Zhendong Yin
Papers
2
Total Citations
13
H-Index
2
About
Zhendong Yin is a researcher whose work bridges the critical domains of navigation systems and embedded deep learning. His research primarily focuses on improving the reliability and performance of integrated navigation technologies, specifically GPS/INS systems, and advancing the deployment of artificial intelligence on resource-constrained hardware. A key contribution is his development of a filter algorithm for GPS/INS integrated navigation using an Interacting Multiple Model Adaptive Filter (IMM-AF). This work, cited 8 times, directly addresses a fundamental challenge: the unreliability of GPS measurement information in Kalman Filter-based systems, offering a more robust solution for maintaining navigation accuracy when GPS signals are lost or degraded. In the field of embedded AI, Yin has made notable strides with his work on scalable FPGA-based accelerators for Convolutional Neural Networks (CNNs). This research, cited 5 times, tackles the computational bottleneck of deploying complex deep learning models for tasks like image classification onto embedded systems, demonstrating a practical path toward efficient, real-time edge computing. Through these contributions, Yin is shaping more reliable navigation and smarter, faster embedded systems.
Research Focus
Key Achievements
Top Papers
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- 2