Xiaojun Zhai
Papers
6
Total Citations
120
H-Index
5
About
Xiaojun Zhai’s research bridges the critical gap between intelligent systems and real-world deployment, with a focus on embedded computing, robotics, and healthcare technology. His work on moving object tracking in clinical scenarios—applied to cardiac surgery and cerebral aneurysm clipping—has garnered 49 citations, demonstrating its impact on surgical precision. Zhai has also advanced autonomous robotics, developing methods to enhance mobile robot localization in distributed sensor environments (22 citations) and analyzing gamma-induced image degradation for nuclear site inspection (15 citations). A key contribution is his reliability-aware scheduling approach (RASA) for FPGA-based resilient embedded systems in extreme environments (17 citations), which ensures robust performance in harsh conditions. Additionally, his work on FPGA-based dynamic deep learning acceleration enables real-time video analytics, pushing the boundaries of edge AI. As editor of a special issue on intelligent IoT systems for healthcare and rehabilitation, Zhai has shaped discourse at the intersection of technology and medicine. His research consistently addresses real-world challenges—from the operating room to nuclear facilities—making him a leading figure in dependable, high-performance embedded systems for critical applications.
Research Focus
Key Achievements
Top Papers
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