Today's low-power electronic systems are designed to handle a high variability in the power demand, for example during transmissions from miniature wireless sensors. However these systems cannot cope with a highly variable power supply. If they are powered by an ambient energy harvester in an environment where the available power is low and sporadic, the system dies once the energy storage becomes depleted or damaged, with start-up being impossible if the power is not increased to a higher steady level. With an increasing number of potential applications of microelectronic systems calling for remote, embedded and miniaturized solutions, sporadic and low power supply and unpredictable energy storage needs to be addressed. This project researches how to design robust and reliable electronics for situations where there is a variable, unreliable source of energy. A number of situations, or states, have been defined, according to the level of depletion of on-board energy storage, and to how variable the power supply is. In the most challenging states, for example where the input power is sporadic and spread over a wide range from nW to mW, modern electronics fails. We call this the "survival zone" and are investigating a combination of techniques from the areas of power electronics and asynchronous microelectronics design to allow devices to operate in this zone. Techniques include control circuits that are able to ride through variable voltages, the detection of states, and reconfigurable hardware resources and control algorithms to suit sporadic and sub-microwatt input power. The chief aim of this project is to produce survival zone design methods for the microelectronics design community.

The nature of the modern battlefield is changing dramatically. Electronic communication is allowing unprecedented interchange of data and information between platforms. Advances in electronics are allowing the possibility of low cost networked unattended sensors. Intelligent and robust processing of the very large amount of multi-sensor data acquired from various networked communications and weapons platforms is, therefore, crucial to retain military advantage and mitigate smart adversaries who present multiple threats within an anarchic and extended operating area (battlespace). Hence we have composed a unique consortium of academic experts from Loughborough, Surrey, Strathclyde and Cardiff universities together with six industrial project partners QinetiQ, Selex-Galileo, Thales, Texas Instruments, PrismTech & Steepest Ascent, to develop transformational new signal processing solutions to the benefit of Dstl, the MoD, and the UK in general. To achieve this goal we are proposing a five-year integrated programme of work composed of the following five interlinked work packages: (1) Automated statistical anomaly detection and classification in high dimensions for the networked battlespace, in which we aim not only to detect anomaly, but also to identify its nature and nuance, when acquired in a high dimensional complex network environment. Data quality and ambiguity measures will be used to ensure the models of normality are not corrupted by unreliable and ambiguous data; (2) Handling uncertainty and incorporating domain knowledge, within which we aim to exploit the world model of the networked battlespace to improve performance and confidence, and to reduce uncertainty to an unprecedented level. Examples for such information are digital maps about terrain and layout of the field, geometric relations between platforms and operational conditions such as weather; (3) Signal separation and broadband distributed beamforming, in which we target at designing low-complexity robust algorithms for underdetermined and convolutive source separation, and broadband distributed beamforming, facilitated by low-rank and sparse representations, and their fast implementations; (4) Multi-input and multi-output (MIMO) and distributed sensing, within which we intend to create novel paradigms for distributed MIMO radar systems operating in the cluttered networked battlespace; and (5) Low complexity algorithms and efficient implementation, in which with Texas Instruments, PrismTech & Steepest Ascent we aim to formulate and realize novel implementation strategies for a range of complex signal processing algorithms in a networked environment. These interlinked workpackages have been very carefully designed to marry up with the research themes and challenges identified by Dstl & the EPSRC and we have clear strategies for attaining datasets, performing evaluation, and communicating findings. We have designed a carefully structured consortium management team including an overarching steering group with renowned external independent experts with expertise covering the scope of the work programme. The operation of the consortium will be the responsibility of the Consortium Director and the Consortium Management Team. A key component of our consortium management is to encourage research staff and students employed to be periodically seconded to the labs of other collaborators within the consortium to benefit from complementary knowledge and skills at partner universities and industry; gain access to privileged datasets and/or equipment; or share resources & provide critical mass when addressing a particular Dstl challenge. The management structure and coordination measures have been designed for the consortium to have the capacity to assume the role of lead consortium, if required, working with Dstl & EPSRC to establish a community of practice in signal and data processing, and to ensure the UK has world leading capability in the area.

The nature of the modern battlefield is changing dramatically. Electronic communication is allowing unprecedented interchange of data and information between platforms. Advances in electronics are allowing the possibility of low cost networked unattended sensors. Intelligent and robust processing of the very large amount of multi-sensor data acquired from various networked communications and weapons platforms is, therefore, crucial to retain military advantage and mitigate smart adversaries who present multiple threats within an anarchic and extended operating area (battlespace). Hence we have composed a unique consortium of academic experts from Loughborough, Surrey, Strathclyde and Cardiff universities together with six industrial project partners QinetiQ, Selex-Galileo, Thales, Texas Instruments, PrismTech & Steepest Ascent, to develop transformational new signal processing solutions to the benefit of Dstl, the MoD, and the UK in general. To achieve this goal we are proposing a five-year integrated programme of work composed of the following five interlinked work packages: (1) Automated statistical anomaly detection and classification in high dimensions for the networked battlespace, in which we aim not only to detect anomaly, but also to identify its nature and nuance, when acquired in a high dimensional complex network environment. Data quality and ambiguity measures will be used to ensure the models of normality are not corrupted by unreliable and ambiguous data; (2) Handling uncertainty and incorporating domain knowledge, within which we aim to exploit the world model of the networked battlespace to improve performance and confidence, and to reduce uncertainty to an unprecedented level. Examples for such information are digital maps about terrain and layout of the field, geometric relations between platforms and operational conditions such as weather; (3) Signal separation and broadband distributed beamforming, in which we target at designing low-complexity robust algorithms for underdetermined and convolutive source separation, and broadband distributed beamforming, facilitated by low-rank and sparse representations, and their fast implementations; (4) Multi-input and multi-output (MIMO) and distributed sensing, within which we intend to create novel paradigms for distributed MIMO radar systems operating in the cluttered networked battlespace; and (5) Low complexity algorithms and efficient implementation, in which with Texas Instruments, PrismTech & Steepest Ascent we aim to formulate and realize novel implementation strategies for a range of complex signal processing algorithms in a networked environment. These interlinked workpackages have been very carefully designed to marry up with the research themes and challenges identified by Dstl & the EPSRC and we have clear strategies for attaining datasets, performing evaluation, and communicating findings. We have designed a carefully structured consortium management team including an overarching steering group with renowned external independent experts with expertise covering the scope of the work programme. The operation of the consortium will be the responsibility of the Consortium Director and the Consortium Management Team. A key component of our consortium management is to encourage research staff and students employed to be periodically seconded to the labs of other collaborators within the consortium to benefit from complementary knowledge and skills at partner universities and industry; gain access to privileged datasets and/or equipment; or share resources & provide critical mass when addressing a particular Dstl challenge. The management structure and coordination measures have been designed for the consortium to have the capacity to assume the role of lead consortium, if required, working with Dstl & EPSRC to establish a community of practice in signal and data processing, and to ensure the UK has world leading capability in the area.

UK Research Councils have set up a RCUK Energy Programme, investing more than £530 million in research and skills to pioneer a low carbon future. Energy is also a major application area funded by TSB. Several major global companies, including BP, Caterpillar, EDF Energy, E.On, Rolls-Royce and Shell, have joined their forces with the UK government to establish the Energy Technologies Institute, creating a potential £1billion investment fund for new energy technologies. The ongoing research programmes cover various aspects of energy from generation, transmission to end use, in order to create affordable, reliable and sustainable energy for heat, power and transport. Increasing the share of renewable energy, e.g. wind, solar, marine and biomass, and improving energy efficiency are the two most important ultimate goals for all energy-related programmes. The renewable energy needs to be connected to the grid, preferably, via inverters in order for them to take part in the grid regulation, in particular, for large-scale renewable installations. However, the capacity of individual power inverters is limited and multiple inverters are needed to be operated in parallel to achieve the power capacity needed. For a 5GW offshore wind power site, 1000 of 5MW inverters are needed. How to make sure that the inverters will share the load proportionally/evenly is a challenge. It should not be assumed that inverters could be connected in parallel automatically. Without proper mechanisms in place, circulating currents may appear and some inverters may be overloaded, which may cause damage. The system may even become unstable and lead to unwanted behaviours. The parallel operation of inverters has been a major problem in industry that prevents the large-scale utilisation of renewable energy sources. This is a simple problem which has not been solved properly for many years. The conventional droop control strategy is a promising technology but the sharing accuracy cannot be guaranteed. Very recently, the PI has revealed that the conventional droop control scheme and its variants do not possess a mechanism to make sure that the sharing accuracy is robust against numerical computational errors, parameter drifts and component mismatches. A robust droop controller is then proposed, which is able to maintain accurate sharing of real power and reactive power at the same time and also to maintain good voltage regulation when the inverters are of the same type. The problem is still unsolved when the inverters are different. The major aims of the project are to develop fundamental understanding about parallel-operated inverters and to develop enabling contorl technologies to facilitate the large-scale utilisation of renewable energy and distributed generation. The ultimate goals of the project are to develop universal control strategies that allow the parallel operation of inverters with different types of output impedances and to develop a fundamental theory to guarantee the stable operation of power systems with parallel-operated inverters.

During the last three hundred years chemical synthesis has come a long way, from the time of Alchemy to the complete synthesis of complex natural products like Taxol, to the assembly of complex nanomolecular particles and devices for dye sensitised solar cells. Today, the availability of fast computers, ubiquitous sensors, imaging techniques, and algorithms are transforming science from electrical engineering to synthetic biology but chemists are yet to embrace the revolution due to the difficulties of integrating chemistry, sensors, software, and material handling. Very recently we have started to explore the development of configurable chemical-robotic platforms for the discovery, optimisation, scale-up and control of syntheses using a range of approaches including flow systems, 3D printing and hybrid robotic platforms. While a number of leading groups internationally and in the UK are aiming to develop new approaches to the optimisation of chemical synthesis, we wish to take the idea a stage further and develop an integrated platform for the discovery of molecular entities (initially focussing on inorganics) and then assess their 'fitness' according to user needs to construct a new library of programmable chemical building blocks leading to new systems that can be rapidly manufactured and tested in a range of application areas. The development of a platform for molecular discovery is unprecedented; this step-change will place the UK as the world leader allowing us to link fundamental discovery with faster, smarter and cleaner manufacturing of new chemical entities with user-driven properties and functions. Therefore we aim to develop a new synthetic chemistry and engineering platform for the discovery of molecules, clusters and nanomaterials using an integrated hybrid chemo-robotic system integrating wetware (chemical reagents), hardware (reactors and sensors) and software (intelligent algorithms). By 'digital' programming it will be possible to optimise / change the course of the wetware as a function of the properties measured using algorithms controlled using a software system utilising the expertise of a team of chemists, electrical engineers and physicists, who share the vision of integration and advanced software control of matter. The chemical inputs will be based upon the assembly of molecular metal oxides (polyoxometalates) with well-defined physical properties using a computer controlled reaction system enabling closed loop chemical synthesis and discovery for the first time. The overall system will target new types of catalytically and electronically active materials with radically new properties via the chemical platform choosing from a Universal Building Block Library (UBBL) approach that links properties of the building blocks with emergent properties of the resulting clusters and materials. The hardware will be built from affordable customisable liquid handling robots, 3D printed reactionware, programmable milli-fluidics as well as linear, networked, and arrayed flow systems with a range of bespoke (CMOS based redox camera / ion sensitive arrays) and off the shelf sensor systems (pH, UV, Raman, mass spectrometry). Targeted properties include photochemical, electrochemical, and catalytically active molecules and materials defined by end-users that will allow us to develop algorithms for the discovery and scale-up of new clusters etc. This programme is supported by a number of partners with support of around £1.9 M in cash, £0.9 M in kind with support from GSK, Unilever, FTDICHIP, ACAL Energy, CMAC, and also with support from the University of Glasgow who will invest ca. £0.5 M equipment funds and 4 PhD students demonstrating a very strong commitment adding value to the EPSRC investment.