Outputs (6)

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.