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这篇关于从心电信号提取呼吸信号的调查，一共介绍了三种方法，即：频率提取，滤波器滤波，小波变换，三种方法。(This extract on the respiratory signal from ECG investigation, presented a total of three methods, namely: the frequency extraction filter filtering, wavelet transform, three ways.)

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.)

同步压缩小波变换（synchrosqueezing wavelet transform）工具箱，用于信号处理时频分析(synchrosqueezing wavelet transform toolbox for time-frequency analysis in signal processing )

由仿真结果时域和频域图可以看出，工频50Hz干扰信号很好被滤除掉，说明所设计的自适应滤波器效果较好。(It can be seen by the domain and frequency domain simulation results in Figure, the frequency 50 Hz interference signals very well be eliminated, the design of the adaptive filter is better.)

基于论文<<Blur Detection for digital images using wavelet transform>>,使用Haar小波变换实现图像模糊度检测。(Based on paper <Blur Detection for digital images using wavelet transform> > , Using the Haar wavelet transform of image blur detection.)

提升小波变换程序， %LWT Lifting wavelet decomposition 1-D. % LWT performs a 1-D lifting wavelet decomposition % with respect to a particular lifted wavelet that you specify.(lifting wavelet transform procedures% Local Walsh Transform Lifting wavelet decomposition 1-D. % Local Walsh Transform performs a 1-D lifting wavelet decomposition% with respect to a particular lifted wavelet that you specify.)

说明：  小波变换算法的C语言实现，包括DWT,INDWT,提升小波，以及小波去噪的MATLAB源代码(wavelet transform algorithm in C language, including DWT, INDWT upgrade wavelet Wavelet denoising and MATLAB source code)

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)

复倒谱分析及最小相位法实验，用MATLAB编程实现求取复倒谱，在求取复倒谱的过程中采用最小相位法避免相位卷绕。 (err)

说明：  本代码利用小波变换，实现了两幅图像的融合，使用了小波分析工具箱wavemenu，使用了小波分析函数wavedec2()进行小波变换(The code using wavelet transform, to achieve a fusion of two images, using wavelet analysis toolbox wavemenu, using wavelet analysis function wavedec2 () wavelet transform)