CDBN_sleepEEG

说明：  Curvelet变换是由一种特殊的滤波过程和多尺度脊波变换组合而成：本程序对彩色图像进行了curvelet变换从而实现图像去噪的效果。首先对图像进行子带分解；然后对不同尺度的子带图像采用不同大小的分块；最后对每个分块进行Ridgelet分析。(Curvelet transform is a combination of a special filtering process and multi-scale ridgelet transform. In this program, curvelet transform is applied to color image to achieve the effect of image denoising. First, the image is decomposed into sub-band; then the sub-band images of different scales are divided into different sizes; finally, each sub-band image is analyzed by ridgelet.)

分别用Pisarenko谐波估计法、MUSIC算法和ESPRIT算法对三正弦叠加高斯白噪声信号源进行频率估计，附件中有代码说明，编译环境为matlab。(Respectively Pisarenko harmonic estimation, MUSIC algorithm and the ESPRIT algorithm superimposed on the three sine Gaussian white noise signal source for frequency estimation, in the annex to the code description, the compiler environment matlab.)

利用CVX进行优化，得到期望加权矢量，从而的得到需要的波束形成(CVX using optimized to give the desired beamforming)

说明：  对脉搏波进行t波，r波进行检测，提取，再将特征值标注到原波形上(Detect and extract the T wave and R wave of pulse wave)

这是三维离散小波变换程序，里面附带程序说明，及实例讲解。(This is a three-dimensional discrete wavelet transform, which comes with the program instructions and examples to explain.)

一个基于小波变换的数据聚类样例 Matlab实现(A sample Matlab based on wavelet transform data clustering)

基于小波变换的信号去噪技术，简单的一维小波去噪程序(Signal de-noising based on wavelet transform technique, a simple one-dimensional wavelet denoising procedure)

小波变换实现微弱信号检测 BPSK QAM FSK(Wavelet transform weak signal detection BPSK QAM FSK)

将图像按位平面分解，然后在最低有效为嵌入一定的信息，达到数字隐藏的作用，是最原始的数字水印技术(image caput plane decomposition, and then embedded in the lowest effective for certain information, to hide the figures, is the most primitive digital watermarking technology)

matlab计算haar 小波各级近似系数和细节系数的频率响应（CA1，CD1，CA2，CD2，CA3），并画出幅频图，便于理解小波变换的意义，(U191 u7197 u5c1 u5c1 u5Fc1 u4Fc u653 u564C u548C u7286 u8282 u7CFB u6570 u7684 u9891 u54CD u5E94 uFF08CA1 uFF0CCD1 uFF0CCA2 uFF0CCD2 UFF0CCA3 uFF09 uFF0C u5E76 u753B u51FA u5E45 u9891 u56FE uFF0C u4FBF u4E8E u7406 u89E3 u5C0F u6CE2 u53D8 u6362 u7684 u610F u4E49 uFF0C)