MATLABsound

对一个全彩色图像进行色彩通道的分解，从而可以看到不同的颜色通道下的图像。(IMCOLORSEP Displays the RGB decomposition of a full-color image)

matlab的数据分析的能力非常强，利用matlab软件进行数据分析，在数学建模中有非常重要的应用(matlab data analysis, data analysis using matlab software has very important applications in the mathematical modeling)

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

基于压缩感知的目标DOA估计，以及常规DOA的方法(DOA Estimation Based on Compression Sensing)

如题，从菜鸟到专家级的MATLAB指导 A Guide to MATLAB for Beginnersand Experienced Users(A Guide to MATLAB for Beginnersand Experienced Users)

说明：  分析了LTE系统频率偏移，定时误差，信道估计问题等，并对广义Rake接收机和CPEDS接收机的性能做了比较。(Analysis of the LTE system frequency offset, timing error, channel estimation problems, and generalized Rake receiver performance receiver and CPEDS were compared.)

multi layer perceptron - MLP sample

可以将matlab中的mat文件转换成txt文件，方便其他的应用程序调用。(Mat files can be converted into a txt file and facilitate the application calls.)

K-MEANS算法程序(MATLAB环境)(K-MEANS algorithm procedure (MATLAB environment))

说明：  深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)