CDBN_sleepEEG

利用小波变换实现对电能质量检测的算法实现(using wavelet transform to detect power quality algorithm)

shiyan图像分析及图像处理（时频变换）的小软件(Shiyan image analysis and image processing (time-frequency transform) a small software)

M. N. Do的经典的关于轮廓波变换的matlab代码，里面有matlab代码、论文、测试图片，很齐全，是很好的学习资料！(The MN Do classic on contourlet transform matlab code matlab code, papers, test image there, very complete, good learning materials.)

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)

基于小波变换的图像分析，它是在matlab中运行的(Image analysis based on wavelet transform, which is run in matlab)

超声图像去噪，基于小波变换的通用超声图像去噪，能够有效的去除相干斑(Ultrasound image denoising based on wavelet transform common ultrasound image denoising, can effectively remove speckle)

说明：  介绍傅立叶变换的漫画，不是大量公式叙述或证明，简洁生动，易于理解。(The caricature that introduces Fourier transform, is not a lot of formula narration or proof, concise and vivid, easy to understand.)

实现小波包的n层分解与重构,以及简单的输入输出方式(Implementation of wavelet packet decomposition and reconstruction of N layer)

运行emd,得到输入信号X的固有模态函数（imf），每列代表着信号X的一阶分量，X就是我们采集的信号。 hhtdn是hht去噪的程序，可以不看，仅看emd 和 findpeaks wvtdn是wavelet去噪程序，如果只是分解信号，仅用下面的语句即可 [C,L] = wavedec(x_noise,dl,wn); x_noise就是我们采集的信号，dl是分解的阶数，wn是分解的母小波名称(% Empiricial Mode Decomposition (Hilbert-Huang Transform) % imf = emd(x) % Func : findpeaks)

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)