Education:
Dr. Yuxing Yang has an extensive academic background in electrical and electronics engineering. He is currently pursuing a PhD in Engineering at Newcastle University, UK, where his research focuses on developing novel frameworks for multi-feature abnormal event detection in video. His PhD work addresses complex challenges in video anomaly detection, employing advanced signal processing and information fusion techniques under the supervision of Dr. Syed Mohsen Naqvi. Dr. Yang also holds a Master of Electrical and Electronics Engineering degree from Newcastle University, where he worked on a dissertation titled “PHD Filter for Multiple Human Tracking.” During his Master’s program, he studied modules such as Signal Processing, Modulation and Coding, Internet of Things, and Wireless Network Technologies. Dr. Yang earned his Bachelor’s degree in Electrical and Electronics Engineering from the University of Liverpool, where his dissertation explored the use of fluorescence for monitoring water quality. His academic journey has equipped him with deep expertise in embedded systems, digital and wireless communications, and engineering management.
Professional Experience:
Dr. Yuxing Yang has gained valuable professional experience through his research and teaching roles at Newcastle University, UK. From 2018 to 2023, he served as an MSc Research Assistant with the Intelligent Sensing and Communications (ISC) research group. In this role, he guided MSc students on projects related to signal processing, machine learning, and video anomaly detection. He played a crucial role in advising students, refining their research findings, and supporting the presentation of their work. Additionally, Dr. Yang worked as a Research Assistant on the 2021 EPSRC IAA project titled “Multimodal Human Security,” where he contributed to enhancing detection accuracy in security surveillance using multimodal data. His work on this project led to a publication at the IEEE International Conference on Information Fusion. Furthermore, Dr. Yang held a position as a Teaching Assistant and Lab Demonstrator at Newcastle University from 2018 to 2022, where he taught and mentored undergraduate and master’s students, providing instruction on theoretical concepts, lab equipment, and experiment conduction, while also assisting with grading and course evaluations.
Research Interests:
Dr. Yuxing Yang’s research interests lie at the intersection of video anomaly detection and human behavior analysis, with a particular focus on advanced techniques in machine learning and signal processing. His work includes multiple human tracking, image segmentation, human action recognition, and multi-feature abnormal event detection in video. Dr. Yang is passionate about developing novel frameworks that enhance the accuracy and efficiency of video surveillance systems, especially in detecting abnormal events in complex environments. His research contributes to advancements in security systems, where multi-modal data fusion and real-time anomaly detection are critical for improving public safety and monitoring.
Skills:
Dr. Yuxing Yang possesses a diverse skill set, with expertise in programming languages and tools critical to his research in video anomaly detection and signal processing. He is proficient in Python, particularly in machine learning frameworks such as Keras, TensorFlow, and PyTorch. Additionally, Dr. Yang is skilled in MATLAB, which he uses extensively for algorithm development and data analysis. His experience with Latex allows him to efficiently prepare academic papers, while his knowledge of Linux Basics ensures a strong foundation in system operations and scripting. These technical skills enable him to develop and implement advanced algorithms for human-related abnormal event detection and security surveillance.
Conclusion:
Dr. Yuxing Yang’s solid academic background, innovative research in abnormal event detection, and extensive teaching experience, coupled with his leadership and contributions to community awareness, make him a suitable and competitive candidate for the Best Researcher Award. His dedication to advancing knowledge in video surveillance and anomaly detection is evident through his numerous publications, research contributions, and awards.
Publication Top Noted:
Abnormal event detection for video surveillance using an enhanced two-stream fusion method
- Authors: Y. Yang, Z. Fu, S. M. Naqvi
- Journal: Neurocomputing
- Year: 2023
- Volume: 553, Article 126561
- Cited by: 15
- DOI: 10.1016/j.neucom.2023.126561
Enhanced adversarial learning-based video anomaly detection with object confidence and position
- Authors: Y. Yang, Z. Fu, S. M. Naqvi
- Conference: 2019 13th International Conference on Signal Processing and Communication
- Year: 2019
- Cited by: 14
- DOI: 10.1109/icspcc46631.2019.8962857
A two-stream information fusion approach to abnormal event detection in video
- Authors: Y. Yang, Z. Fu, S. M. Naqvi
- Conference: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing
- Year: 2022
- Cited by: 13
- DOI: 10.1109/icassp43922.2022.9746515
Pose-driven human activity anomaly detection in a CCTV-like environment
- Authors: Y. Yang, F. Angelini, S. M. Naqvi
- Journal: IET Image Processing
- Year: 2023
- Volume: 17, Issue 3, Pages 674-686
- Cited by: 10
- DOI: 10.1049/ipr2.12876
Video anomaly detection for surveillance based on effective frame area
- Authors: Y. Yang, Y. Xian, Z. Fu, S. M. Naqvi
- Conference: 2021 IEEE 24th International Conference on Information Fusion (FUSION)
- Year: 2021
- Cited by: 8
- DOI: 10.23919/fusion49465.2021.9626892
Skeleton-based fall events classification with data fusion
- Authors: L. Xie, Y. Yang, F. Zeyu, S. M. Naqvi
- Conference: 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
- Year: 2021
- Cited by: 5
- DOI: 10.1109/MFI52462.2021.9604344
One-shot medical action recognition with a cross-attention mechanism and dynamic time warping
- Authors: L. Xie, Y. Yang, Z. Fu, S. M. Naqvi
- Conference: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing
- Year: 2023
- Cited by: 4
- DOI: 10.1109/icassp49357.2023.10095933
Action-based ADHD diagnosis in video
- Authors: Y. Li, Y. Yang, S. M. Naqvi
- Platform: arXiv preprint
- Year: 2024
- Cited by: 2
- DOI: arXiv:2409.02261
Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data
- Authors: L. Xie, Y. Yang, Z. Fu, S. M. Naqvi
- Platform: arXiv preprint
- Year: 2023
- Cited by: 1
- DOI: arXiv:2309.15635