中国科学院机构知识库网格系统: 检索

浏览/检索结果: 共8条，第1-8条

帮助

条数/页：排序方式：
	Downhole microseismic signal recognition and extraction based on sparse distribution features 期刊论文 OAI收割 CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2016, 卷号: 59, 期号: 10, 页码: 3869-3882 作者: Li Wen; Liu Yi-Ke; Liu Bao-Jin \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2017/11/20 Microseismic monitoring Hydraulic fracturing Sparse distribution feature Signal recognition and extraction Wavelet transform De-noise
	Removing Random-Valued Impulse Noises by a Two-Staged Nonlinear Filtering Method 期刊论文 OAI收割 Transactions of Nanjing University of Aeronautics and Astronautics, 2016, 卷号: 33, 期号: 3, 页码: 329-338 作者: Ahmad Ashfaq; Lu Yanting 收藏 \| 浏览/下载：17/0 \| 提交时间：2017/01/17 image de-noising random-valued impulse noise nonlinear filter noisy pixel detection two-stage detection and correction method directional differences
	A review on the applications of wavelet transform in hydrology time series analysis SCI/SSCI论文 OAI收割 2013 Sang Y. F. 收藏 \| 浏览/下载：32/0 \| 提交时间：2014/12/24 Hydrologic time series analysis Wavelet transform Periodicity Trend De-noising Complexity Hydrologic forecasting multiscale entropy analysis artificial neural-networks information-theory noise-reduction rainfall fields trend analysis river flow model uncertainty multiresolution
	STATISTICAL INFERENCE FOR SEMIPARAMETRIC VARYING-COEFFICIENT PARTIALLY LINEAR MODELS WITH ERROR-PRONE LINEAR COVARIATES 期刊论文 OAI收割 ANNALS OF STATISTICS, 2009, 卷号: 37, 期号: 1, 页码: 427-458 作者: Zhou, Yong; Liang, Hua \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2018/07/30 Ancillary variables de-noise linear model errors-in-variable profile least-square-based estimator rational expection model validation data wild bootstrap
	时变带限信道中光通信的均衡与去噪技术期刊论文 OAI收割光子学报, 2008, 卷号: 37, 期号: 6, 页码: 1195, 1199 梁波; 朱海; 陈卫标收藏 \| 浏览/下载：909/147 \| 提交时间：2009/09/18 Convergence (mathematics) De noising Digital equalization Equalization techniques Numerical simulations Optical (PET) (OPET) Optical pulses Optical signalling Poisson noise Semi-blind equalization Soft-threshold Wavelet de noising
	A wavelet based targets detection method for high resolution airborne SAR data 会议论文 OAI收割 Igarss: 2007 Ieee International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing and Understanding Our Planet, New York Tian, Sirui; Wang, Chao; Zhang, Hong; Zhang, Bo; Wu, Fan 收藏 \| 浏览/下载：13/0 \| 提交时间：2014/12/07 targets detection RC-CFAR detector improved wavelet soft-threshold de-noise
	A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China 作者: Li Y.; Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：38/0 \| 提交时间：2013/03/25 This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light and indeed we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm the tracing of WTMM is not just tedious procedure computationally algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.
	Application of dyadic wavelet transform modulus maximum method to denoising of capillary electrophoresis signals 期刊论文 OAI收割 CHEMICAL JOURNAL OF CHINESE UNIVERSITIES-CHINESE, 2002, 卷号: 23, 期号: 5, 页码: 796-800 作者: Zhong, HB; Li, GB; Liu, H; Zheng, JB; Chen, LR \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2019/04/09 Dyadic Wavelet Transform Modulus Maximum De-noise