Dr David Li

Senior Lecturer

Strathclyde Institute of Pharmacy and Biomedical Sciences

Personal statement

David is a senior lecturer in photonics/electronics. He received his PhD in electrical engineering from the National Taiwan University. He then joined the SoC Technology Center (now Information and Communications Research Laboratories), Industrial Technology Research Institute (ITRI), Taiwan, as a R&D engineer working on 1.25-12.5Gbps optical communication chipsets and wireless communication IP, knowledge transfer, and international joint projects with the ECE, Carnegie Mellon University, USA.

From 2007 to 2011, he worked at the Institute for Integrated Micro and Nano Systems, University of Edinburgh, on the European projects "MEGAFRAME" and "METOXIA" for CMOS single-photon avalanche diode (SPAD) based fluorescence lifetime imaging microscopy (FLIM) cameras and analogue front-end circuits. He has invented several hardware-embedded imaging processors which resulted in the first video-rate FLIM imaging on CMOS SPAD arrays.

From January 2014, he joined the Centre for Biophotonics, SIPBS, after his first lecturing position in biomedical engineering and embedded systems at the School of Engineering and Informatics, University of Sussex (where he led a research team working on industry-funded projects).

His current research interests include CMOS imaging and sensor systems, embedded systems and GPU computing, digital signal processing, mixed-signal integrated circuits, fluorescence based sensing systems, electrical impedance sensing systems, forward models of electrical impedance tomography, finite-element/finite-difference and numerical modelling. He has been working with researchers from within the UK and from Netherlands, Switzerland, Belgium, Germany, France, China, USA, and Taiwan.

Please also check: https://scholar.google.co.uk/citations?hl=en&user=UFE7FyIAAAAJ.

David would be delighted to collaborate with colleagues or hear from potential PhD candidates interested in exploring any aspect of imaging/sensing and embedded systems research. 

Funding opportunities for post-graduate students, postdoc researchers and visiting scholars:

Commonwealth Scholaraships: http://cscuk.dfid.gov.uk

EU Marie Sklodowska-Curie Fellowships: http://ec.europa.eu/research/mariecurieactions/

Newton Fellowships: http://www.newtonfellowships.org

Royal Society Newton Mobility Grant: https://royalsociety.org/grants-schemes-awards/grants/newton-mobility-grants/

Royal Academy of Engineering Newton Mobility Grant: http://www.raeng.org.uk/grants-and-prizes/international-research-and-collaborations/newton-fund-programmes/newton-research-collaboration-programme 

 

 

Publications

A low nonlinearity, missing-code free time-to-digital converter based on 28nm FPGAs with embedded bin-width calibrations
Chen Haochang, Zhang Yongliang, Li David Day-Uei
IEEE Transactions on Instrumentation and Measurement Vol 66, pp. 1912 - 1921, (2017)
http://dx.doi.org/10.1109/TIM.2017.2663498
A survey of the state-of-the-art techniques for cognitive impairment detection in the elderly
Fei Zixiang, Yang Erfu, Li David, Butler Stephen, Ijomah Winifred, Mackin Neil
2017 International Conference on Life System Modeling and Simulation & International Conference on Intelligent Computing for Sustainable Energy and Environment, (2017)
Comment on 'A novel method for fast and robust estimation of fluorescence decay dynamics using constrained least-square deconvolution with Laguerre expansion'
Zhang Yongliang, Li David Day-Uei
Physics in Medicine and Biology Vol 62, pp. 1632-1636, (2017)
http://dx.doi.org/10.1088/1361-6560/aa522f
Towards unsupervised fluorescence lifetime imaging using low dimensional variable projection
Zhang Yongliang, Cuyt Annie, Lee Wen-shin, Lo Bianco Giovanni, Wu Gang, Chen Yu, Li David Day-Uei
Optics Express Vol 24, pp. 26777-26791, (2016)
http://dx.doi.org/10.1364/OE.24.026777
Optimizing Laguerre expansion based deconvolution methods for analysing bi-exponential fluorescence lifetime images
Zhang Yongliang, Chen Yu, Li David Day-Uei
Optics Express Vol 24, pp. 13894-13905, (2016)
http://dx.doi.org/10.1364/OE.24.013894
Estimation of fluorescence lifetimes via rotational invariance techniques
Yu Hongqi, Saleeb Rebecca, Dalgarno Paul, Li David Day-Uei
IEEE Transactions on Biomedical Engineering Vol 63, pp. 1292-1300, (2016)
http://dx.doi.org/10.1109/TBME.2015.2491364

more publications

Projects

Big Data-Driven Distributed Microseismic Monitoring Method for Hydrofracturing Oil Exploration
Yang, Erfu (Principal Investigator) Durrani, Tariq (Co-investigator) Li, David (Co-investigator) Post, Mark (Co-investigator)
Period 01-Apr-2017 - 31-Mar-2019
Investigation of a Smart and Low-Cost Autonomous System for Early Detection and Monitoring of Mild Cognitive Impairment in the Elderly
Yang, Erfu (Principal Investigator) Li, David (Co-investigator) Ijomah, Winifred (Co-investigator) Fei, Zixiang (Post Grad Student)
As the number of the elderly people increase, there is an urgent need for the development of advanced assistative technology to ensure their mobility and independent living by early detection and monitoring of the MCI in the low-income community. The main objectives of this project are described as follows: Scientific: (1) To develop novel algorithms for facial recognition and body movement analyses to early detect and monitor the MCI condition in the elderly. (2) To propose a smart tool that can compute the attentional focus of the elderly and determine communication counterparts. (3) To investigate a decision making tool to manage and integrate all the sensory resources of the mobile devices for efficiently executing multiple tasks. Long-term: (1) To improve the performance and scalability of the developed system for the purpose of collective care in the low-income community. (2) To widen the scope of the applicability of the developed system. (3) To lay the foundation for improving the interaction between the elderly and other assistive healthcare device/systems in different environments and chronic conditions. (4) To associate healthcare applications with smart cities, robotics and autonomous systems, signal processing, computer vision and machine learning communities.
Period 01-Oct-2016 - 30-Sep-2019
Big Data-Driven Distributed Microseismic Monitoring Method for Hydrofracturing Oil Exploration
Yang, Erfu (Principal Investigator) Post, Mark (Co-investigator) Li, David (Co-investigator) Durrani, Tariq (Co-investigator)
This joint-project aims to address the fundamental research challenge related to the development of Big Data-driven distributed microseismic monitoring method for hydrofracturing oil exporation. This research challenge is related to microseismic image information processing and utilization, i.e., how to quickly and efficiently extract and analyze the information of interest for oil detection, tracking its distribution/storage from distributed data/images acquired by a huge number of sensors deployed across the oil field, through either fixed locations or autonomous vehicles platforms, such as unmanned ground vehicle (UGV). To address this challenge faced by the O&G industry, this joint project is focusing on novel Big Data-driven solutions which can integrate distributed data filtering, intelligent analytics and machine learning techniques to offer unique benefits in terms of its ability to bring in data from multiple and distributed sensing sources and provide a full suite of descriptive, predictive and prescriptive analytics.
Period 01-Apr-2017 - 31-Mar-2019
David Begg Studentship | Chen, Haochang
Li, David (Principal Investigator) McConnell, Gail (Co-investigator) Chen, Haochang (Research Co-investigator)
Period 01-Nov-2014 - 01-May-2018
Doctoral Training Partnership (DTA - University of Strathclyde) | Chen, Haochang
Li, David (Principal Investigator) McConnell, Gail (Co-investigator) Chen, Haochang (Research Co-investigator)
Period 01-Oct-2014 - 01-Oct-2017
GPU-enhanced Biomedical Image Analysis
Li, David (Principal Investigator)
As recently rapid advances in image sensors and microscopy technologies, image data are usually humongous making image analysis a painstaking task. Without fast analysis strategies, fast acquisition systems cannot make real impact in biomedical or clinical research. This project aims to use the latest graphics processing units for fast fluorescence lifetime imaging and MRI image analysis.
Period 01-Jun-2012 - 31-Aug-2016

more projects

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Hamnett Wing John Arbuthnott Building

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