runge-kutta-of-2stage-4th-order

几个随机分布序列的MATLAB程序，有加入0，1均匀分布的，有加入－1，0，1对称分布的等等，还有加入高斯分布和Pearson分布(Sequence of several randomly distributed MATLAB procedures, were added to 0,1 uniformly distributed, there to join-1,0,1 symmetric distribution, etc., as well as adding Gaussian distribution and the Pearson distribution)

《精通Matlab6.5版》张志勇等编著，书本相关的PPT文件("proficient Matalab 6.5 version of the" Written by Zhang Zhiyong, books related to the PPT document)

能够很好的实现hilbert变化，结果和matlab基本一样，已经通过实验。(Hilbert can achieve very good changes, results and basic matlab, as the experiment has passed.)

说明：  用MATLAB编程，利用FDTD方法模拟了三维结构的场分布(With MATLAB programming, the use of FDTD method to simulate three-dimensional structure of the field distribution)

OFDM信号瑞利衰落信道的matlab仿真，包括一个word文档以及仿真程序。(OFDM signals in Rayleigh fading channel matlab simulation, including a word document as well as the simulation program.)

Character Recognition Example (I&II)- Pre-processing of Image (Matlab Code)(Character Recognition (I)

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)

信号稀疏表示，去噪处理，稀疏分解后重构性能非常好(Signal sparse representation, de-noising, sparse decomposition and reconstruction after the performance is very good)

说明：  几何校正 转换角度 需要的自己下载修改参数(Geometric correction)

m-file调查高功率放大器的影响，channelnoise在ofdm信号，当时介绍卡由于一个paprreductionmethod减少hpa效果。 (m-file investigation of high-power amplifier, channelnoise in ofdm signal, when introduced to reduce card due to a paprreductionmethod hpa effects.)