TAnalysis

此程序实现图象傅立叶变换、离散傅立叶变换和小波变换、可以自己设定小波变换的参数(this procedure image Fourier transform, discrete Fourier transform and wavelet transform, can set their own parameters of wavelet transform)

vc实现虚拟示波器，没有测试过不知道能用吧(vc virtual oscilloscope not tested do not know can)

一维信号的小波去噪,这是一个很好的基于小波变换的小波去噪，是针对一维的，去噪效果非常好！(1DWT wavelet denoide)

希尔伯特变换法提取零相位子波的小程序，很好用！！(Hilbert transform method to extract the zero-phase wavelet procedure, easy to use! !)

利用小波变换进行改进阈值信号去噪 (Improvement threshold signal denoising using wavelet transform)

C语言实现一维离散小波变换，通过mallat算法实现对一维信号的快速重构和分解。(C language implementation of a one-dimensional discrete wavelet transform, fast algorithm by mallat one-dimensional signal reconstruction and decomposition.)

基于小波变换的语音识别，内含毕业论文和摘要以及可执行源程序(Speech recognition based on wavelet transform, containing thesis and summary of executable source code)

FMCW调频连续波雷达的测距测角，可以得到很精确的幅值、频率、相位估计，有小波分析的盲信号处理。( FMCW frequency modulated continuous wave radar range and angular measurements, You can get a very accurate amplitude, frequency, phase estimation, There Wavelet Analysis Blind Signal Processing.)

通过设计Visual C程序源码对简单易懂的一维信号在加上了高斯白噪声之后进行Daubechies小波、Morlet小波与Haar小波变换，从而的到小波分解系数；再通过改变分解的到的各层高频系数数进行信号的小波重构达到消噪噪的目的。在这一程序源码实现的过程中能直观地理解信号小波分解重构的过程与在信号消噪中的重要作用，和在对各层高频系数进行权重处理时系数的选取对信号消噪效果的影响。 可直接 (Design Visual C program source code on a simple one-dimensional signal with a Gaussian white noise Daubechies wavelet, Morlet wavelet and Haar wavelet transform, and thus to the wavelet coefficients and then change the decomposition to each storey wavelet reconstruction frequency coefficient of the number of signal to noise canceling noise. In the process of realization of this program source code can be intuitively understood the signal wavelet decomposition and reconstruction process and an important role in signal denoising, and the selection coefficient in the high frequency coefficients of the layers of the weight of processing noise cancellation signal impact. Can be directly)

说明：  GHM多小波的变换与逆变换，包括预处理和后处理(GHM multi-wavelet transform and inverse transform)