中点画圆法算法实现，能够实现坐标平移，支持鼠标绘图

一个超强的扫描种子填充算法程序，使用VC++开发，效果不错。-a super scan seed filling algorithm, the use of VC development effectiveness.

数字识别代码 第一步：训练网络。使用训练样本进行训练。（此程序中也可以不训练，因为笔者已经将训练好的网络参数保存起来了，读者使用时可以直接识别）第二步：识别。 首先，打开图像（256色） 再次，进行归一化处理。点击“一次性处理” 最后，点击“R”或者使用菜单找到相应项来进行识别识别的结果显示在屏幕上，同时也输出到文件result.txt中 该系统的识别率一般为90％ 另外，也可以单独对打开的图片一步一步进行图像预处理工作。但要注意，每一步工作只能执行一遍，而且要按顺序执行。步骤为：“256色位图转为灰度图”“灰度图二值化”“去噪”“倾斜校正”“分割”“标准化尺寸”“紧缩重排” 另外注意，要识别的图片，要与win.dat、whi.dat位于同一目录。此两文件保存训练后网络的权值参数 -digital identification code first step : training network. Use of training samples for training. (These procedures also can not training because I have trained network parameters saved, Readers can direct identification) second step : identification. First, open the Image (256 colors) again, to a treatment. Click on the "one-stop" Finally, Click on the "R" or use the menu item corresponding to find the identification of the identification results on the screen, also result.txt output to a file which the system"s recognition rate of 90% for the other, can be individually opened to the step-by-step pictures for image prep

中点画圆，bresenham画圆，中点画线，bresenham画仙，-midpoint Circle, bresenham Circle, Stipples line bresenham painting cents

模仿CAD，室内装修 模仿CAD，室内装修-this is like cad software

在matlab下进行图像处理的函数库，包含边缘线段拟合，透视变换，立体图像复原等toolbox中所没有的功能，详见index.html-in Matlab under image processing functions, including marginalized segment fitting, perspective transformation, three-dimensional images rehabilitation toolbox are not functional, as described index.html

c常用算法程序集-c algorithm commonly used procedures set

功能是图象的检测，模板匹配如果上 命令行编译过程如下 vcvars32 rc bmp.rc cl detect.c bmp.res user32.lib gdi32.lib 这里是vc可直接运行 注意事项： 运行时，文件c: est.bmp必须存在-Function of image detection, template matching on the command-line compiler, if the process is as follows vcvars32rc bmp.rccl detect.c bmp.res user32.lib gdi32.lib here can be directly run vc Note: run-time, file c: est.bmp must exist

将bmp文件读取到内存当中，c++语言编写。BMP（全称Bitmap）是Windows操作系统中的标准图像

Cognitive radio frequency spectrum detection-The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum sensing is proposed. The collaboration of multiple SUs can also be considered in order to enhance the detection performance. The collaborative algorithms are compared in terms of the required exchange information among SUs in some collaboration

Implementation of an Interval Iterative Closest Point that uses intervals to found the global optimal solution. -Implementation of an Interval Iterative Closest Point that uses intervals to found the global optimal solution.