Dear all
I was wondering which could be a good method to store in an array the first two columns of the first two lines after the line "Cell (Angstrom):" in the attached file.Dear all
I was wondering which could be a good method to store in an array the first two columns of the first two lines after the line "Cell (Angstrom):" in the attached file. Dear all
I was wondering which could be a good method to store in an array the first two columns of the first two lines after the line "Cell (Angstrom):" in the attached file. text pattern MATLAB Answers — New Questions

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Hello Everyone,

I have a timeseries data from a vehicle having gaps in it. FOr example, when the vehicle was running, there is data, but no data is available when the vehicle is turned off. When i plot now, the missig data is filled witha line connecting last measured data to the next measured data (see figure). How can i remove that line and instead have a continuous line.
I highly appreciate for any efforts.Hello Everyone,

I have a timeseries data from a vehicle having gaps in it. FOr example, when the vehicle was running, there is data, but no data is available when the vehicle is turned off. When i plot now, the missig data is filled witha line connecting last measured data to the next measured data (see figure). How can i remove that line and instead have a continuous line.
I highly appreciate for any efforts. Hello Everyone,

I have a timeseries data from a vehicle having gaps in it. FOr example, when the vehicle was running, there is data, but no data is available when the vehicle is turned off. When i plot now, the missig data is filled witha line connecting last measured data to the next measured data (see figure). How can i remove that line and instead have a continuous line.
I highly appreciate for any efforts. timeseries, data handling, matlab plots MATLAB Answers — New Questions

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There exists a fifth-order implicit function (curve) such that f(x,y)=a (a is a constant)
For example, x^5+x^4*y^1+x^3*y^2+x^2*y^3+x^1*y^4+y^5=10

There exists a 3-dimensional surface such that g(x,y)=z.
For example, z=x^5+xy+y^5

Next, find the cubic curve g(x,y)=z along f(x,y)=a

Then, on the curve z, we want to find (x,y) where dz/dx=0 and dz/dy=0.There exists a fifth-order implicit function (curve) such that f(x,y)=a (a is a constant)
For example, x^5+x^4*y^1+x^3*y^2+x^2*y^3+x^1*y^4+y^5=10

There exists a 3-dimensional surface such that g(x,y)=z.
For example, z=x^5+xy+y^5

Next, find the cubic curve g(x,y)=z along f(x,y)=a

Then, on the curve z, we want to find (x,y) where dz/dx=0 and dz/dy=0. There exists a fifth-order implicit function (curve) such that f(x,y)=a (a is a constant)
For example, x^5+x^4*y^1+x^3*y^2+x^2*y^3+x^1*y^4+y^5=10

There exists a 3-dimensional surface such that g(x,y)=z.
For example, z=x^5+xy+y^5

Next, find the cubic curve g(x,y)=z along f(x,y)=a

Then, on the curve z, we want to find (x,y) where dz/dx=0 and dz/dy=0. implicit function, 3d surface, 2d curve, diff MATLAB Answers — New Questions

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Hello everyone,
For those who don’t know, I am currently working on a supervised learning/RL learning training session with a 3DOF model of a jet aircraft.
My idea is to change, during a training, some parameters such as the number of allowed interventions of the external controller as well as their duration. Another whish is to change accordingly these parametes as the agent makes progress, e.g. its episode reward will increase during training. Is it possible to do this during a training? That is, is it possible to implement some for loops that exploits the trainStats object while the training is runnig? Thank youHello everyone,
For those who don’t know, I am currently working on a supervised learning/RL learning training session with a 3DOF model of a jet aircraft.
My idea is to change, during a training, some parameters such as the number of allowed interventions of the external controller as well as their duration. Another whish is to change accordingly these parametes as the agent makes progress, e.g. its episode reward will increase during training. Is it possible to do this during a training? That is, is it possible to implement some for loops that exploits the trainStats object while the training is runnig? Thank you Hello everyone,
For those who don’t know, I am currently working on a supervised learning/RL learning training session with a 3DOF model of a jet aircraft.
My idea is to change, during a training, some parameters such as the number of allowed interventions of the external controller as well as their duration. Another whish is to change accordingly these parametes as the agent makes progress, e.g. its episode reward will increase during training. Is it possible to do this during a training? That is, is it possible to implement some for loops that exploits the trainStats object while the training is runnig? Thank you reinforcement learning, supervised learning, machine learning, rl, rl block, trainstats, matlab, simulink MATLAB Answers — New Questions

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what function should I write in Simulink matlab function block so it generates square pulses as pulse generator , please helpwhat function should I write in Simulink matlab function block so it generates square pulses as pulse generator , please help what function should I write in Simulink matlab function block so it generates square pulses as pulse generator , please help simulink, matlab function MATLAB Answers — New Questions

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Hello, i am trying to code an automatic detection of alzheimer from EEG signals but my code has an error when using trainNetwork. It worked perfectely with a SVM but doesn’t with a CNN. I tried looking online but nothing seems too work. I got this error :
Error using trainNetwork (line 191)
Too many input arguments.
Error in CNN (line 178)
net = trainNetwork(X_train, y_train, layers, options);
Caused by:
Error using gather
Too many input arguments.
Does anyone have an idea. Here is the part of my code that produce the CNN :
X = all_features{:, 1:end-1}; % Use parentheses () for table indexing
y = all_features.Label;

y = categorical(y);

disp([‘Feature matrix dimensions: ‘, num2str(size(X))]);
disp([‘Labels vector dimensions: ‘, num2str(size(y))]);

X = zscore(X);

numFeatures = size(X, 2);
numObservations = size(X, 1);
X = reshape(X, [numObservations, numFeatures, 1, 1]); % Reshape for CNN

layers = [
imageInputLayer([numFeatures 1 1])
convolution2dLayer([3 1], 8, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
maxPooling2dLayer([2 1], ‘Stride’, 2)
convolution2dLayer([3 1], 16, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];

options = trainingOptions(‘adam’, …
‘MaxEpochs’, 30, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 0.001, …
‘ValidationFrequency’, 10, …
‘Verbose’, false, …
‘Plots’, ‘training-progress’);
% 5-Fold Cross-Validation
cv = cvpartition(y, ‘KFold’, 5, ‘Stratify’, true);

accuracies = zeros(cv.NumTestSets, 1);
confusion_matrices = cell(cv.NumTestSets, 1);

for k = 1:cv.NumTestSets

train_idx = training(cv, k);
test_idx = test(cv, k);

X_train = X(train_idx, :, :, :);
y_train = y(train_idx);
X_test = X(test_idx, :, :, :);
y_test = y(test_idx);

net = trainNetwork(X_train, y_train, layers, options);

y_pred = classify(net, X_test);

confusion_matrices{k} = confusionmat(y_test, y_pred);
cm = confusion_matrices{k};

accuracies(k) = sum(diag(cm)) / sum(cm(:));
end

mean_accuracy = mean(accuracies);
fprintf(‘Mean Accuracy across 5 folds: %.2f%%n’, mean_accuracy * 100);

save(‘eeg_cnn_classifier_cv.mat’, ‘net’);

disp(‘Confusion Matrices for each fold:’);
for k = 1:cv.NumTestSets
disp([‘Fold ‘, num2str(k), ‘:’]);
disp(confusion_matrices{k});
endHello, i am trying to code an automatic detection of alzheimer from EEG signals but my code has an error when using trainNetwork. It worked perfectely with a SVM but doesn’t with a CNN. I tried looking online but nothing seems too work. I got this error :
Error using trainNetwork (line 191)
Too many input arguments.
Error in CNN (line 178)
net = trainNetwork(X_train, y_train, layers, options);
Caused by:
Error using gather
Too many input arguments.
Does anyone have an idea. Here is the part of my code that produce the CNN :
X = all_features{:, 1:end-1}; % Use parentheses () for table indexing
y = all_features.Label;

y = categorical(y);

disp([‘Feature matrix dimensions: ‘, num2str(size(X))]);
disp([‘Labels vector dimensions: ‘, num2str(size(y))]);

X = zscore(X);

numFeatures = size(X, 2);
numObservations = size(X, 1);
X = reshape(X, [numObservations, numFeatures, 1, 1]); % Reshape for CNN

layers = [
imageInputLayer([numFeatures 1 1])
convolution2dLayer([3 1], 8, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
maxPooling2dLayer([2 1], ‘Stride’, 2)
convolution2dLayer([3 1], 16, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];

options = trainingOptions(‘adam’, …
‘MaxEpochs’, 30, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 0.001, …
‘ValidationFrequency’, 10, …
‘Verbose’, false, …
‘Plots’, ‘training-progress’);
% 5-Fold Cross-Validation
cv = cvpartition(y, ‘KFold’, 5, ‘Stratify’, true);

accuracies = zeros(cv.NumTestSets, 1);
confusion_matrices = cell(cv.NumTestSets, 1);

for k = 1:cv.NumTestSets

train_idx = training(cv, k);
test_idx = test(cv, k);

X_train = X(train_idx, :, :, :);
y_train = y(train_idx);
X_test = X(test_idx, :, :, :);
y_test = y(test_idx);

net = trainNetwork(X_train, y_train, layers, options);

y_pred = classify(net, X_test);

confusion_matrices{k} = confusionmat(y_test, y_pred);
cm = confusion_matrices{k};

accuracies(k) = sum(diag(cm)) / sum(cm(:));
end

mean_accuracy = mean(accuracies);
fprintf(‘Mean Accuracy across 5 folds: %.2f%%n’, mean_accuracy * 100);

save(‘eeg_cnn_classifier_cv.mat’, ‘net’);

disp(‘Confusion Matrices for each fold:’);
for k = 1:cv.NumTestSets
disp([‘Fold ‘, num2str(k), ‘:’]);
disp(confusion_matrices{k});
end Hello, i am trying to code an automatic detection of alzheimer from EEG signals but my code has an error when using trainNetwork. It worked perfectely with a SVM but doesn’t with a CNN. I tried looking online but nothing seems too work. I got this error :
Error using trainNetwork (line 191)
Too many input arguments.
Error in CNN (line 178)
net = trainNetwork(X_train, y_train, layers, options);
Caused by:
Error using gather
Too many input arguments.
Does anyone have an idea. Here is the part of my code that produce the CNN :
X = all_features{:, 1:end-1}; % Use parentheses () for table indexing
y = all_features.Label;

y = categorical(y);

disp([‘Feature matrix dimensions: ‘, num2str(size(X))]);
disp([‘Labels vector dimensions: ‘, num2str(size(y))]);

X = zscore(X);

numFeatures = size(X, 2);
numObservations = size(X, 1);
X = reshape(X, [numObservations, numFeatures, 1, 1]); % Reshape for CNN

layers = [
imageInputLayer([numFeatures 1 1])
convolution2dLayer([3 1], 8, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
maxPooling2dLayer([2 1], ‘Stride’, 2)
convolution2dLayer([3 1], 16, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];

options = trainingOptions(‘adam’, …
‘MaxEpochs’, 30, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 0.001, …
‘ValidationFrequency’, 10, …
‘Verbose’, false, …
‘Plots’, ‘training-progress’);
% 5-Fold Cross-Validation
cv = cvpartition(y, ‘KFold’, 5, ‘Stratify’, true);

accuracies = zeros(cv.NumTestSets, 1);
confusion_matrices = cell(cv.NumTestSets, 1);

for k = 1:cv.NumTestSets

train_idx = training(cv, k);
test_idx = test(cv, k);

X_train = X(train_idx, :, :, :);
y_train = y(train_idx);
X_test = X(test_idx, :, :, :);
y_test = y(test_idx);

net = trainNetwork(X_train, y_train, layers, options);

y_pred = classify(net, X_test);

confusion_matrices{k} = confusionmat(y_test, y_pred);
cm = confusion_matrices{k};

accuracies(k) = sum(diag(cm)) / sum(cm(:));
end

mean_accuracy = mean(accuracies);
fprintf(‘Mean Accuracy across 5 folds: %.2f%%n’, mean_accuracy * 100);

save(‘eeg_cnn_classifier_cv.mat’, ‘net’);

disp(‘Confusion Matrices for each fold:’);
for k = 1:cv.NumTestSets
disp([‘Fold ‘, num2str(k), ‘:’]);
disp(confusion_matrices{k});
end cnn, machine learning, deep learning MATLAB Answers — New Questions

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I have this code and i want to save the variable x from workspace into a file clc;
clear;
close all;
% Parameters
sampling_frequency = 1000; % Sampling frequency in Hz
duration = 5; % Duration of signal acquisition in seconds
num_samples = sampling_frequency * duration;
% Initialize variables
time = (0:num_samples-1) / sampling_frequency;
x = zeros(1, num_samples);
% Connect to Arduino
a = arduino();
% Bandpass filter parameters
low_cutoff_frequency = 6; % Low cutoff frequency in Hz
high_cutoff_frequency = 400; % High cutoff frequency in Hz
% Design the bandpass filter
[b, a_filter] = butter(2, [low_cutoff_frequency, high_cutoff_frequency] / (sampling_frequency / 2), ‘bandpass’); %Normalized between [0 1]
% Plot for real-time visualization of raw signal
figure(‘Name’, ‘Real-Time Raw EMG Signal’);
h_raw = plot(nan, nan); % Initialize plot with NaN
grid on;
title(‘Real-Time Raw EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 5]); % Adjust ylim according to expected voltage range
% Plot for real-time visualization of filtered signal
figure(‘Name’, ‘Real-Time Filtered EMG Signal’);
h_filtered = plot(nan, nan); % Initialize plot with NaN
grid on;
title(‘Real-Time Filtered EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 5]); % Adjust ylim according to expected voltage range
% Start loop to acquire signal
for i = 1:num_samples
% Read voltage
raw_voltage = readVoltage(a, ‘A0’);

% Store raw voltage
x(i) = raw_voltage;

% Apply bandpass filter to the signal up to current point if enough data points are collected
if i >= 25
filtered_signal = filtfilt(b, a_filter, x(1:i));

% Remove DC offset by subtracting the mean of the filtered signal
filtered_signal = filtered_signal – mean(filtered_signal);

% Update plot for filtered signal
set(h_filtered, ‘YData’, filtered_signal, ‘XData’, time(1:i));
end

% Update plot for raw signal
set(h_raw, ‘YData’, x(1:i), ‘XData’, time(1:i));

drawnow;

end
save(‘EMG_data.mat’, ‘x’,);
It doesn’t create the .mat file and I suppose the problem is that i stop the iteration very early. But I want to save any values that are obtained until any iteration. How can I fix that?I have this code and i want to save the variable x from workspace into a file clc;
clear;
close all;
% Parameters
sampling_frequency = 1000; % Sampling frequency in Hz
duration = 5; % Duration of signal acquisition in seconds
num_samples = sampling_frequency * duration;
% Initialize variables
time = (0:num_samples-1) / sampling_frequency;
x = zeros(1, num_samples);
% Connect to Arduino
a = arduino();
% Bandpass filter parameters
low_cutoff_frequency = 6; % Low cutoff frequency in Hz
high_cutoff_frequency = 400; % High cutoff frequency in Hz
% Design the bandpass filter
[b, a_filter] = butter(2, [low_cutoff_frequency, high_cutoff_frequency] / (sampling_frequency / 2), ‘bandpass’); %Normalized between [0 1]
% Plot for real-time visualization of raw signal
figure(‘Name’, ‘Real-Time Raw EMG Signal’);
h_raw = plot(nan, nan); % Initialize plot with NaN
grid on;
title(‘Real-Time Raw EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 5]); % Adjust ylim according to expected voltage range
% Plot for real-time visualization of filtered signal
figure(‘Name’, ‘Real-Time Filtered EMG Signal’);
h_filtered = plot(nan, nan); % Initialize plot with NaN
grid on;
title(‘Real-Time Filtered EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 5]); % Adjust ylim according to expected voltage range
% Start loop to acquire signal
for i = 1:num_samples
% Read voltage
raw_voltage = readVoltage(a, ‘A0’);

% Store raw voltage
x(i) = raw_voltage;

% Apply bandpass filter to the signal up to current point if enough data points are collected
if i >= 25
filtered_signal = filtfilt(b, a_filter, x(1:i));

% Remove DC offset by subtracting the mean of the filtered signal
filtered_signal = filtered_signal – mean(filtered_signal);

% Update plot for filtered signal
set(h_filtered, ‘YData’, filtered_signal, ‘XData’, time(1:i));
end

% Update plot for raw signal
set(h_raw, ‘YData’, x(1:i), ‘XData’, time(1:i));

drawnow;

end
save(‘EMG_data.mat’, ‘x’,);
It doesn’t create the .mat file and I suppose the problem is that i stop the iteration very early. But I want to save any values that are obtained until any iteration. How can I fix that? I have this code and i want to save the variable x from workspace into a file clc;
clear;
close all;
% Parameters
sampling_frequency = 1000; % Sampling frequency in Hz
duration = 5; % Duration of signal acquisition in seconds
num_samples = sampling_frequency * duration;
% Initialize variables
time = (0:num_samples-1) / sampling_frequency;
x = zeros(1, num_samples);
% Connect to Arduino
a = arduino();
% Bandpass filter parameters
low_cutoff_frequency = 6; % Low cutoff frequency in Hz
high_cutoff_frequency = 400; % High cutoff frequency in Hz
% Design the bandpass filter
[b, a_filter] = butter(2, [low_cutoff_frequency, high_cutoff_frequency] / (sampling_frequency / 2), ‘bandpass’); %Normalized between [0 1]
% Plot for real-time visualization of raw signal
figure(‘Name’, ‘Real-Time Raw EMG Signal’);
h_raw = plot(nan, nan); % Initialize plot with NaN
grid on;
title(‘Real-Time Raw EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 5]); % Adjust ylim according to expected voltage range
% Plot for real-time visualization of filtered signal
figure(‘Name’, ‘Real-Time Filtered EMG Signal’);
h_filtered = plot(nan, nan); % Initialize plot with NaN
grid on;
title(‘Real-Time Filtered EMG Signal’);
xlabel(‘Time (s)’);
ylabel(‘Voltage (V)’);
xlim([0, duration]); % Adjust xlim as needed
ylim([0, 5]); % Adjust ylim according to expected voltage range
% Start loop to acquire signal
for i = 1:num_samples
% Read voltage
raw_voltage = readVoltage(a, ‘A0’);

% Store raw voltage
x(i) = raw_voltage;

% Apply bandpass filter to the signal up to current point if enough data points are collected
if i >= 25
filtered_signal = filtfilt(b, a_filter, x(1:i));

% Remove DC offset by subtracting the mean of the filtered signal
filtered_signal = filtered_signal – mean(filtered_signal);

% Update plot for filtered signal
set(h_filtered, ‘YData’, filtered_signal, ‘XData’, time(1:i));
end

% Update plot for raw signal
set(h_raw, ‘YData’, x(1:i), ‘XData’, time(1:i));

drawnow;

end
save(‘EMG_data.mat’, ‘x’,);
It doesn’t create the .mat file and I suppose the problem is that i stop the iteration very early. But I want to save any values that are obtained until any iteration. How can I fix that? save variable from workspace, iterations MATLAB Answers — New Questions

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I’m trying to create a motor control simulation.
I would like to implement functions according to the execution cycle as if configuring actual motor control embedded software.
For example, I want to configure the current control subsystem so that this subsystem runs at a 1ms task, and the speed control subsystem calculates at a 5ms task.
How can I use Simulink to implement something like the example?I’m trying to create a motor control simulation.
I would like to implement functions according to the execution cycle as if configuring actual motor control embedded software.
For example, I want to configure the current control subsystem so that this subsystem runs at a 1ms task, and the speed control subsystem calculates at a 5ms task.
How can I use Simulink to implement something like the example? I’m trying to create a motor control simulation.
I would like to implement functions according to the execution cycle as if configuring actual motor control embedded software.
For example, I want to configure the current control subsystem so that this subsystem runs at a 1ms task, and the speed control subsystem calculates at a 5ms task.
How can I use Simulink to implement something like the example? simulink, task MATLAB Answers — New Questions

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Hello, here is my problem: I upgraded from Matlab R2017a to 2021b based on the recommendation for compatibility with Windows 11 (I moved to a new PC). However, I am experiencing problems with the MEX functions I use to connect to hardware (open connexion, close connexion, update status, basic functions). I recompiled the MEX functions (for which I have all the source files) under MATLAB 2021b, using both the -R2017b and -R2018a options, and they recompiled successfully. Despite this, I cannot connect to the hardware. If I use the same MEX functions on the same computer (Windows 11) with MATLAB R2017a, it works fine (not if I compiled the MEX with -R2018a option which makes sense I believe). Additionally, I am using external software from the manufacturer to control the hardware, and it works fine too (driver correctly installed). The issue only occurs with MATLAB 2021b. I compared the verbose outputs of the MEX function compilations with the -R2017b and -R2018a options and found no significant differences. I am confused as to why the MEX functions work with MATLAB R2017a but not with R2021b on the same computer, especially since the functions recompiled with R2021b show no warnings or errors. Do you have any tips or ideas to solve this issue?
Additionnaly, the MEX functions works fine with R2017a, hardware controlled correctly, while with R2021b error code from MEX functions states "device not found". Matlab configuration between installation R2017a and R2021b is the same (paths, compiler, etc.). Thanks !Hello, here is my problem: I upgraded from Matlab R2017a to 2021b based on the recommendation for compatibility with Windows 11 (I moved to a new PC). However, I am experiencing problems with the MEX functions I use to connect to hardware (open connexion, close connexion, update status, basic functions). I recompiled the MEX functions (for which I have all the source files) under MATLAB 2021b, using both the -R2017b and -R2018a options, and they recompiled successfully. Despite this, I cannot connect to the hardware. If I use the same MEX functions on the same computer (Windows 11) with MATLAB R2017a, it works fine (not if I compiled the MEX with -R2018a option which makes sense I believe). Additionally, I am using external software from the manufacturer to control the hardware, and it works fine too (driver correctly installed). The issue only occurs with MATLAB 2021b. I compared the verbose outputs of the MEX function compilations with the -R2017b and -R2018a options and found no significant differences. I am confused as to why the MEX functions work with MATLAB R2017a but not with R2021b on the same computer, especially since the functions recompiled with R2021b show no warnings or errors. Do you have any tips or ideas to solve this issue?
Additionnaly, the MEX functions works fine with R2017a, hardware controlled correctly, while with R2021b error code from MEX functions states "device not found". Matlab configuration between installation R2017a and R2021b is the same (paths, compiler, etc.). Thanks ! Hello, here is my problem: I upgraded from Matlab R2017a to 2021b based on the recommendation for compatibility with Windows 11 (I moved to a new PC). However, I am experiencing problems with the MEX functions I use to connect to hardware (open connexion, close connexion, update status, basic functions). I recompiled the MEX functions (for which I have all the source files) under MATLAB 2021b, using both the -R2017b and -R2018a options, and they recompiled successfully. Despite this, I cannot connect to the hardware. If I use the same MEX functions on the same computer (Windows 11) with MATLAB R2017a, it works fine (not if I compiled the MEX with -R2018a option which makes sense I believe). Additionally, I am using external software from the manufacturer to control the hardware, and it works fine too (driver correctly installed). The issue only occurs with MATLAB 2021b. I compared the verbose outputs of the MEX function compilations with the -R2017b and -R2018a options and found no significant differences. I am confused as to why the MEX functions work with MATLAB R2017a but not with R2021b on the same computer, especially since the functions recompiled with R2021b show no warnings or errors. Do you have any tips or ideas to solve this issue?
Additionnaly, the MEX functions works fine with R2017a, hardware controlled correctly, while with R2021b error code from MEX functions states "device not found". Matlab configuration between installation R2017a and R2021b is the same (paths, compiler, etc.). Thanks ! function, matlab function, mex MATLAB Answers — New Questions

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I have a sparse matrix which contains
aa =
(1,1) 72
(1,2) 101
(1,3) 108

Now I want to convert this into image …?I have a sparse matrix which contains
aa =
(1,1) 72
(1,2) 101
(1,3) 108

Now I want to convert this into image …? I have a sparse matrix which contains
aa =
(1,1) 72
(1,2) 101
(1,3) 108

Now I want to convert this into image …? how to convert sparse matrix into image? MATLAB Answers — New Questions

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