02

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)

基于DB4 的小波变换，实现了小波的分解与重构，可根据实际需要修改阈值的选取方法(Based DB4 wavelet transform to achieve wavelet decomposition and reconstruction, according to the actual need to modify the threshold selection method)

说明：  通过高斯函数导数检测图像边缘，实质是方向可调小波变换检测图像边缘（8个方向变换）。(Gaussian function through derivative edge detection in real terms is adjustable direction edge detection wavelet transform (change the direction of 8).)

在matlab环境下，实现ECG信号R波的检测，使用的方法是小波变换。(Matlab environment, the realization of the R wave of the ECG signal detection, the methods used wavelet transform.)

Harr小波具有紧支撑、零调和性和优良的正交性等优点，因此选择它作为小波变换的基函数。对采样数据进行4层小波包变换得到16维的特征样本。(Harr wavelet has compact support, zero reconcile and excellent orthogonality, etc., so choose it as the base function of wavelet transform. 4 layers of sampled data wavelet packet transform feature samples 16 dimensions.)

Second generation wavelets and applications

波的合成及拍频现象实例，输入两正弦波的振幅、初相位及频率观察波的合成及拍频现象(Synthesis and beat frequency wave phenomena instance, enter the two sine wave amplitude, initial phase and frequency of observation of waves synthesis and beat frequency phenomenon)

整数小波变换，可以直接运行，对图像小波分解(Integer wavelet transform)

matlab提升小波变换与应用初步，包括小波变换的一些改进(the matlab enhance wavelet transform and application of the preliminary)

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.)