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.
VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects’ lean representations as well as their relationships.
Neiwen Ling, Xuan Huang, Zhihe Zhao, Nan Guan, Zhenyu Yan and Guoliang Xing. BlastNet: Exploiting Duo-Blocks for Cross-Processor Real-Time DNN…