The BubCam system presented in this paper estimates the volume of the air bubbles in the ink bags moving on the conveyor belt of the manufacturing line. It employs one main camera and several wireless cameras, whose operations are controlled by a deep reinforcement learning agent. BubCam achieves 1.34 accuracy improvement and 260x latency reduction, compared with manual inspection.

This paper associates the sensing results of mmWave radar and user-held IMUs to track multiple users moving in the same space and estimate the interpersonal distances.

This paper uses a smartphone’s loudspeaker to emit near-inaudible chirps and its microphone to record the acoustic echoes from the indoor environment for localizing the smartphone. Contrasive learning is applied to learn effective location-discriminative features for constructing a SLAM system.

The PriMask system uses personalized lightweight neural networks to obfuscate the data generated by individual users to protect their privacy against curious cloud inference services. At the same time, the obfuscation does not degrade the original inference service quality.

The Telesonar system employs an active sensing approach to probe the existence of the echo channel at the remote end of a phone call as well as a passive sensing approach to analyze the breath timing. This helps decide whether the call is a robocall.