DV_hop_new

Introduction Icasso is based on running FastICA several times (resampling). Icasso pools all the estimates together and forms clusters bottom-up among them. The basic idea is that a tight cluster of estimates is considered to be a candidate for including a "good" estimate. A centroid of such cluster is considered a more reliable estimates than any estimate from an arbitrary run. (Instead of an average as a centroid, Icasso visualizes and returns a centrotype from each cluster. This is the one of the original estimates that is most similar to other estimates in the same cluster. You can compute the average by using Icasso functions.)

Matlab code to reconstruct using simple back projection

This code is related to the course Digital Communications. It easily reproduces the error probability as a function of the SNR per bit for QPSK and 16-QAM systems using MATLAB

this code written using IMRESIZE functions for bilinear

matlab 经典教程学习，为新手提供参考(matlab classic tutorials to learn, provide a reference for the novice)

xs128 SPI 控制数码管 串行通信(the xs128 SPI control digital tube serial communication)

this help to draw delaunay triagles is input x,y to vectors represent coordinate of points for exemple p1(1,2) ,p2(3,4) p3(5,6) then x=[1,3,5] and y=[2,4,6]

L-M(Levenberg—Marquardt)是一种非线性最优化里面最常用的方法之一，改方法能快速而准确的逼近收敛值。本文简述了改方法的操作原理和具体运用，如可以方便的用于有约束与无约束最优化的求解。本文件里面有源码和测试码以及对该方法的具体介绍PDF文件。欢迎下载。(L- M (Levenberg- Marquardt) is a kind of nonlinear optimization inside one of the most commonly used method, change method can fast and accurate approximation convergence value.This paper briefly describes the operating principle of change method and concrete application, such as easy to solve constrained and unconstrained optimization.This document there are source code and test code as well as to the PDF file the detailed introduction of the method.Welcome to download.)

离散快速分数阶傅立叶变换,抽取信号，离散算法(the Discrete Fractional Fourier Transform)

上传的这个Matlab源代码可以用于主成分分析以及核主成分分析，学者们可以通过此方法实现数据的压缩。(this can be used to have a Principal component analysis and a Kernel Principal component analysis by those research works)