MATLAB

Digital Communication:

Signal Processing:

x=[1,5,2,4,2]

y=[3,7]

cl=conv(x,y)

stem(cl,'filled')

ylim=[0 40]

title('Convolution of x and y')

x=randn(6,1);

y=randn(6,1);

A=[x y 2*y+3];

R=corrcoef(A);

y1=fliplr(y)

cl1=conv(x,y1)

stem(cl1,'filled')

ylim=[0 40]

title('Correlation of x and y')

n=0:15;

xn=0.84.^n;

yn=circshift(xn,5)+0.3*randn;

[c,lags]=xcorr(xn,yn);

figure

subplot(2,1,1)

stem(lags,c)

title('Crosscorrelation of x and y')

%%%% Convolution of a rectangular pulse with itself

clc;

clear all;

close all;

x=ones(1,11);%%%%%rectangular pulse sequence

h=ones(1,11);%%%%

nx=[-5:5]; %%%%%(x axis -5 to 5)

LEx=nx(1); %%%%%Left end of x

nh=[-5:5];

LEh=nh(1);

Nx=length(x); %%%%Length of first sequence)

REx=nx(Nx); %%% Right end of x

Nh=length(h); %%%%Length of second sequence)

REh=nh(Nh); %%%Right end ofh

Ny=Nx+Nh-1; %%% Length of output

LEy=LEx+LEh; %%%%%LEft end of output

REy=REx+REh; %%%%Right end of output

ny=LEy:REy; %%%%%Variable for y vector

Xn=[x zeros(1,Nh-1)];

hn=[h zeros(1,Nx-1)];

for i=0:Ny-1

sum=0;

for j=0:i

sum=sum+Xn(j+1)*hn(i-j+1) %%%%Formula for Convolution

end

Y(i+1)=sum;

end

figure,

subplot(1,2,1),stem(nx,x);

subplot(1,2,2),stem(ny,Y);

y=conv(x,h);

ny=-10:1:10

figure, stem(ny,y)

Robotics: