Doctoral research
RF localisation for wireless capsule endoscopy
Radio measurements, antenna geometry and probabilistic inference for capsule position estimation.
Queen Mary University of London
Subramaniam Murugesan
PhD Researcher, Electronic EngineeringResearch Technician, Blizard Institute
My research combines radio-frequency sensing, machine learning and medical imaging.
My PhD investigates localisation for wireless capsule endoscopy. At the Blizard Institute, I contribute to CE-Track through AI-based reconstruction and modelling of clinical imaging data.
My background spans bioinformatics and Big Data Science, connecting biological questions with computational methods.
Research
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CE-Track · Blizard Institute
Medical image analysis and anatomical modelling
AI-based reconstruction and modelling of clinical imaging data for the CE-Track project.
Publications
All publication detailsMachine Learning-Based Anchor-Aware Conditional Flow Matching for RF Localization in Wireless Capsule Endoscopy
20th European Conference on Antennas and Propagation (EuCAP), pp. 1–5
Conditional flow matching for RF localisation, incorporating measurements from multiple anchors and their geometry.
Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing
IEEE Journal of Biomedical and Health Informatics, 28(9), pp. 5043–5054
Neural networks and serverless computing for tuberculosis prediction in an e-health application.
Agentic AI: Vision and challenges
PLOS Complex Systems, 3(8), e0000120
A perspective on agentic AI, its capabilities, implementation approaches and open challenges.
Edge AI: A taxonomy, systematic review and future directions
Cluster Computing, 28, article 18
A systematic review of the architectures, applications and research directions of artificial intelligence at the edge.
GAIKube: Generative AI-Based Proactive Kubernetes Container Orchestration Framework for Heterogeneous Edge Computing
IEEE Transactions on Cognitive Communications and Networking, 11(2), pp. 933–945
Generative AI for proactive container orchestration across heterogeneous edge computing resources.
CloudAIBus: A testbed for AI based cloud computing environments
Cluster Computing, 27, pp. 11953–11981
An AI-based cloud computing testbed for workload forecasting and resource allocation.
Appointments
2024–present
PhD Researcher
Queen Mary University of London · School of Electronic Engineering and Computer Science
EPSRC DTP funded research in RF localisation and machine learning for wireless capsule endoscopy.
May 2026–present
Research Technician
Blizard Institute · Queen Mary University of London
Research Technician for CE-Track, contributing AI-based approaches to clinical imaging reconstruction and anatomical modelling.
Education
View CVPhD in Electronic Engineering
Queen Mary University of London
London, United Kingdom
RF localisation and machine learning for wireless capsule endoscopy.
MSc in Big Data Science
Queen Mary University of London
London, United Kingdom
Distinction.
Bachelor of Technology in Bioinformatics
Thanjavur, India
Recent updates
All updatesVisits & activities
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27–31 July 2026
EME Hub Summer School, Glasgow
Laboratory demonstrations and photographs from the visit.
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19–24 April 2026
EuCAP 2026, Dublin
Conference sessions, fellow attendees and views of Dublin.
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