MATPOWER-in-power-changes

使用matlab实现p-value的算法 具体的函数实现。可用来处理统计推断等有关问题。(Specific algorithm matlab achieve p-value function. It can be used to deal with issues related statistical inference.)

netlab工具包，包含许多matlab图像处理等开发函数，解压到安装目录toolbox文件夹下，在matlab里更新路径后可直接调用(netlab Kit contains many matlab image processing development function, extract to the installation directory Toolbox folder in matlab can be called directly after the update path)

阵列信号处理的高分辨率估计，有CDF三角函数曲线/三维曲线图，能量熵的计算，Matlab实现界面友好，结合PCA的尺度不变特征变换(SIFT)算法，调试通过可以使用。( High-resolution array signal processing estimates, There CDF trigonometric curve/3D graphs, Energy entropy calculation, Matlab to achieve user-friendly, Combined with PCA scale invariant feature transform (SIFT) algorithm, Debugging can be used.)

说明：  数字图像修复算法的评价标准也是从两个方面进行评价的，一个是修 复算法的效率，一个是修复后图像质量。修复算法的效率主要就是看修复 图像所用的时间，可以用时间就行量化。而图像质量很难从量上唯一的进 行评价，目前图像修复质量还没有统一的评价标准，从实际应用出发，修 复后图像质量的好坏大多数是从以下两个方面就行评价，修复后图像给人 主观上的“满意度”和客观上图像的cnr、enl、epi、psnr、snr来评判。(The evaluation criteria of digital image restoration algorithms are also evaluated from two aspects, one is repair. One of the efficiency of complex algorithm is the image quality after restoration. The efficiency of the repair algorithm depends mainly on the repair. The time spent on the image can be quantified in terms of time. But image quality is difficult to improve quantitatively only. At present, there is no uniform evaluation criterion for image restoration quality. Starting from practical application, repair The quality of restored images is mostly evaluated from the following two aspects, and the restored images are given to people. Subjective "satisfaction" and objective image cnr, enl, epi, psnr, SNR to judge.)

说明：  Wavelet Packet Decomposition details and codes

matlab 实现的微粒群算法PSO，比较基本，适合初学者学习参考，自适应权重（非线性）(matlab implementation of PSO PSO, more basic, suitable for beginners to learn reference adaptive weight (nonlinear))

ldpc译码算法，采用LOG算法，减少计算复杂度(ldpc decoding algorithm used LOG algorithm, to reduce the computational complexity)

EasyMod is a MatLab/Scilab toolbox for teaching modal analysis. It provides a set of functions allowing to establish the identification of modal parameters and these validation. Nowadays, circle-fit, line-fit and least square complex exponential methods are implemented. In addition, some relevant functions are offered in order to complete a modal analysis: operations on the frequency response function (FRF) , generation of FRF from mass, damping and stiffness matrices, MAC (modal assurance criterion) and modal collinearity

使用预测控制中的MPC控制算法，利用MATLAB进行编程，对传递函数进行控制。(Use predictive control MPC control algorithm, using MATLAB programming for the transfer function control.)

quickly search for the fourier series