MATLAB_signal_processing

N-way Multivariate ANalysis of COVAriance (MANCOVA

empirical mode decomposition process hilbert huang transform - Matlab.

：给出了一种提取特征点的新方法和合理的特征不变量。该方法根据三角不等式原理利用第三边与另 外两边之和的比值大小来确定角点，以特征点和它相邻的两个特征点所形成的两条直线的夹角正切值以及三角形 第三边与另两边和的比值作为特征不变量。这种特征提取的方法简单，并且由于扩大了模板取值范围，使识别效果受特征点漏检的影响较小。实验表明，该方法不仅简便，而且效果良好。(: In this paper, a new feature point extraction method and the characteristics of rational invariants. The method is based on the principle of triangle inequality with the use of the third side and both sides of the ratio of the size to determine the Corner to feature point and its two adjacent feature points formed by the angle between two tangent linear and the third triangle on both sides and edges with another feature of the ratio as a variable. This feature extraction method is simple, and as a result of the expansion of the scope of the template values to identify the effect of feature points by the impact of smaller undetected. Experiments show that the method is not only simple, but effective.)

a matlab code that can be used for calculating the PCA of any kind of data , using simulink

多目标跟踪的粒子滤波器，各种资源分配算法实现，实现串口的数据采集，调试通过可以使用，滤波求和方式实现宽带波束形成，matlab程序运行时导入数据文件作为输入参数。( Multi-target tracking particle filter, Various resource allocation algorithm, Achieve serial data acquisition, Debugging can be used, Filtering summation way broadband beamforming, Import data files as input parameters matlab program is running.)

使用对称秩1算法求解无约束问题 输入包括初始点，目标函数和梯度 输出包括最优点最优解和迭代次数(Rank 1 using symmetric algorithms for unconstrained problems including initial entry points, the objective function and the gradient of the output, including the most optimal solution and the advantages of iterations)

对陀螺仪导航进行卡尔曼滤波的计算与仿真，效果很好。(For gyro navigation kalman filtering calculation and simulation, the effect is very good)

Source code for Hopfield character Recognition

image proccesing improved by using spatial filtering

This code implements the algorithm described in the paper: Blind deblurring using internal patch recurrence by Tomer Michaeli and Michal Irani