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MATLAB编程基础word版的教程，讲的很好！(Basic word version of MATLAB programming tutorial, pretty well!)

自编烟花的matlab程序，精致的烟花效果。在matlab程序中实现(Self fireworks matlab program, exquisite fireworks effect. Implement the program in matlab)

MATLAB FOR FULL MIMO OFDM CODING

模拟退火法求函数的最小值 结束条件为两次最优解之差小于某小量(End condition of the simulated annealing method and function of the minimum of twice the optimal solution of the difference is smaller than some small amount of)

粒子滤波中的经典matlab仿真程序，辅助粒子滤波，正则粒子滤波，还有说明(Five classic particle filter Matlab simulation program, resampling)

Test the optimized implementation of comparison with single-character strings.

数字信号处理，在圆周上的卡尔曼滤波动态追踪程序(Digital signal processing, dynamic tracking on the circumference of a circle of kalman filtering)

LTE channel model matlab source code

C-均值动态聚类算法 matlab 很简单对 一组样本点的分类和中心点(C- Means clustering algorithm Matlab is very simple sample of a point and focal point for the classification)

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)