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An efficient spatio-temporal index for spatio-temporal query in wireless sensor networks
( Donhee Lee ),( Kyoungro Yoon ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10
Recent research into wireless sensor network (WSN)-related technology that senses various data has recognized the need for spatio-temporal queries for searching necessary data from wireless sensor nodes. Answers to the queries are transmitted from sensor nodes, and for the efficient transmission of the sensed data to the application server, research on index processing methods that increase accuracy while reducing the energy consumption in the node and minimizing query delays has been conducted extensively. Previous research has emphasized the importance of accuracy and energy efficiency of the sensor node's routing process. In this study, we propose an itinerary-based R-tree (IR-tree) to solve the existing problems of spatial query processing methods such as efficient processing and expansion of the query to the spatio-temporal domain.
UAV환경에서 스테레오 비전을 활용한 딥러닝 기반 시차맵 추정
이예지(Yegi Lee),윤경로(Kyoungro Yoon) 대한전기학회 2020 전기학회논문지 Vol.69 No.5
Recently, UAVs(Unmanned Aerial Vehicles) are used in various industries such as military, transportation, agriculture and reconnaissance. However, it is very likely to cause an accident such as a collision or fall, due to external environmental factors, and research is needed to increase safety. To prevent such risks, UAVs are often equipped with sensors such as laser scanners or cameras. But laser scanners are very heavy and consume high power. Stereo cameras are much lighter and use less power than laser scanners, making them ideal for use in small UAV environments. Therefore, in this paper, we introduce a method for estimating the disparity map using a stereo camera and deep learning without using a LiDAR(Lighting Detection And Ranging). The proposed method constructs semi-supervision based neural network to estimate disparity maps. This algorithm can estimate more precise disparity maps than existing matching algorithms.