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	Separating Scholte Wave and Body Wave in OBN Data Using Wave-Equation Migration 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 13 作者: Li, Chao; Wang, Yuan; Zhang, Jinhai; Geng, Jianhua; You, Qingyu \| 收藏 \| 浏览/下载：58/0 \| 提交时间：2022/07/18 Surface waves Sea surface Filtering Imaging Cutoff frequency Background noise Underwater vehicles Body wave demigration migration ocean bottom node (OBN) Scholte wave wavefield separation
	3D Vehicle Detection With RSU LiDAR for Autonomous Mine 期刊论文 OAI收割 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2021, 卷号: 70, 期号: 1, 页码: 344-355 作者: Wang, Guojun; Wu, Jian; Xu, Tong; Tian, Bin \| 收藏 \| 浏览/下载：34/0 \| 提交时间：2021/03/29 Laser radar Three-dimensional displays Filtering Vehicle detection Detectors Roads Feature extraction Background filtering 3D object detection deep learning roadside LiDAR point cloud
	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：59/0 \| 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).
	Speech signal enhancement through wavelet domain MMSE filtering (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Fenghua Z.; Le Y.; Jian W.; Qiang S. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 A new speech enhancement system that combine robust signal enhancement and minimum signal distortion is proposed in this paper. The proposed method introduces frequency depended parametric MMSE filtering techniques that involve wavelet packets. Voice activity detection (VAD) is used to further distinguish speech from noise and help to adaptively remove noise components from color noise eruptive noisy speech while perceptual criteria is also taken into account. Experimental results and objective quality measurement test results validate the proposed speech enhancement system and illustrate the benefit of the proposed wavelet domain MMSE filtering as an excellent speech enhancement method to provide sufficient noise reduction and good intelligibility and perceptual quality without causing considerable signal distortion and musical background noise method. 2010 IEEE.
	Real-time motive vehicle detection with adaptive background updating model and HSV colour space (EI CONFERENCE) 会议论文 OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China Rong-Hui Z.; Bai Y.; Hong-guang J.; Chen T. 收藏 \| 浏览/下载：67/0 \| 提交时间：2013/03/25 In the transportation monitor system we set up the area of interest (AOI) of the vehicle model and adjust the size of AOI dynamically in order to track vehicle accurately. The results of experiment show that motive vehicle detection by adopting digital image is one of key technologies. To detect motive vehicle accurately the arithmetic proposed in the paper can suppress shadow availably we establish an adaptive background updating model firstly. Noise is suppressed by using modality filter detect motive vehicle accurately and satisfy real-time motive vehicle tracking. 2009 SPIE. and we obtain binary image by using maximum entropy to choose dynamic adaptive threshold. Based on positive information of shadow and aspect feature of motive vehicle we adopt HSV colour space and double threshold to solve the problem of vehicle shadow. According to prediction result of Kalman filtering