Array-signal-processing--

程序1实现几个简单函数的响应序列； 程序2实现了两个不同序列的输出响应； 程序3给出了简单序列的零极点图以及输出响应。(Program a response sequence to achieve a few simple functions Procedure 2 implements two different sequences in response to the output Program 3 shows the output response of the pole-zero diagram and simple sequence)

hatch MATLAB and POWER POINT

SVM_GUI_3.1[mcode][by_faruto]1 此SVM_GUI是基于libsvm-FarutoUltimate，所以你需要实现安装该工具箱。会用libsvm- FarutoUltimate的朋友，这个GUI一看就会用，里面的参数的输入就是原来libsvm- FarutoUltimate里面的输入要求(SVM_GUI_3.1 [mcode] [by_faruto] 1 is based on this SVM_GUI libsvm-FarutoUltimate, so you need to implement to install the toolkit. Will use libsvm- FarutoUltimate friend, a look will use this GUI, enter inside the parameters of which is the original input requirements libsvm- FarutoUltimate)

用有限差分法求双脊金属加载矩形波导的基模及第一个高阶模的色散特性曲线( Finite difference method seeking double-ridged metal loaded rectangular waveguide fundamental mode and first higher order mode dispersion characteristic curve)

matlab量子遗传算法及调试信息 txt文档中的是matlab .m文件源码(the quantum genetic algorithms and some of this test info. the txt doc. contain .m(matlab) doc.)

part3 schrodinger solve...

该程序为微粒群算法与遗传算法求解pid参数整定MATLAB程序，优化结果包含参数输出与参数曲线变化图。(The program solves the PID parameter tuning MATLAB program for particle swarm optimization and genetic algorithm, and the optimization results include parameter output and parameter curve change diagram.)

Frame difference is arguably the simplest form of background subtraction. The current frame is simply subtracted from the previous frame, and if the difference in pixel values for a given pixel is greater than a threshold Ts, the pixel is considered part of the foreground.

The sourcecode is about optical character recognision using matlab.

function [a,b,err] = shanks(x,p,q) SHANKS Model a signal using Shanks method ---- Usage: [a,b,err] = shanks(x,p,q) The sequence x is modeled as the unit sample response of a filter having a system function of the form H(z) = B(z)/A(z) The polynomials B(z) and A(z) are formed from the vectors b=[b(0), b(1), ... b(q)] a=[1 , a(1), ... a(p)] The input q defines the number of zeros in the model and p defines the number of poles. The modeling error is returned in err.