sskernel

用matlab做的指纹提取的源码，具有很好的借鉴作用。(Using matlab to do the fingerprint extracted from source code, with a very good reference.)

matlab绘图指令，适合初学者 （ppt）(Matlab drawing instructions（ppt）)

matlab是重要的数值仿真软件，具有强大的数值图示能力，上传几本关于matlab的书籍，希望对大家有帮助。(matlab simulation software is an important value, the value of the icon with a strong ability to upload several books on matlab, I hope to help everyone.)

数字信号处理，现行卷积的matlab代码，和函数文件。(Digital signal processing, the current convolution matlab code, and function files.)

摄像功能实现，Camera function realization(Camera function realization, Camera function realization)

说明：  用MATLAB实现（7，3）循环码算法，很好的体现了循环码的特性(Using MATLAB implementation (7,3) cyclic code algorithm shows good characteristics of cyclic codes)

Bootstrap(自举法)的Matlab程序 (Bootstrap program)

无迹卡尔曼滤波器(Unscented Kalman Filtering)的MATLAB实现(ukf(Unscented Kalman Filtering)MATLAB implementation)

matlab 时频分析技术及其应用：本书的目的主要面向工程应用及实际问题的解决，通过大量的示例和具体应用实例来讲解一些复杂理论的应用。(matlab fft )

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)