Xiaoyue Xu
Papers
1
Total Citations
2
H-Index
1
About
Xiaoyue Xu is a researcher whose work bridges artificial intelligence, robotics, and biomedical signal processing. Her most-cited paper, "Real-time Obstacle Avoidance using Supervised Recurrent Neural Network with Automatic Data Collection and Labeling," demonstrates her core contribution: developing intelligent systems that learn autonomously from real-world data. This work, with 2 citations, introduces a novel framework where recurrent neural networks are trained using automatically collected and labeled data, enabling mobile robots to navigate dynamic environments without manual intervention. Xu’s research integrates deep learning techniques—including convolutional and recurrent neural networks—with practical applications in robotics and healthcare, such as brain-computer interfaces and electroencephalography analysis. Her approach emphasizes efficiency and scalability, reducing the need for human labeling while improving real-time performance. Though early in her career, Xu’s work has been recognized for its potential to advance autonomous systems and medical signal processing. Her contributions are particularly relevant for students and researchers interested in the intersection of machine learning, robotics, and neurotechnology, offering a pathway toward more adaptive and intelligent machines.
Research Focus
Key Achievements
Top Papers
- 1