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Computer Science and Informatics

Overview of Computer Science and Informatics

This submission presented work carried out by researchers in the Departments of Computer Science; Design Engineering and Mathematics; and Natural Sciences at Middlesex University. A total of 69 members of staff were involved, working in five research groups: Interaction Design; Algorithms and Software Engineering; Intelligent Environments; Networks and Distributed Systems; and Artificial Intelligence.

  • REF 2021 Impact Case Studies

    • Digital Twin Specification, Design and Application

      The impact we achieved

      This research project has advanced Digital Twin (DT) research in foundational DT programming technologies and in digital twins for structural health monitoring of large-scale infrastructures. A strong example of how Middlesex researchers collaborate with industry to address emerging problems, the project’s key impacts are:

      • Industry-scale demonstrators for our partner Tata Consultancy Services’ (TCS) clients
      • Design and implementation of Enterprise Simulation Language (ESL), which has been productized by TCS as the Java TwinX™ Library software
      • Design simulations and non-pharmaceutical interventions for managing the COVID-19 Pandemic in Pune, India using ESL and its derivatives
      • Digital Twin representation of Thăng Long bridge in Hanoi, Vietnam to produce a repair and maintenance plan resulting in benefits of £9.1m (£1.5m of savings on repair costs + £7.6m of estimated economy benefit)
      • The developed Digital Twin model and tools have now been explored to improve automation in industry 4.0 in India (tea manufacturing in a UKIERI project).

      The research behind it

      Professor Barn and Professor Clark have developed language-based simulation and modelling techniques to design, analyse and adapt the development of complex enterprise systems. Their construction of an executable modelling language called LEAP, together with a toolset for enterprise simulation, have addressed the unsuitability of the pre-existing approaches to DT representation of enterprise modelling as basis for simulation analysis. This led to the creation of a novel programming language called ESL and an associated development platform called EDB which can be used to aid decision-makers.

      In the area of structural health monitoring of large infrastructure artefacts, Professor Nguyen has been addressing the problem of how to detect damage and predict future maintenance requirements of large infrastructures such as bridges in collaboration with the University of Transport and Communications (UTC), Vietnam. Professor Nguyen has developed a novel hybrid approach delivering highly accurate results in detecting damage and its severity even for multiple damage scenarios. The resulting method is a practical end-to-end data-driven framework for automatically monitoring the operational state of structures.

      The people involved at Middlesex and beyond

      The Middlesex research team behind the project consisted of Professor Balbir Barn, Professor Tony Clark, Professor Huan Nguyen, Dr Mohsin Raza, and Dr Dang Viet Hung.

      Our partners included industry (Tata Consultancy Services (TCS), India), government and academia (Vietnam), and an NGO (India). In 2019 Middlesex established the London Digital Twin Research Centre where the Smart Cyber Factory facility supplied by our partners Festo Didactic and Siemens has been building on the project’s work.

      Read the PDF of the case study submission

      Photo of full-scale testing of a real-life bridge

    • Electrical Impedance Tomography

      The impact we achieved 

      This research project has pioneered the use of Electrical Impedance Tomography (EIT), which can be used to image organ function in real time (100 images/second). Compared with existing technology it is highly portable, inexpensive and lends itself readily to remote imaging to save lives. The project's key impacts are:

      • Provision of imaging algorithms and clinical analysis impacting on clinical software
      • Creation of the largest clinical data store for EIT clinical data in the world (> 50TBytes) for use by clinicians and industrial/academic researchers
      • Development of new wearable hardware for application on patents impacting on clinical usability of EIT
      • Successful use for monitoring pre-term neonates in the largest clinical study undertaken to date and for identifying key parameters for the clinical management of neonates with respiratory conditions impacting on clinical practices
      • Cost saving of €928 to €10,705 per patient in the Dutch setting, or €1,124 to €8,496 per patient in the German setting
      • Ongoing work with Printed Electronics Limited (PEL), a UK-based technology company, to develop print on flexible printed circuits for the EIT neonate system.

      The research behind it

      The impact described above evolved from a series of specific developments employing EIT, including:

      • Successful generation of the first 2D images of impedance change inside the human head using EIT (1996 –2003)
      • A range of clinical applications and the development of a method of automatically generating subject-specific FE models (2003-2007) and, since 2008, the development of a further algorithm
      • Development of algorithms and hardware for image reconstruction, parameter measurement and boundary form generation (since 2016), culminating in the first large scale study monitoring the lung function of 200 neonates (preterm, high risk) for 72 hours each
      • Clinical system for use in neonatal intensive care units, and further developments to clinical hardware for bedside monitoring of lung gestation of pre-term neonates.

      The research continued to flourish and diversify throughout the coronavirus pandemic when we repurposed the hardware and techniques for monitoring COVID-19 pneumonia in adult ITUs.

      The people involved at Middlesex and beyond

      The Middlesex research team behind the project consists of Professor Richard Bayford, Dr Andrew Tizzard, and Dr Andy Bardill.

      Along the way, the team has collaborated with several universities, hospitals and industry partners – locally, nationally and globally – including the Great Ormond Street Hospital and UCL (UK); Oulu University Hospital and University of Oulu (Finland), Nicosia General Hospital (Cyprus), Sentec (previously called Swisstom) and Emergex (UK); and Dartmouth College and Florida State University (USA).

      Read the PDF of the case study submission

    • Intelligent Environments – Engineering and Applications

      The impact we achieved

      This research project has advanced Computer Science through a new system architecture for Intelligent Environments (IE) which generates measurable impacts in several directions and for the benefit of citizen groups – locally and internationally – often neglected by the technology giants. The project’s key impacts are:

      • Contributing to improvements in quality of life of different sectors of society with special needs, such as people with Down’s Syndrome and elderly citizens experiencing early stages of dementia
      • Providing ambient assisted living support for older people in their homes
      • Encouraging citizens to be more physically active.

      We have also developed and finessed methods and tools designed to assist citizens with context awareness, now used by industry, and have shared knowledge with decision-makers, politicians and public sector influencing policy for specific citizen groups, such as those experiencing early symptoms of dementia-like conditions. Concepts developed with our help also led to business development within the European market.

      The research behind it

      Intelligent Environments refer to systems which exist in a physical environment enriched with sensing technology and Artificial Intelligence algorithms to provide context-sensitive help to humans. Since 2013, our Research Group has focused on specific challenges in these systems around the core concepts of contexts and context-awareness, guided by users’ specific needs within practical contexts and by their expectations from system services in those contexts. The underpinning research includes:

      • Creation of our own refined versions of existing approaches to system development, an iterative process centred on stakeholder’s engagement, including our own method to gather requirements for IE and an ethical framework for IE Development
      • Development of specific strategies for context-aware systems’ testing and validation
      • Specialising algorithms to make known AI techniques to work in real life IE scenarios. Those automated learning and reasoning algorithms combined with context-awareness resources and specialized interfaces provide a new system architecture for intelligent environments.

      The people involved

    • Enabling positive engagement between youth workers and young offenders through mobile apps

      The impact we achieved

      This multi-disciplinary research project has focused on advancing technology for social good, particularly for marginalised young people in conflict with the law. Bringing together different computer science, social policy and criminology, our work has made a difference locally, nationally and globally to policy and strategy, public education, and the growth of an international SME (GNB) working in the social enterprise sector. The project’s key impacts are:

      • Development of the UK’s first mobile app to support Youth Offending Teams (YOTs) in the work with young people in the youth justice system. The app, MAYOT, has already been deployed in Bromley and West Marcia YOTs
      • Contribution to international software development which embeds our co-design methodology in its design practice.

      The research behind it

      At the initiation of this research (2013), there were over 20,000 first time entrants into the youth system, with 66,430 young people forming the case load of YOTs nationally (2012 data). To ease this workload, we envisaged the use of a personalised smartphone app to support interactions between case workers and young people. Our research team worked closely with young people and their managers to co-design the app, adopting a value sensitive approach. The resulting conceptual model for value sensitive concerns has formed a substantive body of research reported internationally, on what has since become an important research area. The underpinning research included:

      • App development for social care settings and further research in support of social workers. The latter exposed the importance of understanding users and how users interact and work with mobile devices in challenging settings, as well as the importance of inter-disciplinary approaches to solution development
      • Research consultancy project which increased knowledge of the important role of young people’s relationship with mobile technology and addressed the risk-taking behaviours of young people.

      The people involved at Middlesex and beyond

      The Middlesex research team behind the project were Professor Balbir Barn, Professor Franco Raimondi, and Dr Giuseppe Primiero.

      Our research team has worked in collaboration with several stakeholders across sectors including three Youth Offending Services in England, social enterprise Global Notice Board (GNB), and Royal Holloway, University of London.

      Read the PDF of the case study submission

    • Intelligence-led Policing using VALCRI Visual Analytics Technology

      The impact we achieved

      The VALCRI project (Visual Analytics for sense-making in Criminal Intelligence Analysis) focused on enhancing criminal and intelligence investigations by bringing together academia, law enforcement and industry from across 17 organisations throughout Europe. The project’s output was a visual analysis system using tactile reasoning which enhances criminal and intelligence investigations and its key impacts were:

      • Creation of a visual analysis system using tactile reasoning which enhances criminal and intelligence investigations
      • Financial gains through the commercialisation of the system, for instance the VALCRI IP has already been acquired by Canadian global security systems company, Genetec Inc, with paying customers since September 2019
      • Improvements in performance, practices and policies for police investigations and subsequent societal benefits by raising skills and technology awareness across several police and intelligence communities
      • Informing public debate about intelligence-led policing through its active dissemination, including a presentation ton Latin American government and police leaders.

      The research behind it

      Police intelligence analysts only ever have fragmented data from which to investigate cases and pre-empt terrorist attacks. They also operate in large numbers of datasets and volumes of data, and when they discover relevant information, they assemble evidential chains and narratives that must create a convincing argument and be able to withstand interrogation in court. Police therefore need a combination of tools to discover relevant data across vast data sets. VALCRI’s impact stemmed from its development of technology which addressed this problem by allowing users to interact fluidly with the data and task at hand, using a radically different user-interface based on the concept of tactile reasoning, while ensuring analytic rigour. As a result, hypotheses can be formulated and tested quickly, enabling investigators to discard or modify their hypotheses within minutes and hours, rather than days and weeks.

      The project was underpinned by Professor Wong’s research into the representation design of information and the interaction design of user interfaces to support human decision making in complex dynamic environments. The invention of interaction design INVISQUE (2009) – the interactive visual search and query environment that makes information graspable, enabling ‘tactile reasoning’, an epistemic action that facilitates sense-making and decision making – in combination with other visual analytics research projects including the UKVAC (UK Visual Analytics Consortium) and the EPSRC MakingSense project (2010-13) drove the design of VALCRI.

      The people involved at Middlesex and beyond

      The research team at Middlesex University consisted of Professor B.L. William Wong, Dr Neesha Kodagoda, Dr Chris Rooney, Patrick Seidler, and Stefan Lozovanu.

      The VALCRI consortium – led by Professor Wong – comprised 9 universities and research organisations, 5 Small and Medium Enterprises (SMEs), and 3 Law Enforcement Agencies (LEAs) from across Europe, bringing together 103 scientists and engineers with a diverse set of expertise.

      Read the PDF of the case study submission

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