sendtcpip

教你如何用LabView写IVI驱动，以实现某个测量任务。(Getting Started with IVI Drivers，Your Guide to Using IVI with LabVIEW)

三相光伏并网逆变器完整的仿真模型，希望对您们有用(Complete simulation model of three-phase photovoltaic grid connected inverter)

说明：  计算EEG数据集的锁相值（PLV）。 ％ ％输入参数： eegData是3D矩阵numChannels x numTimePoints x numTrials srate是EEG数据的采样率filtSpec是用于在期望的感兴趣频带中过滤EEG信号的滤波器规范。 它是一个具有两个字段的结构，顺序和范围。 范围指定频带的限制，例如，对于伽马频带，使用filtSpec.range = [35 45]。 在filtSpec.order中指定FIR滤波器的顺序。 一个有用的经验法则可以包括约4到5个周期的所需信号。 例如，对于以500Hz采样的eeg数据，filtSpec.order = 50对应于100ms并且包含~4个周期的γ波段（40Hz）。(Computes the Phase Locking Value (PLV) for an EEG dataset. % % Input parameters: % eegData is a 3D matrix numChannels x numTimePoints x numTrials % srate is the sampling rate of the EEG data % filtSpec is the filter specification to filter the EEG signal in the % desired frequency band of interest. It is a structure with two % fields, order and range. % Range specifies the limits of the frequency % band, for example, put filtSpec.range = [35 45] for gamma band. % Specify the order of the FIR filter in filtSpec.order. A useful % rule of thumb can be to include about 4 to 5 cycles of the desired % signal. For example, filtSpec.order = 50 for eeg data sampled at % 500 Hz corresponds to 100 ms and contains ~4 cycles of gamma band % (40 Hz).)

Android手势识别的实现附上源代码，在屏幕上方划动手指，即可实现手机的响应，执行相应的操作，比如滑动、切换屏幕、图片放大、打开应用程序等，自动判断手势相似度，比较基础的安卓手势操作例子。

说明：  实现简单计算，用户可以进行拓展那功能能。。。。(Implementing Simple Computing)

一个mfc写的石子棋小游戏-a mfc write small stones chess game

说明：  以太网和SONET/SDH中10GB/s光信号的精确消光比测量(Precise Extinction Ratio measurement for optical 10 Gb/s signaling in Ethernet and SONET/SDH)

说明：  lms滤波，可以用于去噪。在标准sin的信号基础上加入高频噪声，均匀白噪声和高斯白噪声，再采用lms滤波，可以得到预期波形(LMS filtering can be used for denoising. The expected waveform can be obtained by adding high frequency noise, uniform white noise and Gaussian white noise on the basis of standard sin signal and using LMS filter.)

使用高阶累积量对MPSK信号进行调制识别，借鉴了主成分分析算法（PCA），完整的基于HMM的语音识别系统。( Using high-order cumulants of MPSK signal modulation recognition, It draws on principal component analysis algorithm (PCA), Complete HMM-based speech recognition system.)