Sheen_MMW imaging for weapon detection

基于WPF的MetroUI事例，demo比较简答属于入门程序(Based on the MTROUI case of WPF, demo is a rudimentary program.)

经典LabWindows CVI方面的教材《LabWindows CVI开发入门和进阶》一书第9章中的例题源码，使用LabWindows CVI的人用得着。麻烦管理员帮我开通下载功能，我急需要本网站上的labwindows/CVI方面的数据库教程，谢谢！-LabWindows CVI aspects of the classic textbook

CCS 4.038 C Compiler For PIC

National Training Team 1999 Proceedings

天门阵游戏源码。是类似连连看的一款游戏，需要通过鼠标拖动牌到相同牌对齐的地方后，消除。最多有30000局。-days Gate Array game source. 1000 block of Terry Avenue is similar to the one game, through licensing the mouse to drag the same alignment of licensing, eliminate. Up to 30,000 Bureau.

说明：  ESP8266串口烧录，delphix7，xe8，xe10(ESP8266 Serial burning,delphix7,xe8,xe10)

一个C#生成随机数字编码的例子，附完整代码文件，可用于设备、包装等编码的生成，随机生成，按照 事先约定好的规则生成，这种随机生成的编码在日常生产中使用频率极高。

计数一个字符串中所有字符出现的频数，并全部输出(Count the number of frequency of all the characters in a string appears, and all output)

椭圆拟合的相关介绍与数学运算方法，包括说明文档及源码、和测试数据-elliptical fitting related presentations and mathematical methods, including documentation, and -Ellipse fitting to introduce the relevant mathematical calculations and methods, including documentation and source code, and test data-elliptical fitting related presentations and mathematical methods, including documentation, and

说明：  K-means算法是基于划分的思想，因此算法易于理解且实现方法简单易行，但需要人工选择初始的聚类数目即算法是带参数的。类的数目确定往往非常复杂和具有不确定性，因此需要专业的知识和行业经验才能较好的确定。而且因为初始聚类中心的选择是随机的，因此会造成部分初始聚类中心相似或者处于数据边缘，造成算法的迭代次数明显增加，甚至会因为个别数据而造成聚类失败的现象。(K-means algorithm is based on the idea of partitioning, so the algorithm is easy to understand and the implementation method is simple and feasible, but it requires manual selection of the initial number of clusters, that is, the algorithm is with parameters. The number of classes is often very complex and uncertain, so professional knowledge and industry experience are needed to better determine. Moreover, because the selection of initial clustering centers is random, some initial clustering centers will be similar or at the edge of data, resulting in a significant increase in the number of iterations of the algorithm, and even the phenomenon of clustering failure due to individual data.)