The reliance of military systems and armed forces on the EM spectrum creates vulnerabilities and opportunities for electronic warfare (EW) in support of military operations. EW is concerned with detecting, recognising then exploiting and countering the enemy's electronic order of battle, and calls for the development of innovative algorithmic solutions for information extraction and delivery of signals in contested electromagnetic environment. Traditionally, the subject of signal sensing/information extraction has been developed separately from the area of signal delivery. In contrast, this visionary project conducted at Imperial College London and University College London aims at leveraging the consortium complementary expertise in various areas of signal processing (sparsity, super-resolution and subspace methods, communications, radar, and machine learning) for civilian and defence applications to design and develop novel and innovative solutions for a cohesive treatment of information extraction and delivery of signals in contested electromagnetic environment. To put together this novel approach in a credible fashion, this project is organized in two major work packages. The first work package will analyze, separate and characterize signals across time, frequency, and space and extract useful information from those signals by developing and leveraging novel super-resolution, subspace and deep learning methods. The second work package will leverage progress made in the first work package and design signals and system responses for sensing and signaling in congested RF environments. Novel waveform design approaches will be derived for sensing using an extended ambiguity function-based framework, for precise spatiotemporal energy delivery using network-wide time-reversal and for joint sensing and signaling. Attention will also be drawn to the design of signals resilient to hardware and nonlinear channel responses. The project will be performed in partnership with academia/research institutes (University of Kansas, Fraunhofer) and industrial leaders in civilian and military equipment design and manufacturing (IBM, US Army Research Lab, Thales). The project demands a strong track record in a wide range of signal processing techniques and it is to be conducted by a unique research consortium with a right mix of theoretical and practical skills. With the above and given the novelty and originality of the topic, the research outcomes will be of considerable value to transform the future of electronic warfare and give the industry and defence a fresh and timely insight into the development of signal processing for contested electromagnetic environment, advancing UK's research profile in the world. Its success would radically change the design of electronic support measures, electronic coutermeasures and electronic counter-coutermeasures and have a tremendous impact on the defence sector and industry.

The UKRI Centre for Doctoral Training in Machine Intelligence for Nano-electronic Devices and Systems (MINDS-CDT) will operate as a centre of training excellence in the next generation of systems that employ Artificial Intelligence (AI) algorithms in low-cost/low-power device technologies: hardware-enabled AI. The use of AI in real-world applications through systems of interconnected devices (so-called Internet of Things) is increasingly important across the global economy. Various market surveys estimate the sector to be valued in the hundreds of billions, and project levels of compound annual growth of 25-30%. Applications of these technologies include smart cities, industrial IoT and robotics, connected health and smart homes. It is widely agreed that new advances in artificial intelligence and machine learning are key to unlocking the potential of these systems. Significant challenges remain, however, in the development of robust algorithms and coordinated systems that are efficient, secure, and work in concert with modern devices. Advances in electronics will soon hit atomic scales, requiring new approaches if we are to continue to improve hardware speed and power consumption. Novel nanotechnologies such as memristors have the potential to play a key role in addressing these challenges, but critical to their employment in real-world applications is how algorithms work in the context of device physics. Further, there are significant challenges around how resources available to devices (energy, memory, etc.) can more effectively adapt to the computational tasks at hand, again requiring us to think about how hardware and software work together. The MINDS CDT is unique in its cross-disciplinary research programme crossing emerging AI algorithms and models with advances in device technologies that underpin and enable their potential. To quote from one of our industry partners, "innovation is to come from software and hardware co-development" and that "this joined-up thinking as a potential game changer". The MINDS-CDT will train a substantial number of experts with the knowledge and skills to lead the development of this next generation of intelligent, embedded systems. The training programme will draw from both computer science and electronics expertise at the University of Southampton, and a substantial network of stakeholders from across industry, government and the broader economy. Core to our training ethos is the up-front investigation of the potential impacts of technological innovation on society, security and safety, and in the engagement of interest groups and the public in understanding the benefits as well as the risks of the use of these new developments in AI and technology for our society and economy. The processes we will use here include that all projects and research activities will be informed by in-depth impact assessment, and we will instigate an ambassadors programme for public engagement and, in particular, the engagement of underrepresented groups in AI and engineering.

The ongoing COVID-19 epidemic requires careful monitoring as a variety of measures such as lockdown and social distancing are introduced and subsequently relaxed, leading to varying levels of demand for and capacity within the healthcare system. The disease has varying expected outcomes depending on the age, sex, and underlying comorbidities of cases. Epidemic dynamics, particularly in the presence of changing control policies, will shift the dominant modes of transmission and hence the distribution of disease. We will develop models to integrate the diverse but often noisy and incomplete datasets available, providing real-time policy support together with quantification of uncertainty. We will address three particular challenges. (1) Understanding spread in closely connected sub-populations in which there are close, repeated contacts capable of spreading disease such as households, hospitals, prisons, and care homes. Data from these contexts allow epidemiological parameters relating to infection risk conditional on contact to be identified in statistical work, and they are also important foci for policies. (2) Making short- and medium-term predictions of the epidemic trajectory and healthcare demand with appropriate uncertainty quantification. (3) Modelling long-term prospects for the epidemic, including the likelihood of eventual endemicity, the consequences of different virological assumptions about SARS-CoV-2, and how the different scenarios in this context will interact with long-term societal and health consequences of the pandemic. The project will use mathematical methodology, integrated with interdisciplinary expertise from social science, biology, clinical medicine, and epidemiology.

The United Nations' 17 Sustainable Development Goals (SDGs) aim to mobilise global efforts to 'transform our world' (UN, 2017) so as to address major challenges facing global society, such as achieving food security and nutrition for all (SDG 1, 2, 3, 8 &12). We will focus on India where agricultural sector which contributes more than 17.5% to its GDP, employs 250 million people and remains the backbone of India's rural population, which comprises almost 67% of the country's 1.3 billion population. Yet, most of India's farmers still remain under poverty. Merely 4% of India's food is moved through the cold chain compared to 70% in the UK, resulting in as much as 40% wastage, particularly in fresh fruits and vegetables, between farm and market. This reduces farmers' income, which in turn limits their capacity to invest and their incentive to grow more nutritious food. Whilst inadequate cold supply chain infrastructure results in large amount of wastage in fresh produce, inadequate value creation and the impact of climate change on agriculture productivity and food loss has led to increasing number of farmers suicide. Moreover, India has highest number of organic farmers globally but these farmers, who produce most of the country's high-value and high-nutrition foods, have little access to integrated cold chains. Indian farmers simply do not have financial resources to invest in precision agriculture and cold chain infrastructure development. With PM Modi's target of "doubling farmers' income by 2022", India necessitates a stronger case of technological intervention along with innovative business models and effective policies that double the income of farmers and maximise value for every stakeholder in the supply chain. The project TRANSSITioN will use a food systems approach to identify relevant STFC and indigenous technologies for digitising small-scale agriculture production, connecting farmers to supply chain, reducing food loss and managing food surplus. We will also identify relevant business and supply chain finance models supporting such technological interventions and ways in which different actors across the cold food chain could be engaged to directly and indirectly shape development outcomes. We will create "Sustainable Cold Food Chain Incubator Hub" (TRANSSITioN Hub) in India built on STFC ground breaking technologies from RAL Space (Thermal modelling, remote sensing, drone applications, Infrared Thermography), cryogenics from ASTeC and Cryox, data science capabilities (big data analytics, artifical intelligence) of STFC and IBM Research at Hartree Centre, along with interdisciplinary team from supply chain management, business sustainability, political science, food science, agriculture and material sciences, international research and stakeholder collaboration. The WPs will be applied to a set of two case studies starting from farms (organic and conventional) to consumption centre, co-identified with in-country partners. Hyderabad and Chennai region have been identified for the pilot project. Being host to companies such as such as Amazon, Flipkart, Jubilant Foods, Johnson & Johnson and Procter & Gamble, this region has become a consumer centric food logistics hub. With an established network of 50,000 organic farmers, processors, technology providers and retailers the selected region strongly aligns with the core competencies of our research agenda. Unfortunately, this region also had the second highest number of farmers suicide in 2016. Project TRANSSITioN, therefore, aims to forge a sustainable framework to meet different economic, social and commercial priorities of varied stakeholders to usher socio-economic change through value maximisation.

The United Nations' 17 Sustainable Development Goals (SDGs) aim to mobilise global efforts to 'transform our world' (UN, 2017) so as to address major challenges facing global society, such as achieving food security and nutrition for all (SDG 1, 2, 3, 8 &12). We will focus on India where agricultural sector which contributes more than 17.5% to its GDP, employs 250 million people and remains the backbone of India's rural population, which comprises almost 67% of the country's 1.3 billion population. Yet, most of India's farmers still remain under poverty. Merely 4% of India's food is moved through the cold chain compared to 70% in the UK, resulting in as much as 40% wastage, particularly in fresh fruits and vegetables, between farm and market. This reduces farmers' income, which in turn limits their capacity to invest and their incentive to grow more nutritious food. Whilst inadequate cold supply chain infrastructure results in large amount of wastage in fresh produce, inadequate value creation and the impact of climate change on agriculture productivity and food loss has led to increasing number of farmers suicide. Moreover, India has highest number of organic farmers globally but these farmers, who produce most of the country's high-value and high-nutrition foods, have little access to integrated cold chains. Indian farmers simply do not have financial resources to invest in precision agriculture and cold chain infrastructure development. With PM Modi's target of "doubling farmers' income by 2022", India necessitates a stronger case of technological intervention along with innovative business models and effective policies that double the income of farmers and maximise value for every stakeholder in the supply chain. The project TRANSSITioN will use a food systems approach to identify relevant STFC and indigenous technologies for digitising small-scale agriculture production, connecting farmers to supply chain, reducing food loss and managing food surplus. We will also identify relevant business and supply chain finance models supporting such technological interventions and ways in which different actors across the cold food chain could be engaged to directly and indirectly shape development outcomes. We will create "Sustainable Cold Food Chain Incubator Hub" (TRANSSITioN Hub) in India built on STFC ground breaking technologies from RAL Space (Thermal modelling, remote sensing, drone applications, Infrared Thermography), cryogenics from ASTeC and Cryox, data science capabilities (big data analytics, artifical intelligence) of STFC and IBM Research at Hartree Centre, along with interdisciplinary team from supply chain management, business sustainability, political science, food science, agriculture and material sciences, international research and stakeholder collaboration. The WPs will be applied to a set of two case studies starting from farms (organic and conventional) to consumption centre, co-identified with in-country partners. Hyderabad and Chennai region have been identified for the pilot project. Being host to companies such as such as Amazon, Flipkart, Jubilant Foods, Johnson & Johnson and Procter & Gamble, this region has become a consumer centric food logistics hub. With an established network of 50,000 organic farmers, processors, technology providers and retailers the selected region strongly aligns with the core competencies of our research agenda. Unfortunately, this region also had the second highest number of farmers suicide in 2016. Project TRANSSITioN, therefore, aims to forge a sustainable framework to meet different economic, social and commercial priorities of varied stakeholders to usher socio-economic change through value maximisation.