DWT_1D

说明：  有小波分解求小波熵，还有小波包技术求解信号特征，适应与分析脑电信号特征(Seeking a wavelet decomposition wavelet entropy, and wavelet packets technique to solve the signal characteristics, and analysis of EEG characteristics of adaptation)

这是一款用来分析心率变异性的matlab程序，有线性分析和非线性分析，包含输出结果包含频谱分布、统计学描述、样本熵、小波分析(This is a matlab program used to analyze heart rate variability, linear analysis and nonlinear analysis contains the output contains spectral distribution, statistical description sample entropy, wavelet analysis)

说明：  直接减压到matlab\work目录下就可以看到运行结果了，其中包括了位平面分解，量化嵌入（提取），图像的金字塔分解等的程序块。(decompression directly to the Matlab \ work on the directory can see the results, which included a bit plane decomposition, quantification embedded (extraction), the image of the pyramid decomposition of the block.)

基于morlet小波的小波熵的源代码，希望有用，共分享(the code of waveletentropy)

使用matlab实现的BPSK信号产生和加入噪声前后的频谱对比以及FFT测频和一阶差分法测频对比(Using matlab to achieve the BPSK signal generator and adding noise before and after the spectrum contrast and FFT frequency measurement and first-order differential method to measure the frequency contrast)

利用小波变换检测回波,经过labview显示，实现了很好的界面效果(Using wavelet transform to detect echoes through labview display interface to achieve a good effect)

说明：  计算小波分解后的能量及小波熵。里面还有OFDM信号和WPM信号的程序。(After calculating the energy of wavelet decomposition and wavelet entropy. There is also OFDM signal and the WPM signal process.)

描述电磁波（电场或磁场）在自由空间传播的Matlab程序。(Description of electromagnetic waves (electric or magnetic) in free-space propagation of Matlab procedures.)

小波变换测试，小波分解的级数设置为3级，可以实现数字图像的小波变化和逆变化。测试代码请使用512*512灰度图像(The wavelet transformation test, wavelet decomposition series is set to 3, digital image wavelet changes and reverse changes.)

小波变换(wavelet transform，WT)是一种新的变换分析方法，它继承和发展了短时傅立叶变换局部化的思想，同时又克服了窗口大小不随频率变化等缺点，能够提供一个随频率改变的"时间-频率"窗口，是进行信号时频分析和处理的理想工具。它的主要特点是通过变换能够充分突出问题某些方面的特征，能对时间(空间)频率的局部化分析，通过伸缩平移运算对信号(函数)逐步进行多尺度细化，最终达到高频处时间细分，低频处频率细分，能自动适应时频信号分析的要求，从而可聚焦到信号的任意细节，解决了Fourier变换的困难问题，成为继Fourier变换以来在科学方法上的重大突破。(Wavelet transform (WT) is a new transform analysis method. It inherits and develops the idea of localization of short-time Fourier transform and overcomes the shortcomings that window size does not vary with frequency. It can provide a frequency-dependent The Time-Frequency window is ideal for time-frequency analysis and processing of signals. Its main feature is that by transforming some features that can fully highlight some aspects of the problem, the localization of the time (space) frequency can be analyzed, and the signal (function) can be gradually and multi-scale refined by the scaling operation, finally reaching the high frequency Time subdivision, frequency subdivision at low frequencies, can automatically adapt to the requirements of time-frequency signal analysis, which can focus on any details of the signal to solve the difficulties of Fourier transform has become a major breakthrough in the scientific method since Fourier transform.)