Manual Code for convolution

Hello there!! Now, I make a code for convolution. data is discrete like

x=[1,3,5,7,9]; y=[5,4,3,2,1];

It mean if t=1, x is 1 and y=5 / if t=3, x is 5, y is 3 ....

So I cannot use 'conv' function. My code is like that

             for t=1:5
                 convolution(t)=0;
                 for j=1:t
                    convolution(t)=convolution(t)+x(t+1-j)*y(j);
                 end
             end

But result is not good. Are there something to fix?

Answer 1

[Birdman]

x=input('Enter x:   ')
h=input('Enter h:   ')
m=length(x);
n=length(h);
X=[x,zeros(1,n)]; 
H=[h,zeros(1,m)]; 
for i=1:n+m-1
Y(i)=0;
for j=1:m
if(i-j+1>0)
Y(i)=Y(i)+X(j)*H(i-j+1);
else
end
end
end
Y

Answer 2

[AKASH KUMAR]

% 
% algorithm to compute convolution 
clc
clear 
close all
x1 = [5,6,1,2];
h = [10,6,4,8,9,5];
N = length(x1)+length(h)-1;
y=conv(x1,h) % Inbuilt matlab function

y = 1×9

50 90 66 90 109 95 55 23 10

x = linearconvolve(x1,h); % Function call
x

x = 1×9

50 90 66 90 109 95 55 23 10

%% User defined function to find linear convolution
function cnv = linearconvolve(a,b)
L = length(a)+length(b)-1;
cnv = zeros(1,L);
a1=[a,zeros(1,L-length(a))]; % define a new vector of a
b1=[b,zeros(1,L-length(b))];
for i=1:L
    c = 0;
    for j=1:i
        c = c + a1(j)*b1(i-j+1);
    end
      cnv(i) = c;
end
end

[AKASH KUMAR]

% 
% algorithm to compute convolution 
% Note : all signals are stored in a row vector

clc
clear 
close all
x1 = [5,6,1,9,1,6,3,6.2,2];
h = [10,6,4];
N = length(x1)+length(h)-1;
y=conv(x1,h) % Inbuilt matlab function

y = 1x11

50.0000 90.0000 66.0000 120.0000 68.0000 102.0000 70.0000 104.0000 69.2000 36.8000 8.0000

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y_filt=filter(h,1,x1)

y_filt = 1x9

50.0000 90.0000 66.0000 120.0000 68.0000 102.0000 70.0000 104.0000 69.2000

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Y1 = linearconvolve1(x1,h) % Function call

Y1 = 1x11

50.0000 90.0000 66.0000 120.0000 68.0000 102.0000 70.0000 104.0000 69.2000 36.8000 8.0000

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Y2= Fir_filter_userDefine(x1,h) % Function call

Y2 = 1x9

50.0000 90.0000 66.0000 120.0000 68.0000 102.0000 70.0000 104.0000 69.2000

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figure(1)
stem(y,'r','LineWidth',3);
hold on
stem(y_filt,'b','LineWidth',1.2);
hold on;
stem(Y1,'g')
stem(Y2,'--m');
legend('conv1','filter','conv2','filter2')

%% User defined function to find linear convolution
function cnv = linearconvolve1(a,b)
L = length(a)+length(b)-1;
cnv = zeros(1,L);
a1=[a,zeros(1,L-length(a))]; % define a new vector of a
b1=[b,zeros(1,L-length(b))];
for i=1:L
    c = 0;
    for j=1:i
        c = c + a1(j)*b1(i-j+1);
    end
      cnv(i) = c;
end
end


% Output length is same as of Input signal
function cnv = Fir_filter_userDefine(X_sig,h_fir)

N=length(X_sig);
L_filt=length(h_fir);
cnv = zeros(1,N);

X_tap=zeros(1,L_filt); % initialize a vector to store incoming signal
for n=1:N

    x_n=X_sig(n); % Step 01: Invert the signal

    X_tap=[x_n,X_tap(1:L_filt-1)]; % Step 02 : sliding operation.
    y_n  = h_fir*X_tap'; % Step 03 Summation 
    % y_n  = sum(h_fir.*X_tap); % Step 03 Summation 
  
    cnv(n) = y_n;
end

end

Answer 3

[keshav kumar]

close all

clear all

x=input('Enter x: ')

h=input('Enter h: ')

m=length(x);

n=length(h);

X=[x,zeros(1,n)];

H=[h,zeros(1,m)];

for i=1:n+m-1

Y(i)=0;

for j=1:m

if(i-j+1>0)

Y(i)=Y(i)+X(j)*H(i-j+1);

else

end

end

end

Y

stem(Y);

ylabel('Y[n]');

xlabel('----->n');

title('Convolution of Two Signals without conv function');