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Wang-Chien Lee
Researcher at Pennsylvania State University
Publications - 367
Citations - 15328
Wang-Chien Lee is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Mobile computing. The author has an hindex of 60, co-authored 366 publications receiving 14123 citations. Previous affiliations of Wang-Chien Lee include Ohio State University & Verizon Communications.
Papers
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Proceedings ArticleDOI
A fully distributed spatial index for wireless data broadcast
Wang-Chien Lee,Baihua Zheng +1 more
TL;DR: Results shows that DSI significantly out-performs R-tree and Hilbert curve index, two state-of-the-art spatial indexing techniques for wireless data broadcast.
Proceedings ArticleDOI
Patent Citation Recommendation for Examiners
TL;DR: An automatic and effective system of patent citation recommendation for patent examiners based on a two-phase ranking approach, which shows that both bibliographic informationand applicant citation information are very useful for examiner citation recommendation, and that the approach significantly outperforms a search engine.
Proceedings ArticleDOI
On theme location discovery for travelogue services
TL;DR: This paper develops a travelogue service that discovers and conveys various travelogue digests, in form of theme locations, geographical scope, traveling trajectory and location snippet, to users and explores the textual and geographical features of locations to develop a co-training model for enhancement of classification performance.
Journal ArticleDOI
Materialized In-Network View for spatial aggregation queries in wireless sensor network
TL;DR: A Materialized In-Network View (MINV) framework is proposed that precalculates aggregated data from clusters of sensor nodes as intermediate query results preserved in the network and made ready for queries, thus prolonging the lifetime of a sensor network.
Proceedings ArticleDOI
Adaptive Lightweight Regularization Tool for Complex Analytics
TL;DR: This paper proposes a general adaptive regularization method based on Gaussian Mixture to learn the best regularization function according to the observed parameters, and develops an effective update algorithm which integrates Expectation Maximization with Stochastic Gradient Descent.