Wireless Sensing

Wireless sensing leverages ambient radio waves, light, or other signals to passively monitor the environment and extract information about objects or activities without requiring direct contact. Current research focuses on improving energy efficiency through techniques like radio-frequency energy harvesting and in-sensor processing, as well as enhancing data reliability by addressing issues like package loss using advanced deep learning models (e.g., BERT, recurrent neural networks) and developing robust algorithms for handling missing data. These advancements are significant for applications ranging from healthcare monitoring and human-computer interaction to industrial automation and environmental sensing, offering improved accuracy, reduced power consumption, and enhanced privacy compared to traditional methods.