56465456456465waveletpackage

利用连续小波变换，交叉小波变换研究ENSO对长江流域径流的影响。(Using the continuous wavelet transform, ENSO cross wavelet transform research on the influence of the Yangtze river basin runoff.)

自己写的小波图像压缩，包括普通压缩和序列图像压缩(himself wrote of wavelet image compression, including the compression and ordinary sequence Image Compression)

小波包分解和重构的过程，在齿轮箱中的应用(Wavelet packet decomposition and reconstruction of the signal in the application of matlab, and energy, frequency )

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)

小波分析用于图像融合和去噪；APES,MUSIC,LMS算法仿真实例；(Wavelet analysis is used to image fusion and noise；APES,MUSIC,LMS )

去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信信号，避免噪声对心电信号特征点的识别与提取造成误判漏判 已通过测试。 (Remove the EMG interference mixed with the signal acquisition process in mind, frequency interference, baseline drift and noise channel signal to avoid noise caused by the misjudgment of the Missing has been tested on ECG feature identification and extraction.)

说明：  主要用于图像融合。 NSCT融合有利于更好的保持图像的边缘信息和轮廓结构，增强图像的平移不变性。(It is mainly used for image fusion. NSCT fusion is conducive to better preserve the edge information and contour structure of the image, and enhance the translation invariance of the image.)

小波基contourlet变换 其中包含spiht编码 去噪 纹理识别(Wavelet transform contourlet transform includes SPIHT coding and denoising texture recognition.)

可用于曲波变换的工具箱，用于含噪声的图像的去噪过程,图像分解，有例子可直接运行。只可用于学术研究目的。(the Latest curvelet transform lab tool ，Non-commercial research use for Academics. Image decomposition, there are examples can be run directly.)

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.)