Browsing by Author "Chang, Chun-Ting"

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A dynamic paging scheme for long-term evolution mobility management
(2015-03-01)
Lin, Yi-Bing
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Liou, Ren-Huang
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Chang, Chun-Ting
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資訊工程學系
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Department of Computer Science
In long-term evolution, the service area is partitioned into several tracking areas (TAs), which comprise one or more cells (the radio coverages of base stations). The TAs are grouped into TA list (TAL). When an incoming call arrives, the network attempts to connect to the user equipment (UE) by paging the cells in the UE's TAL, which may incur large paging traffic that significantly consumes the limited radio resources. To resolve this issue, this paper proposes a dynamic paging scheme that determines the paging sequence of cells in real time according to the UE movement and call behavior. We compare the performance of the dynamic paging with that of the previously proposed Cell-TA-TAL (CTT) paging. Our study indicates that the dynamic paging outperforms the CTT paging when movement pattern is regular and the UE moves frequently. Copyright (c) 2013 John Wiley & Sons, Ltd.
Appliance Control by TDoA-based Localization and Gesture Recognition
(2018-01-01)
Chiang, Ting-Hui
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Chen, Yan-Ann
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Chang, Chun-Ting
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Chen, Ling-Jyh
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Tseng, Yu-Chee
Smart home is one of the major Internet of Things (IoT) applications. When realizing a smart home, designing an intuitive interaction for appliance control has become a crucial issue. A control intention to an appliance comprises two main meanings, identification and control command. But, previous works seldom deliver these two meanings at the same time. To fully convey the control intention, this paper proposes a scheme of intuitive appliance control that tracks and recognizes hand moving trajectory and hand gestures respectively by exploiting acoustic information. We utilize a 3D positioning method based on time difference of arrival (TDoA) of acoustic signals to detect the trajectory of a hand movement for appliance identification and design a gesture recognition method by Doppler effect for control command. We validate the effectiveness of the proposed scheme via real-life experiments. The results show that average locating errors to the targets are 5.24 degrees and 10.35 degrees in the horizontal and vertical planes respectively and the accuracy of gesture recognition is 96.66%. Since human's eyesight is horizontal to the ground, locating an object in the vertical view is error-prone. We also provide a scroll gesture to assist the appliance selection.