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feichouyangxiaobofenjie
1.实现非抽样提升小波包三层分解
2.有效解决了小波包分解频带交错问题
3.有效输出频谱图(1 non-sampling improved wavelet packet three decomposition. Effective solution to the wavelet packet decomposition band interlacing problems. Effective output spectrogram)
- 2013-03-19 20:24:53下载
- 积分：1
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tq1
短期负荷预测（一种基于小波变换支持向量机的负荷预测程序）(Short-term load forecasting (a kind of wavelet transform based on support vector machine load forecasting procedure))
- 2008-06-02 20:33:50下载
- 积分：1
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ECG_FEATURE_WAVELET_ST
ECG feature wavelet transform
- 2012-03-17 21:30:47下载
- 积分：1
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svd_dwt
实现基于离散小波（dwt）的奇异值（svd）分解算法的实现(Decomposition algorithm based on singular value of the discrete wavelet (dwt) (svd))
- 2020-10-23 17:17:22下载
- 积分：1
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BackProjectionbyrao
多点目标 单站SAR的回波信号仿真以及BP后向投影成像
参数见合成孔径雷达成像——算法与实现一书表6.1
运行结果正确，注释比较详细(Percent more single station SAR point target echo signal simulation and backward projection imaging BP parameters, see synthetic aperture radar imaging- algorithm and implementation of a book Table 6.1 run result is correct, more detailed notes)
- 2020-10-28 22:19:58下载
- 积分：1
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kalman
kalman 卡尔曼滤波C代码 用于滤除高斯白噪声的滤波 测试真是可用。(The kalman Kalman filter C code for filtered white Gaussian noise filtering test is really available.)
- 2020-12-01 21:09:26下载
- 积分：1
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Multi_Wavelet
说明： 多小波代码，有CL,GHM,Sa4,Opt-rec四种多小波的变换与反变化，前处理和后处理的代码。(Multiwavelet code, there are CL, GHM, Sa4, Opt-rec four Multiwavelet Transform and anti-change, pre-treatment and post-processing code.)
- 2021-04-28 11:38:44下载
- 积分：1
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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
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Harmnic
谐波小波包分解程序，并且将其应用于声发射信号特征提取(Harmonic wavelet packet decomposition procedure, and will be applied to acoustic emission signal feature extraction)
- 2012-03-04 14:33:21下载
- 积分：1
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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
