AquaScan, the first scanning sonar-based underwater sensing system for human activity monitoring. To
overcome the low frame rate due to the sonar’s physical
limitation, we propose a novel scanning strategy and apply
an image reconstruction method to accelerate the scanning
speed without compromising the performance of motion
detection
Argus, a wearable add-on system based
on stripped-down (i.e., compact, lightweight, low-power, limitedcapability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with
conventional frontal-view mmWave sensing solutions, it addresses
several pain points, such as restricted sensing range, occlusion, and
the multipath effect caused by surroundings.
The core concept of αLiDAR is to expand the operational freedom of a LiDAR sensor through the incorporation of a controllable, active rotational mechanism. This modification allows the sensor to scan previously inaccessible blind spots and focus on specific areas of interest in an adaptive manner.
Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges.
Argus, a wearable add-on system based
on stripped-down (i.e., compact, lightweight, low-power, limitedcapability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with
conventional frontal-view mmWave sensing solutions, it addresses
several pain points, such as restricted sensing range, occlusion, and
the multipath effect caused by surroundings.
Argus, a wearable add-on system based
on stripped-down (i.e., compact, lightweight, low-power, limitedcapability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with
conventional frontal-view mmWave sensing solutions, it addresses
several pain points, such as restricted sensing range, occlusion, and
the multipath effect caused by surroundings.
Argus, a wearable add-on system based
on stripped-down (i.e., compact, lightweight, low-power, limitedcapability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with
conventional frontal-view mmWave sensing solutions, it addresses
several pain points, such as restricted sensing range, occlusion, and
the multipath effect caused by surroundings.
ArtFL, a novel federated learning system designed to support dynamic runtime inference through multi-scale training. The key idea of ArtFL is to utilize the data resolution, i.e., frame resolution of videos, as a knob to accommodate dynamic inference latency requirements. Specifically, we initially propose data-utility-based multi-scale training, allowing the trained model to process data of varying resolutions during inference.
Mozart is a breakthrough mobile sensing system using Time-of-Flight (ToF) cameras to generate high-resolution maps in dark environments. By innovatively manipulating ToF phase components, it enhances texture information. Implemented on Android devices, Mozart operates in real-time, providing a cost-effective, high-performance solution for advanced sensing in the dark.
The core concept of VIMap is to exploit the unique cumulative observations made
by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with
on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map.