Jie Yang

TL;DR: This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops) in a low-cost system that can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles.

TL;DR: A peer assisted localization approach is proposed that can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

TL;DR: The extensive experiments demonstrate that the system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing non-invasive, continuous fine-grained vital signs monitoring without any additional cost.

TL;DR: This work studies the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage and formalizes the problem and develops efficient algorithms to identify the optimal meter set.

TL;DR: Since images of faces often belong to a manifold of intrinsically low dimension, the LLTSA algorithm for effective face manifold learning and recognition is developed, which achieves much higher recognition rates than a few competing methods.