fnnyneering__functional

vc实现http下载的源代码 稍作改动可用来实现多线程 以及断点续传下载 对于想要学习http下载的朋友来说可以作为一个不错的参考(Vc realize the download HTTP source code can be used to make a little change to realize multithreading and netants download to want to learn HTTP download all of us can be used as a good reference)

说明：  能实现ListView的翻页功能,使用的语言是C#和WPF(Page Turning Function of ListView)

在windows下模拟测试P2P试验 在windows下模拟测试P2P试验 在windows下模拟测试P2P试验 在windows下模拟测试P2P试验 在windows下模拟测试P2P试验 在windows下模拟测试P2P试验 在windows下模拟测试P2P试验

说明：  务器端基于select I/O模型。为防止程序界面阻塞，有一个子线程用于不断接收socket并select其中的处理。客户端只有一个线程函数，不过其利用率很高，可用于远程目录交换，请求文件大小，创建若干线程来下载文件。文件传输有上传和下载,还有对等传输，这个项目中，传输文件具体指下载。(Client server based on select I/O model. Programming interfaces in order to prevent obstruction, there is a sub-thread for receiving socket and select which treatment. There is only one client thread function, but its utilization very high, can be used for remote directory exchange, request the file size, create a number of threads to download the file. File transfer are uploading and downloading, as well as peer-to-peer transmission, this project, the specific means to transfer files to download.)

Winsock控件发送Email-Winsock Control Send Email

IEEE所有标准系统的局部电压稳定（L指标）计算的通用程序！适用于任何节点。(IEEE standard system L index calculation of the general program!)

如果其他会员下载一次您上传的源码，您的可用下载数就会增加1次。更多说明请参看

黑客兄弟们的最爱，想成为网络工程师和安全工程师都需要看( Hackers brothers favorites, want to become network engineers and security engineers need to see)

【实例简介】vc 开放的串口通讯程序，经测试特别好用

说明：  每组数据为25维，第1维维类别标识，后24维为语音特征信号。然后把四类语音信号何为一组，抽取1500组数据作为训练数据，500组数据作为测试数据，并对数据进行归一化处理。根据语音类别标识设定每组语音信号的期望输出值，如标识类为1时，期望输出向量为[1 0 0 0]。(Each group of data has 25 dimensions, the first dimension is identified by category, and the last 24 dimensions are speech feature signals.Then, select one group of four speech signals, extract 1500 groups of data as training data and 500 groups of data as test data, and normalize the data.Set the expected output value of each group of speech signals according to the speech category identification. For example, when the identification class is 1, the expected output vector is [1 0 0 0].)