Browsing by Author "Lee, Tsu-Kuang"

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ID-Free Multigroup Cardinality Estimation for Massive RFID Tags in IoT
(2019-01-01)
Lee, Tsu-Kuang
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Chen, Chih-Chieh
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Ren, Yi
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Lin, Cheng-Kuan
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Tseng, Yu-Chee
The Internet of Things (IoT) has become the hottest in both the research community and industry. Among them, Radio Frequency Identification (RFID) plays a key role in IoT. On the RFID tags estimation problem, most existing researches are trying to identifying tags' ID rather than counting the number of tags. But the number of tags is useful information in many applications such as stock management and traffic flow management. Massive tags cause taking a lot of cost and time in the estimate. So an essential problem is how to quickly and accurately estimate the number of massive tags. In order to solve this problem, this paper proposes an accuracy and efficiency hybrid scheme by decreasing time and space complexity. The results of simulation conducted to test the effectiveness of the proposed approach, which matches well with the theoretical analytical model.
Using V2X communications and data fusion to achieve lane-level positioning for road vehicles
(2020-01-01)
Lee, Tsu-Kuang
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Lin, Juyi
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Chen, Jen-Jee
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Tseng, Yu-Chee
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資訊工程學系
Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While global positioning system (GPS) is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using particle filter (PF), which is flexible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: controller area network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localisation with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.
Wireless Location Tracking by a Sensor-Assisted Particle Filter and Floor Plans in a 2.5-D Space
(2018-01-01)
Lo, Chi-Chung
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Chiang, Ting-Hui
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Lee, Tsu-Kuang
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Chen, Ling-Jyh
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Tseng, Yu-Chee
Indoor localization systems have attracted considerable attention recently. A lot of works have used wireless signals from existing base stations to track users' locations. The major challenge to such systems is the signal-drifting problem. A promising direction to conquer this problem is to fuse the tracked wireless signals with inertial sensing data. In this work, we consider location tracking in a multi-floor building, which we call a 2.5-D space, by taking wireless signals, inertial sensing data, and indoor floor plans of a 2.5-D space as inputs and building a SPF (sensor-assisted particle filter) model to fuse these data. Inertial sensors are to capture human mobility, while particles reflect our belief of the user's potential locations. Our work makes the following contributions. First, we propose a model to partition a 2.5-D space into multiple floors connected by stairs and elevators and further partition each floor, according to its floor plan, into logical units connected by passages. Second, based on the 2.5D space model, we then propose particle sampling and resampling mechanisms over the logical units using wireless signal strengths and inertial sensing data to adjust our beliefs of the user's potential locations. Third, to conquer the signal-drifting problem, we propose a weighting mechanism to control the distribution of particles based on user's activities of walking on grounds/stairs and taking elevators. A prototype has been developed and tested to verify the model and its accuracy.