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	Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 卷号: 55, 期号: 1, 页码: 477-490 作者: Gao, Lun; Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Lu, Xiaoman \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2019/09/26 Disaggregation global regression strategy (GWS) land surface temperature (LST) local regression strategy (LWS) moving-window size (MWS) thermal remote sensing
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：57/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	Research on computer control strategy for optical electric tracking system (EI CONFERENCE) 会议论文 OAI收割 2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011, August 7, 2011 - August 10, 2011, Beijing, China 作者: Li M. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 In this paper mathematic model and computer control strategy for optical electrical tracking system have been researched and discussed. First the system's structure and its work process have been analyzed. Second according to the system's moving law the system's mathematic model has been built. As to the computer control strategy trigger guiding two closed loop PID control method as well as compound control method have been applied to satisfy the tracking accuracy. Third we apply the bilinear transformation to get the digital system and the sample time is 800Hz. The simulation results indicate that the maximum tracking error can be limited to less than 0.5 and the regulator time can be 0.04s. Then the control project resonance frequency should be more than 200Hz and the sample frequency should be more than 400Hz to meet the control accuracy requirement. So we can conclude that the system indexes such as swiftness high accuracy as long as real time have been satisfied. And the requirement of mechanical property has been present which is very useful to mechanical working. 2011 IEEE.
	boss: a moving strategy for mobile sinks in wireless sensor networks 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2009, 卷号: 5, 期号: 3, 页码: 173-184 Bi Yanzhong; Sun Limin; Li Na \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2011/03/18 sensor networks mobile sink moving strategy data-gathering applications network lifetime