Long-range imaging and sensing is a rapidly evolving domain of research, relevant to a growing range of industrial applications. However, the current and future systems to be developed critically require efficient computational methods able to handle rapidly large data volumes while being robust to challenging observation conditions. The main objectives of this PhD project are thus to develop new and ground-breaking computational methods for fast and robust sensing and 2D/3D imaging in the low illumination regime. This ambitious project will concentrate on real-time solutions for object detection and 2D/3D reconstruction in the presence of scattering media and atmospheric turbulence.
Nowadays, there is a great demand on advanced materials which show multi-sensing functions for measurement applications in medical and industrial non-destructive testing markets. Among materials, deposited piezoelectric thin films have attracted a lot of attention due to their attractive properties in a wide range of physical and chemical sensors. Current challenges of this thin film sensors technology include low sensitivity but high selectivity, but the advantages include low-cost, wearability, energy-autonomy and cost-effective, scalable manufacturing. This project is an ambitious investigation focused on the development of an ‘ultrasonic thin-film, multimodal, flexible sensor’, with the ultimate aim of commercialising the eventual prototype through the industrial partner.
Retinal imagers, such as the world-leading instruments manufactured by Optos, are the dominant tools for screening and informing treatment for retinal disease. Whereas existing imaging modalities produce images of retinal structure, we will develop new chemical-imaging techniques based on the spectral excitation and fluorescence properties of retinal fluorophores combined with fluorescence-lifetime imaging. We aim to develop new imaging technologies for: enhanced imaging of retinal chromophores such as drusen that are important for diagnosis of eye disease; and for mapping of metabolism indicators, such as flavoproteins, which offer a convenient route to detection of a wide range of systemic diseases.
In this project, multimodal technologies (IoT) based cameras, wearable sensors and RF-based sensing system) which generate a torrent of data that contains key information as well as redundant noise. This will used for in-home monitoring of elderly people living alone with an aim to improve their living quality and their independence. On one hand, it is not cost effective to equip each edge device with the required computational power to extract the required knowledge from the massive noisy data. On the other hand, it is neither reliable nor efficient to transfer all data to the cloud in order to share the centralised computational power. This project will explore optimum event detection using RF and other techniques and intelligent mechanisms that optimise the task delegation between edge and cloud in a context-aware approach that minimises the data transfer and storage while it maximises the knowledge retention and augmentation.
The number of Internet-connected devices is expected to reach 1 trillion by 2035. A large fraction of these devices will become an integral part of households. This can improve the productivity and quality of life of their users, but also exposes them to new cyber security and privacy risks. This project will design intelligent algorithms that can detect and counteract cyber threats originating from or targeting IoT devices in user homes, without requiring manual intervention. Novel mechanisms will also be devised to allow users to operate safely with potentially compromised gadgets, while ensuring such devices will not damage the networking infrastructure or the operation of other equipment in homes. The concepts developed will be prototyped on commodity home routers and the source code of the implementations released into the public domain.