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小波十讲中文、切边优化版本，适合直接在kindle阅读(<Ten Lectures on Wavelets>)

离散小波变换与离散小波反变换 快速小波变换（Mallat小波分解算法）：对一幅图像做2级小波分解（离散小波变换）与合成（离散小波反变换）(Discrete wavelet transform and discrete wavelet inverse transform fast wavelet transform (Mallat wavelet decomposition): For an image to do 2 wavelet transform (DWT) and synthesis (discrete wavelet inverse transform))

Wavelet Neural networks

对信号进行小波去噪再重构，针对不同分解层次的高频系数，采用分层阈值方式来量化高频系数。(Signal is carried out the wavelet denoising again reconstructed to quantify high-frequency coefficients, high-frequency coefficients for different levels of decomposition, hierarchical threshold.)

希尔伯特变换和小波变换检测相位差的matlab程序和一些资料(Hilbert transform and wavelet transform to detect the phase difference matlab program and some of the information)

心电信号分析 （1）设计滤波器，实现对心电信号的噪声抑制和基线纠漂。 （2）时域分析：R波检测算法与实现. （3）功率谱分析：对消噪后的信号进行功率谱分析。要求计算信号的功率谱，功率谱峰值，峰值频率。 (ECG Analysis (1) design filters to achieve noise suppression of ECG and baseline drift correction. (2) time-domain analysis: R-wave detection algorithm and implementation (3) The power spectrum analysis: noise cancellation signal after the power spectrum analysis. Require calculation of the signal power spectrum, peak power spectrum, peak frequency.)

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)

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.)

分别用小波、分形的方法对图像进行去噪，并对这两种方法的结果进行了比较。(Wavelet, respectively, the method of fractal image denoising, and the results of these two methods are compared.)

LeGall(5，3)小波滤波器，其计算复杂度小、存储开销低、压缩效率高，已被JPEG2000标准采用。提升小波实例c语言实现(Lifting Wavelet Example C Language Implementation)