Optimal-Power-Flow-in-Microgrids

rt2870, software, windows, ce

实现了关于MIMO雷达利用ESPRIT算法精确测向问题，精度较高，可以直接使用(The accurate direction finding problem of MIMO radar using ESPRIT algorithm is realized. The accuracy is high, and it can be used directly.)

C++实现电脑屏幕获取和显示，更改一下可以保存为文件(The acquisition and display of computer screen is realized by the C-c)

基于API的套接字编程，可以检测到服务器和当前客户机的IP地址和端口号，并接收客户端信息。-Socket-based API programming, can be detected by the current server and client IP address and port number, and receive client information.

水准网高程平差，C#语言，可以进行水准网的完整平差程序(Leveling adjustment of leveling network)

在WINDOWS下进行网络服务端程序开发，毫无疑问，Winsock 完成端口模型是最高效的。Winsock的完成端口模型借助Widnows的重叠IO和完成端口来实现，完成端口模型懂了之后是比较简单的，但是要想掌握Winsock完成端口模型，需要对WINDOWS下的线程、线程同步，Winsock API以及WINDOWS IO机制有一定的了解。如果不了解，推荐几本书：《Inside Windows 2000，《WINDOWS核心编程》，《WIN32多线程程序设计》、《WINDOWS网络编程技术》。在去年，我在C语言下用完成端口模型写了一个WEBSERVER，前些天，我决定用C++重写这个WEBSERVER，给这个WEBSERVER增加了一些功能，并改进完成端口操作方法，比如采用AcceptEx来代替accept和使用LOOKASIDE LIST来管理内存，使得WEBSERVER的性能有了比较大的提高。 -under the Windows network server program development, no doubt, Winsock completed port model is the most efficient. Winsock port model with complete preview of overlap and complete port IO to achieve complete port after the model to understand is a relatively simple, but want to know the complete Winsock port model, the need for the Windows threads, thread synchronization, Winsock API and Windows IO mechanism of a certain understanding. If we do not understand, recommended several books : "Inside Windows 2000," Windows core programming "," WIN32 multithreaded program

自己做的一个简单的聊天市客户端软件..大家可以作为参考.特别适合一个刚学习C++的新手...感谢大家的支持-do for myself a simple chat client software market .. we can as a reference. Particularly suitable for a just learning Xi C newbie ... thank you for your support

labwindows/CVI 串口上位机源代码(LabWindows/CVI Serial)

visual basic programming textbook

Robotic systems have brought significant socioeconomic impacts to human lives over the past few decades. Cloud Robotics is the latest existing idea tapping into the Internet s vast and rapidly expanding network of computing and data resources. Cloud Robotics has the potential to significantly improve robot performance by indexing libraries of annotated image models to provide semantic information, e.g., CAD models and material properties to facilitate grasping, providing access to massively parallel computation, e.g., video processing, sample-based uncertainty modeling and motion planning, and statistical robot learning, supporting on-demand sharing of data, software, trajectories, and dynamic control policies, obtaining call-center human guidance when all else fails. Cloud computing can enable cheaper, lighter, “smarter” robots. The infrastructure exists and is rapidly evolving in terms of performance and accessibility