPGA platform) and silicon (i.e., physical ICs), and c) ports of programming tool-chains (e.g., GCC and LLVM) and operating systems (e.g., Linux). Similar openness is a core principle in security-critical contexts, contrasting with the alternative often colloquially termed "security by obscurity". This is particularly true in the field of cryptography, a technology routinely tasked with ensuring secrecy, robustness and provenience of our data (communicated or stored), and the authenticity of parties we interact with: open development of cryptographic standards, designs, and implementations is the modern norm. As a result, RISC-V presents various opportunities when used to execute cryptographic software. The proposed research goals capitalise on these opportunities, in a way designed to address advanced, persistent threats to our digital security, and, by extension, society. Specifically: 1) Since RISC-V can be implemented by anyone, it is possible to develop a core hardened against specific types of attack; the focus will be on the threat of side-channel attacks (which is particularly relevant to embedded use-cases, e.g., IoT). As well as doing so, the proposed research will investigation how detailed information about the implementation can be harnessed to produce more effective security evaluations. 2) Since RISC-V can be adapted by anyone, it is possible to develop various cryptography-specific extensions or variants of the ISA that offer either, for example, higher efficiency. If cryptographic software is more efficient it can also be more secure, because, for example, larger keys or more robust attack countermeasures can be deployed without as significant an impact on latency. 3) Evaluation of side-channel security can be prohibitive in the sense it needs various specific items of equipment. Harnessing a platform based on RISC-V, the proposed research with address this problem by offering a "lab. free" (i.e., cloud-based) acquisition and analysis workflow available to anyone.

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  • Funder: UKRI Project Code: EP/N028295/1
    Funder Contribution: 364,323 GBP

    The proposed research applies computer science solutions to an end-user-focussed challenge. The challenge is how to achieve an enhanced customer experience during a journey, through detailed knowledge of an individual traveller, whilst protecting the privacy of their data. As well as developing technical solutions to data privacy, this project aims to encourage passengers to provide this data by developing an evaluation framework to enhance their understanding of how it is used and how they can control it, thus maximising trust in the service. Currently, such a framework does not exist and this is an impediment to the opportunities offered by increased sharing of personal data, i.e. transport customers are, in the majority, unwilling to share personal data due to privacy concerns. The research findings will be applicable to a range of journey modes but the focus here will be on rail travel. The project has been developed closely with the rail industry through partnership with the Association of Train Operating Companies (ATOC) and the Rail Safety and Standards Board (RSSB). In recent years, the availability of data in the rail industry has increased significantly in terms of timetabling, disruption and real-time provision to passengers. Currently there is little in the way of individual customer information but this is increasingly possible through smartphones and other mobile devices and will become more prevalent with the introduction of smartcards and contactless technologies. The industry's Rail Technical Strategy aims to establish rail as customers' preferred form of transport for reliability, ease of use and perceived value. Increased understanding of passengers through information such as their location, their plans, their mobility or luggage limitations, or where they are on the train would enable a more personalised service and an improved experience. The challenge is to assure customers that their data is being protected and used appropriately and that they are fully in control. The consortium assembled for this project brings together the three academic disciplines required to solve this challenge: computer science, to develop the framework and technical solutions (University of Surrey and Royal Holloway, University of London); human factors, to develop the use cases, evaluate passenger perceptions and ensure usable solutions (Loughborough University) and transport systems to bring understanding of the data streams to be integrated (University of Southampton). To ensure the solutions are co-created with the industry and have a direct pathway to impact, ATOC and RSSB have a key role as stakeholders and on the project's External Advisory board, alongside other sector experts such as EnableID (Internet of Things and personal data), the Transport Systems Catapult (the UK government's innovation centre for intelligent mobility knowledge exchange) and ThalesUK (rail technology). The objective is to develop a privacy evaluation framework underpinned by statistical analysis, data provenance and mobile technology. This framework will be integrated with emerging data systems being developed by the rail industry and also into a wider (sector-independent) framework being proposed by the Digital Catapult (the UK government's innovation centre for digital technologies). This will enable better communication to passengers as to why their data is needed and how it will be handled in order to increase trust and feelings of control, thus providing a virtuous circle of data provision, leading to enhanced customer experience and hence further data provision.

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