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Compute the frequency samples of the DTFT

说明：  这个是做模糊图像处理的一些例子，以及代码。各种方式去噪去模糊(This is to do some examples of fuzzy image processing, and code. Denoising a variety of ways to fuzzy)

有限元方法求解laplace 方程(matlab),可以用混合单元，混合边界条件(sloving Laplace eauations with finite element methods. )

压缩感知信号重构方法—cosamp，此方法可以快速精确地恢复原始信号(Compressed sensing signal reconstruction method-cosamp, this method can quickly and accurately recover the original signal)

lab programs for advanced communication experiments using matlab

matlab编写的3维人体运动重建，效果很不错。(Three-dimensional reconstruction of human motion, the effect is very good.)

电容检测不仅用在您的智能手机中；在必须与人体皮肤接触的医疗设备等产品中，它也有用武之地。本文介绍如何使用电容检测来确定设备表面与用户皮肤的接触质量(Capacitive sensing is not only used in your smartphone to be in contact with human skin in medical equipment and other products, it also have its uses. This article describes how to use capacitive sensing to determine the device contacts the skin surface and the quality of the user )

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)

dear sir i have uploaded a few programs for your consideration

svm程序，下载后可以直接运行，但需要结合data数据。没有密码(svm program can be run directly after download, but requires a combination of data data. No password)