wavelet

图象增强小波变换将一幅图象分解为大小、位置与方向都不同的分量。在做逆变换之前能改变小波变换域中某些系数的大小，，这样就能够洋选择的放大所感兴趣的分量而减小不需要的分量。给定一个图象信号，用二维小波分析析对图象进行增强处理。 (Image enhancement wavelet transform decomposition size, location and direction of the different components of an image. Wavelet transform domain, some the size of the coefficient can change before doing the inverse transformation, so can the component of interest to the amplification of the ocean choose to reduce the unwanted weight. Given an image signal, the analysis of two-dimensional wavelet analysis of image enhancement processing.)

对图像进行滤波处理是图像处理中最常见的一种操作类型。而这其中低通滤波（也可以叫做平滑）有事各种滤波处理中最常用的。这里就简单写写 OpenCV 中提供的几种低通滤波方法。 具体思路：对源图像进行离散傅里叶变换，滤波后通过cvDFT进行傅里叶逆变换得到理想低通滤波器、巴特沃斯低通滤波器、高斯低通滤波器后图像

小波神经网络在变压器故障诊断中的应用，介绍一种小波神经网络诊断变压器故障的新方法 好文章大家一起分享(Wavelet neural network in fault diagnosis of transformer applications, introduced a wavelet neural network of the new transformer fault diagnosis method of a good article to share)

说明：  本程序是卡尔曼滤波的源程序包，请大家分享.(Kalman filtering of the source packet, please share.)

PSK信号载波相位符号定时联合估计,载波环采用判决反馈环，去除噪声平方损耗，定时模块采用早迟原理， 里边有详细的注释，希望对大家有用。点击即可运行。(PSK signal carrier phase joint estimation of symbol timing, carrier ring using decision feedback loop, remove the noise square loss, using early-late timing module principle, inside detailed notes, we want to be useful. Click to run.)

生成高斯波形脉冲，并求解高斯脉冲的自相关结果(Gaussian pulse waveform is generated, and solve the results of Gaussian pulse autocorrelation)

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

说明：  实现对离散信号的连续小波变换进行时频域分析，判断信号的稳定性(The continuous wavelet transform of the signal is analyzed in time and frequency domain)

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)

多点目标 单站SAR的回波信号仿真以及BP后向投影成像 参数见合成孔径雷达成像——算法与实现一书表6.1 运行结果正确，注释比较详细(Percent more single station SAR point target echo signal simulation and backward projection imaging BP parameters, see synthetic aperture radar imaging- algorithm and implementation of a book Table 6.1 run result is correct, more detailed notes)