PFfor_lineargaussian

此段代买可以用来实现层次分析法，通过输入参数、更改参数，利用循环自动检验是否符合层次分析法的要求(solve the AHP problem)

张正友标定算法的matlab程序实现摄像机的内外参数的标定(Zhang Zhengyou calibration algorithm matlab program)

信号与信息处理——阵列信号处理DOA估计的matlab算法，这是求根music算法的源代码，非常有用的代码(Signal and information processing- array signal processing matlab algorithm for DOA estimation, this is roots music algorithm source code, very useful code)

建立飞机散射模型，并计算飞机模型的RCS值；雷达目标EKF滤波算法(Aircraft scattering model is established, and calculation model airplanes RCS value; Radar target EKF filtering algorithm)

多用户MIMO预编码技术：块对角化BD，分别采用SVD技术，最大似然检测，最小均方误差检测的误码率性能仿真代码。里面有代码，仿真图等。(Multi-user MIMO precoding techniques: block diagonalization BD, respectively, using the SVD technique, maximum likelihood detection, the minimum mean square error detection BER performance simulation code. There are code, simulation maps.)

空气质量降维B-P神经网络评价法及其MATLAB实现 简介: 用主成分分析将多维空间的样本数据降维到低维空间，将其作为BP网络的输入。BP网络的算法采用LM优化法。描述了该网络的MATLAB程序语言实现过程。(Air quality dimension reduction BP neural network uation method on MATLAB Introduction: Using principal component analysis will drop the sample data in multidimensional space dimension to low-dimensional space, as the input of BP network. BP network algorithm using LM optimization method. The network describes the MATLAB programming language implementation process.)

matlab code for finite element method

SVD based image compression

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

聚类算法，有详细的解释说明，是MATLAB编程。(Clustering algorithm, a detailed explanation, is the MATLAB programming. )