MATLAB Grader can be integrated into Moodle as an external tool. How can you use MATLAB Grader in Moodle Quizzes, to implement MATLAB Grader as a quiz question?MATLAB Grader can be integrated into Moodle as an external tool. How can you use MATLAB Grader in Moodle Quizzes, to implement MATLAB Grader as a quiz question? MATLAB Grader can be integrated into Moodle as an external tool. How can you use MATLAB Grader in Moodle Quizzes, to implement MATLAB Grader as a quiz question? distance_learning, moodle, matlab grader MATLAB Answers — New Questions

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Why am I unable to use web-based applications such as Simulink, App Designer, Simscape, etc.?Why am I unable to use web-based applications such as Simulink, App Designer, Simscape, etc.? Why am I unable to use web-based applications such as Simulink, App Designer, Simscape, etc.? trendmicro, antivirus, exit, code, 1073741819, matlab.internal.cef.webwindow MATLAB Answers — New Questions

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Hi
I’ve written an application using App Designer with 100s of components. When I start the app it freezes during the build of the components in the App Designer generated function createComponents. This happens before my own code starts to execute. The way to avoid this freeze is to add a breakpont far down in the createComponents function. createComponents is called before registerApp and runStartupFcn is called. All the mentioned functions are generated before my own code start to execute and are generated by App Designer and are therefore grayed out which means I can’t do any edits.
Is this freeze problem known to this community and is there a fix for this?
Is there any way for me to edit the App Designer generated functions? I would like to add a pause instead of a breakpoint as a possible way to solve the freeze in createComponents function…
I’m running Matlab R2022a Update 8.
Most grateful for your hints and tipsHi
I’ve written an application using App Designer with 100s of components. When I start the app it freezes during the build of the components in the App Designer generated function createComponents. This happens before my own code starts to execute. The way to avoid this freeze is to add a breakpont far down in the createComponents function. createComponents is called before registerApp and runStartupFcn is called. All the mentioned functions are generated before my own code start to execute and are generated by App Designer and are therefore grayed out which means I can’t do any edits.
Is this freeze problem known to this community and is there a fix for this?
Is there any way for me to edit the App Designer generated functions? I would like to add a pause instead of a breakpoint as a possible way to solve the freeze in createComponents function…
I’m running Matlab R2022a Update 8.
Most grateful for your hints and tips Hi
I’ve written an application using App Designer with 100s of components. When I start the app it freezes during the build of the components in the App Designer generated function createComponents. This happens before my own code starts to execute. The way to avoid this freeze is to add a breakpont far down in the createComponents function. createComponents is called before registerApp and runStartupFcn is called. All the mentioned functions are generated before my own code start to execute and are generated by App Designer and are therefore grayed out which means I can’t do any edits.
Is this freeze problem known to this community and is there a fix for this?
Is there any way for me to edit the App Designer generated functions? I would like to add a pause instead of a breakpoint as a possible way to solve the freeze in createComponents function…
I’m running Matlab R2022a Update 8.
Most grateful for your hints and tips appdesigner, createcomponents MATLAB Answers — New Questions

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Hi guys,
I am creating a mask for my custom block and I want add a checkbox with the following behaviour:
checked: it shows a new port the the block
unhecked: it hides the new port for the block
The integrator block has the same behaviour (see the belows images to understand):

I am struggling how to implement this feature in the Checkbox callback because I can’t find the parameter to show/hide the port.Hi guys,
I am creating a mask for my custom block and I want add a checkbox with the following behaviour:
checked: it shows a new port the the block
unhecked: it hides the new port for the block
The integrator block has the same behaviour (see the belows images to understand):

I am struggling how to implement this feature in the Checkbox callback because I can’t find the parameter to show/hide the port. Hi guys,
I am creating a mask for my custom block and I want add a checkbox with the following behaviour:
checked: it shows a new port the the block
unhecked: it hides the new port for the block
The integrator block has the same behaviour (see the belows images to understand):

I am struggling how to implement this feature in the Checkbox callback because I can’t find the parameter to show/hide the port. simulink, mask, integrator, custom MATLAB Answers — New Questions

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I am using the following two-sample tests for non-normal distributions:
chi2gof
kstest2
ranksum
kruskalwallis
All of them return a p-value, i.e. a p-value for chi2gof, a p-value for kstest2, a p-value for ranksum, and a p-value for kruskalwallis.
Since the p-value is not enough to understand the data/distributions (for example, please see Sullivan & Feinn (2012), Dunkler et al.(2020), Greenland (2016), and du Prel et al. (2009)), I would like to calculate the effect size, the statistical power of the test, and the confidence interval (of hypothesis testing).
I would use the following MATLAB functions:
meanEffectSize (two-sample effect size computations)
sampsizepwr (power of the test)
not found anything about the confidence interval..
However:
I am not sure if both functions, meanEffectSize and sampsizepwr, can be "compatible" with (i) non-normal distributions and (ii) with the 4 previously mentioned tests.
According to the documentation I found on sampsizepwr, it looks like that the calculation of the statistical power of the test works only with normal distributions, but I am not sure.
Those 4 mentioned tests do not provide the confidence interval. Maybe other MATLAB function do, but I was not able to find them.
I have then 2 questions:
Question 1. Is there anyone who could kindly enlighten me on the "compatibility" of meanEffectSize and sampsizepw with with (i) non-normal distributions and (ii) with the 4 previously mentioned tests?
Question 2. Is there anyone who could kindly tell me if there are MATLAB functions to calculate the confidence intervals for the 4 mentioned statistical tests?I am using the following two-sample tests for non-normal distributions:
chi2gof
kstest2
ranksum
kruskalwallis
All of them return a p-value, i.e. a p-value for chi2gof, a p-value for kstest2, a p-value for ranksum, and a p-value for kruskalwallis.
Since the p-value is not enough to understand the data/distributions (for example, please see Sullivan & Feinn (2012), Dunkler et al.(2020), Greenland (2016), and du Prel et al. (2009)), I would like to calculate the effect size, the statistical power of the test, and the confidence interval (of hypothesis testing).
I would use the following MATLAB functions:
meanEffectSize (two-sample effect size computations)
sampsizepwr (power of the test)
not found anything about the confidence interval..
However:
I am not sure if both functions, meanEffectSize and sampsizepwr, can be "compatible" with (i) non-normal distributions and (ii) with the 4 previously mentioned tests.
According to the documentation I found on sampsizepwr, it looks like that the calculation of the statistical power of the test works only with normal distributions, but I am not sure.
Those 4 mentioned tests do not provide the confidence interval. Maybe other MATLAB function do, but I was not able to find them.
I have then 2 questions:
Question 1. Is there anyone who could kindly enlighten me on the "compatibility" of meanEffectSize and sampsizepw with with (i) non-normal distributions and (ii) with the 4 previously mentioned tests?
Question 2. Is there anyone who could kindly tell me if there are MATLAB functions to calculate the confidence intervals for the 4 mentioned statistical tests? I am using the following two-sample tests for non-normal distributions:
chi2gof
kstest2
ranksum
kruskalwallis
All of them return a p-value, i.e. a p-value for chi2gof, a p-value for kstest2, a p-value for ranksum, and a p-value for kruskalwallis.
Since the p-value is not enough to understand the data/distributions (for example, please see Sullivan & Feinn (2012), Dunkler et al.(2020), Greenland (2016), and du Prel et al. (2009)), I would like to calculate the effect size, the statistical power of the test, and the confidence interval (of hypothesis testing).
I would use the following MATLAB functions:
meanEffectSize (two-sample effect size computations)
sampsizepwr (power of the test)
not found anything about the confidence interval..
However:
I am not sure if both functions, meanEffectSize and sampsizepwr, can be "compatible" with (i) non-normal distributions and (ii) with the 4 previously mentioned tests.
According to the documentation I found on sampsizepwr, it looks like that the calculation of the statistical power of the test works only with normal distributions, but I am not sure.
Those 4 mentioned tests do not provide the confidence interval. Maybe other MATLAB function do, but I was not able to find them.
I have then 2 questions:
Question 1. Is there anyone who could kindly enlighten me on the "compatibility" of meanEffectSize and sampsizepw with with (i) non-normal distributions and (ii) with the 4 previously mentioned tests?
Question 2. Is there anyone who could kindly tell me if there are MATLAB functions to calculate the confidence intervals for the 4 mentioned statistical tests? effect size, statistical power, confidence interval, chi2gof, kstest2, ranksum, kruskalwallis MATLAB Answers — New Questions

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hello everyone,

i am having a little problem, i need to figure out what affects one variable. that variable is in a for loop, which is in a function, which is also in a function and several values are also calculated by several other functions…

i do know i can have a chart what functions are involved and how they are affect each other, when i have a project. however, i cant see the variables there, or if i dont know how.

so thats my question, how can i see the functions AND variables that affect one variable.hello everyone,

i am having a little problem, i need to figure out what affects one variable. that variable is in a for loop, which is in a function, which is also in a function and several values are also calculated by several other functions…

i do know i can have a chart what functions are involved and how they are affect each other, when i have a project. however, i cant see the variables there, or if i dont know how.

so thats my question, how can i see the functions AND variables that affect one variable. hello everyone,

i am having a little problem, i need to figure out what affects one variable. that variable is in a for loop, which is in a function, which is also in a function and several values are also calculated by several other functions…

i do know i can have a chart what functions are involved and how they are affect each other, when i have a project. however, i cant see the variables there, or if i dont know how.

so thats my question, how can i see the functions AND variables that affect one variable. variable, function MATLAB Answers — New Questions

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I want to download the MATLAB Runtime (MCR) 7.5 version.
However, when I run the .exe file, I get the following warning.
"Error 1723. There is a problem with this Windows Installer package. A DLL required for this install to complete could not be run. Contact your support or package vendor"
Is there a solution to this?I want to download the MATLAB Runtime (MCR) 7.5 version.
However, when I run the .exe file, I get the following warning.
"Error 1723. There is a problem with this Windows Installer package. A DLL required for this install to complete could not be run. Contact your support or package vendor"
Is there a solution to this? I want to download the MATLAB Runtime (MCR) 7.5 version.
However, when I run the .exe file, I get the following warning.
"Error 1723. There is a problem with this Windows Installer package. A DLL required for this install to complete could not be run. Contact your support or package vendor"
Is there a solution to this? mcr MATLAB Answers — New Questions

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could you please help me to get only the gray scale images without any borders and without any written words or letters created by the device . i want a general code to do this beause i have other images with other border thickness and other written , and i want the gray image entire only
@Image Analyst

Image Analystcould you please help me to get only the gray scale images without any borders and without any written words or letters created by the device . i want a general code to do this beause i have other images with other border thickness and other written , and i want the gray image entire only
@Image Analyst

Image Analyst could you please help me to get only the gray scale images without any borders and without any written words or letters created by the device . i want a general code to do this beause i have other images with other border thickness and other written , and i want the gray image entire only
@Image Analyst

Image Analyst image analyst MATLAB Answers — New Questions

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Activation KeyActivation Key Activation Key provide activation key for matlab MATLAB Answers — New Questions

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Hi, I am trying to use Fourier descriptors to reconstruct the shape of a particle. I have written most of the code (with a lot of help) and only the last step which involves using Fourier descriptors to reconstruct the shape of the original image remains.
I need help to know which functions to use to achieve this. I would really appreciate if anyone could help.
Here is the code:
I have also attached the original image and another file which demonstrates what I need to do.

scale_factor = 100/90; % (units: microns/pixel)
% to convert pixels to microns,
% multiply # of pixels by scale_factor
%1. Open and show image of particle
img = imread(‘1_50.JPG’); %Read image
subplot (3,3,1)
imshow(img) %show image

%2. Binarize and show image
BW = im2bw(img,0.45); %binarize image with a threshold value of 0.45
img1= bwareaopen (BW, 1000); %Remove small objects
img2= imfill(img1, ‘holes’); %fill holes
subplot(3,3,2)
imshow(img2) %show binarized image

%3. Setting up the axes
% for plotting into multiple axes in one loop, it’s convenient to set up
% all the axes first, before anything is plotted into them

% 3.1. set up the 2nd axes:
ax3 = subplot(3,3,3);
hold on
box on %draws a box that encompasses the entire axes area
axis image %sets the aspect ratio of the axes (ax2) to be equal and
%adjusts the limits of the axes so that the data units are
%of equal size along both the x-axis and y-axis.
set(ax3,’YDir’,’reverse’) %reverses the y-axis to begin from the top
%since the origin of the image is at the top left-corner
% 3.2. set up the 3rd axes:
ax4 = subplot(3,3,4);
hold on

%4. Compute centroid of binarized image
centriod_value= regionprops(img2, ‘Centroid’); %Finds centroid of image
centroid = cat(1,centriod_value.Centroid); %Stores centroid coordinates in a two-column matrix

%5. Tracing the boundary of image
p_boundary= bwboundaries(img2); % trace the exterior boundaries of particle
%(in row, column order, not x,y)
number_of_boundaries = size(p_boundary,1); %specify only one boundary
centroid = centroid*scale_factor;

% 6. Setting up equally spaced angles before initing the loop
%use 129 points to have an angle spacing of 360/128
N_angles = 129;
interp_angles = linspace(0,360,N_angles).’;

%7. Initiate for loop to plot boundary, centroid, & a graph of radius vs angle

for k = 1 : number_of_boundaries % initiate loop to go round the selected boundary
thisBoundary = p_boundary{k}*scale_factor;
y = thisBoundary(:,1); % rows
x = thisBoundary(:,2); % columns

plot(ax3, x, y, ‘g’, ‘LineWidth’, 2);

plot(ax3, centroid(:,1),centroid(:,2),’b.’)

% 7.1. Calculate the angles in degrees
deltaY = thisBoundary(:,1) – centroid(k,2); % boundary(:,1) is y, but centroid(k,2) is y
deltaX = thisBoundary(:,2) – centroid(k,1); % boundary(:,2) is x, but centroid(k,1) is x
% angles = atand(deltaY ./ deltaX); % atand gives angles in range [-90,90]
angles = atan2d(deltaY,deltaX); % use atan2d to get angles in range [-180,180]

% 7.2. Calculate the radii
radius = sqrt(deltaY.^2 + deltaX.^2); % use deltaX and deltaY, which were just calculated

% 7.3. Add 360 degrees to negative angles so that range is [0,360] instead of [-180,180]
idx = angles < 0; %idx reps angles less than 0 degreees
angles(idx) = angles(idx)+360; %360 degrees is added to idx angles

%7.4. Sort angles in ascending order
% sort the angles, so the first one is smallest (closest to zero)
% and the last one is largest (closest to 360)
% this is useful for getting a smooth line that goes from 0 to 360 in the plot
% I returns the corresponding indices of the angles
[angles,I] = sort(angles);

%7.5. Reorder distances the same way angles was reordered using I

radius = radius(I);

%7.6. Use only unique angles in case an angle is repeated
% in case any angles are repeated, use only the unique set and
% corresponding distances in interp1 (in this data, one angle is
% repeated twice, with the same distance each time)
%The ~ (tilde) here is a placeholder indicating that we’re not interested
%in capturing the actual unique elements, only their indices
[~,I] = unique(angles);

% 7.7. perform linear interpolation/extrapolation:
%angles(I)= sample points, radius(I)= correspoinding points
%and interp_angles = coordinates of the querry points (QP)
%In general, the functions returns interpolated values at specific QP
interp_distances = interp1(angles(I),radius(I),interp_angles,’linear’,’extrap’);

% 7.8 Plot distance vs. angle.
plot(ax4,interp_angles,interp_distances)

end

%8. Apply fft algorithm to values of radius
L = length(interp_distances); %Define the number of the radiuses (points)
f = 0:L-1; %Define the sampling frequencies
A = fft(interp_distances); %Transforming the radius function into frequency domain using FFT
B = (abs(A)/2535.2); % Normalize the amplitude of fourier descriptors using the first descriptors

%9. Plot fft descriptor vs. frequency
subplot(3,3,5)
bar(f,B,3) %plot the frequencies vs. the nornalised descriptors
xlim([0 128])

%10. Plot phase angle vs. frequency
subplot(3,3,6)
bar(f, angle(A),3) % plot the frequencies vs phase angles
xlim([0 128])
ylim([-4 4])

%11. Create a table of values
table1 = table(A, f’, abs(A), B, angle(A));
table1.Properties.VariableNames = {‘FFT_coeffs’, ‘Frequency’, ‘Amplitude’, ‘Norm_Amplitude’ ‘Phase’};
disp(table1);

%12. Reconstruct particle boundary using FFT descriptors ?????Hi, I am trying to use Fourier descriptors to reconstruct the shape of a particle. I have written most of the code (with a lot of help) and only the last step which involves using Fourier descriptors to reconstruct the shape of the original image remains.
I need help to know which functions to use to achieve this. I would really appreciate if anyone could help.
Here is the code:
I have also attached the original image and another file which demonstrates what I need to do.

scale_factor = 100/90; % (units: microns/pixel)
% to convert pixels to microns,
% multiply # of pixels by scale_factor
%1. Open and show image of particle
img = imread(‘1_50.JPG’); %Read image
subplot (3,3,1)
imshow(img) %show image

%2. Binarize and show image
BW = im2bw(img,0.45); %binarize image with a threshold value of 0.45
img1= bwareaopen (BW, 1000); %Remove small objects
img2= imfill(img1, ‘holes’); %fill holes
subplot(3,3,2)
imshow(img2) %show binarized image

%3. Setting up the axes
% for plotting into multiple axes in one loop, it’s convenient to set up
% all the axes first, before anything is plotted into them

% 3.1. set up the 2nd axes:
ax3 = subplot(3,3,3);
hold on
box on %draws a box that encompasses the entire axes area
axis image %sets the aspect ratio of the axes (ax2) to be equal and
%adjusts the limits of the axes so that the data units are
%of equal size along both the x-axis and y-axis.
set(ax3,’YDir’,’reverse’) %reverses the y-axis to begin from the top
%since the origin of the image is at the top left-corner
% 3.2. set up the 3rd axes:
ax4 = subplot(3,3,4);
hold on

%4. Compute centroid of binarized image
centriod_value= regionprops(img2, ‘Centroid’); %Finds centroid of image
centroid = cat(1,centriod_value.Centroid); %Stores centroid coordinates in a two-column matrix

%5. Tracing the boundary of image
p_boundary= bwboundaries(img2); % trace the exterior boundaries of particle
%(in row, column order, not x,y)
number_of_boundaries = size(p_boundary,1); %specify only one boundary
centroid = centroid*scale_factor;

% 6. Setting up equally spaced angles before initing the loop
%use 129 points to have an angle spacing of 360/128
N_angles = 129;
interp_angles = linspace(0,360,N_angles).’;

%7. Initiate for loop to plot boundary, centroid, & a graph of radius vs angle

for k = 1 : number_of_boundaries % initiate loop to go round the selected boundary
thisBoundary = p_boundary{k}*scale_factor;
y = thisBoundary(:,1); % rows
x = thisBoundary(:,2); % columns

plot(ax3, x, y, ‘g’, ‘LineWidth’, 2);

plot(ax3, centroid(:,1),centroid(:,2),’b.’)

% 7.1. Calculate the angles in degrees
deltaY = thisBoundary(:,1) – centroid(k,2); % boundary(:,1) is y, but centroid(k,2) is y
deltaX = thisBoundary(:,2) – centroid(k,1); % boundary(:,2) is x, but centroid(k,1) is x
% angles = atand(deltaY ./ deltaX); % atand gives angles in range [-90,90]
angles = atan2d(deltaY,deltaX); % use atan2d to get angles in range [-180,180]

% 7.2. Calculate the radii
radius = sqrt(deltaY.^2 + deltaX.^2); % use deltaX and deltaY, which were just calculated

% 7.3. Add 360 degrees to negative angles so that range is [0,360] instead of [-180,180]
idx = angles < 0; %idx reps angles less than 0 degreees
angles(idx) = angles(idx)+360; %360 degrees is added to idx angles

%7.4. Sort angles in ascending order
% sort the angles, so the first one is smallest (closest to zero)
% and the last one is largest (closest to 360)
% this is useful for getting a smooth line that goes from 0 to 360 in the plot
% I returns the corresponding indices of the angles
[angles,I] = sort(angles);

%7.5. Reorder distances the same way angles was reordered using I

radius = radius(I);

%7.6. Use only unique angles in case an angle is repeated
% in case any angles are repeated, use only the unique set and
% corresponding distances in interp1 (in this data, one angle is
% repeated twice, with the same distance each time)
%The ~ (tilde) here is a placeholder indicating that we’re not interested
%in capturing the actual unique elements, only their indices
[~,I] = unique(angles);

% 7.7. perform linear interpolation/extrapolation:
%angles(I)= sample points, radius(I)= correspoinding points
%and interp_angles = coordinates of the querry points (QP)
%In general, the functions returns interpolated values at specific QP
interp_distances = interp1(angles(I),radius(I),interp_angles,’linear’,’extrap’);

% 7.8 Plot distance vs. angle.
plot(ax4,interp_angles,interp_distances)

end

%8. Apply fft algorithm to values of radius
L = length(interp_distances); %Define the number of the radiuses (points)
f = 0:L-1; %Define the sampling frequencies
A = fft(interp_distances); %Transforming the radius function into frequency domain using FFT
B = (abs(A)/2535.2); % Normalize the amplitude of fourier descriptors using the first descriptors

%9. Plot fft descriptor vs. frequency
subplot(3,3,5)
bar(f,B,3) %plot the frequencies vs. the nornalised descriptors
xlim([0 128])

%10. Plot phase angle vs. frequency
subplot(3,3,6)
bar(f, angle(A),3) % plot the frequencies vs phase angles
xlim([0 128])
ylim([-4 4])

%11. Create a table of values
table1 = table(A, f’, abs(A), B, angle(A));
table1.Properties.VariableNames = {‘FFT_coeffs’, ‘Frequency’, ‘Amplitude’, ‘Norm_Amplitude’ ‘Phase’};
disp(table1);

%12. Reconstruct particle boundary using FFT descriptors ????? Hi, I am trying to use Fourier descriptors to reconstruct the shape of a particle. I have written most of the code (with a lot of help) and only the last step which involves using Fourier descriptors to reconstruct the shape of the original image remains.
I need help to know which functions to use to achieve this. I would really appreciate if anyone could help.
Here is the code:
I have also attached the original image and another file which demonstrates what I need to do.

scale_factor = 100/90; % (units: microns/pixel)
% to convert pixels to microns,
% multiply # of pixels by scale_factor
%1. Open and show image of particle
img = imread(‘1_50.JPG’); %Read image
subplot (3,3,1)
imshow(img) %show image

%2. Binarize and show image
BW = im2bw(img,0.45); %binarize image with a threshold value of 0.45
img1= bwareaopen (BW, 1000); %Remove small objects
img2= imfill(img1, ‘holes’); %fill holes
subplot(3,3,2)
imshow(img2) %show binarized image

%3. Setting up the axes
% for plotting into multiple axes in one loop, it’s convenient to set up
% all the axes first, before anything is plotted into them

% 3.1. set up the 2nd axes:
ax3 = subplot(3,3,3);
hold on
box on %draws a box that encompasses the entire axes area
axis image %sets the aspect ratio of the axes (ax2) to be equal and
%adjusts the limits of the axes so that the data units are
%of equal size along both the x-axis and y-axis.
set(ax3,’YDir’,’reverse’) %reverses the y-axis to begin from the top
%since the origin of the image is at the top left-corner
% 3.2. set up the 3rd axes:
ax4 = subplot(3,3,4);
hold on

%4. Compute centroid of binarized image
centriod_value= regionprops(img2, ‘Centroid’); %Finds centroid of image
centroid = cat(1,centriod_value.Centroid); %Stores centroid coordinates in a two-column matrix

%5. Tracing the boundary of image
p_boundary= bwboundaries(img2); % trace the exterior boundaries of particle
%(in row, column order, not x,y)
number_of_boundaries = size(p_boundary,1); %specify only one boundary
centroid = centroid*scale_factor;

% 6. Setting up equally spaced angles before initing the loop
%use 129 points to have an angle spacing of 360/128
N_angles = 129;
interp_angles = linspace(0,360,N_angles).’;

%7. Initiate for loop to plot boundary, centroid, & a graph of radius vs angle

for k = 1 : number_of_boundaries % initiate loop to go round the selected boundary
thisBoundary = p_boundary{k}*scale_factor;
y = thisBoundary(:,1); % rows
x = thisBoundary(:,2); % columns

plot(ax3, x, y, ‘g’, ‘LineWidth’, 2);

plot(ax3, centroid(:,1),centroid(:,2),’b.’)

% 7.1. Calculate the angles in degrees
deltaY = thisBoundary(:,1) – centroid(k,2); % boundary(:,1) is y, but centroid(k,2) is y
deltaX = thisBoundary(:,2) – centroid(k,1); % boundary(:,2) is x, but centroid(k,1) is x
% angles = atand(deltaY ./ deltaX); % atand gives angles in range [-90,90]
angles = atan2d(deltaY,deltaX); % use atan2d to get angles in range [-180,180]

% 7.2. Calculate the radii
radius = sqrt(deltaY.^2 + deltaX.^2); % use deltaX and deltaY, which were just calculated

% 7.3. Add 360 degrees to negative angles so that range is [0,360] instead of [-180,180]
idx = angles < 0; %idx reps angles less than 0 degreees
angles(idx) = angles(idx)+360; %360 degrees is added to idx angles

%7.4. Sort angles in ascending order
% sort the angles, so the first one is smallest (closest to zero)
% and the last one is largest (closest to 360)
% this is useful for getting a smooth line that goes from 0 to 360 in the plot
% I returns the corresponding indices of the angles
[angles,I] = sort(angles);

%7.5. Reorder distances the same way angles was reordered using I

radius = radius(I);

%7.6. Use only unique angles in case an angle is repeated
% in case any angles are repeated, use only the unique set and
% corresponding distances in interp1 (in this data, one angle is
% repeated twice, with the same distance each time)
%The ~ (tilde) here is a placeholder indicating that we’re not interested
%in capturing the actual unique elements, only their indices
[~,I] = unique(angles);

% 7.7. perform linear interpolation/extrapolation:
%angles(I)= sample points, radius(I)= correspoinding points
%and interp_angles = coordinates of the querry points (QP)
%In general, the functions returns interpolated values at specific QP
interp_distances = interp1(angles(I),radius(I),interp_angles,’linear’,’extrap’);

% 7.8 Plot distance vs. angle.
plot(ax4,interp_angles,interp_distances)

end

%8. Apply fft algorithm to values of radius
L = length(interp_distances); %Define the number of the radiuses (points)
f = 0:L-1; %Define the sampling frequencies
A = fft(interp_distances); %Transforming the radius function into frequency domain using FFT
B = (abs(A)/2535.2); % Normalize the amplitude of fourier descriptors using the first descriptors

%9. Plot fft descriptor vs. frequency
subplot(3,3,5)
bar(f,B,3) %plot the frequencies vs. the nornalised descriptors
xlim([0 128])

%10. Plot phase angle vs. frequency
subplot(3,3,6)
bar(f, angle(A),3) % plot the frequencies vs phase angles
xlim([0 128])
ylim([-4 4])

%11. Create a table of values
table1 = table(A, f’, abs(A), B, angle(A));
table1.Properties.VariableNames = {‘FFT_coeffs’, ‘Frequency’, ‘Amplitude’, ‘Norm_Amplitude’ ‘Phase’};
disp(table1);

%12. Reconstruct particle boundary using FFT descriptors ????? fft, shape reconstruction, fourier descriptor MATLAB Answers — New Questions

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