EM

Among the high-complexity methods, two methods dominate the literature Kalman filtering and Mixture of Gaussians (MoG). Both have their advantages, but Kalman filtering gets slammed in every paper for leaving object trails that can t be eliminated. As this seems like a possible deal breaker for many applications, I went with MoG. Also, MoG is more robust, as it can handle multi-modal distributions. For instance, a leaf waving against a blue sky has two modes—leaf and sky. MoG can filter out both. Kalman filters effectively track a single Gaussian, and are therefore unimodal: they can filter out only leaf or sky, but usually not both.

PLS回归模型matlab代码，可直接调用(PLSregression model matlab code，it can be called directly)

运用matlab环境，编写仿真程序，采用Radon计算线性调频信号的斜率(Use matlab environment simulation program written using Radon calculate the slope of the linear FM signal)

加arnold置乱的数字水印在matlab下的ＤＦＴ仿真实现整个程序运行还行，共享给大家-jpeg of the Matlab Simulation, and the whole process has run OK, for everyone to share (Add arnold Scrambling of digital watermarking in matlab under the DFT simulation run also realize the whole process line, to share to everyone-jpeg of the Matlab Simulation, and the whole process has run OK, for everyone to share)

matlab学习指南-编写与调用函数教程,非常实用!(matlab study guide- prepare and call the function tutorial, very useful!)

MatlabBGL. A Matlab Graph Library. MatlabBGL is a Matlab package for working with graphs. It uses the Boost Graph Library to efficiently implement the graph algorithms. MatlabBGL is designed to work with large sparse graphs with hundreds of thousands of nodes.

Convert a vector auto-regressive model of order k to state-space form

m-rife,单频信号的频率估计，适合初学者学习(m-rife, single-frequency signal frequency estimation, suitable for beginners to learn)

Bayesian macroeconometrics

Descriptor for simple shapes (e.g letters). Computes the descriptor twice in order to describe horizontally and vertically with the same accuracy. Input: image (2-D, binary or gray) depth of recursion (default 7), plotFlag(if on - plot Illustration) Output: vec - descriptor: Division locations, values around zero. Of length 2*(2^depth - 2) (levels: the depth of the division locations.)