I am currently running 2023b update 8 with the MATLAB Support Package for Apple iOS Sensors on an iPhone 8. The maximum resolution when taking images is 720p. Is there any way to get full resolution images? Alternatively is there a way to connect the iPhone to matlab over a wire for full resolution pictures, without the cloud streaming method?I am currently running 2023b update 8 with the MATLAB Support Package for Apple iOS Sensors on an iPhone 8. The maximum resolution when taking images is 720p. Is there any way to get full resolution images? Alternatively is there a way to connect the iPhone to matlab over a wire for full resolution pictures, without the cloud streaming method? I am currently running 2023b update 8 with the MATLAB Support Package for Apple iOS Sensors on an iPhone 8. The maximum resolution when taking images is 720p. Is there any way to get full resolution images? Alternatively is there a way to connect the iPhone to matlab over a wire for full resolution pictures, without the cloud streaming method? sensors, mobile, device, camera, iphone, resolution, image acquisition, phone MATLAB Answers — New Questions

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Error using trainNetwork (line 150)
Invalid training data. Sequence responses must have the same sequence length as the corresponding predictors.

Error in Untitled (line 92)
net = trainNetwork(x_train_seq, y_train_seq, layers, options);
% Load the files
file_path_e = ‘sig.xlsx’;
file_path_sig = ‘E.xlsx’;
% Read the files
data_e = readtable(file_path_e);
data_sig = readtable(file_path_sig);
% Prepare data
x = table2array(data_e);
y = table2array(data_sig);
% Ensure x and y have the same length
min_length = min(length(x), length(y));
x = x(1:min_length);
y = y(1:min_length);

% Display initial data types
disp(‘Initial data types:’);
disp([‘x type: ‘, class(x)]);
disp([‘y type: ‘, class(y)]);

% Convert to numeric arrays if not already
x = str2double(x);
y = str2double(y);
% Display number of NaNs before removing them
fprintf(‘Number of NaNs in x before removal: %dn’, sum(isnan(x)));
fprintf(‘Number of NaNs in y before removal: %dn’, sum(isnan(y)));
% Handle non-numeric entries by removing NaNs
valid_indices = ~isnan(x) & ~isnan(y);
x = x(valid_indices);
y = y(valid_indices);

% Display number of valid data points after removal
fprintf(‘Number of valid data points after preprocessing: %dn’, length(x));

% Ensure x and y have the same length again after removing NaNs
min_length = min(length(x), length(y));
x = x(1:min_length);
y = y(1:min_length);

% Check if there are enough valid entries
if min_length <= 1
error(‘Not enough valid data points after preprocessing.’);
end

% Reshape data to be compatible with LSTM input (samples, timesteps, features)
x = reshape(x, [], 1);
y = reshape(y, [], 1);
% Scale data using min-max normalization
x_scaled = (x – min(x)) / (max(x) – min(x));
y_scaled = (y – min(y)) / (max(y) – min(y));
% Split data into train and test sets
cv = cvpartition(length(x_scaled), ‘HoldOut’, 0.2);
x_train = x_scaled(training(cv));
y_train = y_scaled(training(cv));
x_test = x_scaled(test(cv));
y_test = y_scaled(test(cv));

% Create sequences for LSTM
seq_length = 10;
[x_train_seq, y_train_seq] = create_sequences(x_train, y_train, seq_length);
[x_test_seq, y_test_seq] = create_sequences(x_test, y_test, seq_length);

% Reshape for LSTM
x_train_seq = reshape(x_train_seq, [size(x_train_seq, 1), seq_length, 1]);
x_test_seq = reshape(x_test_seq, [size(x_test_seq, 1), seq_length, 1]);
% Build the LSTM model
layers = [
sequenceInputLayer(1)
lstmLayer(20, ‘OutputMode’, ‘sequence’)
dropoutLayer(0.2)
lstmLayer(20)
dropoutLayer(0.2)
fullyConnectedLayer(1)
regressionLayer];
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 300, …
‘MiniBatchSize’, 20, …
‘InitialLearnRate’, 0.001, …
‘ValidationData’, {x_test_seq, y_test_seq}, …
‘Plots’, ‘training-progress’, …
‘Verbose’, 0);
% Train the model
net = trainNetwork(x_train_seq, y_train_seq, layers, options);

% Plot loss curve
training_info = net.TrainingHistory;
figure;
plot(training_info.TrainingLoss, ‘DisplayName’, ‘Train’);
hold on;
plot(training_info.ValidationLoss, ‘DisplayName’, ‘Validation’);
title(‘Model loss’);
xlabel(‘Epoch’);
ylabel(‘Loss’);
legend(‘show’);
hold off;
% Function to create sequences
function [xs, ys] = create_sequences(x_data, y_data, seq_length)
xs = [];
ys = [];
for i = 1:(length(x_data) – seq_length)
x_seq = x_data(i:i+seq_length-1);
y_seq = y_data(i+seq_length-1); % Adjust index to ensure same length
xs = [xs; x_seq’];
ys = [ys; y_seq’];
end
endError using trainNetwork (line 150)
Invalid training data. Sequence responses must have the same sequence length as the corresponding predictors.

Error in Untitled (line 92)
net = trainNetwork(x_train_seq, y_train_seq, layers, options);
% Load the files
file_path_e = ‘sig.xlsx’;
file_path_sig = ‘E.xlsx’;
% Read the files
data_e = readtable(file_path_e);
data_sig = readtable(file_path_sig);
% Prepare data
x = table2array(data_e);
y = table2array(data_sig);
% Ensure x and y have the same length
min_length = min(length(x), length(y));
x = x(1:min_length);
y = y(1:min_length);

% Display initial data types
disp(‘Initial data types:’);
disp([‘x type: ‘, class(x)]);
disp([‘y type: ‘, class(y)]);

% Convert to numeric arrays if not already
x = str2double(x);
y = str2double(y);
% Display number of NaNs before removing them
fprintf(‘Number of NaNs in x before removal: %dn’, sum(isnan(x)));
fprintf(‘Number of NaNs in y before removal: %dn’, sum(isnan(y)));
% Handle non-numeric entries by removing NaNs
valid_indices = ~isnan(x) & ~isnan(y);
x = x(valid_indices);
y = y(valid_indices);

% Display number of valid data points after removal
fprintf(‘Number of valid data points after preprocessing: %dn’, length(x));

% Ensure x and y have the same length again after removing NaNs
min_length = min(length(x), length(y));
x = x(1:min_length);
y = y(1:min_length);

% Check if there are enough valid entries
if min_length <= 1
error(‘Not enough valid data points after preprocessing.’);
end

% Reshape data to be compatible with LSTM input (samples, timesteps, features)
x = reshape(x, [], 1);
y = reshape(y, [], 1);
% Scale data using min-max normalization
x_scaled = (x – min(x)) / (max(x) – min(x));
y_scaled = (y – min(y)) / (max(y) – min(y));
% Split data into train and test sets
cv = cvpartition(length(x_scaled), ‘HoldOut’, 0.2);
x_train = x_scaled(training(cv));
y_train = y_scaled(training(cv));
x_test = x_scaled(test(cv));
y_test = y_scaled(test(cv));

% Create sequences for LSTM
seq_length = 10;
[x_train_seq, y_train_seq] = create_sequences(x_train, y_train, seq_length);
[x_test_seq, y_test_seq] = create_sequences(x_test, y_test, seq_length);

% Reshape for LSTM
x_train_seq = reshape(x_train_seq, [size(x_train_seq, 1), seq_length, 1]);
x_test_seq = reshape(x_test_seq, [size(x_test_seq, 1), seq_length, 1]);
% Build the LSTM model
layers = [
sequenceInputLayer(1)
lstmLayer(20, ‘OutputMode’, ‘sequence’)
dropoutLayer(0.2)
lstmLayer(20)
dropoutLayer(0.2)
fullyConnectedLayer(1)
regressionLayer];
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 300, …
‘MiniBatchSize’, 20, …
‘InitialLearnRate’, 0.001, …
‘ValidationData’, {x_test_seq, y_test_seq}, …
‘Plots’, ‘training-progress’, …
‘Verbose’, 0);
% Train the model
net = trainNetwork(x_train_seq, y_train_seq, layers, options);

% Plot loss curve
training_info = net.TrainingHistory;
figure;
plot(training_info.TrainingLoss, ‘DisplayName’, ‘Train’);
hold on;
plot(training_info.ValidationLoss, ‘DisplayName’, ‘Validation’);
title(‘Model loss’);
xlabel(‘Epoch’);
ylabel(‘Loss’);
legend(‘show’);
hold off;
% Function to create sequences
function [xs, ys] = create_sequences(x_data, y_data, seq_length)
xs = [];
ys = [];
for i = 1:(length(x_data) – seq_length)
x_seq = x_data(i:i+seq_length-1);
y_seq = y_data(i+seq_length-1); % Adjust index to ensure same length
xs = [xs; x_seq’];
ys = [ys; y_seq’];
end
end Error using trainNetwork (line 150)
Invalid training data. Sequence responses must have the same sequence length as the corresponding predictors.

Error in Untitled (line 92)
net = trainNetwork(x_train_seq, y_train_seq, layers, options);
% Load the files
file_path_e = ‘sig.xlsx’;
file_path_sig = ‘E.xlsx’;
% Read the files
data_e = readtable(file_path_e);
data_sig = readtable(file_path_sig);
% Prepare data
x = table2array(data_e);
y = table2array(data_sig);
% Ensure x and y have the same length
min_length = min(length(x), length(y));
x = x(1:min_length);
y = y(1:min_length);

% Display initial data types
disp(‘Initial data types:’);
disp([‘x type: ‘, class(x)]);
disp([‘y type: ‘, class(y)]);

% Convert to numeric arrays if not already
x = str2double(x);
y = str2double(y);
% Display number of NaNs before removing them
fprintf(‘Number of NaNs in x before removal: %dn’, sum(isnan(x)));
fprintf(‘Number of NaNs in y before removal: %dn’, sum(isnan(y)));
% Handle non-numeric entries by removing NaNs
valid_indices = ~isnan(x) & ~isnan(y);
x = x(valid_indices);
y = y(valid_indices);

% Display number of valid data points after removal
fprintf(‘Number of valid data points after preprocessing: %dn’, length(x));

% Ensure x and y have the same length again after removing NaNs
min_length = min(length(x), length(y));
x = x(1:min_length);
y = y(1:min_length);

% Check if there are enough valid entries
if min_length <= 1
error(‘Not enough valid data points after preprocessing.’);
end

% Reshape data to be compatible with LSTM input (samples, timesteps, features)
x = reshape(x, [], 1);
y = reshape(y, [], 1);
% Scale data using min-max normalization
x_scaled = (x – min(x)) / (max(x) – min(x));
y_scaled = (y – min(y)) / (max(y) – min(y));
% Split data into train and test sets
cv = cvpartition(length(x_scaled), ‘HoldOut’, 0.2);
x_train = x_scaled(training(cv));
y_train = y_scaled(training(cv));
x_test = x_scaled(test(cv));
y_test = y_scaled(test(cv));

% Create sequences for LSTM
seq_length = 10;
[x_train_seq, y_train_seq] = create_sequences(x_train, y_train, seq_length);
[x_test_seq, y_test_seq] = create_sequences(x_test, y_test, seq_length);

% Reshape for LSTM
x_train_seq = reshape(x_train_seq, [size(x_train_seq, 1), seq_length, 1]);
x_test_seq = reshape(x_test_seq, [size(x_test_seq, 1), seq_length, 1]);
% Build the LSTM model
layers = [
sequenceInputLayer(1)
lstmLayer(20, ‘OutputMode’, ‘sequence’)
dropoutLayer(0.2)
lstmLayer(20)
dropoutLayer(0.2)
fullyConnectedLayer(1)
regressionLayer];
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 300, …
‘MiniBatchSize’, 20, …
‘InitialLearnRate’, 0.001, …
‘ValidationData’, {x_test_seq, y_test_seq}, …
‘Plots’, ‘training-progress’, …
‘Verbose’, 0);
% Train the model
net = trainNetwork(x_train_seq, y_train_seq, layers, options);

% Plot loss curve
training_info = net.TrainingHistory;
figure;
plot(training_info.TrainingLoss, ‘DisplayName’, ‘Train’);
hold on;
plot(training_info.ValidationLoss, ‘DisplayName’, ‘Validation’);
title(‘Model loss’);
xlabel(‘Epoch’);
ylabel(‘Loss’);
legend(‘show’);
hold off;
% Function to create sequences
function [xs, ys] = create_sequences(x_data, y_data, seq_length)
xs = [];
ys = [];
for i = 1:(length(x_data) – seq_length)
x_seq = x_data(i:i+seq_length-1);
y_seq = y_data(i+seq_length-1); % Adjust index to ensure same length
xs = [xs; x_seq’];
ys = [ys; y_seq’];
end
end accuracy, lstm MATLAB Answers — New Questions

​

Hello community.
I’d like to have your support to find a soluion.
I have several ‘.dat’ files, I’m usinng mdfimport to export the variables I need and then save them into ‘.mat’ format.
my code:
clear all;
clc;
[FileName,PathName] = uigetfile(‘*.dat’,’Select the .dat-file(s)’,’MultiSelect’, ‘on’);

if class(FileName) == char(‘cell’);
FileName = FileName’;
end
if class(FileName)==char(‘char’);
FileName = {FileName};
end
%========================================================================%
V = {
[‘EnvT_t’]
[‘CtT_flgHeal’]
[‘CtT_flgEna’]
[‘Epm_nEng’]
[‘CTM_Delta’]
[‘CTM_Flag’]
[‘CTM_Sum’]
};
V=V’;
for k=1:length(FileName)
%———————————————————————-
progress = [‘Working on file ‘ int2str(k) ‘ of ‘ int2str(length(FileName)) ‘…’];
disp(progress);
disp(FileName(k));
LoadPath = char(strcat(PathName, FileName(k)));
mdfimport(LoadPath,[],V, ‘resample_1’);
save([‘@’ num2str(k) ‘.mat’])
end
let’s say I have below data:
Carr1.dat
Carr2.dat
Carr3.dat
Carr4.dat
my code will go trhoug each .dat file, will extract defined variables and then it will save it as follows:
@1.mat
@2.mat
@3.mat
@4.mat
how can I use save command to keep original file name? I’m looking to get this:
Carr1.mat
Carr2.mat
Carr3.mat
Carr4.mat
I’ve tryied different things but always get an error.

as always your feedback will be highly appreciatedHello community.
I’d like to have your support to find a soluion.
I have several ‘.dat’ files, I’m usinng mdfimport to export the variables I need and then save them into ‘.mat’ format.
my code:
clear all;
clc;
[FileName,PathName] = uigetfile(‘*.dat’,’Select the .dat-file(s)’,’MultiSelect’, ‘on’);

if class(FileName) == char(‘cell’);
FileName = FileName’;
end
if class(FileName)==char(‘char’);
FileName = {FileName};
end
%========================================================================%
V = {
[‘EnvT_t’]
[‘CtT_flgHeal’]
[‘CtT_flgEna’]
[‘Epm_nEng’]
[‘CTM_Delta’]
[‘CTM_Flag’]
[‘CTM_Sum’]
};
V=V’;
for k=1:length(FileName)
%———————————————————————-
progress = [‘Working on file ‘ int2str(k) ‘ of ‘ int2str(length(FileName)) ‘…’];
disp(progress);
disp(FileName(k));
LoadPath = char(strcat(PathName, FileName(k)));
mdfimport(LoadPath,[],V, ‘resample_1’);
save([‘@’ num2str(k) ‘.mat’])
end
let’s say I have below data:
Carr1.dat
Carr2.dat
Carr3.dat
Carr4.dat
my code will go trhoug each .dat file, will extract defined variables and then it will save it as follows:
@1.mat
@2.mat
@3.mat
@4.mat
how can I use save command to keep original file name? I’m looking to get this:
Carr1.mat
Carr2.mat
Carr3.mat
Carr4.mat
I’ve tryied different things but always get an error.

as always your feedback will be highly appreciated Hello community.
I’d like to have your support to find a soluion.
I have several ‘.dat’ files, I’m usinng mdfimport to export the variables I need and then save them into ‘.mat’ format.
my code:
clear all;
clc;
[FileName,PathName] = uigetfile(‘*.dat’,’Select the .dat-file(s)’,’MultiSelect’, ‘on’);

if class(FileName) == char(‘cell’);
FileName = FileName’;
end
if class(FileName)==char(‘char’);
FileName = {FileName};
end
%========================================================================%
V = {
[‘EnvT_t’]
[‘CtT_flgHeal’]
[‘CtT_flgEna’]
[‘Epm_nEng’]
[‘CTM_Delta’]
[‘CTM_Flag’]
[‘CTM_Sum’]
};
V=V’;
for k=1:length(FileName)
%———————————————————————-
progress = [‘Working on file ‘ int2str(k) ‘ of ‘ int2str(length(FileName)) ‘…’];
disp(progress);
disp(FileName(k));
LoadPath = char(strcat(PathName, FileName(k)));
mdfimport(LoadPath,[],V, ‘resample_1’);
save([‘@’ num2str(k) ‘.mat’])
end
let’s say I have below data:
Carr1.dat
Carr2.dat
Carr3.dat
Carr4.dat
my code will go trhoug each .dat file, will extract defined variables and then it will save it as follows:
@1.mat
@2.mat
@3.mat
@4.mat
how can I use save command to keep original file name? I’m looking to get this:
Carr1.mat
Carr2.mat
Carr3.mat
Carr4.mat
I’ve tryied different things but always get an error.

as always your feedback will be highly appreciated matlab, data import, save MATLAB Answers — New Questions

​