In Europe, the total value of sewer assets amounts to 2 trillion Euros. The US Environmental Protection Agency estimates that water collection systems in the USA have a total replacement value between $1 and $2 trillion. Similar figures can be assigned to other types of buried pipe assets which supply clean water and gas. In China alone 40,000 km of new sewer pipes are laid every year. However, little is known about the condition of these pipes despite the pressure on water and gas supply utility companies to ensure that they operate continuously, safely and efficiently. In order to do this properly, the utility operator must identify the initial signs of failure and then respond to the onset of failure rapidly enough to avoid loss of potable water supply, wastewater flooding or gas escape. This is attempted through targeted inspection which is typically carried out through man-entry or with CCTV approaches, although more sophisticated (e.g. tethered) devices have been developed and are used selectively. Nevertheless, and in spite of the fact that the UK is a world leader in this research area, these approaches are slow and labour intensive, analysis is subjective, and their deployment disrupts traffic. Moreover, because these inspections are necessarily infrequent and only cover a small proportion of the pipe network, serious degradation is often missed and pipe failures occur unexpectedly, requiring emergency repairs that greatly disrupt life of the road and adjacent buried utility infrastructure. This Programme Grant proposes a radical change in terms of buried pipe sensing in order to address the issues of pipe inspection and rehabilitation. It builds upon recent advances in sensors, nano- and micro-electronics research, communication and robotic autonomous systems and aims to develop a completely new pervasive robotics sensing technology platform which is autonomous and covers the entire pipe network. These robots will be able to travel, cooperate and interrogate the pipes from the inside, detect the onset of any defects continuously, navigate to and zoom on sub-millimetre scale defects to examine them in detail, communicate and guide any maintenance equipment to repair the infrastructure at an early sign of deterioration. By being tiny, they do not present a danger of being stuck, blocking the pipe if damaged or run out of power. By being abundant, they introduce a high level of redundancy in the inspection system, so that routine inspection can continue after a loss of a proportion of the sensors in the swarm. By making use of the propagation of sonic waves and other types of sensing these robots can monitor any changes in the condition of the pipe walls, joints, valves and lateral connections; they can detect the early development and growth of sub-millimetre scale operational or structural faults and pipe corrosion. An important benefit of this sensing philosophy is that it mimics nature, i.e. the individual sensors are small, cheap and unsophisticated, but a swarm of them is highly capable and precise. This innovation will be the first of its kind to deploy swarms of miniaturised robots in buried pipes together with other emerging in-pipe sensor, navigation and communication solutions with long-term autonomy. Linked to the related previous work, iBUILD (EP/K012398), ICIF (EP/K012347) and ATU's Decision Support System (EP/K021699), this Programme Grant will create the technology that has flexibility to adapt to different systems of governance globally. This work will be done in collaboration with a number of industry partners who will help to develop a new set of requirements for the new pervasive robotic sensing platform to work in clean water, wastewater and gas pipes. They will support the formation and operation of the new research Centre of Autonomous Sensing for Buried Infrastructure in the UK and ensure that the results of this research have strong practical outcomes.

The Solar Aviator project seeks to demonstrate a lightweight solution to develop light-powered and wireless electronic devices used by defence personnel for communication and data acquisition. The Royal Airforce require an ongoing energy source for ground troops who secure and protect airfields. Such operations require ground troops to carry a lot of heavy kit. Additionally, where personnel are in the field and battery energy sources run out, light weight solar energy sources would help maintain contact on operations such as an evacuation. We have developed a solution to this operational need: printable solar cells which can be deposited on fabric, plastic, or foil for integration into portable electronic devices or wearable technology under various light conditions. This project exploits recent results proving that the power generated under ambient light by a high efficiency photovoltaics with the area equivalent to a mobile phone can power sensors and IoT devices. We now propose a wearable self-powered communication technology.

This cross-disciplinary project aims to develop novel data mining and visualization tools and techniques, which will transform people's ability to analyse quantitative and coded longitudinal data. Such data are common in many sectors. For example, health data is classified using a hierarchy of hundreds of thousands of Read Codes (a thesaurus of clinical terms), with analysts needing to provide business intelligence for clinical commissioning decisions, and researchers tacking challenges such modelling disease risk stratification. Retailers such as Sainsbury's sell 50,000+ types of products, and want to combine data from purchasing, demographic and other sources to understand behavioural phenomena such as the convenience culture, to guide investment and reduce waste. To solve these needs, public and private sector organisations require an infrastructure that provides far more powerful analytical tools than are available today. Today's analysis tools are deficient because they (a) are crude for assessing data quality, (b) often involve analysis techniques are designed to operate on aggregated, rather than fine-grained, data, and (c) are often laborious to use, which inhibits users from discovering important patterns. The QuantiCode project will address these deficiencies by bringing together experts in statistics, modelling, visualization, user evaluation and ethics. The project will be based in the Leeds Institute for Data Analytics (LIDA), which houses the ESRC Consumer Data Research Centre (£5m ES/L011891/1) and the MRC Medical Bioinformatics Centre (£7m ES/L011891/1), and provides a development facilities complete with high-performance computing (HPC), visualization and safe rooms for sensitive data. Our project will deliver proof of concept visual analytic systems, which we will evaluate with a wide variety of users drawn from our partners and researchers/external users based in LIDA. At the outset of the project we will engage with our partners to identify analysis use cases and requirements that drive the details of our research, which is divided into four workpackages (WPs). WP1 (Data Fusion) will develop governance principles for the analysis of fine-grained data from multiple sources, implement tools to substantially reduce the effort of linking those sources, and develop new techniques to visualize completeness, concordance, plausibility, and other aspects of data quality. WP2 (Analytical Techniques) and WP3 (Abstraction Models) are the project's technical core. WP2 will deliver a new, robust approach for modelling data as they appear naturally in health and retail data (irregularly dispersed or sampled over time), scaling that approach with stochastic control to guide learning and resource usage, and developing a low-effort 'question-posing' visual interface to drastically lower the human effort of investigating data and finding patterns. WP3 (Abstraction Models) focuses on data granularity, and will deliver a tool that implements a working version of the governance principles we develop in WP1, and new computational and interactive techniques for exploring abstraction spaces to create inputs suited to each aspect of analysis. WP4 will implement the above tools and techniques in three versions of our proof of concept system, evaluating each with our partners and LIDA researchers/users. This will ensure that our solutions are compatible with, and scale to, challenging real-world data analysis problems. Success criteria will be time saved, increased analysis scope, notable insights, and tackling previously unfeasible types of analysis - all compared against a baseline provided by users' current analysis tools. We will encourage adoption via showcases, workshops and licensed installations at our partners' sites. The project's legacy will include tools that are embedded as an integral part of the LIDA infrastructure, a plan for their on-going development, and a research roadmap.