activity
20142023
most citedNILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

500 citations · 523 across the 8 of their papers we have counts for

collaborators

17 papers

eess.SP2024

Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer

Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +5

Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing a…

eess.SP20249 cited

Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data

Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +2

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a pr…

cs.CV2024

FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors

Jason Wu, Ziqi Wang, Xiaomin Ouyang +5

Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sen…

cs.CV20241 cited

Principles of Designing Robust Remote Face Anti-Spoofing Systems

Xiang Xu, Tianchen Zhao, Zheng Zhang +4

Protecting digital identities of human face from various attack vectors is paramount, and face anti-spoofing plays a crucial role in this endeavor. Current approaches primarily foc…

cs.LG2024

On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study

Tomoyoshi Kimura, Jinyang Li, Tianshi Wang +9

This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a cla…

cs.AI20243 cited

LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Traces

Xiaomin Ouyang, Mani Srivastava

Most studies on machine learning in sensing systems focus on low-level perception tasks that process raw sensory data within a short time window. However, many practical applicatio…