SphereSurface

用EKF/UKF的Matlab仿真程序包(With EKF/UKF the Matlab simulation package)

matlab中所有基本函数功能实现的源代码，可以利用以下函数实现matlab中信号处理的傅里叶等函数变换。(matlab function to achieve the basic function of all the source code, you can use the following function implementation matlab signal processing functions such as Fourier transform.)

taylor green vortex runing on matlab, using two relaxation time method.

对一维稳态热传导方程采用matlab语言编程求解。(The one-dimensional steady-state heat conduction equation is solved by MATLAB language programming.)

matlab function for estimation gaussian and motion blur parapeters

附件程序为图像稀疏表示的源代码，程序用matlab编写，能正常运行。(Sparse representation of the image attachment program source code, programs written with matlab able to function properly.)

说明：  该功能适用于单尺度自商图像算法的图像的MATLAB程序(The function applies the single scale self quotient image algorithm to an image. )

marmousi模型，用于地震资料的反演方法，可作为验证反演方法精度的模型(marmousi model for seismic data inversion method can be used as a method to verify the accuracy of the model inversion)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

laplacian of gaussian test