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.
DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments.
VILAM, a novel framework that leverages intelligent roadside infrastructures to realize high-precision and globally consistent localization and mapping on autonomous vehicles. VILAM utilize the scene measurement from the infrastructure to correct errors in the vehicle map.
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.
BeamSense adopts a novel multi-path estimation algorithm that can efficiently and accurately map bidirectional CBR to a multi-path channel based on intrinsic fingerprints. We implement BeamSense on several prevalent models of Wi-Fi devices and evaluated its performance with microbenchmarks and three representative Wi-Fi sensing applications.
EdgeFM, a novel edge-cloud cooperative system with open-set recognition capability. EdgeFM selectively uploads unlabeled data to query the FM on the cloud and customizes the specific knowledge and architectures for edge models. Meanwhile, EdgeFM conducts dynamic model switching at run-time taking into account both data uncertainty and dynamic network variations, which ensures the accuracy always close to the original FM.
Harmony, a new system for heterogeneous multi-modal federated learning. Harmony disentangles the multi-modal network training in a novel two-stage framework, namely modality-wise federated learning and federated fusion learning.
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.