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小波变换光谱数据预处理阈值平滑处理用于光谱数据(xiaobobianhuan yuchuli )

说明：  python实现小波和短时傅里叶变换，源代码和测试算例(Python implementation of wavelet and short time Fourier transform)

kalman 卡尔曼滤波C代码 用于滤除高斯白噪声的滤波 测试真是可用。(The kalman Kalman filter C code for filtered white Gaussian noise filtering test is really available.)

整数小波变换，可以直接运行，对图像小波分解(Integer wavelet 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.)

本代码是小波包学习和使用的初学代码，简单改造可对信号进行滤波，去噪，实现我们需要的各种功能(xiaobobao )

利用连续小波变换，交叉小波变换研究ENSO对长江流域径流的影响。(Using the continuous wavelet transform, ENSO cross wavelet transform research on the influence of the Yangtze river basin runoff.)

说明：  广义S变换及其逆变换代码，可以直接进行属性分析(Generalized S-transform and its inverse transform code can be used for attribute analysis directly)

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.)

对基于小波变换的电能质量扰动检测和识别方法进行研究，在总结前人经验的基础上提出自己对算法的改进，并且利用MATLAB软件对改进算法进行仿真分析,取得令人满意的效果,验证所用方法的可行性。 (Power quality disturbances detection and identification method based on wavelet transform and make their own improvements on the algorithm on the basis of previous experience, and improved algorithm for the simulation analysis using MATLAB software, obtain satisfactory results, verify thethe feasibility of the method.)