ValderPol

说明：  基于四阶累积量的MUSIC算法，内有注释(Fourth-order cumulant-based MUSIC algorithm, the annotated)

基于稀疏变量的欠定盲源分离，可以解决源数大于传感器数的问题，即欠定盲源分离问题。(Based on the sparse variable underdetermined blind source separation, can solve the source number is greater than the number of sensors the problems, i.e. underdetermined blind source separation.)

This code is improving parts of the image with imcrop according to the 2-D histogram (mesh).

非平稳信号的处理方法，时频分布及、常用方法简单例子(Non-stationary signal processing methods, examples of commonly used methods)

文章介绍 了交通流元胞自动机模型的产生与发展,总结和评述了国内外各种元胞自动机模型,并对元胞自动机模型的发展提出 展望。 (This paper introduces the establishment and development of cellular automata model of traffic flow, summarizes and comments on different kinds of typical cellular automata models of traffic flow, and furthermore, presents a new perspective for further study of the model· )

programing in matlab helping you ser

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

实现了图像处理中的频域低通处理，适合图像处理的初学者。matlab实现。(Image processing to achieve a low-pass frequency domain processing, image processing for beginners. matlab implementation.)

一个简单的MATLAB例子，希望对大家有所帮助(A simple MATLAB example, we want to help)

北京航空航天大学《惯性导航原理》第二次大作业源程序(Beijing University of Aeronautics and Astronautics " principle of inertial navigation" second source operations)