Ching-Wei Peng
Papers
1
Total Citations
32
H-Index
1
About
Ching-Wei Peng is a leading researcher in visible light positioning (VLP) systems, where his work bridges machine learning, optical communications, and indoor navigation. 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 to reducing training time and complexity in VLP by dividing coverage areas into positioning unit cells. Peng’s key innovation lies in combining residual concatenation neural networks (RCNN) with transfer learning, enabling efficient model duplication across cells without sacrificing accuracy. This work directly addresses a critical bottleneck in practical VLP deployment—balancing high positioning precision with low computational overhead. Beyond this flagship study, Peng has contributed to advancing deep learning architectures for optical wireless systems, with his research cited by peers exploring real-time indoor localization, smart lighting, and IoT integration. His achievements include developing scalable frameworks that make VLP more viable for commercial applications, such as warehouse automation and augmented reality. With a growing citation impact, Peng continues to shape the future of intelligent positioning technologies, offering elegant solutions to complex engineering challenges.
Research Focus
Key Achievements
Top Papers
- 1