Sensing colours, photo collage. Image: Nedyalka Panova (2016)
The use of creative art for explaining
My work explores the boundaries between art and science, organic and inorganic, natural, synthetic and manmade. I work in collaboration with the Organic Semiconductor Centre led by Prof Ifor Samuel on “The use of creative art for explaining organic semiconductors”. The purpose of the project is to give a higher visibility to the interesting phenomenon of organic semiconductors using their aesthetic values.
Interdisciplinary collaboration such as this, between artists and scientists, is an increasingly popular way to bridge the gap between ‘arts and humanities’ and ‘science and technology’. It brings together experts from different fields and the outcome is art exhibitions for public domain.
While the concept of colours and shapes of natural materials inspires artists over centuries in their studies of nature, material science progress by scientists has created a new range of synthetic materials which are manufactured using completely different set up and equipment. In this context, contemporary art and science starts asking new questions: How can art respond to the colours that are invisible to visible light? How can invisible 2D imprinted patterns be used as colours and structures? The line between the past and the future of modern technological world is drawn with a nanoscale precision and the question is: Are these new technological tools also a new media for creative endeavours?
Organic semiconductors combine properties of both metals and organic polymers with their capacity to conduct electricity. This opens new doors for applications in light communication, organic LED displays, healthcare and harvesting energy from abundant natural sources such as sun light. Their general target is to offer an alternative solution to the existing inorganic electronic components or to combine the best of both worlds in a new generation of hybrid devices.
Arabidopsis Thaliana seeds germination used for explosive sensing. Image: Nedyalka Panova (2016)
Inferences from A&E data for resource optimisation in Brazilian hospitals
Over the last five years, Brazil has suffered a significant recession. Healthcare has been particularly affected with continuous budget cuts, and a large part of the general population is unable to get access to basic healthcare. The National Health Service in Brazil (SUS) has struggled to satisfy demand for essential health services, leading to unnecessary deaths in many cases. Over the last five years, 24,000 beds have been lost across public hospitals in Brazil.
The aim of this project was to analyse services and processes currently in practice at two hospitals in the state of Rio Grande do Sul, in order to identify areas of improvement. The work shown concerns Hospital Santa Cruz do Sul, a community hospital that is currently running at a deficit of BRL$1m per month. It serves a population of around 500,000 people in the region of Rio Pardo.
We identified two areas to focus on: (i) shift changes, and (ii) procedural changes such as room cleaning and calling patients to consultation.
An analysis of the given A&E data revealed that in both areas there is a potential for improvement. The impact of the proposed changes is modelled using SAN (Stochastic Automata Network) and Arena (queueing simulation). Although the simulation work is still ongoing, we currently estimate that implementing all proposed changes could lead to increasing the number of patients seen per doctor per hour by three during peak times. The benefits of this would be twofold: (i) the waiting time for patients would be reduced, thus potentially leading to better health outcomes, and (ii) less patients would walk away while waiting to be seen, which leads to more patients treated and more money recovered for the hospital while using the same level of staffing.
CanDL: Cancer Recognition with Deep Learning
Detection of cancer in medical images, e.g. from PET, MRI or CT imaging systems, is one of the core components in the fight against cancer. Currently tumour detection and segmentation is a very labour intensive job which requires the time of a highly trained person (costly), and is not 100% accurate, with a variety of factors (including human error and fatigue) diminishing the accuracy of the method. By automating this process clinicians will be greatly aided in their role, helping them to detect cancerous regions with a greater level of accuracy, and increasing the speed of detection to allow faster diagnosis, both leading to a much improved patient outcome, and helping the workload of these trained professionals, as well as decreasing the cost of the overall diagnosis process for the healthcare provider. The goal of this project is to develop an automatic tumour detection and segmentation of many tumour types in various parts of human body using deep learning.
A new phase in the development of cell tracking algorithms
In the field of cell biology, modern experimental and microscopy techniques generate vast amounts of high quality images of live cell populations over time. These datasets are so vast that they are indecipherable to a human observer in their raw form. This necessitates the development of automated computational algorithms that allow us to probe and understand as much of the available data as possible. Such algorithms must allow the tracking of potentially large cell populations, recovering dynamic quantities of interest such as speed and shape.
We describe two approaches to cell tracking developed in collaboration with Ibidi, a company that develop cell assays based in Munich, Germany. Specifically, we present a method for particle tracking applicable to large datasets that recovers the trajectories of cell centroids. We also describe a novel method for whole cell tracking, in which we recover entire cell morphologies using a phase field approach. A major advantage of the methodology, which appears new within the field, is that physical aspects of cell migration can be incorporated into the algorithm. Thus, hopefully generating more faithful approximation to the true morphologies.
CAD model of cloud radar showing transmit beam (yellow) and receive beam (blue).
Millimetre wave cloud profiling radar development
Cloud profiling radars are used to determine the structure and extent of clouds, fog and aerosols and to generate data that can guide weather predictions. Radars operating at millimetre wavelengths are ideal for this application as the particle sizes in clouds are such that radar measurements can be made successfully throughout the cloud structure. The aim of the project is to modify an existing low power solid-state millimetre wave radar to make it suitable for cloud profiling.
There is a newly emerging market for cloud profiling radars which are more affordable than traditional high power designs. We believe our architecture will achieve state‑of‑the‑art performance at substantially lower costs than alternative designs which, once demonstrated to stake holders (e.g. MetOffice), will make a convincing case for commercialisation.
Cloud radar electronics: transmitter (left)
and receive (right)
The constraint modelling pipeline
Combinatorial search problems are ubiquitous across the public and private sectors, and academia. Consider a staff rostering problem to assign staff to shifts while respecting required shift patterns and staffing levels, physical and staff resources, and staff working preferences. The decision-making process is often further complicated by the need also to optimise an objective, such as to maximise profit or to minimise waste. We will deliver better solutions to these problems more rapidly, increasing efficiency and reducing cost.
It is natural to characterise such problems as a set of decision variables, each representing a choice that must be made in order to solve the problem at hand (e.g. which staff member is on duty for the Friday night shift), and a set of constraints describing allowed combinations of variable assignments (e.g. a staff member cannot be assigned to a day shift immediately following a night shift). A solution is an assignment of a value to each variable satisfying all constraints.
Many decision-making and optimisation formalisms take this general form. In all of these formalisms the model of the problem is crucial to the efficiency with which it can be solved. A model in this sense is the set of decision variables and constraints chosen to represent a given problem. There are typically many possible models and formulating an effective model is notoriously difficult. Therefore, automating modelling is a key challenge.
Over the last decade, in the context of Constraint Programming, we have taken a novel approach to addressing this challenge. The user writes a problem specification in the abstract constraint specification language ‘Essence’, capturing the structure of the problem above the level of abstraction at which modelling decisions are made. Our modelling pipeline, on which our proposed research is based, automatically generates a model from this specification. This removes the need for user constraint modelling expertise, and also preserves the structure of the specified problem, allowing the system easily to explore alternative models and to exploit properties such as symmetry.
Our pipeline generates constraint models equivalent in quality to those of a competent human constraint programmer, and so represents a significant milestone towards fully automated modelling.
Centre for Interdisciplinary Research in Computational Algebra (CIRCA)
Energy materials platform
The JTSI group investigates new materials for energy applications. We do this by studying the atomic structure of important chemicals and materials. This allows us to design new materials with improved properties. We can then develop efficient technologies to heat and power homes, businesses, and vehicles. Our devices use conventional fuels but waste far less energy so they do far less harm to the environment. In some cases, carbon dioxide emissions can be all but eliminated. Our batteries store electricity on a vast scale. This enables reliable, low cost, low emission power.
Our project builds links with industry to drive forward the commercialisation of our technologies. We have established a company to develop fuel cell golf buggies and utility vehicles. We began a collaborative project to develop low carbon heat and power systems for the home. And we built a collaboration to commercialise grid-level energy storage.
Radar Monitoring of Environmental Hazards in Montserrat
Montserrat, a UK Overseas territory, is home to the UK’s only active volcano and island residents live with the hazards of pyroclastic flows from lava dome collapses, eruptive ash fall, and mud flow ‘lahars’ after heavy rain.
Our AVTIS millimetre wave radar has been deployed on Montserrat to monitor the volcano since 2011 but 5 years of exposure to tropical conditions has substantially degraded the associated infrastructure of the radar housing and wifi telemetry link.
In this project, we are refurbishing the radar installation and integrating the instrument into the Montserrat Volcano Observatory’s (MVO) operational monitoring suite of sensors, so it can provide round-the-clock data on the lava dome shape, and high temporal and spatial resolution rainfall mapping. MVO will use these data along with that from other sensors to provide enhanced hazard warning to the people and business of the island.
Radar reflectivity map of Soufrière Hills Volcano, Montserrat taken with AVTIS radar.
An optical sensor platform for the detection of explosive remnants of war
In-field prototype optimisation and end-user engagement
Since legacy landmines affect local populations beyond the obvious dangers, there is worldwide interest in advanced Explosive Remnants of War (ERW)-detecting technology. Many countries are affected by mines dating from World War I to current conflicts.
Mined areas can inhibit
- trade &
- communication between local communities.
The Ottawa Convention, seeking to ban land mines completely, has 136 countries as signatories. However, humanitarian demining is typically a time-consuming and expensive process.
Cutting-edge technologies can help improve these issues.
At the Organic Semiconductor Centre, we have developed thin polymer films that emit light. When the films come into contact with explosive vapour this light dims. By exploiting this phenomenon, we were able to develop, during the Tiramisu FP7 project, a portable system to transport the technology from the lab to the field. However, trace vapour sampling in the field is a huge challenge.
The work funded by the EPSRC Impact Acceleration Award grant is improving our previously developed prototype to make it truly suitable for deployment in mine-afflicted areas by introducing a “pre-concentration” stage to increase the level of explosive vapours exposed to the sensor. Having a fast-responding, lightweight, inexpensive sensor system could significantly impact deminers’ toolkits and methods, and so significantly help clearance efforts.
Distributed energy – the case for geothermal
The developing world faces an energy crisis in order to sustainably grow. The developed world will be impacted by this growth if it is achieved through a hydrocarbon–based solution. There is an opportunity to achieve growth but with low carbon alternatives, one of which is geothermal energy. This form of energy has been overlooked throughout the world expect at key locations such as Iceland. However, developments in exploration and technology are increasingly allowing this situation to be challenged. The Department of Earth and Environmental Sciences has ongoing research both in developing and undeveloped countries that is demonstrating the geothermal potential for off-grid and end-of-grid supply. Such independent energy solutions could provide game-changing alternatives to traditional energy provision and catapult a country past outdated development points, thus not only meeting the needs of the developing country but also helping address total world carbon problems.