Research output per year
Research output per year
PROF, Director of Electronics and Electrical Engineering
Accepting PhD Students
PhD projects
<p>PhD Studentship Available: Adaptive time-resolved optical imaging for medical applications. Closing date, 13th June 2022.</p>
<p>This PhD position looks to use advanced optical imaging techniques to develop new methods for accessing optical “fingerprints” of disease, tailored to translatable medical applications. We are seeking an outstanding physics/engineering/computing student with great interest in understanding scanned optical image generation and processing to design and develop novel new adaptive image acquisition algorithms and image processing methods.</p>
<p>For more information, <a href="https://www.findaphd.com/phds/project/adaptive-time-resolved-optical-imaging-for-medical-applications/?p144853">please click here</a></p><p>Also, offering projects in Machine Learning and Statistical Signal Processing, including Probabilistic Graphical Models for Information Fusion. For further information, please see my range of interests below. </p>
PhD Studentship Available: Adaptive time-resolved optical imaging for medical applications (Closing date: 13th June 2022) |
CURRENT PROJECT: 2020-2023: EPSRC Healthcare Impact Partnership project on "Next-Generation Sensing For Human In Vivo Pharmacology - Accelerating Drug Development In Inflammatory Diseases (EP/S025987/1)." Signal Processing and Machine Learning for Microendoscopy Imaging: developing signal and image processing and machine learning algorithms for a breakthrough healthcare imaging system, addressing problems in modern drug development. |
CURRENT PROJECT: 2019-2024: Developing algorithms for Probabilistic Graphical Models in Multi-Target Tracking at the School of Engineering at the University of Edinburgh, through the University Defence Research Collaboration (UDRC) phase 3 project jointly funded by EPSRC and UK Ministry of Defence. This work contributes to the UDRC3 under Work Package 2.1, within the Institute for Digital Communications at the University of Edinburgh. Multi-target tracking (MTT) is an important problem in many defence and civilian applications, from tracking airborne targets, to maritime scenarios, to tracking people in urban environments. Recent work has been concerned with tracking an unknown number of targets from multiple multimodal asynchronous sensors, such as combining detections from multiple-radars and infra-red. Moreover, strong MoD investment in modular hierarchical autonomous sensor systems such as SAPIENT requires the development of inference algorithms for fixed-resource middleware platforms that scale with the variety, velocity, and veracity of the data, especially in cases where the sensor head has pre-processing capability. This project was formally advertised here: https://www.findaphd.com/phds/project/probabilistic-graphical-models-in-multi-target-tracking/?p108113 |
James Hopgood is a Senior Lecturer in the Institute for Digital Communications, within the School of Engineering, at the University of Edinburgh, Scotland. He works in the disciplines of Data Science and Machine Learning within the field of Statistical Signal Processing, a branch of Electronic Engineering.
James's research specialisation include model-based Bayesian signal processing, speech and audio signal processing in adverse acoustic environments, including blind dereverberation and multi-target acoustic source localisation and tracking, single channel signal separation, distant speech recognition, audio-visual fusion, medical imaging, blind image deconvolution, and general statistical signal and image processing.
James received the M.A., M.Eng. degree in Electrical and Information Sciences in 1997 and a Ph.D. in July 2001 in Statistical Signal Processing, part of Information Engineering, both from the University of Cambridge, England. He was then a Post-Doctoral Research Associate for the year after his Ph.D within the same group, at which point he became a Research Fellow at Queens’ College continuing his research in the Signal Processing Laboratory in Cambridge. James joined the University of Edinburgh in April 2004.
Since September 2011, he is Editor-in-Chief for the IET Journal of Signal Processing. James is the Programme Director for the MSc in Signal Processing and Communications at the University of Edinburgh.
James’s interest in audio-signal processing in adverse acoustic environments covers a variety of research topics including: acoustic source localisation and tracking; blind enhancement of de-reverberation of speech and music; environmental noise reduction for acoustic sensing from remote mobile platforms; acoustic weapons fire and gunshot localisation; intelligent sensing form mobile robots; and ad-hoc acoustic sensor networks.
You can listen to some of James's recent work in his short public lecture talk from Edinurgh's Innovative Learning Week entitled "Separation and Marriage: Signal Processing for Life Science".
Other PhD Positions available: Microphone array based acoustic event analysis, Distributed Source Localisation. Please contact me for further information.
Teaches the undergraduate course, Sensor Networks and Data Analysis (formally Signals and Communications 2) and previously tuaght Signals and Communications 3, in the second and third year respectively. James has over 16 years experience of teaching undergraduate and masters students, and is Director of Electronics and Electrical Engineering. James was previously Programme Director for the MSc in Signal Processing and Communications
Also teaches a 20-credit (double) Masters course on "Probability, Estimation Theory, and Random Signals (PETARS)". Extremely comprehensive courses notes are available for all four courses, along with a series of lecture recordings, tutorial questions, and solutions.
I also teach on the UDRC Summer School at Master's level. For more information, watch this video!
James has been using lecture recording technologies for the last seven years for supporting the student experience. His pedagogical approach is to provide visualisation, intuition, and real-world examples for describing complex topics, while providing teaching materials suitable for a range of learning needs.
Student name: Saleh Hanano
Provisional thesis title: Machine Learning for Real-time Classification of Liver Disease using Ultrasound & Opto-acoustic Imaging
Dates: Commenced 1st October, 2017
Funding: Optical Medical Imaging CDT (Optima)
Institution: University of Newcastle
Department: School of Engineering
Student Name: Yang Sun
Date: September 2019
Thesis title: "Deep Neural Networks for Monaural Source Separation"
Supervisor: Dr Mohsen Naqvi
Institution: University of Surrey
Department: Centre for Vision, Speech and Signal Processing Faculty of Engineering and Physical Sciences
Student Name: Atiyeh Alinaghi
Date: September 2016
Thesis title: "Blind Convolutive Stereo Speech Separation and Dereverberation"
Supervisor: Drs Philip JB Jackson and Wenwu Wang
Institution: University of Cambridge
Department: Signal Processing and Communications Laboratory, Department of Engineering
Student name: ZHANG, Xiao
Date: October July, 2016
Thesis title: Probablistic Models & Filters for Financial Time Series.
Supervisor: Prof. Simon Godsill
Institution: Imperical College London
Department: Communications and Signal Processing Group, Electrical & Electronic Engineering Department
Student name: WANG, Yu
Date: October 2015
Thesis title: Speech Enhancement in the Modulation Domain
Supervisor: Dr Mike Brooks
Institution: University of Cambridge
Department: Signal Processing and Communications Laboratory, Department of Engineering
Student name: Maurice Fallon
Date: Septemer 2008
Thesis title: Acoustic Source Tracking using Sequential Monte Carlo
Supervisor: Prof. Simon Godsill
Institution: University of Cambridge
Department: Signal Processing and Communications Laboratory, Department of Engineering
Student name: Zaifei Liu
Date: October 2006
Thesis title: Monte Carlo Methods for Bayesian Inference in Digital Communications
Supervisor: Prof. Arnaud Doucet
External examiner for over 20 candidates at the University of Edinburgh.
Doctor of Philosophy (PhD), Nonstationary Signal Processing with Application to Reverberation Cancellation in Acoustic Environments, University of Cambridge
Award Date: 1 Jan 2001
Master of Arts, University of Cambridge
Award Date: 1 Jan 2000
Master of Engineering, Electrical and Information Sciences (EIST), University of Cambridge
Award Date: 1 Jan 1997
Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Research output: Contribution to journal › Article › peer-review
James Hopgood (Speaker) & David Laurenson (Speaker)
Activity: Academic talk or presentation types › Oral presentation
James Hopgood (Speaker)
Activity: Academic talk or presentation types › Invited talk
James Hopgood (Peer reviewer)
Activity: Publication peer-review and editorial work types › Publication peer-review
James Hopgood (Peer reviewer)
Activity: Publication peer-review and editorial work types › Publication peer-review
James Hopgood (Peer reviewer)
Activity: Publication peer-review and editorial work types › Publication peer-review
Wang, Q., Akram, A., Hopgood, J. & Dorward, D.
1/01/24 → 31/12/24
Project: Research
Wang, Q., Dhaliwal, K., Akram, A., Hopgood, J. & Dorward, D.
1/01/24 → 31/12/24
Project: Research
1/10/23 → 31/03/27
Project: Research
1/10/23 → 31/03/27
Project: Research
Connelly, M. (Creator), McHoull, B. (Creator), Troy, D. (Creator) & Hopgood, J. (Depositor), Edinburgh DataShare, 26 Apr 2018
DOI: 10.7488/ds/2342
Dataset
Diamantis, K. (Creator) & Hopgood, J. (Creator), Edinburgh DataShare, 9 Dec 2019
DOI: 10.7488/ds/2723
Dataset
Hopgood, J. (Creator) & Taimori, A. (Creator), Edinburgh DataShare, 4 Apr 2022
DOI: 10.7488/ds/3430, https://www.techrxiv.org/articles/preprint/Fast_and_robust_single-exponential_decay_recovery_from_noisy_fluorescence_lifetime_imaging/17264756
Dataset
Herbert, S. (Creator), Hopgood, J. (Supervisor) & Mulgrew, B. (Supervisor), Edinburgh DataShare, 3 Aug 2018
DOI: 10.7488/ds/2403
Dataset