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	IPIML: A Deep-Scan Earthquake Detection and Location Workflow Integrating Pair-Input Deep Learning Model and Migration Location Method 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 卷号: 61, 页码: 9 作者: Mohammadigheymasi, Hamzeh; Shi, Peidong; Tavakolizadeh, Nasrin; Xiao, Zhuowei; Mousavi, S. Mostafa \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2024/01/02 Earthquake detection and location pair-input deep learning (PIDL) waveform migration location (MIL) method
	The capability analysis of the bistatic radar system based on Tianlai radio array for space debris detection 期刊论文 OAI收割 ADVANCES IN SPACE RESEARCH, 2019, 卷号: 64, 期号: 9, 页码: 1652-1661 作者: Li, Jiawei; Gao, Pengqi; Shen, Ming; Zhao, You \| 收藏 \| 浏览/下载：23/0 \| 提交时间：2020/03/10 Tianlai radio array Space debris The bistatic radar system Detection and location method
	A sub-pixel extraction method for the center of the circular structure from spot array calibration target (EI CONFERENCE) 会议论文 OAI收割 2012 3rd International Conference on Information Technology for Manufacturing Systems, ITMS 2012, September 8, 2012 - September 9, 2012, Qingdao, China 作者: Liu Y.; Zhang T.; Yang H.; Gao X.; Liu Y. 收藏 \| 浏览/下载：78/0 \| 提交时间：2013/03/25 The imaging measurement system must be calibrated before application. In calibration procedure sub-pixel center extraction is the crucial step for the final accuracy. In this paper a new sub-pixel extraction method is proposed. Edge detection at pixel level is obtained using LOG operator then sub-pixel edge detection is realized using the Facet model. Finally the least squares ellipse fitting is operated on the sub-pixel edge to determine the sub-pixel center. Experiment on a series of simulated images indicates that this algorithm is able to realize the center location accuracy of 0.01-0.02 pixel and meets the requirement of sub-pixel level. (2012) Trans Tech Publications Switzerland.
	Study particle filter tracking and detection algorithms based on DSP signal processors (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 Dong Y.; Chuan W. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 In Video tracking detection and tracking usually need two algorithms. The process is complex and need much time which detection and tracking are. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance flag and of location. Particle filter-based method implements detection and tracking at one time. In order to reduce process time and think of pixel position in tracking field feature histogram of luminance is as observe vector and used posterior estimate. In this paper the luminance component is derived and target is recognized and tracked through image processor based on DSP in order to implementing real-time. The experimental results confirm that method can detect and track the object in real-time successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2010 IEEE.
	Study on color image tracking and detection algorithms based on particle filter (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, June 17, 2009 - June 19, 2009, Beijing, China Wu C.; Sun H.-J.; Yang D. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 In Video tracking detection and tracking need two algorithms. The process is complex and need much time which detection and tracking is. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance and of location. Particle filter-based method implements detection and tracking. In order to reduce process time and think of pixel position in tracking field feature histogram of color-based is as observe vector and used posterior estimate. The experimental results confirm that method can detect and track object in 17.68ms successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2009 SPIE.
	Multiwavelet based multispectral image fusion for corona detection (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang X.; Yang H.-J.; Sui Y.-X.; Yan F.; Yan F. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 Image fusion refers to the integration of complementary information provided by various sensors such that the new images are more useful for human or machine perception. Multiwavelet transform has simultaneous orthogonality symmetry compact support and vanishing moment which are not possible with scalar wavelet transform. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. In this paper a new image fusion algorithm based on discrete multiwavelet transform (DMWT) to fuse the dual-spectral images generated from the corona detection system is presented. The dual-spectrum detection system is used to detect the corona and indicate its exact location. The system combines a solar-blind UV ICCD with a visible camera where the UV image is useful for detecting UV emission from corona and the visible image shows the position of the corona. The developed fusion algorithm is proposed considering the feature of the UV and visible images adequately. The source images are performed at the pixel level. First a decomposition step is taken with the DMWT. After the decomposition step a pyramid for each source image in each level can be obtained. Then an optimized coefficient fusion rule consisting of activity level measurement coefficient combining and consistency verification is used to acquire the fused coefficients. This process reduces the impulse noise of UV image. Finally a new fused image is obtained by reconstructing the fused coefficients using inverse DMWT. This image fusion algorithm has been applied to process the multispectral UV/visible images. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach.