Mobile and People-centric Systems and Sensing
The design of mobile devices that collect and reason over people-centric data raise a unique combination of sensing, networking and systems challenges. We study how to achieve high-fidelity privacy-preserving continuous mobile sensing of a rich cross-section of the lives of both individuals and groups (e.g., health, workplaces, social interactions). Primarily, this activity considers how such goals can be accomplished through a mix of innovation spanning: networking, protocols and cloud resources; learning and systems algorithms; and, hardware/sensor design.
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Chris Xiaoxuan Lu‚ Xuan Kan‚ Bowen Du‚ Changhao Chen‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni and John Stankovic
In The Web Conference (WWW). 2019.
Simultaneous Localization and Mapping with Power Network Electromagnetic Field
Chris Xiaoxuan Lu‚ Yang Li‚ Peijun Zhao‚ Changhao Chen‚ Linhai Xie‚ Hongkai Wen‚ Rui Tan and Niki Trigoni
In Annual International Conference on Mobile Computing and Networking (MobiCom). 2018.
DeepAuth: In−situ Authentication for Smartwatches via Deeply Learned Behavioural Biometrics
Chris Xiaoxuan Lu‚ Bowen Du‚ Peijun Zhao‚ Hongkai Wen‚ Yiran Shen‚ Andrew Markham and Niki Trignoni
In International Symposium on Wearable Computers (ISWC). 2018.