Mr. Xiaowo Xu, University of Electronic Science and Technology of China
Xiaowo Xu is a Ph.D. candidate in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC), where he has been honing his research skills since September 2022. His academic journey began with a Bachelor of Engineering in Electronic Information Engineering from Sichuan University, followed by a Master of Engineering in the same field at UESTC. His research interests focus on deep learning applications, particularly in object categorization, object detection, instance segmentation, and moving object tracking. Currently, he is dedicated to the intelligent interpretation of synthetic aperture radar (SAR) images. Xiaowo has received several prestigious awards, including the 1st Scholarship for Doctoral Candidates and the Special Scholarship for Doctoral Candidates from UESTC, along with an “Honor Academic” Award and the Outstanding Graduate Student Award for the 2022-2023 academic year.
Professional Profile:
ORCID
Summary of Suitability for Best Researcher Award
Xiaowo Xu’s research focus on deep learning applications, particularly in object detection, segmentation, and synthetic aperture radar (SAR) image interpretation, positions him well for the Best Researcher Award. His expertise aligns with cutting-edge areas like object categorization and moving object tracking, essential topics in remote sensing and computer vision, which are currently high-impact fields in academia and industry.
Education:
- Sep. 2022 – Present: Ph.D. candidate in Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC).
- Sep. 2020 – Sep. 2022: Master of Engineering in Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC).
- Sep. 2016 – Jun. 2020: Bachelor of Engineering in Electronic Information Engineering, Sichuan University (SCU).
Work and Research Experience:
- Ph.D. Research (2022 – Present): Xiaowo Xu is currently pursuing a Ph.D. in Information and Communication Engineering at UESTC, focusing on deep learning applications in synthetic aperture radar (SAR) image intelligent interpretation. His research areas encompass object categorization, detection, instance segmentation, and moving object tracking using deep learning techniques.
- Master’s Research (2020 – 2022): During his master’s studies at UESTC, he deepened his expertise in information and communication engineering, developing skills in Python, MATLAB, and deep learning frameworks like PyTorch and TensorFlow.
- Academic Communication and Conferences (2022 – Present): Xiaowo Xu has presented his research through posters at prestigious IEEE conferences, including the International Geoscience and Remote Sensing Symposium and the Radar Conference. His work has been showcased internationally, including in the USA, Malaysia, and China.
Publication top Notes:
A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception
A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection
RBFA-Net: A Rotated Balanced Feature-Aligned Network for Rotated SAR Ship Detection and Classification
A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering
Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images