机器人PID控制仿真

用滤波器祛除图象噪声，matlab环境运行(Eliminate the image noise with filters, matlab environment running)

联合开发网 - pudn.com

基于matlab编写的基本的M序列和gold序列的生成，作为其他伪随机码生成的基础。对于初学者很有帮助的(Prepared based on matlab basic sequence of M sequence and the generation of gold, as other pseudo-random code generated basis. Very helpful for beginners)

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)

Jakes Simulation Models With Correct Statistical Properties for Rayleigh Fading Channels

说明：  采用K-SVD对图像进行去噪处理。可以对不同类型的图像进行去噪处理。(Image denoising using K-SVD)

matlab环境下计算MFCC参数（mel倒谱参数），lpcc参数（线性预测倒谱）和pitch（基因）(calculate mfcc lpcc pitch )

编程,逆向及数据分析 编程逆向及数据分析. :数据分析的几个方面的经验总结.(Programming, reverse and data analysis 编程逆向及数据分析. Programming of reverse engineering and data analysis :数据分析的几个方面的经验总结. : data analysis of several aspects of the experience )

多重信号分类（MUSIC）算法，进行线列阵测向问题研究(Multi-signal Classification (MUSIC) Algorithms for Linear Array Direction Finding)

对有噪声的图像进行图像复原，通过迭代算法实现此功能(Image restoration for noisy images, this function is implemented by an iterative algorithm)