men-in-black-iii_english-620159

基於 Matlab 系统辨识工具箱的系统建模(Matlab System Identification Toolbox for System Modeling)

说明：  该文件提供了基于图像颜色特征检索的源代码(this artical provides the code based of the color of a image)

Detect the type and convert the image to one out of four types: BW, LABELED, CONTOUR,LIKELIHOOD. This program can be used as a part of automatic image recognition tool.

说明：  获取图像的mser区域，使用matlab语言(Get image mser area, using matlab language)

[x,f]= L01p_e(c,A,b,N)用枚举法求解下列 0-1线性规划问题min f=c *x, s.t. A*x<=b,x的分量全为整数0或1, 其中N表示约束条件 Ax ≤ b中的前N个是等式，N= 0时可以省略。 返回结果x是最优解，f是最优解处的函数值。 ([X, f] = L01p_e (c, A, b, N) with the following enumeration method for solving linear programming problems 0-1 min f = c ' * x, st A* x < = b, x is an integer of all components 0 or 1, where N represents the constraints Ax ≤ b in the equation of the first N, N = 0 can be omitted. Return result x is the optimal solution, f is the function value at the optimal solution.)

一个语音识别的程序，用matlab进行训练、识别。(A speech recognition procedure, using matlab for training, identification.)

运用matlab来编写关于图像处理所需要处理的实验，熟悉matalab(Use matlab to prepare a report on the need to deal with image processing experiments, familiar matalab)

MATLAB实现EMD分析振动信号时的波形匹配延拓，此方法可以一定程度上降低希尔伯特黄变换引起的端点效应(MATLAB to achieve EMD analysis of vibration signals when the waveform matching extension, this method can be reduced to some extent, the Hilbert yellow transform caused by the endpoint effect)

采用加权网络中节点强度和权重都是幂率分布的模型，分析了该信号的时域、频域、倒谱，循环谱等,粒子图像分割及匹配均为自行编制的子例程。( Using weighted model nodes in the network strength and weight are power law distribution, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. Particle image segmentation and matching subroutines themselves are prepared.)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)