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掌握和精通matlab之GUI设计，找了好久才找到的资料，大家看看吧。(Matlab master and master of GUI design, looking for a long time to find the information, let us look at it.)

说明：  2011数学建模a题，运用地理统计学内梅洛指数法，和综合指数法，简单实用(2011 Mathematical modeling of a problem, the use of geographic statistical Neimei Luo index method, and the composite index method, simple and practical)

wpdv-hop算法,dvhop算法的一种改进，粒子群算法与dv-hop算法的改进(Improved wpdv-hop algorithm, dvhop algorithm, PSO algorithm with dv-hop Improvement)

simulation of communication systems and software defined radio from the book chapter 4

设计一维纳滤波器 (1)产生三组观测数据：首先根据s(n)=a s(n-1)+w(n)产生信号s(n)，将其加噪（信噪比分别为20dB,10 dB，6 dB），得到观测数据x1(n), x2(n), x31(n). (2)估计xi(n)，i=1,2,3的AR模型参数。假设信号长度为L，AR模型阶数为N，分析实验结果，并讨论改变L,N对实验结果的影响。 (A Wiener filter design (1) to produce three sets of observations: first generating a signal s (n) according to s (n) = as (n-1)+w (n), to add noise (20dB SNR, respectively , 10 dB, 6 dB), to obtain observational data x1 (n), x2 (n), x31 (n). (2) estimate xi (n), i = AR model parameters 1,2,3. Assuming the signal length L, AR model of order N, analysis of experimental results and discuss the impact changing L, N of the experimental results.)

此代码展示了在mimo系统中分别用ML MMSE ZF和MMSE-SIC检测算法的不同误码率曲线(This code shows the mimo system respectively ML MMSE ZF and MMSE-SIC detection algorithm different BER curves)

说明：  信号x（n）=sin(2*pi*0.05*n)+2*cos(2*pi*0.12*n)+w（n），求N=365的相关函数和功率谱(Signal x (n) = sin (2 * pi * 0.05 * n) + 2 * cos (2 * pi * 0.12 * n) + W (n), the correlation function and power spectrum of n = 365 are obtained)

说明：  盲源分离FastICA、matalab程序，(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)

MAtlab code for finding transfer function

Keypress operated interactive peak detector function.