Outputs (45)

Mediation role of anxiety on social support and depression among diabetic patients in elderly caring social organizations in China during COVID-19 pandemic: a cross-sectional study (2023)
Journal Article
Zhao, L., Xu, F., Zheng, X., Xu, Z., Osten, B., Ji, K., …Chen, R. (2023). Mediation role of anxiety on social support and depression among diabetic patients in elderly caring social organizations in China during COVID-19 pandemic: a cross-sectional study. BMC Geriatrics, 23(1), Article 790. https://doi.org/10.1186/s12877-023-04502-z

Background: Diabetes has become a prominent global public health problem, which is an important cause of death, disease burden, and medical and health economic burden. Previous studies have reported that majority of persons diagnosed with diabetes la... Read More about Mediation role of anxiety on social support and depression among diabetic patients in elderly caring social organizations in China during COVID-19 pandemic: a cross-sectional study.

Learning based motion artifacts processing in fNIRS: a mini review (2023)
Journal Article
Zhao, Y., Luo, H., Chen, J., Loureiro, R., Yang, S., & Zhao, H. (2023). Learning based motion artifacts processing in fNIRS: a mini review. Frontiers in Neuroscience, 17, Article 1280590. https://doi.org/10.3389/fnins.2023.1280590

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA... Read More about Learning based motion artifacts processing in fNIRS: a mini review.

Building a Reusable and Extensible Automatic Compiler Infrastructure for reconfigurable devices (2023)
Conference Proceeding
Zang, Z., Dolinsky, U., Ghiglio, P., Cherubin, S., Goli, M., & Yang, S. (2023). Building a Reusable and Extensible Automatic Compiler Infrastructure for reconfigurable devices. In 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL) (351-352). https://doi.org/10.1109/FPL60245.2023.00062

Multi-Level Intermediate Representation (MLIR) is gaining increasing attention in reconfigurable hardware communities due to its capability to represent various abstract levels for software compilers. This project aims to be the first to provide an e... Read More about Building a Reusable and Extensible Automatic Compiler Infrastructure for reconfigurable devices.

A holistic human activity recognition optimisation using AI techniques (2023)
Journal Article
Li, Z., Liu, Y., Liu, B., Le Kernec, J., & Yang, S. (2024). A holistic human activity recognition optimisation using AI techniques. IET Radar, Sonar & Navigation, 18(2), 256-265. https://doi.org/10.1049/rsn2.12474

Building on previous radar-based human activity recognition (HAR), we expand the micro-Doppler signature to 6 domains and exploit each domain with a set of handcrafted features derived from the literature and our patents. An adaptive thresholding met... Read More about A holistic human activity recognition optimisation using AI techniques.

FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms (2023)
Presentation / Conference
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023, September). FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms. Poster presented at FPL 2023: 33rd International Conference on Field-Programmable Logic and Applications, Gothenburg, Sweden

This demonstration showcases a novel FPGA development pipeline for developing a low-power and real-time motion artefact detection module for a wearable functional near-Infrared spectroscopy (fNIRS) processing system. We provide a brief overview of th... Read More about FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms.

A Fast Optical Coherence Tomography Angiography Image Acquisition and Reconstruction Pipeline for Skin Application (2023)
Journal Article
Liao, J., Yang, S., Zhang, T., Li, C., & Huang, Z. (2023). A Fast Optical Coherence Tomography Angiography Image Acquisition and Reconstruction Pipeline for Skin Application. Biomedical Optics Express, 14(8), 3899-3913. https://doi.org/10.1364/BOE.486933

Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only... Read More about A Fast Optical Coherence Tomography Angiography Image Acquisition and Reconstruction Pipeline for Skin Application.

The satisfaction of elderly people with elderly caring social organizations and its relationship with social support and anxiety during the COVID-19 pandemic: a cross-sectional study (2023)
Journal Article
Ding, S., Liu, G., Xu, F., Ji, K., Zhao, L., Zheng, X., …Chen, R. (2023). The satisfaction of elderly people with elderly caring social organizations and its relationship with social support and anxiety during the COVID-19 pandemic: a cross-sectional study. BMC Public Health, 23(1), Article 1206. https://doi.org/10.1186/s12889-023-15951-x

Background: With the deepening of China’s aging population, higher demands have been placed on the supply of elderly care services. As one of the main sources of providing elderly care services, the quality of service provided by elderly caring socia... Read More about The satisfaction of elderly people with elderly caring social organizations and its relationship with social support and anxiety during the COVID-19 pandemic: a cross-sectional study.

A hand‐held optical coherence tomography angiography scanner based on angiography reconstruction transformer networks (2023)
Journal Article
Liao, J., Yang, S., Zhang, T., Li, C., & Huang, Z. (2023). A hand‐held optical coherence tomography angiography scanner based on angiography reconstruction transformer networks. Journal of Biophotonics, 16(9), Article e202300100. https://doi.org/10.1002/jbio.202300100

Optical coherence tomography angiography (OCTA) has successfully demonstrated its viability for clinical applications in dermatology. Due to the high optical scattering property of skin, extracting high‐quality OCTA images from skin tissues requires... Read More about A hand‐held optical coherence tomography angiography scanner based on angiography reconstruction transformer networks.

Review of recent advances in frequency-domain near-infrared spectroscopy technologies (2023)
Journal Article
Zhou, X., Xia, Y., Uchitel, J., Collins-Jones, L., Yang, S., Loureiro, R., …Zhao, H. (2023). Review of recent advances in frequency-domain near-infrared spectroscopy technologies. Biomedical Optics Express, 14(7), 3234-3258. https://doi.org/10.1364/BOE.484044

Over the past several decades, near-infrared spectroscopy (NIRS) has become a popular research and clinical tool for non-invasively measuring the oxygenation of biological tissues, with particular emphasis on applications to the human brain. In most... Read More about Review of recent advances in frequency-domain near-infrared spectroscopy technologies.

Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset (2023)
Conference Proceeding
Zhao, Y., Xia, Y., Loureiro, R., Zhao, H., Dolinsky, U., & Yang, S. (2023). Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset. In IWOCL '23: Proceedings of the 2023 International Workshop on OpenCL. https://doi.org/10.1145/3585341.3585380

Machine Learning has potential applications across a wide spectrum of devices. However, current approaches for domain-specific accelerators have encountered difficulties in satisfying the most recent computational demands for machine learning applica... Read More about Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset.

Radar-based Human Activity Recognition with Adaptive Thresholding towards Resource Constrained Platforms (2023)
Journal Article
Li, Z., Le Kernec, J., Abbasi, Q., Fioranelli, F., Yang, S., & Romain, O. (2023). Radar-based Human Activity Recognition with Adaptive Thresholding towards Resource Constrained Platforms. Scientific Reports, 13, Article 3473. https://doi.org/10.1038/s41598-023-30631-x

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms... Read More about Radar-based Human Activity Recognition with Adaptive Thresholding towards Resource Constrained Platforms.

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University (2023)
Journal Article
Yang, S., Kernec, J. L., Romain, O., Fioranelli, F., Cadart, P., Fix, J., …Jin, T. (2023). The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University. IEEE Journal of Biomedical and Health Informatics, 27(4), 1813-1824. https://doi.org/10.1109/jbhi.2023.3240895

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi,... Read More about The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University.

Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures (2022)
Conference Proceeding
Zheng, C., Zhen, C., Xie, H., & Yang, S. (2022). Towards Secure Multi-Agent Deep Reinforcement Learning: Adversarial Attacks and Countermeasures. In 2022 IEEE Conference on Dependable and Secure Computing (DSC). https://doi.org/10.1109/dsc54232.2022.9888828

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., …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.

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.

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., …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.

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation (2022)
Journal Article
Thomson, H., Yang, S., & Cochran, S. (2022). Machine learning-enabled quantitative ultrasound techniques for tissue differentiation. Journal of Medical Ultrasonics, 49, 517-528. https://doi.org/10.1007/s10396-022-01230-6

Purpose: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for t... Read More about Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

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., …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., …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)
Conference Proceeding
Ji, Y., Yang, S., Zhou, K., Li, C., & Huang, Z. (2021). A Machine Learning Based Quantitative Data Analysis for Screening Skin Diseases Based on Optical Coherence Tomography Angiography (OCTA). In 2021 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/IUS52206.2021.9593642

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)
Conference Proceeding
Thomson, H., Yang, S., Cochran, S., Stritch, T., & Baldwin, M. (2020). Effect of Freezing and Fixation on Quantitative Ultrasound Parameters in Phantoms of Brain and Brain Tumour. In 2020 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/ius46767.2020.9251780

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)
Conference Proceeding
Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Cochran, S., & Nathke, I. (2020). Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound. In 2020 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/ius46767.2020.9251280

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., …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., …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)
Conference Proceeding
Loukas, C., Fioranelli, F., Le Kernec, J., & Yang, S. (2018). Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers. In 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). https://doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.00088

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.

A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection (2018)
Conference Proceeding
Yu, Z., Yang, S., Zhou, K., & Aggoun, A. (2019). A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection. In Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence (169-178). https://doi.org/10.1007/978-3-319-97982-3_14

In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to tr... Read More about A Low Computational Approach for Assistive Esophageal Adenocarcinoma and Colorectal Cancer Detection.

A Highly Integrated Hardware/Software Co-Design and Co-Verification Platform (2018)
Journal Article
Yang, S., & Yu, Z. (2019). A Highly Integrated Hardware/Software Co-Design and Co-Verification Platform. IEEE Design and Test, 36(1), 23-30. https://doi.org/10.1109/mdat.2018.2841029

This article presents a platform for hardware/software co-design and co-verification with a flexible hardware/software interface. The platform has been applied to verification of a pedestrian tracking application to demonstrate its effectiveness.

A single chip system for sensor data fusion based on a Drift-diffusion model (2018)
Conference Proceeding
Yang, S., Wong-Lin, K., Rano, I., & Lindsay, A. (2018). A single chip system for sensor data fusion based on a Drift-diffusion model. In 2017 Intelligent Systems Conference (IntelliSys). https://doi.org/10.1109/intellisys.2017.8324291

Current multisensory system face data communication overhead in integrating disparate sensor data to build a coherent and accurate global phenomenon. We present here a novel hardware and software co-design platform for a heterogeneous data fusion sol... Read More about A single chip system for sensor data fusion based on a Drift-diffusion model.

Towards a scalable hardware/software co-design platform for real-time pedestrian tracking based on a ZYNQ-7000 device (2018)
Conference Proceeding
Yu, W., Yang, S., Sillitoe, I., & Buckley, K. (2018). Towards a scalable hardware/software co-design platform for real-time pedestrian tracking based on a ZYNQ-7000 device. . https://doi.org/10.1109/icce-asia.2017.8307853

Currently, most designers face a daunting task to research different design flows and learn the intricacies of specific software from various manufacturers in hardware/software co-design. An urgent need of creating a scalable hardware/software co-des... Read More about Towards a scalable hardware/software co-design platform for real-time pedestrian tracking based on a ZYNQ-7000 device.

Interactive Reading Using Low Cost Brain Computer Interfaces (2017)
Conference Proceeding
Loizides, F., Naughton, L., Wilson, P., Loizou, M., Yang, S., Hartley, T., …Zaphiris, P. (2017). Interactive Reading Using Low Cost Brain Computer Interfaces. In Human-Computer Interaction – INTERACT 2017 Proceedings, Part IV (450-454). https://doi.org/10.1007/978-3-319-68059-0_49

This work shows the feasibility for document reader user applications using a consumer grade non-invasive BCI headset. Although Brain Computer Interface (BCI) type devices are beginning to aim at the consumer level, the level at which they can actual... Read More about Interactive Reading Using Low Cost Brain Computer Interfaces.

An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture (2017)
Journal Article
Jiménez Serrata, A. A., Yang, S., & Li, R. (2017). An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture. EURASIP Journal on Embedded Systems, 2017(1), Article 27 (2017). https://doi.org/10.1186/s13639-017-0075-9

The simultaneous localisation and mapping (SLAM) algorithm has drawn increasing interests in autonomous robotic systems. However, SLAM has not been widely explored in embedded system design spaces yet due to the limitation of processing recourses in... Read More about An intelligible implementation of FastSLAM2.0 on a low-power embedded architecture.

A neuro-inspired visual tracking method based on programmable system-on-chip platform (2017)
Journal Article
Yang, S., Wong-Lin, K., Andrew, J., Mak, T., & McGinnity, T. M. (2018). A neuro-inspired visual tracking method based on programmable system-on-chip platform. Neural Computing and Applications, 30(9), 2697-2708. https://doi.org/10.1007/s00521-017-2847-5

Using programmable system-on-chip to implement computer vision functions poses many challenges due to highly constrained resources in cost, size and power consumption. In this work, we propose a new neuro-inspired image processing model and implement... Read More about A neuro-inspired visual tracking method based on programmable system-on-chip platform.

Modelling nanoplasmonic device based on an off-shelf hybrid desktop supercomputing platform (2013)
Conference Proceeding
Yang, S., Li, R., & Hillenbrand, D. (2013). Modelling nanoplasmonic device based on an off-shelf hybrid desktop supercomputing platform. In 2013 13th IEEE International Conference on Nanotechnology (IEEE-NANO 2013). https://doi.org/10.1109/nano.2013.6720891

Designing nanoplasmonic devices presents a number of unique challenges. The time domain modelling and simulation of electromagnetic (EM) wave interaction with nanoplasmonic devices, at high spatial and time resolution, requires high computational pow... Read More about Modelling nanoplasmonic device based on an off-shelf hybrid desktop supercomputing platform.

Adaptive Proactive Inhibitory Control for Embedded Real-Time Applications (2012)
Journal Article
Yang, S., McGinnity, T. M., & Wong-Lin, K. (2012). Adaptive Proactive Inhibitory Control for Embedded Real-Time Applications. Frontiers in Neuroengineering, 5, https://doi.org/10.3389/fneng.2012.00010

Psychologists have studied the inhibitory control of voluntary movement for many years. In particular, the countermanding of an impending action has been extensively studied. In this work, we propose a neural mechanism for adaptive inhibitory control... Read More about Adaptive Proactive Inhibitory Control for Embedded Real-Time Applications.

A case for spiking neural network simulation based on configurable multiple-FPGA systems (2011)
Journal Article
Yang, S., Wu, Q., & Li, R. (2011). A case for spiking neural network simulation based on configurable multiple-FPGA systems. Cognitive Neurodynamics, 5(3), Article 301 (2011). https://doi.org/10.1007/s11571-011-9170-0

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of t... Read More about A case for spiking neural network simulation based on configurable multiple-FPGA systems.

A Token-Managed Admission Control System for QoS Provision on a Best-Effort GALS Interconnect (2009)
Journal Article
Yang, S., Furber, S. B., Shi, Y., & Plana, L. A. (2009). A Token-Managed Admission Control System for QoS Provision on a Best-Effort GALS Interconnect. Fundamenta Informaticae, 95(1), 53-72. https://doi.org/10.3233/fi-2009-142

A Token-ManagedAdmission Control (TMAC) mechanism is introduced in order to provide efficient Quality-of-Service (QoS) support for different types of application on a best-effort Globally-Asynchronous Locally-Synchronous (GALS) interconnect fabric. T... Read More about A Token-Managed Admission Control System for QoS Provision on a Best-Effort GALS Interconnect.

A GALS Infrastructure for a Massively Parallel Multiprocessor (2007)
Journal Article
Plana, L. A., Furber, S. B., Temple, S., Khan, M., Shi, Y., Wu, J., & Yang, S. (2007). A GALS Infrastructure for a Massively Parallel Multiprocessor. IEEE Design and Test of Computers, 24(5), 454-463. https://doi.org/10.1109/mdt.2007.149

This case study focuses on a massively parallel multiprocessor for real-time simulation of billions of neurons. Every node of the design comprises 20 ARM9 cores, a memory interface, a multicast router, and two NoC structures for communicating between... Read More about A GALS Infrastructure for a Massively Parallel Multiprocessor.