PQ_Control

压缩感知算法,提出了一种新型的信道估计算法(compressed sensing although，A novel channel estimation algorithm is proposed)

常规PID与模糊PID的比较，模糊调节的速度更快。(Comparison between conventional PID and fuzzy PID)

说明：  ARMA模型拟分为（一）（二）两部分发布，第一部分主要包括ARMA模型简介，模拟ARMA数据、拟合ARMA模型,单纯的AR模型或MA模型的定阶。第二部分主要包括ARMA模型的定阶策略、模型选择、残差分析。(ARMA model fitting is divided into two parts: (1) and (2). The first part mainly includes the introduction of ARMA model, ARMA data simulation, ARMA model fitting, AR model or MA model order.The second part mainly includes the fixed-order strategy, model selection and residual analysis of ARMA model.)

包括图像分析的四部分代码：matlab扩散和高斯函数，线性扩散，线性复扩散，非线性扩散。(It contains four parts： [1]MATLAB function:diffusion.m gauss.m [2]Linear diffusion Applying linear diffusion to images creating linear scale-space. MATLAB code: demo_lin.m Image: haifa1.bmp [3]Linear complex diffusion Applying linear complex diffusion creating Gaussian and Laplacian scale-spaces. MATLAB code: demo_cmplin.m [4]Nonlinear diffusions: Perona-Malik ["Anisotropic diffusion"] Catte et al. regularization of P-M Complex ramp-preserving diffusion Nonlinear edge preserving diffusions.The classical Perona-Malik process: the value of the diffusion coefficient is reduced near edges estimated by the first derivative. Best applied to step edges. A regularized version by Catte et al- the gradient estimation for controlling the process is smoothed by a Gaussian. Proved to be mathematically well-posed. Ramp preserving complex diffusion- best for ramp-type edges. Results are smoother with almost no staircasing effects. MATLAB code: demo_nldif.m Image: ct_scan.bmp)

一篇经典的2Ｄ转３Ｄ方面的文章，研究3DTV领域的很有用(ａclassic paper on 2D to 3D)

单色光（波）的双缝干涉实验的简单模拟：电磁场与微波技术、光学实验中，经常需要进行类似的干涉衍射实验，但受实验环境影响，结果误差大，用仿真模拟可以获得直观且纯净的结果。(Monochromatic light (wave), a simple simulation of the single-slit diffraction experiment: the electromagnetic field and microwave technology, optical experiments, often need similar interference diffraction experiments, but experimental environmental impact, results of large errors, the simulation can be intuitive and pure results.)

code in matlab for penny shaped crack

说明：  压缩感知Basis Pursuit基追踪算法，稀疏度和正确率的关系以及采样率和正确率的关系(Basis Pursuit based on compressed sensing and tracking relationships between sampling rate and accuracy of the algorithm, and the correct rate of sparsity)

悬置功能计算的，能量解耦的方法，优化及其位移的计算(The calculation of suspended function, energy decoupling method, optimization and calculation of displacement)

统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines( This section should give the reader a quick overview of the methods implemented in STPRtool. • Analysis of linear discriminant function: Perceptron algorithm and multiclass modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection of known algorithms solving the Generalized Anderson’s Task. • Feature extraction: Linear Discriminant Analysis. Principal Component Analysis (PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis. • Probability distribution estimation and clustering: Gaussian Mixture Models. Expectation-Maximization algorithm. Minimax probability estimation. K-means clustering. • Support Vector and other Kernel Machines: Sequential Minimal Optimizer (SMO). Matlab Optimization toolbox based algorithms. Interface to the SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers. Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell- Demyanov-Molozenov algorithm )