Tengfei-Shen

This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images.

MATLAB——通讯录软件分享 使用MATLAB GUIDE，可以载入已存在的数据库，可以新增新的数据库（数据用excel文件存放的）。(MATLAB- Address Book software to share with MATLAB GUIDE, you can load an existing database, you can add new database (data storage with the excel file.))

求离散超混沌系统的自相关和互相关函数，本程序使用的是kawakami超混沌系统(for discrete hyperchaotic system autocorrelation and cross-correlation function, This procedure is used in super-chaotic system kawakami)

PROGRAM IS USED FOR Interface matrix keyboard with 8051.

Libelas(大规模高效立体匹配库)是cross-platfrom c++ 图书馆与MATLAB计算包装差异大的地图图像。输入 是一双纠正灰度立体图像的大小相同。输出相应的 差距的地图。(Libelas (LIBrary for Efficient LArge-scale Stereo matching) is a cross-platfrom C++ library with MATLAB wrappers for computing disparity maps of large images. Input is a rectified greyscale stereo image pair of same size. Output are the corresponding disparity maps.)

说明：  matlab用的惯性导航工具箱，用来实现算法仿真。(Matlab toolbox with inertial navigation, used to realize the algorithm simulation.)

说明：  连续波信号调频的雷达处理,利用MATLAB/Simulink进行编程和仿真(FM continuous wave radar signal processing using MATLAB/Simulink programming and simulation)

Particle swarm optimization has been used to solve many optimization problems since it was proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and several papers on this topic [5][7][13][15], one of which did a study on its performance using four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO, each particle moves in the search space with a velocity according to its own previous best solution and its group’s previous best solution. The dimension of the search space can be any positive integer.

说明：  0案例分析\MATLAB神经网络30个案例分析\源程序\案例1 BP神经网络的数据分类-语音特征信号分类(0 Case Study \ MATLAB neural network analysis of 30 cases \ source \ Case 1 BP neural network data classification- voice characteristic signal classification)

We see from the graphs , that as the number of samples increased, the estimation gets better. As now for increased number of samples , variance of estimators are reduced. So, we get better estimation.