Browsing by Author "Lin, Jun-Han"

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ArduTalk: An Arduino Network Application Development Platform Based on IoTtalk
(2019-03-01)
Lin, Yun-Wei
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Lin, Yi-Bing
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Yang, Ming-Ta
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Lin, Jun-Han
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資訊工程學系
Several tools provide popular solutions for creating innovative Internet of Things (IoT) applications on single Arduino board. However, interactions among multiple Arduino boards need significant effort to be established. This paper proposes ArduinoIo-Ttalk (ArduTalk), a graphical user interface (GUI)-based platform aims to develop IoT network applications for interaction among multiple Arduino boards. ArduTalk utilizes IoTtalk, an IoT device management platform for quickly establishing connections and meaningful interactions between IoT devices without concerning the lower layer communication protocols. In this paper, the IoTtalk GUI has been significantly enhanced for ArduTalk. By integrating Arduino with the enhanced IoTtalk, ArduTalk allows a user to arbitrarily link and relink sensors to actuators without or with little programming effort, and quickly generate Arduino applications for different purposes. We conduct measurements to investigate the time complexity for data delivery among multiple Arduino boards, and propose a damping mechanism to address the fairness issue caused by the discrepancies between local and remote delays among the Arduino boards.
UP-Miner: A Utility Pattern Mining Toolbox
(2015)
Tseng, Vincent S.
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Wu, Cheng-Wei
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Lin, Jun-Han
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Fournier-Viger, Philippe
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資訊工程學系
Utility pattern mining has received extensive attentions in recent years due to the wide and novel applications in various fields like e-commerce, Web mining, finance, biomedicine, etc. However, there exists not yet a toolbox for utility pattern mining so far. In this work, we address this issue by proposing a first-of-its-kind toolbox named UP-Miner (Utility Pattern Miner) that provides various functions for utility pattern mining. The main merits of UP-Miner lie in three aspects: First, it offers implementations of thirteen state-of-the-art algorithms for efficiently mining different types of utility patterns, such as high utility itemsets, high utility episodes and utility-based sequential patterns, as well as four functionalities for processing utility-based databases. Second, it is a cross-platform system implemented in Java with a user-friendly graphical interface. Third, the toolbox and relevant materials, including source codes, benchmark datasets and data generators, are made public on Web (http://bigdatalab.cs.nctu.edu.tw/software.htm) for benefiting the research community.