Chi‐Wai Chow

About

Chi-Wai Chow is a leading researcher in the field of optical wireless communications, with a primary focus on visible light positioning (VLP) and indoor navigation systems. His major contributions center on the integration of advanced machine learning techniques to overcome fundamental challenges in VLP accuracy and real-time performance. Notably, he pioneered the use of Residual Concatenation Neural Networks (RCNN) with transfer learning to reduce training complexity in large-area positioning, and introduced the Two-Stage Neural Network (TSNN) with Received Intensity Selective Enhancement (RISE) to achieve the first-ever demonstration of a 3D visible light-based indoor positioning system. His work has garnered significant attention, with his most cited paper accumulating 32 citations, and his real-time VLP system employing Long Short-Term Memory (LSTM) networks with Principal Component Analysis (PCA) represents a critical advancement for emerging applications in augmented reality, IoT, and autonomous mobile robotics. Through these innovations, Chow has established himself as a key figure in making high-precision, real-time indoor positioning a practical reality for next-generation smart environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP)

   32 citations · 2021
2. 2
   3D Visible Light-Based Indoor Positioning System Using Two-Stage Neural Network (TSNN) and Received Intensity Selective Enhancement (RISE) to Alleviate Light Non-Overlap Zones

   19 citations · 2022
3. 3
   Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal Component Analysis (PCA)

   13 citations · 2024

Key Collaborators

Contact & Links