Yun-Shen Lin
Papers
1
Total Citations
32
H-Index
1
About
Yun-Shen Lin is a leading researcher in visible light positioning (VLP) systems, where his work bridges optical communications and machine learning to achieve unprecedented indoor localization accuracy. His most cited paper, "Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP)" (2021, 32 citations), introduces a novel approach that divides large positioning areas into smaller unit cells, dramatically reducing training time and computational complexity. By combining residual concatenation neural networks with transfer learning, Lin's method enables high-precision positioning without the need for exhaustive data collection across entire spaces—a critical advancement for practical VLP deployment in smart buildings, warehouses, and autonomous navigation. His work directly addresses the scalability challenge that has limited VLP's real-world adoption, demonstrating that machine learning can overcome traditional trade-offs between accuracy and efficiency. With 32 citations in just a few years, Lin's contributions are already shaping how researchers approach indoor positioning systems, offering a blueprint for combining deep learning with optical wireless technologies. His innovative use of transfer learning to adapt models across different unit cells marks a significant step toward plug-and-play VLP solutions.
Research Focus
Key Achievements
Top Papers
- 1