案例5

Denmark Technical University的大牛Hansen，第二版风力机空气动力学。(Denmark Technical University' s Daniel Hansen, the second edition of wind turbine aerodynamics.)

数字信号处理功率谱估计自相关法——列文森递推法的MATLAB实现(Digital signal processing (DSP) power spectrum estimate autocorrelation method levinson recursive method of MATLAB)

SPA投影 用于 光谱分析 选取特征波长(SPA projection used for spectral analysis selected characteristic wavelengths)

omid code image for image rf processing

用matlab分析由n个单胞组成的桁架在受到最外节点处的集中力是的位移及应力应变，并对整个结构进行优化设计。(Matlab analysis by single cell formed by the n truss subjected to the most outside the nodes in the concentration of power is the displacement, stress and strain, and the entire structure is optimized.)

产生常用的M序列，M序列是模拟以及仿真常用的序列。以及产生逆M序列。(M arrey)

基于Opencv的视频序列前景检测模块，可以用来检测自动化目标跟踪的初始位置(Opencv based video sequences foreground detection module,can be used to detect the initial position in automated target tracking )

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)

example of texture segmentation using matlab code

数字信号处理及其matlab实现，绝对好资料，实用，方便新人学习(Matlab digital signal processing and its realization)