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matlab中PID控制系统仿真模型，算法简单、稳定性好、可靠性高(PID control system simulation model in matlab, simple algorithm, good stability, high reliability)

大家看看，关于最优单纯形法非线性方程matlab源代码(We can see that on the optimal simplex method matlab source code. . .)

用互信息作为两幅图像的相似性度量进行配准(Using mutual information as the two images of the similarity measure for registration)

图形旋转的matlab程序，可以进行任意角度的旋转 请参看(Graphic rotation matlab program, can be at any angle of rotation see)

通过前期统计的数字，直接求马尔科夫链的转移矩阵，很经典(The Markoff transfer matrix )

基于waston的自适应数字水印算法，部分可用，需要的朋友请参考。(Adaptive digital watermarking algorithm based waston partially available, please refer friends.)

使用matlab对基于最小错误率的Bayes分类器进行仿真，编写了相应的程序，分为协方差相等和不相等两种情况，最后画出了三类的分界线(Using matlab to the smallest error rate based on the Bayes classifier to carry out simulation, the preparation of the corresponding procedures, divided into equal covariance and unequal both cases, the final draw of the three types of line)

MATLAB中包络线的画法，子程序带DEMO(MATLAB envelope painting, subroutines with DEMO)

说明：  MUSIC算法 DOA（Direction Of Arrival）波达方向定位技术主要有ARMA谱分析、最大似然法、熵谱分析法和特征分解法，特征分解法主要有MUSIC算法、ESPRIT算法WSF算法等。 MUSIC (Multiple Signal Classification)算法，即多信号分类算法，由Schmidt等人于1979年提出。MUSIC算法是一种基于子空间分解的算法，它利用信号子空间和噪声子空间的正交性，构建空间谱函数，通过谱峰搜索，估计信号的参数。对于声源定位来说，需要估计信号的DOA。MUSIC算法对DOA的估计有很高的分辨率，且对麦克风阵列的形状没有特殊要求，因此应用十分广泛。(DOA (direction of arrival) positioning technology mainly includes spectrum analysis, maximum likelihood method, entropy spectrum analysis method and feature decomposition method. The feature decomposition method mainly includes music algorithm, ESPRIT algorithm, algorithm, etc. Music (multiple signal classification) algorithm, namely multi signal classification algorithm, was proposed by Schmidt In 1979. MUSIC algorithm is an algorithm based on subspace decomposition. It uses the orthogonality of signal subspace and noise subspace to construct spatial spectrum function, and estimates signal parameters through spectrum peak search. For sound source localization, DOA of signal needs to be estimated. MUSIC algorithm has high resolution for DOA estimation, and has no special requirements for the shape of microphone array, so it is widely used.)

图像 稀疏分解方法 Random Sampling of Sparse Trigonometric Polynomials II - Orthogonal Matching Pursuit versus Basis Pursuit - (Preprint arXiv:math.CA/0604429) by Stefan Kunis and Holger Rauhut(Image sparse decomposition method Random Sampling of Sparse Trigonometric Polynomials II- Orthogonal Matching Pursuit versus Basis Pursuit- (Preprint arXiv: math.CA/0604429) by Stefan Kunis and Holger Rauhut)