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About

Wei Xi is a researcher advancing the frontiers of sensing and localization, with key contributions in radio-frequency identification (RFID), depth estimation, and non-invasive fall detection. His most influential work, "Trio: Utilizing Tag Interference for Refined Localization of Passive RFID" (2018, 47 citations), introduced a novel approach to high-precision object positioning within small regions like tabletops—critical for cyber-physical systems in industrial automation. By harnessing tag interference, Xi’s method achieves refined localization without expensive hardware, offering a cost-effective solution for smart environments. More recently, he has tackled challenges in autonomous driving and robotic perception through "Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion" (2024, 7 citations), improving depth accuracy by addressing sparse and non-uniform LiDAR data. Xi also addresses health-tech needs with "mm-Fall: Practical and Robust Fall Detection via mmWave Signals" (2025, 4 citations), leveraging millimeter-wave signals for privacy-preserving, non-wearable fall detection in elderly care. His work consistently bridges theory and application, earning recognition for practical, robust sensing systems. With a growing citation footprint and a focus on real-world impact, Xi is shaping the next generation of ubiquitous computing and human-centric IoT technologies.