Thales are the leading company in Europe for high performance long wavelength infra-red (LWIR) imagers. Thales has been developing thermal imagers for more than 40 years, and is currently working on a unique polarimetric thermal imaging camera concept - the Polarimetric Catherine MP. Thermal imagers provide day and night imaging capability with good object discrimination (for example, telling the difference between animals and vehicles). Further development work has been identified to progress the current camera capabilities. This work includes advanced signal, data and image processing development, some of which are already underway in house. The proposed project is integral part of this effort as it will address fundamental issues about the operation and performance of the detector, as well as investigating a novel approach to utilising the camera data (thermal and polarisation imagery) for deployment as part of a multi-modal imaging system. This will be achieved primarily through the application of existing expertise in Bayesian inference, imaging and polarisation in STFC-funded research groups (Astronmy and Institute of Gravitational Research) at the University of Glasgow. Algorithms will be developed with an aim to diagnosing and improving flat-fielding and polarimetric contrast. These algorithms will be tested using simulated data and test data acquired through experimentation and test field imaging. This project will coordinate and support in-house R&D of Thales polarimetric imagers and help the company gain a better understanding at all levels of this technology and maximise its application in different markets

Diamond and fibre are a natural match that provides a platform to take high-power lasers into hitherto unattainable parameter regimes and to serve new applications. Though attractive in its simplicity, this area remains largely unexplored. Here, we propose a partnership that will enable high-impact applications through careful investigation of the underpinning device science. This will lead to fibre-pumped diamond Raman lasers with properties tailored to applications in LIDAR and clear plastics processing. We aim to lay the foundations for this to become the preferred approach for a number of important laser applications. Fibre lasers are the laser of choice from medicine to materials processing thanks to their reliability, low cost of ownership, proven performance, and outstanding power scalability. While moderate laser parameters and standard wavelengths suffice for many applications, many more require better beam quality, narrower linewidths, specific wavelengths, or well-controlled high-energy pulses - but still at hundreds of watts of output power. Fibre lasers can only rarely simultaneously satisfy these requirements. In this project, we aim to overcome these generic limitations of fibre sources by employing diamond to shift fibre lasers further into infrared via stimulated Raman scattering (SRS) with simultaneous brightness enhancement and, in the case of pulses, spectral narrowing towards the transform-limit. The UK is established as a world leader in fibre laser research and has played a leading role in pioneering the use of diamond in Raman lasers. Both fibre lasers and diamond are recognized as being superbly power scalable thanks to superior optical and thermal properties. Our approach will harness the advantages of fibre systems - efficiency, compactness, and reliability - while modifying their output to better address key industrial challenges. While the combination of fibre and diamond is a platform solution that can address a wide range of wavelength-specific applications, especially in the near IR range, in this project we aim to prove the technology in two areas that are important for our industrial partners. This proposal will deliver a new type of laser that is uniquely capable of the combination of power, brightness, spectral purity and wavelength required for industrially important applications in LIDAR and clear plastic processing.

Image processing is playing an increasingly important role in our lives whether this is the numerous sources of social provision e.g. TV, or the increased reliance on security to protect our everyday lives through the proliferation of security cameras in airports and town centres. There are also healthcare applications with increased need for 3-dimensional (3D) images such as in viewing 3D computerised tomography scans to provide much more intelligent treatment. In automotive applications, cameras are used for quality assurance in manufacture and situational awareness in use. In security applications, organisations are keen to have more intelligent views of scenes to highlight security risks and dangers. This has increased the amount of visual information that we process and store, and has placed increasing importance on the users' ability to process data where it is received, thus pushing for more intelligent image processing. Whilst a lot of innovative work has been done to derive the algorithms to provide this intelligence, there is a clear need for suitable, high performance, lower power hardware to provide the processing as in many cases, these systems may be remote e.g. security cameras with limited interconnection. We could wait for technology evolutions to provide the increased performance as before, but the warnings on process variability below 45-nm CMOS technology suggest that this might not be forthcoming and implies an increased focus on novel processor architectures is required. Whilst multi-core and application specific processors such as graphical processing units (GPUs) have been proposed, the gains have been limited. In addition, the rapid developments in the acquisition and interpretation of images together with intelligent algorithmic development, have not been matched by sound software engineering principles to develop and transform code into hardware implementations efficient in speed, memory and power. In many cases, image sensors comprise simple processing engines which communicate to some central resource for further processing. For a lot of medical and security applications, there is a need for more intelligent image acquisition, multi-view video processing (merging many views into a more useful, higher-level representation) and more context-aware acquisition devices which are aware of the existence of other cameras which can contribute to the creation of the full scene. This requires a step change in how we design and program these systems. Current FPGA technology such as the Xilinx Virtex-7 FPGA, offers a huge performance capability (over 6.7 Giga Multiply-Accumulate per second and up to 30 Terabits/s of memory bandwidth) and better power efficiency than GPUs. Currently FPGA solutions are created by aggregating powerful intellectual property (IP) cores together with soft cores, but the resulting performance is limited by the overall systems architecture and programmability is severely limited. Hence, there is a clear need to derive a FPGA system architecture that best matches the algorithmic requirements but that is programmable in software for a range of algorithms in the application domain. By considering the model of computation and programming model from the outset, we propose to create a highly powerful platform for a range of image processing algorithms. The proposal combines the FPGA processor design expertise in Queen's University (Woods), with the software language and compiler research (Michaelson) and image processing expertise (Wallace) at Heriot-Watt University. A key aspect is to ensure close interaction between the processor development and software languages and representation, in order to ensure the creation of a processor architecture configuration that is programmable in software. The research looks to radically alter the design of front end image processing systems by offering the performance of FPGA solutions with the programmability of processor solution

Our tangible cultural heritage, both historic and contemporary, is made from a plethora of complex multilayer materials. What we see is often only the surface and form of an object. Hidden below are the materials and evidence of the processes by which the objects were originally created. By using state of the art imaging / spectroscopy systems which can map the composition and reveal the stages of their creation, we gain an understanding about the meaning and significance, both in their original context and our present day. This is at the heart of the disciplines of technical art history, archaeology and material culture studies. It also informs collections care, access policies and conservation of cultural heritage. Infrared imaging and spectroscopy is particularly well suited to looking below the surface, as the scattering which normally occurs with visible light is usually much less. Thus the infrared penetrates further into the object. Depending on the material and its structure the infrared light will be absorbed or reflected. This can either be directly imaged or modulated (Fourier Transform Spectroscopy) to acquire spectroscopic information indicating the chemical composition. Most techniques employed at present within the field of cultural heritage can only make spot measurements; to map large areas would take hours to days to acquire the data and therefore is not usually viable or suitable for in-situ measurements. Other techniques require samples to be taken and are therefore invasive. We aim to explore state of the art IR imaging strategies that will be "fit for the job". This implies wide bandwidth, full field and fast techniques coupled with signal processing/ photonics methods to analyse, visualise and manipulate large multivariate data sets. By exploiting state-of-the-art laser sources developed at Heriot-Watt and providing massively tunable infrared light, we will explore and develop several complementary strategies for 4-dimensional imaging (3 x spatial, 1 x wavelength). Compressive sensing illumination techniques and machine-learning based data processing will allow us to image rapidly and efficiently while also extracting the maximum value from our datasets by automatically classifying surface and sub-surface features. In this way we expect to produce outcomes of shared value for both the ICT and Technical Art History researchers in our team. Contextual information from art history will inform the photonic design and computational anaylsis strategies we deploy, while powerful ICT-led techniques will provide the Technical Art History community with new technical capabilities that reveal previously hidden structure and history. The significance to the public of our cultural heritage has motivated us to integrate outreach activity from the start, in particular a dynamic website using 4D data to allow an interactive tool for exploring the chosen case studies, reflecting the People at the Heart of ICT priority. The project includes industrial partners who will contribute resources and expertise in mid-IR lasers (Chromacity Ltd.) and mid-IR cameras (Thales Optronics Ltd.). Our partners have committed substantial in-kind support in the form of access to their technology and contributions of staff time. Furthermore, their engagement ensures that activities within the project, and the outcomes these generate, can be rapidly evaluated for adjacent commercial opportunities. EPSRC priorities are reflected in the project's structure. Cross-Disciplinarity is embedded as collaborations within the ICT community (Photonics & AI Technologies researchers) and with researchers from the AHRC-funded Cultural Heritage community. Co-Creation is essential: only by combining the distinct technical, contextual and material resources of each research group in our team will the project succeed in delivering new capabilities for IR imaging and analysis and new insights into culturally important objects of shared value.

Sensors are everywhere, facilitating real-time decision making and actuation, and informing policy choices. But extracting information from sensor data is far from straightforward: sensors are noisy, prone to decalibrate, and may be misplaced, moved, compromised, and generally degraded over time. We understand very little about the issues of programming in the face of pervasive uncertainty, yet sensor-driven systems essentially present the designer with uncertainty that cannot be engineered away. Moreover uncertainty is a multi-level phenomenon in which errors in deployment can propagate through to incorrectly-positioned readings and then to poor decisions; system layering breaks down when exposed to uncertainty. How can we be assured a sensor system does what we intend, in a range of dynamic environments, and how can we make a system ``smarter'' ? Currently we cannot answer these questions because we are missing a science of sensor system software. We will develop the missing science that will allow us to engineer for the uncertainty inherent in real-world systems. We will deliver new principles and techniques for the development and deployment of verifiable, reliable, autonomous sensor systems that operate in uncertain, multiple and multi-scale environments. The science will be driven and validated by end-user and experimental applications.