Papers
6
Total Citations
67
H-Index
4
About
Shuxue Ding is a pioneering researcher in machine learning and robotics, with a primary focus on environmental sound recognition and intelligent robotic systems. His groundbreaking work on environmental sound recognition using multilayered neural networks (2004, 26 citations) established foundational methods for robotic audition, addressing the challenge of recognizing diverse sounds without perfect databases. He further advanced this field by developing time-frequency intersection patterns (2012, 11 citations), which combine instantaneous power and frequency features for robust sound classification in robots and intelligent systems. Ding has also made significant contributions to device-free localization (DFL) in IoT environments (2020, 20 citations), enabling target detection without wearable devices—critical for applications like intrusion detection and mobile robot localization. His recent work includes innovative path planning methods using extended random artificial potential fields (2023) and visual positioning for nasal swab robots through hierarchical decision-making (2023), demonstrating his ongoing impact in medical robotics. With over 67 citations across his most-cited works, Ding’s research continues to shape the intersection of machine learning, robotics, and IoT, offering practical solutions for real-world sensing and automation challenges.
Research Focus
Key Achievements
Top Papers
- 1Environmental sound recognition by multilayered neural networks26 citations · 2004
- 2
- 3Environmental Sound Recognition Using Time-Frequency Intersection Patterns11 citations · 2012
- 4
- 5Path Planning Method Based on Extended Random Artificial Potential Field3 citations · 2023
- 6Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision3 citations · 2023