Xiaotian Dai
Papers
2
Total Citations
69
H-Index
2
About
Xiaotian Dai is a researcher whose work bridges the critical intersection of real-time systems and computer vision. His primary research areas include multiprocessor scheduling, directed acyclic graph (DAG) analysis, and edge-based detection algorithms. Dai's most significant contribution is his pioneering work on DAG scheduling for multiprocessor systems, where he addresses the challenge of exploiting parallelism while managing functional dependencies in complex real-time applications. His 2020 paper on this topic has garnered 66 citations, reflecting its substantial impact on the field of embedded and real-time computing. This work is particularly relevant as modern systems demand ever-higher performance from multiprocessor architectures. In a different vein, Dai has also contributed to computer vision with his "Line–Circle–Square (LCS)" geometric filter for edge-based detection, demonstrating his versatility across research domains. His achievements highlight a commitment to solving practical problems in both theoretical scheduling and applied perception, making his work valuable for students and researchers working on real-time systems, autonomous vehicles, and high-performance embedded computing.
Research Focus
Key Achievements
Top Papers
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