Octave Programming Examples

crc1

octave

clc;
clear all;
n=input(&#39;Enter the value of n:&#39;);
k=input(&#39;Enter the vaue of k:&#39;);
disp(&#39;Message bits&#39;);
m=randint(1,k,[0,1]);
disp(m);
disp(&#39;Cyclic Polynomial&#39;);
p=cyclpoly(n,k,&#39;min&#39;);
disp(p);
disp(&#39;Encoded Word&#39;);
x=encode(m,n,k,&#39;cyclic/fmt&#39;,p);
disp(x);
disp(&#39;Error Pattern Generation&#39;);
e=randerr(1,n,[1,0]);
disp(e);
disp(&#39;Received Code Vector&#39;);
r=rem(plus(x,e),2);
disp(r);
disp(&#39;Decode Message Bits&#39;);
msg=decode(r,n,k,&#39;Cyclic&#39;);
disp(msg);

ghfj

octave

clc
clear all

t = -10:0.1:10;

% señal cuadrada
s = F_Eunit0(t,-5) - F_Eunit0(t,5);
figure(1)
plot(t.s);xlabel('t');ylabel('s(t)')
axis([-10 10 0 1.2])
title('Diferencia de dos Señales Escalon')

hihihi

octave

clc;
clear all;
close all;
fc=input('Enter the carrier frequency,fc=');
fm=input('Enter the modulating frequency,fm=');
ac=input('Enter the carrier amplitude,ac=');
am=input('Enter the modulating signal amplitude,am=');
fs=input('Enter the sampling frequency,fs=');
t=0:1/fs:1;
Am=am*cos(2*pi*fm*t);
Ac=ac*cos(2*pi*fc*t);
m=am/ac;
y=Ac.*(1+m*cos(2*pi*fm*t));
subplot(3,1,1);
plot(t,Am);
xlabel('Time');
ylabel('Amplitude of modulating signal');
title('Modulating signal');
subplot(3,1,2);
plot(t,Ac);
xlabel('Time');
ylabel('Amplitude of carrier signal');
title('Carrier signal');
subplot(3,1,3);
plot(t,y);
xlabel('Time');
ylabel('Amplitude of modulated signal');
title('Modulated signal');
xlabel('Time');
ylabel('Amplitude of carrier signal');
title('Carrier signal');
subplot(3,1,3);
plot(t,y);
xlabel('Time');
ylabel('Amplitude of modulated signal');
title('Modulated signal');

aaaa

octave

x = [1 2 3 4 5 6 7 8 9 10];
y1 = [.16 .08 .04 .02 .013 .007 .004 .002 .001 .0008 ];
y2 = [.16 .07 .03 .01 .008 .003 .0008 .0003 .00007 .00002 ];

semilogy(x,y1,'-bo;y1;',x,y2,'-kx;y2;');
title('Plot title');
xlabel('X Axis');
ylabel('Y Axis');

print -dpng figure.png

Execute MATLAB/Octave Online

octave

clear all
clc

X = [0 1 2 3 4 5 6 7 8 9 10 11];
Y = [47.22	45.57	47.35	49.41	48.58	51.05	52.41	52.23	50.17	50.29	50.75	45.51];

% X = [0	 2	4	6 ];
% Y = [47.22	47.35	48.58	52.41];

% Polynomial Order Setting
% M = length(X)-1;
M = 4;

% A = [x^M ... x^0]
Order = M:-1:0;
rep_Order = repmat(Order,length(X),1);
rep_X     = repmat(X',1,M+1)
A  = rep_X.^rep_Order
Y'


% Data Normalize - Important for Gradient Descent
nor_A = ones(length(X),M+1);
varA = zeros(1,M);
mn_A = zeros(1,M);

for iM = 1:M
    varA(iM) = var(A(:,iM));
    mn_A(iM) = mean(A(:,iM));
    nor_A(:,iM) = (A(:,iM)-mn_A(iM))/varA(iM);
end



if(M == (length(X)-1) )
  w = inv(A)*Y';
else
   % ML Close Form Solution
   %   w = inv(A'*A)*A'*Y'
    
  A = nor_A;  
  w = zeros(M+1,1);

Max_Big_Loop = 10;
Max_Small_Loop = 1000;
eta = 0.01;
h = 0.1;
tolerant = 10;
  
Ew_Rec = zeros(1,Max_Big_Loop);

  for iBig = 1:Max_Big_Loop
  for iw = 1:length(w)
      
      for iSmall = 1:Max_Small_Loop
          
        w_h = w;
        w_h(iw) = w_h(iw) + h ;
        E_w = sum((A*w - Y').^2);
        E_w_h = sum((A*w_h - Y').^2);
 

        grad_Ew = (E_w_h - E_w)/h;
        w(iw) = w(iw) - eta*grad_Ew;

      if (min([E_w E_w_h])<tolerant) , break, end ;

      end
  end
  Ew_Rec(iBig)=min([E_w E_w_h]);
  end

end





plt_X = 0:0.2:12;

rep_Order = repmat(Order,length(plt_X),1);
rep_plt_X = repmat(plt_X',1,M+1);


A_plt  = rep_plt_X.^rep_Order;
nor_A_plt = ones(length(plt_X),M+1);

for iM = 1:M
    nor_A_plt(:,iM) = (A_plt(:,iM)-mn_A(iM))/varA(iM);
end

if(M == (length(X)-1) )
    Y_Estimation = A_plt*w;
else
    Y_Estimation = nor_A_plt*w;
end

fig = figure;

line1 = plot(X,Y);
hold on;
line2 = plot(plt_X,Y_Estimation);
 
set(line1,'color','r','linestyle','-','marker','*');
set(line2,'color','b','linestyle','-','marker','o');

title('X-Y Curve Fitting');
xlabel('X ');
ylabel('Y ');

cdad

octave

files = dir('*.jpg');
for k = length(files(:,1))
	% Leer la matricula
	mat = imread(files(k).name);

	% Preparar la carpeta para el output
	[filepath,name,ext] = fileparts(files(k).name);
	mkdir name;
	
	% Matricula a escala de grises
	mat_gris = rgb2gray(mat);
	threshold = graythresh(mat_gris);

	% Binarizar en funcion del color del fondo
	mat_binaria = imcomplement(imbinarize(mat_gris, threshold*0.8));
	mat_binaria_menos_ruido = bwareaopen(mat_binaria, 200);

	% Extraer los componentes
	[L, n] = bwlabel(mat_binaria_menos_ruido);

	% Calcular los BoundingBox de cada comonente
	bb = regionprops(L,'BoundingBox');

	% 
	comp = cell(n,1);
	for i = 1:n
		bb_i = ceil(bb(i).BoundingBox);
		idx_x = [bb_i(1) bb_i(1)+bb_i(3)];
		idx_y = [bb_i(2) bb_i(2)+bb_i(4)];

		im = L==i;
      		comp{i} = im(idx_y(1):idx_y(2),idx_x(1):idx_x(2));
	end

	% Guardar los componentes en imagenes
	for i = 2:n
	    imwrite(comp{i}, strcat(strcat(strcat(name,'/'),int2str(i-1)),'.jpg'));
	end
end

x=4:1:10;y=5*x;plot(x,y)

octave

clear all, clc, close all
%% 1. Setup
L = 1; % [m] Length
W = 0.5; % [m] Width
h = 10; % [W/m^2 K] Convection coefficient [south face]
Tinf = .5; % [K] Temperature for convection [south face]
qs = 10; % [W/m^2] incident heat flux [north face]
k = 1; % [W/m K] thermal conductivity
q0 = 0; % [W/m^3] generation rate
N =50; % nodes for discretization (x)
M =50; % nodes for discretization (t)
dx = L/(N-1); % Step size
dy = W/(M-1); % Step size
x = 0:dx:L; % vector of positions
y = 0:dy:W; % vector of positions
A = zeros(N*M,N*M);
b = zeros(N*M,1);
%% 2. Interior Nodes
for i1 = 2:N-1
for i2 = 2:M-1
in = i1+(i2-1)*N;
A(in,in) = -2/(dx)^2 - 2/(dy)^2;
A(in,in-1) = 1/dx^2;
A(in,in+1) = 1/dx^2;
A(in,in+N) = 1/dy^2;
A(in,in-N) = 1/dy^2;
b(in,1) = -q0/k;
end
end
%% 3. Boundary Nodes
% East Face [incl. NorthEast and SouthEast Corners]
i1 = N;
for i2 = 1:M
in = i1+(i2-1)*N;
A(in,in) = 1;
b(in,1) = 0;
end
% West Face
i1 = 1;
for i2 = 2:M-1
in = i1+(i2-1)*N;
A(in,in) = -2/(dx)^2 - 2/(dy)^2;
A(in,in+1) = 2/dx^2;
A(in,in+N) = 1/dy^2;
A(in,in-N) = 1/dy^2;
b(in,1) = -q0/k;
end
% North Face
i2 = M;
for i1 = 2:N-1
in = i1+(i2-1)*N;
A(in,in) = -1/(dx)^2 - 1/(dy)^2;
A(in,in-1) = 1/(2*dx^2);
A(in,in+1) = 1/(2*dx^2);
A(in,in-N) = 1/dy^2;
b(in,1) = -q0/(2*k)-qs/(k*dy);
end
% South Face
i2 = 1;
for i1 = 2:N-1
in = i1+(i2-1)*N;
A(in,in) = -1/(dx)^2 - 1/(dy)^2 - h/(k*dy);
A(in,in-1) = 1/(2*dx^2);
A(in,in+1) = 1/(2*dx^2);
A(in,in+N) = 1/dy^2;
b(in,1) = -q0/(2*k)-h/(k*dy)*Tinf;
end
% SouthWest Corner [in-1 = in +1]
i1 = 1; i2 = 1;
in = i1+(i2-1)*N;
A(in,in) = -1/(dx)^2 - 1/(dy)^2-h/(k*dy);
A(in,in+1) = 2/(2*dx^2);
A(in,in+N) = 1/dy^2;
b(in,1) = -q0/(2*k)-h/(k*dy)*Tinf;
% NorthWest Corner [in-1 = in +1]
i1 = 1; i2 = M;
in = i1+(i2-1)*N;
A(in,in) = -1/(dx)^2 - 1/(dy)^2;
A(in,in+1) = 2/(2*dx^2);
A(in,in-N) = 1/dy^2;
b(in,1) = -q0/(2*k)-qs/(k*dy);
%% 4. Solve
T = A\b;
%% 5. Plot
Tij = reshape(T,M,N)';
figure(1), clf
% surf(x,y,Tij), shading interp,
colorbar
% % xlabel('y')
% % ylabel('x')
% % zlabel('T')
contourf(x,y,Tij,25)%, shading interp
xlabel('x')
ylabel('y')
print -dpng figure.png

count objects

octave

img1=imread('Lines.jpg');
imshow(img1)

img1=rgb2gray(img1);
imshow(img1)

img2=im2bw(img1,graythresh(img1));
imshow(img2)

img2=~img2;
imshow(img2)

B = bwboundaries(img2);
imshow(img2)
text(10,10,strcat('\color{green}Objects Found:',num2str(length(B))))
hold on

for k = 1:length(B)
boundary = B{k};
plot(boundary(:,2), boundary(:,1), 'g', 'LineWidth', 0.2)
end

hbagaria

octave

I = imread('cameraman.tif');

compress

octave

 lines (13 sloc)  428 Bytes
% Create a Gaussian pyramid
% Essentially this uses reduce for a total of levels times.
% Each image in one scale is half the size of the previous scale
% First scale is simply the original image
function [gaussout] = gauss_pyramid(im, levels)
    gaussout = cell(1,levels+1);
    gaussout{1} = im;
    subsample = im;
    for i = 2 : levels+1
        subsample = reduce(subsample);
        gaussout{i} = subsample;
    end
end

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