Digital signal processing is a powerful technique for storing, analysing and manipulating digital signals. Ultimately, the quality of the signal to be processed is determined by the performance of the analogue-to-digital converter (ADC) which is used to sample the original analogue signal in the first place and produce a digital representation of it. Electronic ADCs are embedded ubiquitously in numerous everyday items, such as mobile phones, digital thermometers and computer mice to name a few. As the speed of electronic ADCs continues to increase, more and more sophisticated applications including medical imaging and cognitive radar can benefit from the use of ADCs and digital signal processing. Photonics has been used to increase the performance of electronic ADCs since the 1970s, forming what is now generally termed the photonic ADC. Most photonic ADCs with sampling rates as high as 1 THz (1,000,000,000,000 Hz) have invariably employed mode-locked lasers as they can produce very high power optical pulses with very short pulse widths and low jitters, both in the femto second region. Such ultra-short and stable optical pulses are ideal for sampling microwave and millimetre-wave signals at a sampling rate which is beyond what is achievable using conventional electronic ADCs. However, most mode-locked laser sources are bulky, expensive and require constant stability adjustments. Therefore they have not found widespread commercial application to date. Furthermore, the repetition rates of most mode-locked laser pulse sources cannot be readily adjusted and as a result, the sampling rates of photonic ADCs using such sources are fixed and cannot be varied to suit the input signal frequency and bandwidth. In this application, we seek support to investigate a new, high-performance photonic sampling technique based on an optical comb generator instead of the traditional mode-locked lasers. In this novel approach, continuous sampling at flexible sampling frequencies are possible, unlike the mode-locked laser approach. We have also calculated that the combined jitter level due to the linewidth of a typical DFB laser and the phase noise of a mm-wave generator to be used in this technique is less than 5 fs (RMS) and the corresponding effective number of bits (ENOB) of resolution is 10 which is superior to the state-of-the-art CMOS electronic ADC and the all-optical ADC at the same 40 GHz sampling frequency. Such high-performance photonic sampling technique is expected to attract wide attention from both the research community and the industry.

Protection of homeland territory, offshore and overseas assets and related national economic and political interests are strategically important priorities for the UK and the world community. Worldwide economical and political crisis over the last few years has deepened this challenge and the UK witnesses the consequences of it, resulting in increased illegal immigration entries, piracy, and threats to commercial and national assets. Technological advances become quickly available to criminals so that flexibility of contra-measures, including development of deployable sensor networks, re-use of existing communication technologies with multi-mode operation, advanced signal processing is required to tackle the modern challenges. This requires targeted R&D of high-performance cost-effective electronic security (ESS) systems, including practical implementation and development of efficient digital signal processing algorithms. ESS is one of the world's largest (and growing) markets worth about $62 bn a year with UK companies fundamentally involved at the hi-tech end of this industry. An essential segment of the ESS market relates to perimeter/border protection solutions to provide situational awareness and, importantly, real-time recognition and identification of intruders, based on reliable all weather, day and night operation in complex environmental conditions. There is no single solution, so that general approach is to use all technologies and systems available, which can complement each other by providing additional information or data fusion. Widely used for surveillance, electro-optical or mm wave real time imaging systems are not efficient in the absence of line-of-sight and poor transparency of propagation media: walls, foliage, fog, smoke, snow, etc. In contrast, relatively low frequency radio signals penetrate such obstacles and this is the reason why all long-range surveillance and security missions are entrusted to radars. In traditional radar which process the reflections from the target, a target is viewed as a set of bright points, scintillating in amplitude and changing position with aspect angle, as it is composed of many scatterers. Thus even in high performance radar, automatic target recognition remains the most difficult task. At the same time the value of virtually all wide area surveillance radar is substantially reduced by the absence of reliable target classification functionality. This project addresses an important application area - that of low observable or, so-called 'difficult' target imaging in low-cost deployable radio frequency (RF) forward scatter (FS) perimeter protection radar networks. This radar has already proven its excellent detection and target parameter estimation ability. The highly sought-after recognition capability for such a radar network will be provided by combining, for the first time, the Target Shadow Profile Reconstruction (TSPR) technique with MIMO approaches. The novel imaging approach will be based on accurate solution of inverse diffraction problem to reconstruct the target silhouette by a network of distributed RF sensors, configured as a multi-tier chain of RF transmitters and receivers. Each pair of separated transmitter and receiver forms a section of an 'electronic fence', so that each crossing of the baseline is registered and processed in real time. A multi-tier configuration will provide crossings of multiple baselines by the same target allowing multi-perspective images, so that non-coherent MIMO will be exploited for enhanced imaging capability. Coherent synchronized virtual MIMO array will be also investigated on its ability to form an improved multi-perspective target shape outline. The reconstructed target profiles will be a base for the automatic target recognition (ATR).The introduction of target imaging by FS sensors will facilitate implementation of the fully functional radar system for perimeter protection and surveillance.

Automotive industry and the consumers are eager for smart features on new cars and more efficient vehicles. Modern cars are not considered as mere means for travelling from point A to B anymore, but rather smart systems that offer personalised services and have the capability to adapt to the user's preferences and needs. They are expected to become intelligent agents that learn from their environments and exploit various sources of information to become increasingly autonomous systems that relieve the driver from tedious tasks, such as parking, and improve safety, efficiency, and desirability of the future cars. From a wider angle, today's land transportation systems claim about 1.3 million lives and 7 million injuries in road accidents, according to a recent report by CISCO. The increasing number of cars results in traffic jams costing about 90 billion of lost hours for the drivers and the passengers. In addition, transportation accounts for about 26% of the total greenhouse gas emission from human activities. While public transport can help, cars remain to be the desired means of transport according to a recent report by the Department of Transport in 2014. These market forces in addition to the environmental, economic and social impacts of transport systems demand a timely and transformative research to rethink the automotive control systems and revolutionise vehicle design for future cars. There have been two trends towards this objective in the past decade: in the one hand the research in autonomous systems, inspired by unmanned space vehicles, gave birth to driver-less concept cars such as Google robotic car; on the other hand, modern wireless communications enabled cars to talk to each other and the roadside infrastructures, resulting in the concept of connected cars. However, driver-less cars remain to be too expensive for commercial vehicles (Google's cars cost about £100,000 only for sensing equipment) and connected vehicles can offer little if not properly integrated into smart and autonomous features. This ambitious research is defined by a number of world-class academic institutions and leading industrial partners to work with Jaguar Land Rover, a market leader in high end cars, to design and validate a framework that combines the power of connected vehicles concept with the notion of autonomous systems and build a novel platform for cost-effective deployment of autonomous features and ultimately realisation of connected and fully autonomous cars. This can be made possible thanks to modern wireless technologies and the power of cloud computing that allows sharing expensive computing resources (hence, reducing costs per vehicle) and provides access to information that are only available on the cloud. To realise the ambition of the project, a number of key challenges in the areas of ultra-low-latency wireless technologies, cloud computing, distributed control systems, and human interaction issues will be addressed in this project. In addition, potential security threats will be identified and analysed to assess the potential risks for the public and reputational damage for car manufacturers should such technologies be commercialised. At the end of the project, the technical solutions will be integrated into a single framework and will be validated by example applications, characterising technical and service-level performance of the framework, and providing a basis for the future direction of enhanced automated services. While the objective here is to ultimately enable affordable driver-less cars, in the short term, this project aims to enable a number of demonstrable autonomous features in a test environment.

Automotive industry and the consumers are eager for smart features on new cars and more efficient vehicles. Modern cars are not considered as mere means for travelling from point A to B anymore, but rather smart systems that offer personalised services and have the capability to adapt to the user's preferences and needs. They are expected to become intelligent agents that learn from their environments and exploit various sources of information to become increasingly autonomous systems that relieve the driver from tedious tasks, such as parking, and improve safety, efficiency, and desirability of the future cars. From a wider angle, today's land transportation systems claim about 1.3 million lives and 7 million injuries in road accidents, according to a recent report by CISCO. The increasing number of cars results in traffic jams costing about 90 billion of lost hours for the drivers and the passengers. In addition, transportation accounts for about 26% of the total greenhouse gas emission from human activities. While public transport can help, cars remain to be the desired means of transport according to a recent report by the Department of Transport in 2014. These market forces in addition to the environmental, economic and social impacts of transport systems demand a timely and transformative research to rethink the automotive control systems and revolutionise vehicle design for future cars. There have been two trends towards this objective in the past decade: in the one hand the research in autonomous systems, inspired by unmanned space vehicles, gave birth to driver-less concept cars such as Google robotic car; on the other hand, modern wireless communications enabled cars to talk to each other and the roadside infrastructures, resulting in the concept of connected cars. However, driver-less cars remain to be too expensive for commercial vehicles (Google's cars cost about £100,000 only for sensing equipment) and connected vehicles can offer little if not properly integrated into smart and autonomous features. This ambitious research is defined by a number of world-class academic institutions and leading industrial partners to work with Jaguar Land Rover, a market leader in high end cars, to design and validate a framework that combines the power of connected vehicles concept with the notion of autonomous systems and build a novel platform for cost-effective deployment of autonomous features and ultimately realisation of connected and fully autonomous cars. This can be made possible thanks to modern wireless technologies and the power of cloud computing that allows sharing expensive computing resources (hence, reducing costs per vehicle) and provides access to information that are only available on the cloud. To realise the ambition of the project, a number of key challenges in the areas of ultra-low-latency wireless technologies, cloud computing, distributed control systems, and human interaction issues will be addressed in this project. In addition, potential security threats will be identified and analysed to assess the potential risks for the public and reputational damage for car manufacturers should such technologies be commercialised. At the end of the project, the technical solutions will be integrated into a single framework and will be validated by example applications, characterising technical and service-level performance of the framework, and providing a basis for the future direction of enhanced automated services. While the objective here is to ultimately enable affordable driver-less cars, in the short term, this project aims to enable a number of demonstrable autonomous features in a test environment.

The evolution of cyber space is transforming the way our infrastructure is managed. Industrial control systems, that is those systems that manage critical utility infrastructure such as Energy, Water and Transport are increasingly interacting with enterprise IT systems in intricate fashions. This leads to an increase in the level of threats to these critical infrastructures. This is only too evident from cyber weapons such as Stuxnet which targeted centrifuges in Iran's nuclear facilities and more recent news that over 60,000 exposed control systems were accessible online. The US Defence Secretary Leon Panetta described a recent spate of cyber attacks against critical infrastructures as a "pre-9/11 moment". The cyber attack surface of future generations of control systems is likely to increase further with new technologies and working practices such as the use of autonomous software agents in their operation and handheld wireless devices in control and maintenance. Given the importance of industrial control systems to society, it is important that decision-makers are able to effectively articulate the risks posed to them from cyber space. Even more importantly, decision-makers should be able to understand and respond to such risks from a business continuity and recovery perspective in order to evaluate and prioritise their mitigation responses. However, to date, metrics for articulating cyber risk in such settings have largely been driven by technical measures pertaining to security of information or resilience of the control system itself. Though important, these metrics bear little relationship to typical factors used in business risk analysis, such as business continuity, disaster recovery, cost, reputation, impact on resources, etc. The MUMBA project takes the perspective that metrics for articulating cyber risk (in industrial control systems) as business risk only make sense in the context of what we understand the larger system to be, and cannot sensibly be designed without a model of this system. Post-hoc mapping of security and resilience metrics to business risk fails to account for the complex socio-technical landscape in which current and future generations of control systems reside. Effective articulation of cyber risk as business risk requires multi-faceted metrics that are first and foremost driven by business risk concepts. Such metrics consider business risk both along and across various facets of an industrial control system setting i.e., the control system itself, enterprise systems, business processes, people, third party organisations in the product/service supply chain and new/emergent technologies (and associated working practices). Furthermore, the project addresses the need to contextualise these metrics to a particular critical infrastructure domain to ensure meaningful interpretation of business risks and prioritisation and implementation of responses (i.e., whether to mitigate, transfer, accept or avoid particular risks). The project involves a world-leading multi-disciplinary team of researchers in cyber security, resilient industrial control systems, risk management and social anthropology from the Security Lancaster research centre. This academic expertise is complemented by practical insights provided by four industry partners: Airbus, Thales, Atkins Global and Raytheon. Through its research into the complex socio-technical processes at play in contemporary industrial control system settings, new metrics and how to instrument such environments to gather relevant data to compute such metrics, the project aims to become a cornerstone for future research and practice on articulating cyber risk as business risk.