AMI

matlab得毕业设计源码 请选择性的使用 内容详细(matlab code of graduation thesis)

matlab的电子书籍，对初学者非常有用。书籍的内容涵盖比较详细(Matlab electronic books, very useful for beginners. The content of the book covers more detail)

汽车高斯曲面拟合 --- 2程序，以适应到表面二维高斯： 子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的 这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。 autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。 autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种(Auto Gaussian Surface fit --- 2 routines to fit a 2D Gaussian to a surface: zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters. autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit. autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals))

This is BOC AWGN Simulation zip file. It contains one folder(build setting) and 9 files(m file, mdl files etc..) Thank you

数字图像处理的一些经典matlab源程序，对于加深理解很有帮助(Some of the classic digital image processing matlab source, useful for better understanding)

NRZ encoder for matlab

matlab几种典型的混沌吸引子，经典中的经典！谢谢！！！(matlab several typical chaotic attractor, the classic of classics! Thank you! ! !)

lists all possible ways to permute k elements out of the vector v, with replacement.

方位估计是阵列信号处理的重要内容，使用单个矢量传感器估计信号方位近几年才发展起来(DOA estimation array signal processing is an important aspect of using a single vector-sensor estimate signal directions only in recent years developed)

this project compares two text in txt format for plagiarisim detection.this script show how works winnoing method and return similarity in form of figure between 0 to 100.