imagereconstruction

用迭代法生成haar子程序上 以及信息隐藏的详细功能、包含内容说明(Iterative method to generate a the haar subroutine and information hidden features, including content description)

图像小波变换，可以求图像小波分解系数图，阶数可以自己设定(tuxiangxiaobobianhuan)

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.)

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.)

说明：  用MATLAB编程实现实现滤波，与数字信号处理相关(Implementing filtering)

一维小波变换的MATLAB实现方法，包括一维连续小波变换和离散小波变换。(MATLAB in a one-dimensional wavelet transform methods, including the one-dimensional continuous wavelet transform and discrete wavelet transform.)

此函数基于二进小波的分解、重构的图像去噪(This function is based on the dyadic wavelet decomposition, reconstruction of the image denoising)

说明：  压缩包中为小波变换的程序，可在MATLAB 中使用，可进行时频分析(The compression package is a wavelet transform program, which can be used in MATLAB and can be used for time-frequency analysis)

此为提供初学计算电磁学应用於色散介质的人，一维色散介质-Lorentz的程序(It is fortran code to provide a chance to learn the computational EM wave application of dispersive media for Lorentz model)

说明：  提出了一种基于复解析小波变换的相位诊断齿轮故障的方法。复解析小波变换将Hilbert变换与小波分析紧密结合在一起,具有自适应分析能力。利用复解析小波变换得到齿轮振动信号的相位,其功率谱图上可提取调制边频带结构来识别故障模式。试验结果表明这种方法可有效应用于齿轮局部故障诊断中。(A new method using the phase based on complex analytical wavelet transform for fault diagnosis of gears was proposed. The phase of vibration signals of gears is got by complex analytical wavelet transform, which combines Hilbert transform with wavelet transform tightly and possesses adaptive analysis ability. By making spectrum analysis of the phase, the sideband structure in power spectrum can be protruded, and the fault pattern can be identified. Experimental results show that the proposed method is effective in local defect detection of gears.)