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Can extroversion congruence promote online interaction? Evidence from college first-year students 期刊论文
PSYCH JOURNAL, 2022, 页码: 9
作者:  Wang, Kexin;  Chen, Shuang;  Zhang, Jianxin;  Deng, Yang;  Zhang, Zheng
收藏  |  浏览/下载：39/0  |  提交时间：2022/02/11
团体依恋的概念与测量：项目因子分析方法的应用 期刊论文
北京大学学报 自然科学版, 2012
李同归; 刘飏
收藏  |  浏览/下载：3/0  |  提交时间：2015/11/12
Motion artifact cancellation in electrogastrograms using reconstruction of the wavelet transform modulus maxima 期刊论文
2010, 2010
Peng Cheng; Ye Datian
收藏  |  浏览/下载：4/0
A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
Hu S.; Zhu M.; Wu C.; Song H.-J.
收藏  |  浏览/下载：12/0  |  提交时间：2013/03/25
In many computer vision tasks  in order to improve the accuracy and robustness to the noise  wavelet analysis is preferred for the natural multi-resolution property. However  the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour  the Zernike moments are introduced  and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours  and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments  consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image  which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient  precise  and robust.  
A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Li Y.; Ni H.; Pang W.; Hao Z.
收藏  |  浏览/下载：25/0  |  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.  
Adaptive Kalman filtering deconvolution based on dyadic wavelet transform 期刊论文
Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2001, 卷号: 29, 期号: 1, 页码: 64-67
作者:  Dong, E. Q.;  Liu, G. Z.;  Zhang, Z. P.
收藏  |  浏览/下载：10/0  |  提交时间：2020/01/07
Q spaces of several real variables 其他
2000-01-01
Essen, M; Janson, S; Peng, LZ; Xiao, J
收藏  |  浏览/下载：2/0  |  提交时间：2015/11/16
Seismic data time-frequency domain denoising based on dyadic wavelet transform 期刊论文
Acta Geophysica Sinica, 1996, 卷号: 39, 期号: 2
作者:  Zhang, Ke;  Liu, Gui-Zhong;  Zou, Da-Wen;  Qian, Jun-Sheng;  Li, Feng-Qi
收藏  |  浏览/下载：6/0  |  提交时间：2020/01/07

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