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于 2021-04-05 发布文件大小:4KB

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利用小波变换，EMD以及两者的综合分别进行轴承信号背景噪声去噪分析(Using wavelet transform, EMD and the combination of the two separate bearing signal denoising of background noise)

文件列表：

背景去噪
........\M4.m,1728,2009-09-23
........\m5.m,1320,2009-10-14
........\quzao.m,2871,2009-12-07
........\QUZAO1.m,2676,2009-11-20
........\rl.m,1183,2009-10-15

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小波包分析
说明：关于小波包分析的图像绘制，小波分析，三次分解，单支重构故障信号(Image registration algorithms based on piecewise linear interpolators (spatiotemporal gradients or block matching) are analyzed to determine subpixel registration accuracy.)

2020-04-20 15:44:46下载

积分：1
arrayxy
mimo_BP用于十字型阵列雷达，利用匹配滤波理论进行算法仿真(mimo_BP Used for cross-array radar, using matched filter theory for algorithm simulation)

2018-05-05 15:07:36下载

积分：1
duocengmo
小波分析，及小波能量谱分析，信号分析与处理技术小波分析，及小波能量谱分析，信号分析与处理技术(wavelet analysis, and wavelet energy spectrum analysis, signal analysis and processing technology wavelet analysis, and wavelet energy spectrum analysis, Signal Analysis and Processing Technology)

2007-01-19 22:44:42下载

积分：1
OFDM
OFDM系统完整程序，小波/小波包变换在OFDM中的应用的程序。(Complete program of the OFDM system, wavelet/wavelet packet transform in OFDM applications program.)

2012-05-17 23:28:28下载

积分：1
eegwave
说明：基于小波包分解的脑电信号特征提取.格式是caj后缀的，大家看的时候可能得有CAJViewer 这个软件(Based on wavelet packet decomposition of the EEG feature extraction. CAJ suffix format is, we see there may be a time when the software CAJViewer)

2008-10-14 13:24:37下载

积分：1
matlab谐波分析
说明：可导入comtrade格式录波文件，进行谐波分析(It can import COMTRADE format recording file for harmonic analysis)

2020-06-17 23:20:01下载

积分：1
wavelet-package
使用小波包分析信号的MATLAB程序，对初学者有用~~~~·( analysis Signal by Wavelet packet..matlab~~)

2010-05-11 15:42:49下载

积分：1
wavelet-singularity
基于小波变换的信号奇异性检测，小波模极大值的计算(The signal singularity detection based on wavelet transform )

2020-08-24 19:18:15下载

积分：1
project
说明：利用小波变换去除图像中噪声，对比其他去噪方法(Using Wavelet Transform to Remove Noise from Image)

2020-12-27 17:23:08下载

积分：1
BCS-SPL-1.5-new
Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

2020-11-23 19:29:34下载

积分：1

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