leidaxinhaoyuchuli

小波图像去噪源码，很不错，内有程序的运行结果(Image denoising source, very good, there are programs running results)

本书简明扼要地介绍了时间序列及其相关领域的基本概念和基本理论，对ARMA序列预测、时间序列的统计分析、时间序列的时频分析和时间序列的小波变换等给出了有关分析计算方法，结合MATLAB编程应用，介绍了MATLAB时间序列分析有关函数的功能和用法，阐述了如何利用这些函数解决工程应用中的问题。(This book succinctly describes the time sequence and its related fields of basic concepts and theories of the ARMA series forecasting, time series statistical analysis, time series analysis and time-frequency wavelet transform of time series analysis and calculation are given about methods, combined with MATLAB programming applications, the introduction of time series analysis MATLAB functions about functions and usage, explains how to use these functions to solve engineering problems in the application.)

说明：  实现f-k变换，功能强大，各种滤波处理，地震波等都能用(wavelet transformF -k transform, powerful, a variety of filtering, seismic waves can be used)

利用小波包分析信号的matlab程序，大家可以参考(Matlab program using wavelet packet analysis of the signal, we can refer to)

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.)

小波神经网络,实现曲线拟和，数据分类，故障诊断，学习速度快于传统BP。(Wavelet neural network, and realize curve fitting, data classification, fault diagnosis, learning faster than the traditional BP.)

此程序仿真了一个被污染了的信号源，然后通过小波去噪，成功取出了信号中的杂波(This procedure simulation has polluted the supply oscillator, then through the wavelet denoising, the success took out in the signal clutter )

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)

相关性小波去噪的程序实现，相关性去噪的程序实现(Wavelet denoising procedures related implementation procedures related to implementation of denoising)

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)