Skip to main content

Research Repository

Advanced Search

All Outputs (32)

Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures (2022)
Presentation / Conference Contribution
Zheng, C., Zhen, C., Xie, H., & Yang, S. (2022, June). Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures. Presented at 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom

Reinforcement Learning (RL) is one of the most popular methods for solving complex sequential decision-making problems. Deep RL needs careful sensing of the environment, selecting algorithms as well as hyper-parameters via soft agents, and simultaneo... Read More about Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures.

Semi-supervised Representative Learning for Measuring Epidermal Thickness in Human Subjects in Optical Coherence Tomography by Leveraging Datasets from Rodent Models (2022)
Journal Article
Ji, Y., Yang, S., Zhou, K., Lu, J., Wang, R., Rocliffe, H. R., Pellicoro, A., Cash, J. L., Li, C., & Huang, Z. (2022). Semi-supervised Representative Learning for Measuring Epidermal Thickness in Human Subjects in Optical Coherence Tomography by Leveraging Datasets from Rodent Models. Journal of Biomedical Optics, 27(8), Article 085002. https://doi.org/10.1117/1.JBO.27.8.085002

Aim: Morphological changes in the epidermis layer are critical for the diagnosis and assessment of various skin diseases. Due to its non-invasiveness, optical coherence tomography (OCT) is a good candidate for observing microstructural changes of ski... Read More about Semi-supervised Representative Learning for Measuring Epidermal Thickness in Human Subjects in Optical Coherence Tomography by Leveraging Datasets from Rodent Models.

Co-optimization method to improve lateral resolution in photoacoustic computed tomography (2022)
Journal Article
Zhang, Y., Yang, S., Xia, Z., Hou, R., Xu, B., Hou, L., Marsh, J. H., Jiangmin Hou, J., Mojtaba Rezaei Sani, S., Liu, X., & Xiong, J. (2022). Co-optimization method to improve lateral resolution in photoacoustic computed tomography. Biomedical Optics Express, 13(9), 4621-4636. https://doi.org/10.1364/BOE.469744

In biomedical imaging, photoacoustic computed tomography (PACT) has recently gained increased interest as this imaging technique has good optical contrast and depth of acoustic penetration. However, a spinning blur will be introduced during the image... Read More about Co-optimization method to improve lateral resolution in photoacoustic computed tomography.

Thermography for Disease Detection in Livestock: A Scoping Review (2022)
Journal Article
McManus, R., Boden, L., Weir, W., Viora, L., Barker, R., Kim, Y., …Yang, S. (2022). Thermography for Disease Detection in Livestock: A Scoping Review. Frontiers in Veterinary Science, 9, Article 965622. https://doi.org/10.3389/fvets.2022.965622

Infra-red thermography (IRT) offers potential opportunities as a tool for disease detection in livestock. Despite considerable research in this area, there are no common standards or protocols for managing IRT parameters in animal disease detection r... Read More about Thermography for Disease Detection in Livestock: A Scoping Review.

A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework (2022)
Book Chapter
Yang, S., & Li, Y. (2022). A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework. In C. H. Chen (Ed.), Computational Intelligence and Image Processing in Medical Applications (157-174). World Scientific Publishing. https://doi.org/10.1142/9789811257452_0010

Deep neural network powered semantic segmentation implementation has great advantages of providing accurate object detection using pixel-based classification; however, when this technique is applied within resource-constrained platforms, such as mobi... Read More about A Fast Inference Framework for Medical Image Semantic Segmentation Tasks Using Deep Learning Framework.

Deep Learning Approach for Automated Thickness Measurement of Epithelial Tissue and Scab using Optical Coherence Tomography (2022)
Journal Article
Ji, Y., Yang, S., Zhou, K., Rocliffe, H. R., Pellicoro, A., Cash, J. L., Wang, R., Li, C., & Huang, Z. (2022). Deep Learning Approach for Automated Thickness Measurement of Epithelial Tissue and Scab using Optical Coherence Tomography. Journal of Biomedical Optics, 27(1), https://doi.org/10.1117/1.JBO.27.1.015002

Significance: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound healing, especially re-epithelialization. Epidermis and scab detection is of importance in the wound... Read More about Deep Learning Approach for Automated Thickness Measurement of Epithelial Tissue and Scab using Optical Coherence Tomography.

Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning (2021)
Journal Article
Parra-Ullauri, J. M., Garcoa-Dominguez, A., Bencomo, N., Zheng, C., Zhen, C., Boubeta-Puig, J., Ortiz, G., & Yang, S. (2022). Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning. Software and Systems Modeling, 21, 1091-1113. https://doi.org/10.1007/s10270-021-00952-4

Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is... Read More about Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning.

A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA) (2021)
Presentation / Conference Contribution
Ji, Y., Yang, S., Zhou, K., Li, C., & Huang, Z. (2021, September). A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA). Presented at 2021 IEEE International Ultrasonics Symposium, Xi'an, China [Online]

Lack of accurate and standard quantitative evaluations limit the progress of applying the OCTA technique into skin clinical trials. More systematic research is required to investigate the possibility of using quantitative OCTA techniques for screenin... Read More about A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA).

Reward-Reinforced Generative Adversarial Networks for Multi-agent Systems (2021)
Journal Article
Zheng, C., Yang, S., Parra-Ullauri, J., Garcia-Dominguez, A., & Bencomo, N. (2022). Reward-Reinforced Generative Adversarial Networks for Multi-agent Systems. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(3), 479 - 488. https://doi.org/10.1109/TETCI.2021.3082204

Multi-agent systems deliver highly resilient and adaptable solutions for common problems in telecommunications, aerospace, and industrial robotics. However, achieving an optimal global goal remains a persistent obstacle for collaborative multi-agent... Read More about Reward-Reinforced Generative Adversarial Networks for Multi-agent Systems.

The use of artificial intelligence and robotics in regional anaesthesia (2021)
Journal Article
McKendrick, M., Yang, S., & McLeod, G. A. (2021). The use of artificial intelligence and robotics in regional anaesthesia. Anaesthesia, 76(S1), 171-181. https://doi.org/10.1111/anae.15274

The current fourth industrial revolution is a distinct technological era characterised by the blurring of physics, computing and biology. The driver of change is data, powered by artificial intelligence. The UK National Health Service Topol Report em... Read More about The use of artificial intelligence and robotics in regional anaesthesia.

Effect of Freezing and Fixation on Quantitative Ultrasound Parameters in Phantoms of Brain and Brain Tumour (2020)
Presentation / Conference Contribution
Thomson, H., Yang, S., Cochran, S., Stritch, T., & Baldwin, M. (2020, September). Effect of Freezing and Fixation on Quantitative Ultrasound Parameters in Phantoms of Brain and Brain Tumour. Presented at 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA

Quantitative ulltrasound (QUS) analyzes unprocessed radio frequency data from an ultrasound transducer or array and infers properties about tissue microstructure. Whilst it has shown success in diagnosing various soft tissue diseases, there have been... Read More about Effect of Freezing and Fixation on Quantitative Ultrasound Parameters in Phantoms of Brain and Brain Tumour.

Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound (2020)
Presentation / Conference Contribution
Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Cochran, S., & Nathke, I. (2020, September). Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound. Presented at 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA

With histological information on inflammation status as the ground truth, deep learning methods can be used as a classifier to distinguish different stages of bowel inflammation based on microultrasound (μUS) B-scan images. However, it is extremely t... Read More about Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound.

A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract (2020)
Journal Article
Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Näthke, I., & Cochran, S. (2021). A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract. IEEE Transactions on Medical Imaging, 40(1), 38-47. https://doi.org/10.1109/tmi.2020.3021560

Inflammation of the gastrointestinal (GI) tract accompanies several diseases, including Crohn's disease. Currently, video capsule endoscopy and deep bowel enteroscopy are the main means for direct visualisation of the bowel surface. However, the use... Read More about A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract.

Hierarchical Radar Data Analysis for Activity and Personnel Recognition (2020)
Journal Article
Li, X., Li, Z., Fioranelli, F., Yang, S., Romain, O., & Kernec, J. L. (2020). Hierarchical Radar Data Analysis for Activity and Personnel Recognition. Remote Sensing, 12(14), Article 2237. https://doi.org/10.3390/rs12142237

Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find... Read More about Hierarchical Radar Data Analysis for Activity and Personnel Recognition.

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy (2020)
Journal Article
Ali, S., Zhou, F., Braden, B., Bailey, A., Yang, S., Cheng, G., Zhang, P., Li, X., Kayser, M., Soberanis-Mukul, R. D., Albarqouni, S., Wang, X., Wang, C., Watanabe, S., Oksuz, I., Ning, Q., Yang, S., Khan, M. A., Gao, X. W., Realdon, S., …Rittscher, J. (2020). An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Scientific Reports, 10(1), Article 2748 (2020). https://doi.org/10.1038/s41598-020-59413-5

We present a comprehensive analysis of the submissions to the first edition of the Endoscopy Artefact Detection challenge (EAD). Using crowd-sourcing, this initiative is a step towards understanding the limitations of existing state-of-the-art comput... Read More about An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy.

A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life (2019)
Journal Article
Abdur Rahman, M., Rashid, M. M., Le Kernec, J., Philippe, B., Barnes, S. J., Fioranelli, F., Yang, S., Romain, O., Abbasi, Q. H., Loukas, G., & Imran, M. (2019). A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life. Sensors, 19(23), Article 5258. https://doi.org/10.3390/s19235258

Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as “after cancer treatment”. Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a... Read More about A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life.

Radar sensing for healthcare: Associate Editor Francesco Fioranelli on the applications of radar in monitoring vital signs and recognising human activity patterns (2019)
Journal Article
Fioranelli, D. F., Shah, D. S. A., Li1, H., Shrestha, A., Yang, D. S., & Kernec, D. J. L. (2019). Radar sensing for healthcare: Associate Editor Francesco Fioranelli on the applications of radar in monitoring vital signs and recognising human activity patterns. Electronics Letters, 55(19), 1022-1024. https://doi.org/10.1049/el.2019.2378

Although traditionally associated with defence and security domains, radar sensing has attracted significant interest in recent years in healthcare applications. These include the monitoring of vital signs such as respiration, heartbeat, and blood pr... Read More about Radar sensing for healthcare: Associate Editor Francesco Fioranelli on the applications of radar in monitoring vital signs and recognising human activity patterns.

A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks (2019)
Journal Article
Bassoy, S., Imran, M. A., Yang, S., & Tafazolli, R. (2019). A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks. IEEE Access, 7, 92693-92708. https://doi.org/10.1109/access.2019.2927093

Coordinated multi-point (CoMP) transmission is one of the key features for long term evolution advanced (LTE-A) and a promising concept for interference mitigation in 5th generation (5G) and beyond future densely deployed wireless networks. Due to th... Read More about A Load-Aware Clustering Model for Coordinated Transmission in Future Wireless Networks.

Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers (2018)
Presentation / Conference Contribution
Loukas, C., Fioranelli, F., Le Kernec, J., & Yang, S. (2018, August). Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers. Presented at 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Athens, Greece

This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative app... Read More about Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers.

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests (2018)
Journal Article
Lin, Y., Le Kernec, J., Yang, S., Fioranelli, F., Romain, O., & Zhao, Z. (2018). Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests. IEEE Sensors Journal, 18(23), 9669-9681. https://doi.org/10.1109/jsen.2018.2872849

The accurate classification of activity patterns based on radar signatures is still an open problem and is a key to detect anomalous behavior for security and health applications. This paper presents a novel iterative convolutional neural network str... Read More about Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests.