The acoustics industry contributes £4.6 billion to the UK's economy annually, employing more than 16,000 people, each generating over £65,000 in gross value added across over 750 companies nationwide. The productivity of acoustics industry is similar to that of other enabling technologies, for example the UK photonics industry (£62k per employee in 2014). Innovation through research in acoustics is a key to its industry success. The UK's acoustics industry and research feeds into many major global markets, including the $10 billion market for sound insulation materials in construction, $7.6 billion ultrasound equipment market and $31 billion market for voice recognition. This is before the vital role of acoustics in automotive, aerospace, marine and defence is taken into consideration, or that of the major UK industries that leverage acoustics expertise, or the indirect environmental and societal value of acoustics is considered. All the four Grand Challenges identified in the 2017 UK Industrial Strategy require acoustics innovation. The Industrial Strategy Challenge Fund (ISCF, https://www.ukri.org/innovation/industrial-strategychallenge-fund/) focuses on areas all of which need support from acoustics as an enabling technology. The future of acoustics research in the UK depends on its ability to contribute to the Four Grand Challenges. Numerous examples are emerging to demonstrate the central role of acoustics in addressing the four Grand Challenges and particularly through more focused research. The acoustics-related research base in the UK is internationally competitive, but it is important to continue to link this research directly to the four Grand Challenges. In this process, the role of UK Acoustics Network (UKAN) is very important. The Network unites over 870 members organised in 15 Special Interest Groups (www.acoustics.ac.uk) who represent industry, academia and various non-academic organisations which success relies on the quality of acoustics related research in the UK. UKAN was funded by the EPSRC as a standard Network grant with the explicit aim of pulling together the formerly disparate and disjoint acoustics community in the UK, across both industry and academia. UKAN has been remarkably successful. Its success is manifested in the large number of its members, numerous network events it has run since its inception in November 2017 and contribution it has made to the acoustics research community. Unfortunately, UKAN has not been in the position to fund new, pilot adventurous or translational projects nor has it any funding support for on-going research or knowledge transfer (KT) activities. The purpose of UKAN+ is to move beyond UKAN, create strategic connections between acoustics challenges and the Grand Challenges and to tackle these challenges through pilot studies leading in turn to full-scale grant proposals and systematic research and KT projects involving a wider acoustics community. There is a great opportunity for the future of the UK's acoustics related research to move on beyond this point, build upon the assembled critical mass and explore the trans-disciplinary work initiated by UKAN. Therefore, this proposal is for UKAN+ to take this community to the next stage, connect this Network more widely in the UK and internationally to contribute through coordinated research to the solution of Grand Challenges set by the government. UKAN+ will develop a new roadmap for acoustics research in the UK related to Grand Challenges, award exploratory (pilot) cross-disciplinary research projects to the wider community to support adventure research and knowledge transfer activities agreed in the roadmap and support the development of develop full-scale bids to the government research funding bodies which are aligned with the Grand Challenges. UKAN+ will also set up a National Centre or Coordination of Acoustics Research, achieve full sustainability and support best Equality, Diversity and Inclusion practices.

We propose to construct a system for 3D face recognition. We propose to use photometric stereo for face reconstruction in order to by pass the problems of conventional stereo (that needs to solve the matching problem first), structured light (that does not supply colour information) and photometric stereo with spectrally distinct light sources (that relies on the assumption of uniformly coloured imaged objects). Photometric stereo (PS) can reproduce structural details and colour on a per pixel basis in a way that no other 3D system can. The proposed scheme will be appropriate for use in a controlled environment for authentication purposes, but also in a general environment e.g. the entrance of a public event. We shall use two routes: surface reconstruction from the data and direct extraction of facial characteristics from the PS set. In the first approach, once surface normal and albedo is recovered, images of the face may be synthetically rendered under arbitrary new pose and illumination conditions to allow novel viewing conditions. We also aim to use a new multi-scale facial feature matching approach in the recognition process, where facial features range from overall face and head shape to fine skin dermal topography, reflectance and texture. The latter may be thought of as a form of detailed surface bump map forming a unique skin-print or signature and represents a new approach. Hence both the 3D shape and 2D intensity data will be used in recognition or authentication tasks. We propose to use scalable methods for matching, so we can cope with large databases. 3D matching will be done with the newly proposed invaders algorithm which is FFT cross-correlation based, and more detailed matching will be done by using features and classifier combination. The novelty of our approach lies in the use of PS to extract 3D information, the use of detailed facial characteristics like moles, scratches, and skin texture, and in the design of the system so that it can operate while the person is moving, with minimum intrusion and maximum efficiency. We have two industrial collaborators who will contribute in system design, data gathering and exploitation and support from the Home Office. We shall evaluate our system following three possible scenaria: a face searched in the crowd (real time face recognition), a person has to be identified (off-line face recognition) and a person has to be checked against a claimed identity (face authentication). We shall install the first prototype system in the offices of one of our industrial partners in month 12, so that data can be collected. We envisage a door like structure with lights flashing in succession as a person walks through, while a camera is capturing images. We propose to investigate the optimal number of lights in terms of efficiency and accuracy of the reconstruction, and the option of using non-visible light to avoid problems with people sensitive to flashes. We shall also investigate the relationship between detail that has to be captured and the geometry of the construction.

This proposal aims to advance the state-of-the-art in 3D face recognition by means of a novel, non-intrusive and highly efficient skin reflectance capture technology. The techniques developed will, in-turn, enable rapid facial geometry analysis and enhanced recognition rates.Face recognition is currently a rapidly growing area of research within industry and academia. Indeed, 2D face recognition is now at a stage where a few industrial applications are possible. However, these methods, which just use a single 2D image of a face to perform the recognition, are excessively limited by the fact that the face becomes unrecognisable when variations such as pose, illumination, make-up or expression are present. However, the 3D shape of the face does not change at all with many of these variations, and changes only minimally with expression. Consequently, an increasing amount of face recognition research is focussing on ways to use the 3D shape of the face for identification.Here, we are proposing to use a Photometric Stereo (PS) method for 3D shape estimation. The main advantages of the proposed method compared to other 3D face shape capture devices will be (1) cheaper to construct hardware, (2) fast acquisition and processing, (3) largely unaffected by ambient illumination, (4) person-specific reflectance considered, (5) more accurate than standard PS, (6) possibility of using the reflectance properties to aid recognition, and (7) minimal calibration required.A large number of methods for using the 3D facial geometry have been proposed in the scientific literature and very promising results have been attained. However, the question of how to capture a subject's 3D face shape prior to recognition is an open one. Existing approaches use technology that is too expensive and too slow for most applications. This proposal is motivated by the need to address this question.The main contributions of the proposed work will be in two areas: photometric stereo (PS) and reflectance analysis. Photometric stereo is a method of estimating the 3D geometry of an object by imaging it under three or more illumination directions. For this project, we will be using five light sources, and aim to simultaneously acquire both shape and reflectance information. We will be using a high speed light-camera synchronisation device developed here at UWE for this task. This will allow deducing a mapping between the orientations of the recovered surface and the measured pixel intensities which will form a quantitative measure of the skin reflectance properties. An iterative method will then be used to update the surface estimate and the reflectance properties until convergence. Thus, we will arrive at a lookup-table set of reflectance measurements and an optimal shape estimate, which will allow for improved face recognition. This is a novel approach to PS and should allow us to diminish some of the strong assumptions on surface orientation that most current methods impose. The main challenge here will be in forming the relationships between the image-based skin reflectance measurements and the skin orientation for the whole face in order to acquire the optimal 3D shape estimate.The final stage of the project will involve applying face recognition methods developed previously both at the MVL and at other institutions for a comparative analysis. This will demonstrate improvements in recognition rates compared to 3D methods using standard PS and other techniques.

The international offshore energy industry currently faces the triple challenges of an oil price expected to remain less than $50 a barrel, significant expensive decommissioning commitments of old infrastructure (especially North Sea) and small margins on the traded commodity price per KWh of offshore renewable energy. Further, the offshore workforce is ageing as new generations of suitable graduates prefer not to work in hazardous places offshore. Operators therefore seek more cost effective, safe methods and business models for inspection, repair and maintenance of their topside and marine offshore infrastructure. Robotics and artificial intelligence are seen as key enablers in this regard as fewer staff offshore reduces cost, increases safety and workplace appeal. The long-term industry vision is thus for a completely autonomous offshore energy field, operated, inspected and maintained from the shore. The time is now right to further develop, integrate and de-risk these into certifiable evaluation prototypes because there is a pressing need to keep UK offshore oil and renewable energy fields economic, and to develop more productive and agile products and services that UK startups, SMEs and the supply chain can export internationally. This will maintain a key economic sector currently worth £40 billion and 440,000 jobs to the UK economy, and a supply chain adding a further £6 billion in exports of goods and services. The ORCA Hub is an ambitious initiative that brings together internationally leading experts from 5 UK universities with over 30 industry partners (>£17.5M investment). Led by the Edinburgh Centre of Robotics (HWU/UoE), in collaboration with Imperial College, Oxford and Liverpool Universities, this multi-disciplinary consortium brings its unique expertise in: Subsea (HWU), Ground (UoE, Oxf) and Aerial robotics (ICL); as well as human-machine interaction (HWU, UoE), innovative sensors for Non Destructive Evaluation and low-cost sensor networks (ICL, UoE); and asset management and certification (HWU, UoE, LIV). The Hub will provide game-changing, remote solutions using robotics and AI that are readily integratable with existing and future assets and sensors, and that can operate and interact safely in autonomous or semi-autonomous modes in complex and cluttered environments. We will develop robotics solutions enabling accurate mapping of, navigation around and interaction with offshore assets that support the deployment of sensors networks for asset monitoring. Human-machine systems will be able to co-operate with remotely located human operators through an intelligent interface that manages the cognitive load of users in these complex, high-risk situations. Robots and sensors will be integrated into a broad asset integrity information and planning platform that supports self-certification of the assets and robots.

The international offshore energy industry is undergoing as revolution, adopting aggressive net-zero objectives and shifting rapidly towards large scale offshore wind energy production. This revolution cannot be done using 'business as usual' approaches in a competitive market with low margins. Further, the offshore workforce is ageing as new generations of suitable graduates prefer not to work in hazardous places offshore. Operators therefore seek more cost effective, safe methods and business models for inspection, repair and maintenance of their topside and marine offshore infrastructure. Robotics and artificial intelligence are seen as key enablers in this regard as fewer staff offshore reduces cost, increases safety and workplace appeal. The long-term industry vision is thus for a digitised offshore energy field, operated, inspected and maintained from the shore using robots, digital architectures and cloud based processes to realise this vision. In the last 3 years, we has made significant advances to bring robots closer to widespread adoption in the offshore domain, developing close ties with industrial actors across the sector. The recent pandemic has highlighted a widespread need for remote operations in many other industrial sectors. The ORCA Hub extension is a one year project from 5 UK leading universities with over 20 industry partners (>£2.6M investment) which aims at translating the research done into the first phase of the Hub into industry led use cases. Led by the Edinburgh Centre of Robotics (HWU/UoE), in collaboration with Imperial College, Oxford and Liverpool Universities, this multi-disciplinary consortium brings its unique expertise in: Subsea (HWU), Ground (UoE, Oxf) and Aerial robotics (ICL); as well as human-machine interaction (HWU, UoE), innovative sensors for Non Destructive Evaluation and low-cost sensor networks (ICL, UoE); and asset management and certification (HWU, UoE, LIV). The Hub will provide remote solutions using robotics and AI that are applicable across a wide range of industrial sectors and that can operate and interact safely in autonomous or semi-autonomous modes in complex and cluttered environments. We will develop robotics solutions enabling accurate mapping , navigation around and interaction with assets in the marine, aerial and ground environments that support the deployment of sensors for asset monitoring. This will be demonstrated using 4 industry led use cases developed in close collaboration with our industry partners and feeding directly into their technology roadmaps: Offshore Renewable Energy Subsea Inspection in collaboration with EDF, Wood, Fugro, OREC, Seebyte Ltd and Rovco; Aerial Inspection of Large Infrastructures in Challenging Conditions in collaboration with Barrnon, BP, Flyability, SLAMCore, Voliro and Helvetis; Robust Inspection and Manipulation in Hazardous Environments in collaboration with ARUP, Babcock, Chevron, EMR, Lafarge, Createc, Ross Robotics; Symbiotic Systems for Resilient Autonomous Missions in collaboration with TLB, Total Wood and the Lloyds Register. This will see the Hub breach into new sectors and demonstrate the potential of our technology on a wider scale.