S
Shaowei Lin
Researcher at Singapore University of Technology and Design
Publications - 53
Citations - 2225
Shaowei Lin is an academic researcher from Singapore University of Technology and Design. The author has contributed to research in topics: Wireless sensor network & Marginal likelihood. The author has an hindex of 18, co-authored 53 publications receiving 1829 citations. Previous affiliations of Shaowei Lin include University of California, Berkeley & Institute for Infocomm Research Singapore.
Papers
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Journal ArticleDOI
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
TL;DR: An extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in WSNs is presented and a comparative guide is provided to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.
Journal ArticleDOI
Mobile big data analytics using deep learning and apache spark
TL;DR: An overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark that speeds up the learning of deep models consisting of many hidden layers and millions of parameters.
Posted Content
Deep Activity Recognition Models with Triaxial Accelerometers
TL;DR: This paper shows that deep activity recognition models provide better recognition accuracy of human activities, and avoid the expensive design of handcrafted features in existing systems, and utilize the massive unlabeled acceleration samples for unsupervised feature extraction.
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Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey
TL;DR: This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs, and various solution methods are discussed and compared to serve as a guide for using MDPs in W SNs.
Journal ArticleDOI
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
TL;DR: In this article, an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem.