Matlab Code Arduino Read Write

global com;

arduino = serial(com,'BaudRate',9600);
try
    fopen(arduino);
catch
    instrreset;
    fopen(arduino);
end

baca=fscanf(arduino, '%s');%1x7 14  char
fclose(arduino);
cell = strsplit(baca,'#');
sensor=cell2mat(cell(1,1));
val=cell2mat(cell(1,2));
set(handles.txt12,'String',sensor);



clear all
clc

serialPort=serial('COM4','BaudRate',9600);
warning('off','MATLAB:serial:fscanf:unsuccessfulRead');
fopen(serialPort);
volt=[10,10,10,101,10,10,10,10];

%Voltages are mapped from 0-10 to 0-100000
for i=1:length(volt)
    voltmapped(i)=map2(volt(i),0,10,0,100000);
end
display('Press any button to continue.');
pause
for i=1:length(voltmapped)
    fprintf(serialPort,'%d',voltmapped(i));
end
fclose(serialPort);

Referensi Montecarlo

link download :
https://www.youtube.com/watch?v=Xr4a6Dw6qcc

simple code:

%  Monte Carlo computation of pi.

n = input(' Enter n: ');
count = 0;

%  Generate random points in the square [-1,1]X[-1,1].
%  The fraction of these that lie in the unit disk
%  x^2+y^2 <= 1 will be approximately pi/4.

%  Think of this as taking the average of N independent
%  identically distributed random variables X_i, where
%  X_i = 1 if point i lies in the disk, 0 otherwise.

Eofxsq = 0;   % Compute expected value of X_i^2 to use in error estimate.

for i=1:n,
  x = 2*rand-1;  y = 2*rand-1;
  if x^2 + y^2 <= 1,  count = count + 1;  Eofxsq = Eofxsq + 1^2;  end;
end;

pi_approx = 4*(count/n),
err = pi - pi_approx,

Eofxsq = Eofxsq/n;
varx = Eofxsq - (count/n)^2; % Variance in individual approximations to pi/4.
sigx = sqrt(varx);           % Std dev  in individual approximations to pi/4.
sigma = 4*sigx/sqrt(n),      % Std dev in total approximation to pi.

fprintf('Error should be less than %f, 68.3 percent of the time\n',sigma)
fprintf('Error should be less than %f, 95   percent of the time\n',2*sigma)

Selamat Mencoba.....

Matlab Code SImple LVQ / SOM

function scan(img)

files = dir('*.jpg');

hist = [];

for n = 1 : length(files)

    filename = files(n).name;

    file = imread(filename);

 

    hist = [hist, imhist(rgb2gray(imresize(file,[ 50 50])))]; %#ok

end

 

som = selforgmap([10 10]);

som = train(som, hist);

t   = som(hist); %extract class data

 

net = lvqnet(10);

net = train(net, hist, t);

 

like(img, hist, files, net)

end




====================




function like(im, hist, files , net)

    hs = imhist(rgb2gray(imresize(im,[50 50])));

    cls = vec2ind(net(hs));

 

    [~, n] = size(hist);

    for i = 1 : n

        if(cls == vec2ind(net(hist(:, i))))

            figure('name', files(i).name);

            imshow(imresize(imread(files(i).name), [100 100]))

        end

    end

end
















Reff:

https://scriptbucket.wordpress.com/2012/09/21/image-classification-using-matlab-somlvq/