RSA加密

说明：  IEEE_SSR标准PSCAD算例，实用算例亲测有效(An example of IEEE SSR standard PSCAD)

冈萨雷斯版数字图像处理源码第二部分，非常全，非常好用(Gonzales digital image processing source code, very full, very easy to use)

说明：  再来一个绘图程序，花了我一个上午时间才搞定。(then a drawing program, I spent a morning time to get.)

计算图像的质心，用图像的算发进行操作。这样可以有效的算出图像的质心(The centroid of the calculated image, with an image of the operator hair operation. This can effectively calculating the image centroid)

点云特征提取 详细 实用 经典的讲述过程及方法(Point Cloud Feature Extraction practical details about the processes and methods of classic)

基于区域统计的融合规则，内含源码和源图像以及contourlet工具箱，直接出结果(Fusion rule based on regional statistics, containing source code and source images, and contourlet toolbox, directly results from)

本代码是描述图像融合的一个综合性代码，其中包括对图像变换提升等一系列试验方案(The code is described in an integrated image fusion of code, including the transformation of images, such as a series of pilot programs to enhance)

说明：  官方出版的opencv教程，学习opencv必备。(Officially published opencv tutorials are essential for learning.)

LSB信息隐藏算法，就是通常把信息隐藏在图像像素的最后一位，可能对你们有帮助(LSB information hiding algorithm, the information is usually hidden in the image pixels of the last one, you may have to help)

电压跌落是最严重的动态电能质量问题之一， 精确定位电压跌落起止时间是应对电压跌落问题的 重要前提和基础。由于电压采样信号往往有噪声分 量，现有的方法在定位电压跌落的起止时间时存在 局限性。本文提出利用多小波变换及相邻系数去噪 的电压跌落定位方法。多小波兼有对称性、正交性、 有限支撑性和二阶消失矩等优异的信号处理性能， 利用GHM多小波可以准确定位电压跌落起止时间。 多小波变换系数在每层之间具有对应关系，多小波 相邻系数将紧相邻的若干个系数作为一个整体来确 定阈值，考虑了系数之间的相关性，能获得更好的 去噪效果。通过 Matlab 进行仿真验证，仿真结果表 明，所提出的方法的正确性。 (Voltage sag is one of the most serious dynamic power quality problems. Critical start-time and end-time are important indices for voltage sags. But the sampling signals often have noisy component, the locations of start-time and end-time are hard to get. Wavelet is an effective tool for those non-stationary signal processing and has been used in this field. Local feature in the signal can be enlarged after the transformation using the scalar wavelet. But scalar wavelets cannot contain orthogonality, symmetry, compact support and higher order of vanishing moments simultaneously. In this thesis, multi-wavelets GHM is used to detect and locate power quality disturbances. Multi-wavelets offer many excellent properties such as the same approximation order but more compact support. The dependence of the multi-wavelets coefficients varies with the level, so neighboring coefficien)