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CORC

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STABILITY AND ERROR ANALYSIS FOR A SECOND-ORDER FAST APPROXIMATION OF THE ONE-DIMENSIONAL SCHRODINGER EQUATION UNDER ABSORBING BOUNDARY CONDITIONS 期刊论文
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2018, 卷号: 40, 期号: 6
作者:  Li, Buyang;  Zhang, Jiwei;  Zheng, Chunxiong
收藏  |  浏览/下载：4/0  |  提交时间：2019/12/05
MAXIMAL FUNCTION CHARACTERIZATIONS OF MUSIELAK-ORLICZ-HARDY SPACES ASSOCIATED TO NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES 期刊论文
COMMUNICATIONS ON PURE AND APPLIED ANALYSIS, 2016, 卷号: 15, 期号: 1-6, 页码: 2135-2160
作者:  Yang, DC;  Yang, SB
收藏  |  浏览/下载：6/0  |  提交时间：2017/05/09
Comparing three methods for solving probabilistic optimal power flow 期刊论文
ELECTRIC POWER SYSTEMS RESEARCH, 2015, 卷号: 124, 页码: 92-99
作者:  Xiao, Qing[1]
收藏  |  浏览/下载：4/0  |  提交时间：2019/04/26
Gaussian-mixture probability hypothesis density filter for multiple extended targets 期刊论文
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2014, 卷号: 48, 期号: [db:dc_citation_issue], 页码: 95-101
作者:  Han, Yulan;  Zhu, Hongyan;  Han, Chongzhao;  Wang, Jing
收藏  |  浏览/下载：5/0  |  提交时间：2019/12/03
A method by using Gaussian estimation for the semi-parametrical panel data models 会议论文
作者:  Yan, G. Y.;  Liu, L. Q.
收藏  |  浏览/下载：2/0  |  提交时间：2019/12/05
Stochastic differential equations with coefficients in Sobolev spaces 期刊论文
JOURNAL OF FUNCTIONAL ANALYSIS, 2010, 卷号: 259, 期号: 5, 页码: 1129-1168
作者:  Fang, Shizan;  Luo, Dejun;  Thalmaier, Anton
收藏  |  浏览/下载：13/0  |  提交时间：2018/07/30
Speech enhancement algorithm based on spectral subtraction 期刊论文
2010, 2010
Li Ye; Cui Huijuan; Tang Kun
收藏  |  浏览/下载：3/0
Feedback frequency domain equalization for approximate parallel receiver 期刊论文
2010, 2010
Dang Xiaochuan; Wu Youshou; Fu Jian
收藏  |  浏览/下载：2/0
Phase error estimation with broadband white light by Phase Diversity (EI CONFERENCE) 会议论文
6th International Symposium on Precision Engineering Measurements and Instrumentation, August 8, 2010 - August 11, 2010, Hangzhou, China
Wu Y.; Wang B.; Wang Z.; Cao J.; Zhang X.
收藏  |  浏览/下载：13/0  |  提交时间：2013/03/25
The technique of Phase Diversity (PD) is widely adopted to measure the wavefront error caused by atmosphere turbulence and system error. PD solves the Zernike coefficients of the wavefront by utilizing two images obtained with different defocus value. In this paper  we propose a method to restore the image and estimate the wavefront error of an imaging system. And we use present white noises to verify the robustness of the algorithm. We design an experiment system and implement it in our laboratory. The proposed algorithm is validated by computer simulations and experimental results. To use a broadband white object in experimental system a reasonable simplification for the system model is done. Our results show that the robustness against Gaussian white noises of the method is better then the case when the variance value is 0.03. The proposed method can be used as a wavefront sensor and restore the degradative images by photoelectric image system. 2010 SPIE.  
CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
Wang J.-L.; Wang Y.-P.; Li D.-Y.; Li S.-W.; Kui H.-L.
收藏  |  浏览/下载：17/0  |  提交时间：2013/03/25
In the procedure of computed radiography imaging  we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing  we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail  the author think that the major two noises are Gaussian white noise and Poisson noise. Then  the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images  a multiscale image filtering algorithm  which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model  has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix  then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM  according to scale size  and achieve the experiments as well as the comparison with other denoising methods were presented at last.  

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