wenjian

这是一个好用的频偏估计算法的matlab仿真程序，谢谢使用。(This is a nice offset estimation algorithm matlab simulation program, thank you to use.)

这是多目标跟踪的入门文献，希望研究高斯概率假设密度的朋友共勉。文中详细描述了多目标跟踪的基本原理及最新方法。(This is the entry of multi-target tracking literature study friends encourage each of the Gaussian probability hypothesis density. The paper describes in detail the basic principles of multi-target tracking and the latest methods.)

matlab程序，很多好的小程序可以用！适用于初学者(Matlab procedures, the many excellent small procedures can be used. Apply to beginners)

matlab解用分支定界法解整数规划的程序。请大家参考(matlab solution using branch and bound method for solving integer programming procedures. Please refer to)

WiMAX物理层平台搭建，并实现了物理层小数时延和整数时延的添加，并并一定方法实现对时延的估计(WiMAX physical platform building, and implements the fractional time delay and integer delay to add the physical, and a certain method of time delay estimation)

用matlab编写的家庭安全监控系统,运用无线网络摄像头，同软件实时监控家庭实际情况。(Home Security System write by matlab.)

储能电池在电网备用中的matlab图解 清晰展示了电池如何控制及如何与电网连接(battery is connected to grid through 3-phase inverter. PI based controller is developed for control of inverter according to Line to Line voltage of grid. and load is connected in between grid and battery. 100Km length of transmission is considered here.)

自己搭建的模型，采用VF控制方法，用MATLAB完成(The model built by ourselves is completed by using VF control method and MATLAB.)

现代通信系统matlab版（中文）用超星打开(Modern communications systems matlab version (Chinese) open with Chaoxing)

自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。 在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。 二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。 BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods. Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs. Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)