Wen-Yuah Shih

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The global positioning system (GPS) is widely used for localization. However, GPS does not perform well in an indoor environment due to that it is hard to receive satellite signal inside a building. A huge body of work utilized signal strength of short range signal (such as WiFi, Bluetooth, ultra sound or Infrared) to build a radio map for indoor(More)
We implement pedestrian dead reckoning (PDR) for indoor localization. With a waist-mounted PDR based system on a smart-phone, we estimate the user’s step length that utilizes the height change of the waist based on the Pythagorean Theorem. We propose a zero velocity update (ZUPT) method to address sensor drift error: Simple harmonic motion and a low-pass(More)
Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive.(More)
A huge body of work utilized signal strength of short range signal (such as WiFi, Bluetooth, ultra sound or Infrared) to build a radio map for indoor localization, by deploying a great number of beacon nodes in the building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system is costly and(More)
In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q, l,w,n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several(More)
With the advances of video streaming technology, live streaming services also become more and more popular. However, there may be thousands of online channels at the same time, and most viewers are not willing to go through one by one for finding the channels they are interested in. In view of this, we propose a hybrid preference-aware recommendation(More)
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