The aim of this fellowship is to develop disruptive approaches through theory and experiment to unlock the capacity of future information systems. To go beyond current channel limits is arguably the greatest challenge faced by digital optical communications. To target it, the proposed research will combine techniques from information theory, coding, higher-dimensional modulation formats, digital signal processing, advanced photonic design, and machine learning to make possible breakthrough developments to ensure a robust communications infrastructure beyond tomorrow. Optical communications have to-date been able to fulfil the ever-growing data demand whilst simultaneously reducing cost and energy-per bit. However, it is now recognised that systems are rapidly approaching the fundamental information capacity of current transmission technologies, a trend with potential negative impact on the economy and social progress. To meet future demands with prospective cost and energy savings and avoid the impending exhaust of fibre capacity, the only solution is the emergent technology of spatial division multiplexing (SDM). It provides much wider conduits of information by offering additional means for transporting channels over one single fibre, using multi-mode and multi-core fibres. However, SDM has not yet found a viable path to access this much higher information capacity. State-of-the-art SDM transceivers are only compatible with few-mode/few-core fibres (~10 paths) given the requirement to multiplex/demultiplex over all the fibre pathways to successfully estimate and unravel pathways crosstalk and walk-off. This completely defeats SDM's purpose, the installation of new fibres must allow for several decades of capacity growth to offset the high deployment costs of new cables. This fellowship envisages how to transform SDM technology to drive future optical networks by addressing the key issue overlooked by the research community since the introduction of SDM concepts: optical transceivers must undergo >100-fold integration to enable the benefits of multi-mode/core. Focus on new transceivers capable of digital space modulation will enable scalability of all data pathways to reduce the cost and energy-consumption per bit. Digital spatial modulation in novel coherent transmission schemes, i.e. the pathway index itself is used to carry information, will open fundamentally new theoretical and experimental possibilities up to now unexplored. These new transceivers will be capable of exploiting the multidimensional channel properties in the linear and nonlinear regimes through new spatial modulation formats and coding guided by new information theory and nonlinear science methods. Two main challenges are to construct a high-speed digital spatial modulator capable of dynamically addressing different groups of paths (potentially with tens of paths) in massive multi-path fibres and to develop new learning algorithms (guided by new theory methods) suitable of being embedded in spatial-adaptable transceivers to reach the ultimate capacity of nonlinear multi-dimensional channels.

It is currently a very exciting and challenging time for video compression. The predicted growth in demand for bandwidth, especially for mobile services is driven largely by video applications and is probably greater now than it has ever been. There are four reasons for this: (i) Recently introduced formats such as 3D and multiview, coupled with increasing dynamic range, spatial resolution and framerate, all require increased bit-rate to deliver improved immersion; (ii) Video-based web traffic continues to grow and dominate the internet; (iii) User expectations coninue to drive flexibility and quality, with a move from linear to non-linear delivery; (iv) Finally the emergence of new services, in particular mobile delivery through 4G/LTE to smart phones. While advances in network and physical layer technologies will no doubt contribute to the solution, the role of video compression is also of key importance. This research project is underpinned by the assumption that, in most cases, the target of video compression is to provide good subjective quality rather than to minimise the error between the original and coded pictures. It is thus possible to conceive of a compression scheme where an analysis/synthesis framework replaces the conventional energy minimisation approach. Such a scheme could offer substantially lower bitrates through reduced residual and motion vector coding. The approach proposed will model scene content using combinations of waveform coding and texture replacement, using computer graphic models to replace target textures at the decoder. These not only offer the potential for dramatic improvements in performance, but they also provide an inherent content-related parameterisation which will be of use in classification and detection tasks as well as facilitating integration with CGI. This has the potential to create a new content-driven framework for video compression. In this context our aim is to shift the video coding paradigm from rate-distortion optimisation to rate-quality modelling, where region-based parameters are combined with perceptual quality metrics to inform and drive the coding and synthesis processes. However it is clear that a huge amount of research needs to be done in order to fully exploit the method's potential and to yield stable and efficient solutions. For example, mean square error is no longer a valid objective function or measure of quality, and new embedded perceptually driven quality metrics are essential. The choice of texture analysis and synthesis models are also important, as is the exploitation of long-term picture dependencies.

The UK has fallen significantly behind other countries when it comes to adopting robotics/automation within factories. Collaborative automation, that works directly with people, offers fantastic opportunities for strengthening UK manufacturing and rebuilding the UK economy. It will enable companies to increase productivity, to be more responsive and resilient when facing external pressures (like the Covid-19 pandemic) to protect jobs and to grow. To enable confident investment in automation, we need to overcome current fundamental barriers. Automation needs to be easier to set up and use, more capable to deal with complex tasks, more flexible in what it can do, and developed to safely and intuitively collaborate in a way that is welcomed by existing workers and wider society. To overcome these barriers, the ISCF Research Centre in Smart, Collaborative Robotics (CESCIR) has worked with industry to identify four priority areas for research: Collaboration, Autonomy, Simplicity, Acceptance. The initial programme will tackle current fundamental challenges in each of these areas and develop testbeds for demonstration of results. Over the course of the programme, CESCIR will also conduct responsive research, rapidly testing new ideas to solve real world manufacturing automation challenges. CESCIR will create a network of academia and industry, connecting stakeholders, identifying challenges/opportunities, reviewing progress and sharing results. Open access models and data will enable wider academia to further explore the latest scientific advances. Within the manufacturing industry, large enterprises will benefit as automation can be brought into traditionally manual production processes. Similarly, better accessibility and agility will allow more Small and Medium sized Enterprises (SMEs) to benefit from automation, improving their competitiveness within the global market.