H
Hao-Hua Chu
Researcher at National Taiwan University
Publications - 142
Citations - 5727
Hao-Hua Chu is an academic researcher from National Taiwan University. The author has contributed to research in topics: Mobile computing & Ubiquitous computing. The author has an hindex of 43, co-authored 142 publications receiving 5535 citations. Previous affiliations of Hao-Hua Chu include NTT DoCoMo & University of Illinois at Urbana–Champaign.
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Patent
Layout of platform specific graphical user interface widgets migrated between heterogeneous device platforms
TL;DR: In this article, a scaleable graphical user interface system utilizes a device platform independent model to provide dynamic layout of graphical user interfaces migrated between any of a plurality of heterogeneous device platforms.
Patent
Dynamic adaptation of GUI presentations to heterogeneous device platforms
TL;DR: In this article, a system for dynamically adapting a presentation generated with a scalable application to a display screen of any of a plurality of heterogeneous device platforms is presented, which includes a device platform and a transformation module.
Proceedings ArticleDOI
Playful bottle: a mobile social persuasion system to motivate healthy water intake
Meng-Chieh Chiu,Shih-Ping Chang,Yu-Chen Chang,Hao-Hua Chu,Cheryl Chia-Hui Chen,Fei-Hsiu Hsiao,Ju-Chun Ko +6 more
TL;DR: Results from 7-week user study with 16 test subjects suggest that both hydration games are effective for encouraging adequate and regular water intake by users and that adding social reminders to the hydration game is more effective than system reminders alone.
Proceedings ArticleDOI
Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics
TL;DR: A sensor-assisted adaptation method that employs RFID sensors and environment sensors to adapt the location systems automatically to the changing environmental dynamics and shows that the proposed adaptive method can avoid adverse reduction in positioning accuracy under changed environmental dynamics.
Journal ArticleDOI
Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization
TL;DR: Experimental results demonstrate that the proposed learning method improves positional accuracy within 100 s of learning time, and was designed and implemented in a working WiFi positioning system and evaluated using different WiFi devices with diverse RSS signal patterns.