Ultra-Fast Device-Free Visible Light Sensing and Localization via Reflection-Based RSS and Deep Learning
Abstract
We propose an Ultra-Fast, Device-Free Visible Light Sensing and Positioning system that captures spatiotemporal variations in single-LED VLC channel responses, using ceiling-mounted photodetectors, to accurately and non-intrusively infer human presence and position through optical signal reflection modeling. The system is highly adaptive and ready to serve different real-world sensing and positioning scenarios using one or more ML based models from the library of multi-architecture deep neural network ensembles we have developed.
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