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wavelet-matlab

于 2011-11-01 发布 文件大小:1KB
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  使用小波包变换分析信号的MATLAB程序, 使用小波包变换分析两个信号的特征向量和各频率成分的功率谱,很完整的一个应用程序 (Signal using wavelet packet analysis of MATLAB program, using wavelet packet analysis of two signal eigenvectors and the power spectrum of each frequency component, an application is complete)

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使用小波包变换分析信号的MATLAB程序.txt,4712,2011-10-31

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    2010-12-13 13:31:49下载
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    以labview为平台通过小波分析方法对波形进行处理。(Labview as a platform for analysis of the waveform by wavelet processing.)
    2011-12-28 16:12:20下载
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    一个基于小波阈值的信号处理程序,通过自适应阈值方式抑制信号噪声(A threshold based on wavelet signal processing procedures, through the adaptive threshold signal noise suppression methods)
    2008-12-23 21:23:01下载
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    小波变换的数字水印算法,附有论文和源程序.(Wavelet Transform Digital Watermarking Algorithm with thesis and source code.)
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  • xinhaoquzao
    利用小波变换进行改进阈值信号去噪 (Improvement threshold signal denoising using wavelet transform)
    2012-10-29 16:35:35下载
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    gabor二维滤波器,用与表情识别!非常好用!做人脸识别,表情识别的朋友可以下载!(Facial expression recognition based on Gabor wavelets tranform!It is very useful to the people who research facial expression !)
    2010-06-23 16:42:56下载
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  • 61
    说明:  提出了一种结合SVD的小波变换方法,对其在外弹道测量数据中的野值剔除进行了研究。对观测数据进行小波分解,将小波分解后的近似分量和细节分量组合实现相空间重构,作为SVD方法的输入观测矩阵,根据奇异 熵增量准则,对奇异值进行筛选,根据SVD逆变换重构原信号。这一方法克服了Hankel矩阵相空间构建方法数据 端点失真问题。以小波分解后分量重构的相空间可以满足正交性,进一步提高了SVD进行数据降噪和野值检测的精度。仿真数据和试验数据处理结果证明了这一方法的有效性。(Proposed a method of combining the SVD wavelet transform its outer ballistic measurement data in excluding outliers were studied. Observational data on wavelet decomposition, wavelet decomposition of the approximate combined component and detail component to achieve the phase space reconstruction, as the input observation matrix SVD method, based on singular entropy increment standards, singular value filter, according to the inverse transform heavy SVD structure of the original signal. This approach overcomes the Hankel matrix phase space construction method data endpoint distortion. Wavelet decomposition component of reconstruction phase space satisfy orthogonality, to further improve the SVD for data noise reduction and outlier detection accuracy. Simulation data and experimental data processing results prove the effectiveness of this approach.)
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