All Outputs (12)

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.

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.

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.

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).

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.

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.

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 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.

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.