3DFDTDm

MATLAB的几个讲解实例！！！！！很好有。有要的就下吧！(MATLAB)

Matlab examples of "Multi sensor data fusion with Matlab" Book

修改过的比较实用易于理解的gabor滤波，附有主函数调用部分，便于使用(Modified more practical easy to understand gabor filtering, with the main part of the function call, easy to use)

Leach protocol for wireless sensors

This function made recognize of letters

产生两不迥频率的信号，并对两信号作复数FFT变换，找出频率为50Hz的信号对其进行能量计算(Two distant signal is not generated frequency, and two signals for complex FFT transform to identify the frequency of 50Hz signal to calculate its energy)

基于MUSIC算法的OFDM系统子载波个数盲估计(Bind Estimation of the number of subcarriers of the OFDM system based on MUSIC algorithm)

基本粒子群算法源程序，开发环境是MATLAB，吐血推荐，易改实用！(Particle swarm algorithm source)

It simulates a simple circuit analysis phasors of current and voltages.

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)