Tag Archives: matlab
trying perform an image classification using RL but facing error, Any suggestion?
clc;
%% Load Dataset
imds = imageDatastore(‘D:2023thesiswaveletImagesSubsetImages’, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);
% Ensure the dataset is balanced
DataSetInfo = countEachLabel(imds);
% Split Data into Training and Validation sets
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.80, ‘randomized’);
%% Load Pretrained Network
net = vgg16;
inputSize = net.Layers(1).InputSize;
% Feature extraction layer
featureLayer = ‘fc7’;
% Augment data to match input size of the network
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
% Extract Features using Pretrained CNN
trainFeatures = activations(net, augimdsTrain, featureLayer, ‘OutputAs’, ‘rows’);
validationFeatures = activations(net, augimdsValidation, featureLayer, ‘OutputAs’, ‘rows’);
% Extract labels
trainLabels = grp2idx(imdsTrain.Labels);
validationLabels = grp2idx(imdsValidation.Labels);
%% RL Environment Setup
% Define Observation and Action spaces
numFeatures = size(trainFeatures, 2); % Number of features (e.g., 4096 for ‘fc7’ layer)
ObservationInfo = rlNumericSpec([numFeatures, 1], …
‘LowerLimit’, -inf, ‘UpperLimit’, inf, …
‘Name’, ‘Features’);
ObservationInfo.Description = ‘Feature vector extracted from CNN’;
% Define Action space (each action corresponds to a class label)
ActionInfo = rlFiniteSetSpec(1:max(trainLabels)); % Class labels as actions
ActionInfo.Name = ‘Class Labels’;
% Define Step and Reset functions
stepFunction = @(Action, LoggedSignals) stepFunctionRL(Action, trainFeatures, trainLabels, LoggedSignals);
resetFunction = @() resetFunctionRL(trainFeatures);
% Create RL Environment
env = rlFunctionEnv(ObservationInfo, ActionInfo, stepFunction, resetFunction);
%% Define RL Agent
% Define critic network
statePath = [
featureInputLayer(numFeatures, ‘Normalization’, ‘none’, ‘Name’, ‘state’)
fullyConnectedLayer(128, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu1’)
fullyConnectedLayer(64, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu2’)
fullyConnectedLayer(numel(ActionInfo.Elements), ‘Name’, ‘fcOutput’)]; % Number of actions
criticNet = dlnetwork(layerGraph(statePath));
critic = rlQValueFunction(criticNet, ObservationInfo, ActionInfo, ‘ObservationInputNames’, ‘state’);
% Agent options
agentOpts = rlDQNAgentOptions( …
‘ExperienceBufferLength’, 1e5, …
‘DiscountFactor’, 0.99, …
‘MiniBatchSize’, 64, …
‘TargetSmoothFactor’, 1e-3, …
‘TargetUpdateFrequency’, 4);
agent = rlDQNAgent(critic, agentOpts);
%% Train RL Agent
trainOpts = rlTrainingOptions( …
‘MaxEpisodes’, 500, …
‘MaxStepsPerEpisode’, size(trainFeatures, 1), …
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘StopTrainingCriteria’, ‘EpisodeCount’, …
‘StopTrainingValue’, 500);
trainingStats = train(agent, env);
%% Reset Function
function [InitialObservation, LoggedSignals] = resetFunctionRL(Features)
% Reset function for the RL environment
% This function resets the environment to its initial state at the start
% of each new episode.
% Initialize the LoggedSignals structure (holds environment state)
LoggedSignals = struct();
% Set the initial observation index to the first data point
LoggedSignals.CurrentIndex = 1;
% Fetch the initial observation: the first feature vector
% Ensure that the observation is formatted as a column vector [numFeatures, 1]
InitialObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to column vector
% Check if the initial observation matches the expected size
if size(InitialObservation, 2) ~= 1
error(‘InitialObservation must be a column vector of size [numFeatures, 1]’);
end
% Optionally, initialize any other properties you want in LoggedSignals
% (e.g., additional state variables, environment-specific flags)
LoggedSignals.EpisodeStartTime = datetime(‘now’); % Track the start time for each episode
end
%% Step Function
function [NextObservation, Reward, IsDone, LoggedSignals] = stepFunctionRL(Action, Features, Labels, LoggedSignals)
% Step function for the RL environment
idx = LoggedSignals.CurrentIndex;
% Assign reward based on action correctness
correctLabel = Labels(idx); % Correct label for the current feature
if Action == correctLabel
Reward = 1; % Positive reward for correct classification
else
Reward = -1; % Negative reward for incorrect classification
end
% Update index and check if episode is done
LoggedSignals.CurrentIndex = idx + 1;
IsDone = LoggedSignals.CurrentIndex > size(Features, 1);
% Ensure the next observation is a column vector of size [numFeatures, 1]
if ~IsDone
NextObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to ensure column vector [numFeatures x 1]
else
NextObservation = Features(idx, :)’; % Dummy to avoid dimension mismatch
end
end
AND THIS IS THE ERROR
Error using rl.env.MATLABEnvironment/validateEnvironment (line 58)
Environment ‘ObservationInfo’ does not match observation output from reset function. Check the data type, dimensions, and range.
Error in rl.env.rlFunctionEnv (line 82)
validateEnvironment(this);
Error in rlFunctionEnv (line 45)
env = rl.env.rlFunctionEnv(varargin{:});clc;
%% Load Dataset
imds = imageDatastore(‘D:2023thesiswaveletImagesSubsetImages’, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);
% Ensure the dataset is balanced
DataSetInfo = countEachLabel(imds);
% Split Data into Training and Validation sets
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.80, ‘randomized’);
%% Load Pretrained Network
net = vgg16;
inputSize = net.Layers(1).InputSize;
% Feature extraction layer
featureLayer = ‘fc7’;
% Augment data to match input size of the network
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
% Extract Features using Pretrained CNN
trainFeatures = activations(net, augimdsTrain, featureLayer, ‘OutputAs’, ‘rows’);
validationFeatures = activations(net, augimdsValidation, featureLayer, ‘OutputAs’, ‘rows’);
% Extract labels
trainLabels = grp2idx(imdsTrain.Labels);
validationLabels = grp2idx(imdsValidation.Labels);
%% RL Environment Setup
% Define Observation and Action spaces
numFeatures = size(trainFeatures, 2); % Number of features (e.g., 4096 for ‘fc7’ layer)
ObservationInfo = rlNumericSpec([numFeatures, 1], …
‘LowerLimit’, -inf, ‘UpperLimit’, inf, …
‘Name’, ‘Features’);
ObservationInfo.Description = ‘Feature vector extracted from CNN’;
% Define Action space (each action corresponds to a class label)
ActionInfo = rlFiniteSetSpec(1:max(trainLabels)); % Class labels as actions
ActionInfo.Name = ‘Class Labels’;
% Define Step and Reset functions
stepFunction = @(Action, LoggedSignals) stepFunctionRL(Action, trainFeatures, trainLabels, LoggedSignals);
resetFunction = @() resetFunctionRL(trainFeatures);
% Create RL Environment
env = rlFunctionEnv(ObservationInfo, ActionInfo, stepFunction, resetFunction);
%% Define RL Agent
% Define critic network
statePath = [
featureInputLayer(numFeatures, ‘Normalization’, ‘none’, ‘Name’, ‘state’)
fullyConnectedLayer(128, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu1’)
fullyConnectedLayer(64, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu2’)
fullyConnectedLayer(numel(ActionInfo.Elements), ‘Name’, ‘fcOutput’)]; % Number of actions
criticNet = dlnetwork(layerGraph(statePath));
critic = rlQValueFunction(criticNet, ObservationInfo, ActionInfo, ‘ObservationInputNames’, ‘state’);
% Agent options
agentOpts = rlDQNAgentOptions( …
‘ExperienceBufferLength’, 1e5, …
‘DiscountFactor’, 0.99, …
‘MiniBatchSize’, 64, …
‘TargetSmoothFactor’, 1e-3, …
‘TargetUpdateFrequency’, 4);
agent = rlDQNAgent(critic, agentOpts);
%% Train RL Agent
trainOpts = rlTrainingOptions( …
‘MaxEpisodes’, 500, …
‘MaxStepsPerEpisode’, size(trainFeatures, 1), …
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘StopTrainingCriteria’, ‘EpisodeCount’, …
‘StopTrainingValue’, 500);
trainingStats = train(agent, env);
%% Reset Function
function [InitialObservation, LoggedSignals] = resetFunctionRL(Features)
% Reset function for the RL environment
% This function resets the environment to its initial state at the start
% of each new episode.
% Initialize the LoggedSignals structure (holds environment state)
LoggedSignals = struct();
% Set the initial observation index to the first data point
LoggedSignals.CurrentIndex = 1;
% Fetch the initial observation: the first feature vector
% Ensure that the observation is formatted as a column vector [numFeatures, 1]
InitialObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to column vector
% Check if the initial observation matches the expected size
if size(InitialObservation, 2) ~= 1
error(‘InitialObservation must be a column vector of size [numFeatures, 1]’);
end
% Optionally, initialize any other properties you want in LoggedSignals
% (e.g., additional state variables, environment-specific flags)
LoggedSignals.EpisodeStartTime = datetime(‘now’); % Track the start time for each episode
end
%% Step Function
function [NextObservation, Reward, IsDone, LoggedSignals] = stepFunctionRL(Action, Features, Labels, LoggedSignals)
% Step function for the RL environment
idx = LoggedSignals.CurrentIndex;
% Assign reward based on action correctness
correctLabel = Labels(idx); % Correct label for the current feature
if Action == correctLabel
Reward = 1; % Positive reward for correct classification
else
Reward = -1; % Negative reward for incorrect classification
end
% Update index and check if episode is done
LoggedSignals.CurrentIndex = idx + 1;
IsDone = LoggedSignals.CurrentIndex > size(Features, 1);
% Ensure the next observation is a column vector of size [numFeatures, 1]
if ~IsDone
NextObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to ensure column vector [numFeatures x 1]
else
NextObservation = Features(idx, :)’; % Dummy to avoid dimension mismatch
end
end
AND THIS IS THE ERROR
Error using rl.env.MATLABEnvironment/validateEnvironment (line 58)
Environment ‘ObservationInfo’ does not match observation output from reset function. Check the data type, dimensions, and range.
Error in rl.env.rlFunctionEnv (line 82)
validateEnvironment(this);
Error in rlFunctionEnv (line 45)
env = rl.env.rlFunctionEnv(varargin{:}); clc;
%% Load Dataset
imds = imageDatastore(‘D:2023thesiswaveletImagesSubsetImages’, …
‘IncludeSubfolders’, true, …
‘LabelSource’, ‘foldernames’);
% Ensure the dataset is balanced
DataSetInfo = countEachLabel(imds);
% Split Data into Training and Validation sets
[imdsTrain, imdsValidation] = splitEachLabel(imds, 0.80, ‘randomized’);
%% Load Pretrained Network
net = vgg16;
inputSize = net.Layers(1).InputSize;
% Feature extraction layer
featureLayer = ‘fc7’;
% Augment data to match input size of the network
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation);
% Extract Features using Pretrained CNN
trainFeatures = activations(net, augimdsTrain, featureLayer, ‘OutputAs’, ‘rows’);
validationFeatures = activations(net, augimdsValidation, featureLayer, ‘OutputAs’, ‘rows’);
% Extract labels
trainLabels = grp2idx(imdsTrain.Labels);
validationLabels = grp2idx(imdsValidation.Labels);
%% RL Environment Setup
% Define Observation and Action spaces
numFeatures = size(trainFeatures, 2); % Number of features (e.g., 4096 for ‘fc7’ layer)
ObservationInfo = rlNumericSpec([numFeatures, 1], …
‘LowerLimit’, -inf, ‘UpperLimit’, inf, …
‘Name’, ‘Features’);
ObservationInfo.Description = ‘Feature vector extracted from CNN’;
% Define Action space (each action corresponds to a class label)
ActionInfo = rlFiniteSetSpec(1:max(trainLabels)); % Class labels as actions
ActionInfo.Name = ‘Class Labels’;
% Define Step and Reset functions
stepFunction = @(Action, LoggedSignals) stepFunctionRL(Action, trainFeatures, trainLabels, LoggedSignals);
resetFunction = @() resetFunctionRL(trainFeatures);
% Create RL Environment
env = rlFunctionEnv(ObservationInfo, ActionInfo, stepFunction, resetFunction);
%% Define RL Agent
% Define critic network
statePath = [
featureInputLayer(numFeatures, ‘Normalization’, ‘none’, ‘Name’, ‘state’)
fullyConnectedLayer(128, ‘Name’, ‘fc1’)
reluLayer(‘Name’, ‘relu1’)
fullyConnectedLayer(64, ‘Name’, ‘fc2’)
reluLayer(‘Name’, ‘relu2’)
fullyConnectedLayer(numel(ActionInfo.Elements), ‘Name’, ‘fcOutput’)]; % Number of actions
criticNet = dlnetwork(layerGraph(statePath));
critic = rlQValueFunction(criticNet, ObservationInfo, ActionInfo, ‘ObservationInputNames’, ‘state’);
% Agent options
agentOpts = rlDQNAgentOptions( …
‘ExperienceBufferLength’, 1e5, …
‘DiscountFactor’, 0.99, …
‘MiniBatchSize’, 64, …
‘TargetSmoothFactor’, 1e-3, …
‘TargetUpdateFrequency’, 4);
agent = rlDQNAgent(critic, agentOpts);
%% Train RL Agent
trainOpts = rlTrainingOptions( …
‘MaxEpisodes’, 500, …
‘MaxStepsPerEpisode’, size(trainFeatures, 1), …
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘StopTrainingCriteria’, ‘EpisodeCount’, …
‘StopTrainingValue’, 500);
trainingStats = train(agent, env);
%% Reset Function
function [InitialObservation, LoggedSignals] = resetFunctionRL(Features)
% Reset function for the RL environment
% This function resets the environment to its initial state at the start
% of each new episode.
% Initialize the LoggedSignals structure (holds environment state)
LoggedSignals = struct();
% Set the initial observation index to the first data point
LoggedSignals.CurrentIndex = 1;
% Fetch the initial observation: the first feature vector
% Ensure that the observation is formatted as a column vector [numFeatures, 1]
InitialObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to column vector
% Check if the initial observation matches the expected size
if size(InitialObservation, 2) ~= 1
error(‘InitialObservation must be a column vector of size [numFeatures, 1]’);
end
% Optionally, initialize any other properties you want in LoggedSignals
% (e.g., additional state variables, environment-specific flags)
LoggedSignals.EpisodeStartTime = datetime(‘now’); % Track the start time for each episode
end
%% Step Function
function [NextObservation, Reward, IsDone, LoggedSignals] = stepFunctionRL(Action, Features, Labels, LoggedSignals)
% Step function for the RL environment
idx = LoggedSignals.CurrentIndex;
% Assign reward based on action correctness
correctLabel = Labels(idx); % Correct label for the current feature
if Action == correctLabel
Reward = 1; % Positive reward for correct classification
else
Reward = -1; % Negative reward for incorrect classification
end
% Update index and check if episode is done
LoggedSignals.CurrentIndex = idx + 1;
IsDone = LoggedSignals.CurrentIndex > size(Features, 1);
% Ensure the next observation is a column vector of size [numFeatures, 1]
if ~IsDone
NextObservation = Features(LoggedSignals.CurrentIndex, :)’; % Transpose to ensure column vector [numFeatures x 1]
else
NextObservation = Features(idx, :)’; % Dummy to avoid dimension mismatch
end
end
AND THIS IS THE ERROR
Error using rl.env.MATLABEnvironment/validateEnvironment (line 58)
Environment ‘ObservationInfo’ does not match observation output from reset function. Check the data type, dimensions, and range.
Error in rl.env.rlFunctionEnv (line 82)
validateEnvironment(this);
Error in rlFunctionEnv (line 45)
env = rl.env.rlFunctionEnv(varargin{:}); rl, image-classification MATLAB Answers — New Questions
error in testing matpower
hi everyone, I want to install matpower 6.0 in my matlab 2019b but after I install it same as instruction I get errors in testing it.
when I write test_matpower in command window I recieve these errors.
please help me to install it successfully.
>> test_matpower
t_test_fcns………….ok
t_nested_struct_copy….ok
t_feval_w_path……….ok
t_mpoption…………..ok
t_loadcase…………..Error using save
Unable to write file t_mat9_opf.mat:
permission denied.
Error in t_loadcase (line 40)
eval([‘save ‘ matfile ‘.mat baseMVA bus
gen branch areas gencost’]);
Error in t_run_tests (line 58)
feval( test_names{k}, ~verbose );
Error in test_matpower (line 128)
t_run_tests( tests, verbose );hi everyone, I want to install matpower 6.0 in my matlab 2019b but after I install it same as instruction I get errors in testing it.
when I write test_matpower in command window I recieve these errors.
please help me to install it successfully.
>> test_matpower
t_test_fcns………….ok
t_nested_struct_copy….ok
t_feval_w_path……….ok
t_mpoption…………..ok
t_loadcase…………..Error using save
Unable to write file t_mat9_opf.mat:
permission denied.
Error in t_loadcase (line 40)
eval([‘save ‘ matfile ‘.mat baseMVA bus
gen branch areas gencost’]);
Error in t_run_tests (line 58)
feval( test_names{k}, ~verbose );
Error in test_matpower (line 128)
t_run_tests( tests, verbose ); hi everyone, I want to install matpower 6.0 in my matlab 2019b but after I install it same as instruction I get errors in testing it.
when I write test_matpower in command window I recieve these errors.
please help me to install it successfully.
>> test_matpower
t_test_fcns………….ok
t_nested_struct_copy….ok
t_feval_w_path……….ok
t_mpoption…………..ok
t_loadcase…………..Error using save
Unable to write file t_mat9_opf.mat:
permission denied.
Error in t_loadcase (line 40)
eval([‘save ‘ matfile ‘.mat baseMVA bus
gen branch areas gencost’]);
Error in t_run_tests (line 58)
feval( test_names{k}, ~verbose );
Error in test_matpower (line 128)
t_run_tests( tests, verbose ); matpower MATLAB Answers — New Questions
how to identify the cracks from the image
<</matlabcentral/answers/uploaded_files/129987/el2.jpg>><</matlabcentral/answers/uploaded_files/129987/el2.jpg>> <</matlabcentral/answers/uploaded_files/129987/el2.jpg>> ir image, cracks, image segmentation, tiles MATLAB Answers — New Questions
Crack Detection
I have problem for detection for surface ceramics image, how i can detect crack surface , pls give me some advice. this my code for detect crack surface
function [img1_array, img2_array,img3_array, img4_array,Zme]= DefectScan(input_path,input_path2);
% clear all;
% close all;
I = imread(input_path2);
J = imread(input_path);
I = rgb2gray(I);
J = rgb2gray(J);
% f=figure,imshow(I);
% g=figure,imshow(J);
hy = fspecial(‘sobel’);
hx = hy’;
Iy = imfilter(double(I), hy, ‘replicate’);
Ix = imfilter(double(I), hx, ‘replicate’);
ey = fspecial(‘sobel’);
fx = ey’;
Jy = imfilter(double(J), ey, ‘replicate’);
Jx = imfilter(double(J), fx, ‘replicate’);
gradmag = sqrt(Ix.^2 + Iy.^2);
gradmag2 = sqrt(Jx.^2 + Jy.^2);
K=figure,imshow(gradmag,[]);
L=figure,imshow(gradmag2,[]);
set(K, ‘visible’,’off’);
set(L, ‘visible’,’off’);
filename = ‘temp_file.jpg’
filename2 = ‘temp_file2.jpg’
saveas(K, filename)
saveas(L, filename2)
i1 = imread(filename)
i2 = imread(filename2)
delete(filename)
delete(filename2)
[x, y, rgb] = ind2sub([size(i1,1) size(i1,2) size(i1,3)], find(i1 ~= 255));
A = i1(min(x):max(x)-1,min(y):max(y)-1,:);
[x, y, rgb] = ind2sub([size(i2,1) size(i2,2) size(i2,3)], find(i2 ~= 255));
B = i2(min(x):max(x)-1,min(y):max(y)-1,:);
A = rgb2gray(A)
B = rgb2gray(B)
I = edge(A,’sobel’)
J = edge(B,’sobel’)I have problem for detection for surface ceramics image, how i can detect crack surface , pls give me some advice. this my code for detect crack surface
function [img1_array, img2_array,img3_array, img4_array,Zme]= DefectScan(input_path,input_path2);
% clear all;
% close all;
I = imread(input_path2);
J = imread(input_path);
I = rgb2gray(I);
J = rgb2gray(J);
% f=figure,imshow(I);
% g=figure,imshow(J);
hy = fspecial(‘sobel’);
hx = hy’;
Iy = imfilter(double(I), hy, ‘replicate’);
Ix = imfilter(double(I), hx, ‘replicate’);
ey = fspecial(‘sobel’);
fx = ey’;
Jy = imfilter(double(J), ey, ‘replicate’);
Jx = imfilter(double(J), fx, ‘replicate’);
gradmag = sqrt(Ix.^2 + Iy.^2);
gradmag2 = sqrt(Jx.^2 + Jy.^2);
K=figure,imshow(gradmag,[]);
L=figure,imshow(gradmag2,[]);
set(K, ‘visible’,’off’);
set(L, ‘visible’,’off’);
filename = ‘temp_file.jpg’
filename2 = ‘temp_file2.jpg’
saveas(K, filename)
saveas(L, filename2)
i1 = imread(filename)
i2 = imread(filename2)
delete(filename)
delete(filename2)
[x, y, rgb] = ind2sub([size(i1,1) size(i1,2) size(i1,3)], find(i1 ~= 255));
A = i1(min(x):max(x)-1,min(y):max(y)-1,:);
[x, y, rgb] = ind2sub([size(i2,1) size(i2,2) size(i2,3)], find(i2 ~= 255));
B = i2(min(x):max(x)-1,min(y):max(y)-1,:);
A = rgb2gray(A)
B = rgb2gray(B)
I = edge(A,’sobel’)
J = edge(B,’sobel’) I have problem for detection for surface ceramics image, how i can detect crack surface , pls give me some advice. this my code for detect crack surface
function [img1_array, img2_array,img3_array, img4_array,Zme]= DefectScan(input_path,input_path2);
% clear all;
% close all;
I = imread(input_path2);
J = imread(input_path);
I = rgb2gray(I);
J = rgb2gray(J);
% f=figure,imshow(I);
% g=figure,imshow(J);
hy = fspecial(‘sobel’);
hx = hy’;
Iy = imfilter(double(I), hy, ‘replicate’);
Ix = imfilter(double(I), hx, ‘replicate’);
ey = fspecial(‘sobel’);
fx = ey’;
Jy = imfilter(double(J), ey, ‘replicate’);
Jx = imfilter(double(J), fx, ‘replicate’);
gradmag = sqrt(Ix.^2 + Iy.^2);
gradmag2 = sqrt(Jx.^2 + Jy.^2);
K=figure,imshow(gradmag,[]);
L=figure,imshow(gradmag2,[]);
set(K, ‘visible’,’off’);
set(L, ‘visible’,’off’);
filename = ‘temp_file.jpg’
filename2 = ‘temp_file2.jpg’
saveas(K, filename)
saveas(L, filename2)
i1 = imread(filename)
i2 = imread(filename2)
delete(filename)
delete(filename2)
[x, y, rgb] = ind2sub([size(i1,1) size(i1,2) size(i1,3)], find(i1 ~= 255));
A = i1(min(x):max(x)-1,min(y):max(y)-1,:);
[x, y, rgb] = ind2sub([size(i2,1) size(i2,2) size(i2,3)], find(i2 ~= 255));
B = i2(min(x):max(x)-1,min(y):max(y)-1,:);
A = rgb2gray(A)
B = rgb2gray(B)
I = edge(A,’sobel’)
J = edge(B,’sobel’) detection crack surface, doit4me, sendit2me MATLAB Answers — New Questions
Crack detection on concrete
Hi, i have problem in measuring the length and width the crack image.
i have follow the code but it only measure straight line (endpoint). I want the measurement by following the crack pattern. Please help me on this.
%load image
path=imgetfile();
originalImage1=imread(path);
imshow(originalImage1);
%image to grayscale
originalImage = rgb2gray(originalImage1);
figure
imshow(originalImage);
%treshold
thresholdValue = 100;
binaryImage = originalImage > thresholdValue;
binaryImage = imfill(binaryImage, ‘holes’);
hold on;
% maxYValue = ylim;
% line([thresholdValue, thresholdValue], maxYValue, ‘Color’, ‘r’);
% annotationText = sprintf(‘Thresholded at %d gray levels’, thresholdValue);
% text(double(thresholdValue + 5), double(0.5 * maxYValue(2)), annotationText, ‘FontSize’, 10, ‘Color’, [0 .5 0]);
% text(double(thresholdValue – 70), double(0.94 * maxYValue(2)), ‘Background’, ‘FontSize’, 10, ‘Color’, [0 0 .5]);
% text(double(thresholdValue + 50), double(0.94 * maxYValue(2)), ‘Foreground’, ‘FontSize’, 10, ‘Color’, [0 0 .5]);
figure
imshow(binaryImage);
% bw = bwareaopen(binaryImage,100,8);
bw= bwareaopen(binaryImage, round(0.1*numel(binaryImage)));
figure
imshow(bw)
g=bwmorph(bw,’clean’);
figure
imshow(g);
% %labeled image (on hold)
labeledImage = bwlabel(g,4);
figure
imshow(labeledImage, []);
%colored label image
coloredLabels = label2rgb (labeledImage, ‘hsv’, ‘k’, ‘shuffle’);
figure
imshow(coloredLabels);
axis image;
%blobmeasurement
blobMeasurements = regionprops(labeledImage, originalImage, ‘all’);
numberOfBlobs = size(blobMeasurements, 1);
figure
imshow(originalImage);
axis image; % Make sure image is not artificially stretched because of screen’s aspect ratio.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k};
plot(thisBoundary(:,2), thisBoundary(:,1), ‘g’, ‘LineWidth’, 2);
end
hold off;
% Measure the area
measurements = regionprops(labeledImage, ‘Area’);
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for blobIndex = 1 : numberOfBoundaries
thisBoundary = boundaries{blobIndex};
x = thisBoundary(:, 2); % x = columns.
y = thisBoundary(:, 1); % y = rows.
% Find which two bounary points are farthest from each other.
maxDistance = -inf;
for k = 1 : length(x)
distances = sqrt( (x(k) – x) .^ 2 + (y(k) – y) .^ 2 );
[thisMaxDistance, indexOfMaxDistance] = max(distances);
if thisMaxDistance > maxDistance
maxDistance = thisMaxDistance;
index1 = k;
index2 = indexOfMaxDistance;
end
end
% Find the midpoint of the line.
xMidPoint = mean([x(index1), x(index2)]);
yMidPoint = mean([y(index1), y(index2)]);
longSlope = (y(index1) – y(index2)) / (x(index1) – x(index2))
perpendicularSlope = -1/longSlope
% Use point slope formula (y-ym) = slope * (x – xm) to get points
y1 = perpendicularSlope * (1 – xMidPoint) + yMidPoint;
y2 = perpendicularSlope * (columns – xMidPoint) + yMidPoint;
% Get the profile perpendicular to the midpoint so we can find out when if first enters and last leaves the object.
[cx,cy,c] = improfile(binaryImage,[1, columns], [y1, y2], 1000);
% Get rid of NAN’s that occur when the line’s endpoints go above or below the image.
c(isnan(c)) = 0;
firstIndex = find(c, 1, ‘first’);
lastIndex = find(c, 1, ‘last’);
% Compute the distance of that perpendicular width.
perpendicularWidth = sqrt( (cx(firstIndex) – cx(lastIndex)) .^ 2 + (cy(firstIndex) – cy(lastIndex)) .^ 2 );
% Get the average perpendicular width. This will approximately be the area divided by the longest length.
averageWidth = measurements(blobIndex).Area / maxDistance;
% Plot the boundaries, line, and midpoints over the two images.
% Plot the boundary over the gray scale image
subplot(2, 2, 3);
plot(x, y, ‘y-‘, ‘LineWidth’, 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], ‘Color’, ‘r’, ‘LineWidth’, 3);
plot(xMidPoint, yMidPoint, ‘r*’, ‘MarkerSize’, 15, ‘LineWidth’, 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], ‘Color’, ‘g’, ‘LineWidth’, 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], ‘Color’, ‘m’, ‘LineWidth’, 3);
% Plot the boundary over the binary image
subplot(2, 2, 4);
plot(x, y, ‘y-‘, ‘LineWidth’, 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], ‘Color’, ‘r’, ‘LineWidth’, 3);
plot(xMidPoint, yMidPoint, ‘r*’, ‘MarkerSize’, 15, ‘LineWidth’, 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], ‘Color’, ‘g’, ‘LineWidth’, 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], ‘Color’, ‘m’, ‘LineWidth’, 3);
message = sprintf(‘The longest line is red.nPerpendicular to that, at the midpoint, is green.nMax distance for blob #%d = %.2fnPerpendicular distance at midpoint = %.2fnAverage perpendicular width = %.2f (approximatelynArea = %d’, …
blobIndex, maxDistance, perpendicularWidth, averageWidth, measurements(blobIndex).Area);
fprintf(‘%sn’, message);
uiwait(helpdlg(message));
end
hold off;Hi, i have problem in measuring the length and width the crack image.
i have follow the code but it only measure straight line (endpoint). I want the measurement by following the crack pattern. Please help me on this.
%load image
path=imgetfile();
originalImage1=imread(path);
imshow(originalImage1);
%image to grayscale
originalImage = rgb2gray(originalImage1);
figure
imshow(originalImage);
%treshold
thresholdValue = 100;
binaryImage = originalImage > thresholdValue;
binaryImage = imfill(binaryImage, ‘holes’);
hold on;
% maxYValue = ylim;
% line([thresholdValue, thresholdValue], maxYValue, ‘Color’, ‘r’);
% annotationText = sprintf(‘Thresholded at %d gray levels’, thresholdValue);
% text(double(thresholdValue + 5), double(0.5 * maxYValue(2)), annotationText, ‘FontSize’, 10, ‘Color’, [0 .5 0]);
% text(double(thresholdValue – 70), double(0.94 * maxYValue(2)), ‘Background’, ‘FontSize’, 10, ‘Color’, [0 0 .5]);
% text(double(thresholdValue + 50), double(0.94 * maxYValue(2)), ‘Foreground’, ‘FontSize’, 10, ‘Color’, [0 0 .5]);
figure
imshow(binaryImage);
% bw = bwareaopen(binaryImage,100,8);
bw= bwareaopen(binaryImage, round(0.1*numel(binaryImage)));
figure
imshow(bw)
g=bwmorph(bw,’clean’);
figure
imshow(g);
% %labeled image (on hold)
labeledImage = bwlabel(g,4);
figure
imshow(labeledImage, []);
%colored label image
coloredLabels = label2rgb (labeledImage, ‘hsv’, ‘k’, ‘shuffle’);
figure
imshow(coloredLabels);
axis image;
%blobmeasurement
blobMeasurements = regionprops(labeledImage, originalImage, ‘all’);
numberOfBlobs = size(blobMeasurements, 1);
figure
imshow(originalImage);
axis image; % Make sure image is not artificially stretched because of screen’s aspect ratio.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k};
plot(thisBoundary(:,2), thisBoundary(:,1), ‘g’, ‘LineWidth’, 2);
end
hold off;
% Measure the area
measurements = regionprops(labeledImage, ‘Area’);
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for blobIndex = 1 : numberOfBoundaries
thisBoundary = boundaries{blobIndex};
x = thisBoundary(:, 2); % x = columns.
y = thisBoundary(:, 1); % y = rows.
% Find which two bounary points are farthest from each other.
maxDistance = -inf;
for k = 1 : length(x)
distances = sqrt( (x(k) – x) .^ 2 + (y(k) – y) .^ 2 );
[thisMaxDistance, indexOfMaxDistance] = max(distances);
if thisMaxDistance > maxDistance
maxDistance = thisMaxDistance;
index1 = k;
index2 = indexOfMaxDistance;
end
end
% Find the midpoint of the line.
xMidPoint = mean([x(index1), x(index2)]);
yMidPoint = mean([y(index1), y(index2)]);
longSlope = (y(index1) – y(index2)) / (x(index1) – x(index2))
perpendicularSlope = -1/longSlope
% Use point slope formula (y-ym) = slope * (x – xm) to get points
y1 = perpendicularSlope * (1 – xMidPoint) + yMidPoint;
y2 = perpendicularSlope * (columns – xMidPoint) + yMidPoint;
% Get the profile perpendicular to the midpoint so we can find out when if first enters and last leaves the object.
[cx,cy,c] = improfile(binaryImage,[1, columns], [y1, y2], 1000);
% Get rid of NAN’s that occur when the line’s endpoints go above or below the image.
c(isnan(c)) = 0;
firstIndex = find(c, 1, ‘first’);
lastIndex = find(c, 1, ‘last’);
% Compute the distance of that perpendicular width.
perpendicularWidth = sqrt( (cx(firstIndex) – cx(lastIndex)) .^ 2 + (cy(firstIndex) – cy(lastIndex)) .^ 2 );
% Get the average perpendicular width. This will approximately be the area divided by the longest length.
averageWidth = measurements(blobIndex).Area / maxDistance;
% Plot the boundaries, line, and midpoints over the two images.
% Plot the boundary over the gray scale image
subplot(2, 2, 3);
plot(x, y, ‘y-‘, ‘LineWidth’, 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], ‘Color’, ‘r’, ‘LineWidth’, 3);
plot(xMidPoint, yMidPoint, ‘r*’, ‘MarkerSize’, 15, ‘LineWidth’, 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], ‘Color’, ‘g’, ‘LineWidth’, 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], ‘Color’, ‘m’, ‘LineWidth’, 3);
% Plot the boundary over the binary image
subplot(2, 2, 4);
plot(x, y, ‘y-‘, ‘LineWidth’, 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], ‘Color’, ‘r’, ‘LineWidth’, 3);
plot(xMidPoint, yMidPoint, ‘r*’, ‘MarkerSize’, 15, ‘LineWidth’, 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], ‘Color’, ‘g’, ‘LineWidth’, 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], ‘Color’, ‘m’, ‘LineWidth’, 3);
message = sprintf(‘The longest line is red.nPerpendicular to that, at the midpoint, is green.nMax distance for blob #%d = %.2fnPerpendicular distance at midpoint = %.2fnAverage perpendicular width = %.2f (approximatelynArea = %d’, …
blobIndex, maxDistance, perpendicularWidth, averageWidth, measurements(blobIndex).Area);
fprintf(‘%sn’, message);
uiwait(helpdlg(message));
end
hold off; Hi, i have problem in measuring the length and width the crack image.
i have follow the code but it only measure straight line (endpoint). I want the measurement by following the crack pattern. Please help me on this.
%load image
path=imgetfile();
originalImage1=imread(path);
imshow(originalImage1);
%image to grayscale
originalImage = rgb2gray(originalImage1);
figure
imshow(originalImage);
%treshold
thresholdValue = 100;
binaryImage = originalImage > thresholdValue;
binaryImage = imfill(binaryImage, ‘holes’);
hold on;
% maxYValue = ylim;
% line([thresholdValue, thresholdValue], maxYValue, ‘Color’, ‘r’);
% annotationText = sprintf(‘Thresholded at %d gray levels’, thresholdValue);
% text(double(thresholdValue + 5), double(0.5 * maxYValue(2)), annotationText, ‘FontSize’, 10, ‘Color’, [0 .5 0]);
% text(double(thresholdValue – 70), double(0.94 * maxYValue(2)), ‘Background’, ‘FontSize’, 10, ‘Color’, [0 0 .5]);
% text(double(thresholdValue + 50), double(0.94 * maxYValue(2)), ‘Foreground’, ‘FontSize’, 10, ‘Color’, [0 0 .5]);
figure
imshow(binaryImage);
% bw = bwareaopen(binaryImage,100,8);
bw= bwareaopen(binaryImage, round(0.1*numel(binaryImage)));
figure
imshow(bw)
g=bwmorph(bw,’clean’);
figure
imshow(g);
% %labeled image (on hold)
labeledImage = bwlabel(g,4);
figure
imshow(labeledImage, []);
%colored label image
coloredLabels = label2rgb (labeledImage, ‘hsv’, ‘k’, ‘shuffle’);
figure
imshow(coloredLabels);
axis image;
%blobmeasurement
blobMeasurements = regionprops(labeledImage, originalImage, ‘all’);
numberOfBlobs = size(blobMeasurements, 1);
figure
imshow(originalImage);
axis image; % Make sure image is not artificially stretched because of screen’s aspect ratio.
hold on;
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for k = 1 : numberOfBoundaries
thisBoundary = boundaries{k};
plot(thisBoundary(:,2), thisBoundary(:,1), ‘g’, ‘LineWidth’, 2);
end
hold off;
% Measure the area
measurements = regionprops(labeledImage, ‘Area’);
boundaries = bwboundaries(binaryImage);
numberOfBoundaries = size(boundaries, 1);
for blobIndex = 1 : numberOfBoundaries
thisBoundary = boundaries{blobIndex};
x = thisBoundary(:, 2); % x = columns.
y = thisBoundary(:, 1); % y = rows.
% Find which two bounary points are farthest from each other.
maxDistance = -inf;
for k = 1 : length(x)
distances = sqrt( (x(k) – x) .^ 2 + (y(k) – y) .^ 2 );
[thisMaxDistance, indexOfMaxDistance] = max(distances);
if thisMaxDistance > maxDistance
maxDistance = thisMaxDistance;
index1 = k;
index2 = indexOfMaxDistance;
end
end
% Find the midpoint of the line.
xMidPoint = mean([x(index1), x(index2)]);
yMidPoint = mean([y(index1), y(index2)]);
longSlope = (y(index1) – y(index2)) / (x(index1) – x(index2))
perpendicularSlope = -1/longSlope
% Use point slope formula (y-ym) = slope * (x – xm) to get points
y1 = perpendicularSlope * (1 – xMidPoint) + yMidPoint;
y2 = perpendicularSlope * (columns – xMidPoint) + yMidPoint;
% Get the profile perpendicular to the midpoint so we can find out when if first enters and last leaves the object.
[cx,cy,c] = improfile(binaryImage,[1, columns], [y1, y2], 1000);
% Get rid of NAN’s that occur when the line’s endpoints go above or below the image.
c(isnan(c)) = 0;
firstIndex = find(c, 1, ‘first’);
lastIndex = find(c, 1, ‘last’);
% Compute the distance of that perpendicular width.
perpendicularWidth = sqrt( (cx(firstIndex) – cx(lastIndex)) .^ 2 + (cy(firstIndex) – cy(lastIndex)) .^ 2 );
% Get the average perpendicular width. This will approximately be the area divided by the longest length.
averageWidth = measurements(blobIndex).Area / maxDistance;
% Plot the boundaries, line, and midpoints over the two images.
% Plot the boundary over the gray scale image
subplot(2, 2, 3);
plot(x, y, ‘y-‘, ‘LineWidth’, 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], ‘Color’, ‘r’, ‘LineWidth’, 3);
plot(xMidPoint, yMidPoint, ‘r*’, ‘MarkerSize’, 15, ‘LineWidth’, 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], ‘Color’, ‘g’, ‘LineWidth’, 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], ‘Color’, ‘m’, ‘LineWidth’, 3);
% Plot the boundary over the binary image
subplot(2, 2, 4);
plot(x, y, ‘y-‘, ‘LineWidth’, 3);
% For this blob, put a line between the points farthest away from each other.
line([x(index1), x(index2)], [y(index1), y(index2)], ‘Color’, ‘r’, ‘LineWidth’, 3);
plot(xMidPoint, yMidPoint, ‘r*’, ‘MarkerSize’, 15, ‘LineWidth’, 2);
% Plot perpendicular line. Make it green across the whole image but magenta inside the blob.
line([1, columns], [y1, y2], ‘Color’, ‘g’, ‘LineWidth’, 3);
line([cx(firstIndex), cx(lastIndex)], [cy(firstIndex), cy(lastIndex)], ‘Color’, ‘m’, ‘LineWidth’, 3);
message = sprintf(‘The longest line is red.nPerpendicular to that, at the midpoint, is green.nMax distance for blob #%d = %.2fnPerpendicular distance at midpoint = %.2fnAverage perpendicular width = %.2f (approximatelynArea = %d’, …
blobIndex, maxDistance, perpendicularWidth, averageWidth, measurements(blobIndex).Area);
fprintf(‘%sn’, message);
uiwait(helpdlg(message));
end
hold off; image processing, crack detection, length, width, regionprops, crack MATLAB Answers — New Questions
How to detect and show the crack in this image and eliminate the rest of the objects (noises)?
I have tried used the morphological structuring element (STREL-line),but there is one problem with my images, the line crack is fixed not only in horizontal and vertical line , it has its degree(0-360), which is this strel-line can’t be used. I used strel-line in order to maintain the line cracks and remove any shapes in the images which is I can’t solve that. I got an idea how to differentiate between the lines and the noises, low gray-level value and high gradient, however this seems not work to this image since the value of gray scale and gradient almost similar to the noises.Can you show me the right way? Attached is the image.I have tried used the morphological structuring element (STREL-line),but there is one problem with my images, the line crack is fixed not only in horizontal and vertical line , it has its degree(0-360), which is this strel-line can’t be used. I used strel-line in order to maintain the line cracks and remove any shapes in the images which is I can’t solve that. I got an idea how to differentiate between the lines and the noises, low gray-level value and high gradient, however this seems not work to this image since the value of gray scale and gradient almost similar to the noises.Can you show me the right way? Attached is the image. I have tried used the morphological structuring element (STREL-line),but there is one problem with my images, the line crack is fixed not only in horizontal and vertical line , it has its degree(0-360), which is this strel-line can’t be used. I used strel-line in order to maintain the line cracks and remove any shapes in the images which is I can’t solve that. I got an idea how to differentiate between the lines and the noises, low gray-level value and high gradient, however this seems not work to this image since the value of gray scale and gradient almost similar to the noises.Can you show me the right way? Attached is the image. image processing, crack detection MATLAB Answers — New Questions
Using sigstrength() for raytracing propagation model
I’m trying to run a ray tracing simulation for a simple ground reflection scenario. I have an STL file with a "floor" that is 1m wide (in y-direction) and 25m long (in the x-direction). I place a transmitter at the location [0.1;0.5;3]. I place 1000 receivers collinear with the transmitter, and at the same height:
clear all;clc;close all;
stlFile = ’25m1m_floor.stl’;
viewer = siteviewer(‘SceneModel’,stlFile); % Required to link STL for ray tracing
tx = txsite(‘cartesian’, ‘AntennaPosition’, [0.1;0.5;3], ‘TransmitterFrequency’, 900e6, ‘TransmitterPower’, 0.001);
numPointsX = 1000;numPointsY = 1;
x = linspace(0.1, 25, numPointsX); y = linspace(0.5, 0.5, numPointsY); [X, Y] = meshgrid(x, y);
Z = 3 * ones(size(X));
for j = 1:numPointsX
for i = 1:numPointsY
rxs(i,j) = rxsite(‘cartesian’, ‘AntennaPosition’, [X(i,j); Y(i,j); Z(i,j)], ‘CoordinateSystem’, ‘cartesian’);
end
end
%To display the tx and rx in siteviewer
%{
show(tx,"ShowAntennaHeight",false);
for i = 1:numel(rxs)
show(rxs(i),"ShowAntennaHeight",false);
end
%}
pm = propagationModel(‘raytracing’, ‘Method’, ‘sbr’, ‘MaxNumReflections’, 1,…
‘SurfaceMaterial’, ‘concrete’, ‘CoordinateSystem’, ‘cartesian’);
ss(1,:) = sigstrength(rxs,tx,pm);
packetLength = 33;
txPower = 0.01;
for j = 1:numPointsX
for i = 1:numPointsY
rx = rxs(i,j);
rays_out = raytrace(tx, rx, pm);
rays = rays_out{:};
[RSS(i,j), rxPower(i,j)] = calcRSSRays(rays,packetLength,txPower);
end
end
figure;plot(x,RSS);
hold;plot(x,ss);
function [RSS,rxPower] = calcRSSRays(rays,packetLength,txPower)
data = randi([0 3], packetLength, 1);
txPacket = sqrt(txPower)*pskmod(data, 4, pi/4);
rxPacket = zeros(1, packetLength);
pathGains = -[rays.PathLoss];
phaseShifts = [rays.PhaseShift];
pathGainsLinear = 10.^(pathGains / 10);
for s = 1:packetLength
E_ray = sqrt(pathGainsLinear) .* exp(-1j .* phaseShifts);
E_total = sum(E_ray * txPacket(s),"all");
rxPacket(s) = E_total;
end
rxPower = mean(abs(rxPacket).^2);
RSS = 10 * log10(rxPower./txPower);
end
The transmitter frequency is 900Mhz so the wavelength is around 0.33m. I’ve specified MaxNumReflections as 1, which I think should capture the ground reflection, and when I plot for one tx-rx pair, I can see that:
Since the tx and rx are all at the same height, I think I should get an RSS profile which decays with distance according to the power rule (i.e. 1/d^2), but with undulations where the ground reflected path interferes constructively and destructively with the line of sight ray.
However, when I plot the output from the sigstrength() function, there are no undulations. I’ve also tried calculating RSS from a packet-level simulation that I wrote, taking the rays output from the raytrace function – I think this is correct, because I do see the interference pattern in it:
Does anyone know if I’ve made a mistake in the ray-tracing/use of sigstrength()? Or is any further calculation required on it?
Or is this something to ask Mathworks support?
NOTE 1: Question editor doesn’t recognize .stl file extension for upload, so I’ve changed the extension to .txt. Extension needs to be changed to .stl to run it.
NOTE 2: I posted this question earlier, but I’ve come up with a better version of the simulation, so I’ve deleted that one before any answers/comments were posted on it.I’m trying to run a ray tracing simulation for a simple ground reflection scenario. I have an STL file with a "floor" that is 1m wide (in y-direction) and 25m long (in the x-direction). I place a transmitter at the location [0.1;0.5;3]. I place 1000 receivers collinear with the transmitter, and at the same height:
clear all;clc;close all;
stlFile = ’25m1m_floor.stl’;
viewer = siteviewer(‘SceneModel’,stlFile); % Required to link STL for ray tracing
tx = txsite(‘cartesian’, ‘AntennaPosition’, [0.1;0.5;3], ‘TransmitterFrequency’, 900e6, ‘TransmitterPower’, 0.001);
numPointsX = 1000;numPointsY = 1;
x = linspace(0.1, 25, numPointsX); y = linspace(0.5, 0.5, numPointsY); [X, Y] = meshgrid(x, y);
Z = 3 * ones(size(X));
for j = 1:numPointsX
for i = 1:numPointsY
rxs(i,j) = rxsite(‘cartesian’, ‘AntennaPosition’, [X(i,j); Y(i,j); Z(i,j)], ‘CoordinateSystem’, ‘cartesian’);
end
end
%To display the tx and rx in siteviewer
%{
show(tx,"ShowAntennaHeight",false);
for i = 1:numel(rxs)
show(rxs(i),"ShowAntennaHeight",false);
end
%}
pm = propagationModel(‘raytracing’, ‘Method’, ‘sbr’, ‘MaxNumReflections’, 1,…
‘SurfaceMaterial’, ‘concrete’, ‘CoordinateSystem’, ‘cartesian’);
ss(1,:) = sigstrength(rxs,tx,pm);
packetLength = 33;
txPower = 0.01;
for j = 1:numPointsX
for i = 1:numPointsY
rx = rxs(i,j);
rays_out = raytrace(tx, rx, pm);
rays = rays_out{:};
[RSS(i,j), rxPower(i,j)] = calcRSSRays(rays,packetLength,txPower);
end
end
figure;plot(x,RSS);
hold;plot(x,ss);
function [RSS,rxPower] = calcRSSRays(rays,packetLength,txPower)
data = randi([0 3], packetLength, 1);
txPacket = sqrt(txPower)*pskmod(data, 4, pi/4);
rxPacket = zeros(1, packetLength);
pathGains = -[rays.PathLoss];
phaseShifts = [rays.PhaseShift];
pathGainsLinear = 10.^(pathGains / 10);
for s = 1:packetLength
E_ray = sqrt(pathGainsLinear) .* exp(-1j .* phaseShifts);
E_total = sum(E_ray * txPacket(s),"all");
rxPacket(s) = E_total;
end
rxPower = mean(abs(rxPacket).^2);
RSS = 10 * log10(rxPower./txPower);
end
The transmitter frequency is 900Mhz so the wavelength is around 0.33m. I’ve specified MaxNumReflections as 1, which I think should capture the ground reflection, and when I plot for one tx-rx pair, I can see that:
Since the tx and rx are all at the same height, I think I should get an RSS profile which decays with distance according to the power rule (i.e. 1/d^2), but with undulations where the ground reflected path interferes constructively and destructively with the line of sight ray.
However, when I plot the output from the sigstrength() function, there are no undulations. I’ve also tried calculating RSS from a packet-level simulation that I wrote, taking the rays output from the raytrace function – I think this is correct, because I do see the interference pattern in it:
Does anyone know if I’ve made a mistake in the ray-tracing/use of sigstrength()? Or is any further calculation required on it?
Or is this something to ask Mathworks support?
NOTE 1: Question editor doesn’t recognize .stl file extension for upload, so I’ve changed the extension to .txt. Extension needs to be changed to .stl to run it.
NOTE 2: I posted this question earlier, but I’ve come up with a better version of the simulation, so I’ve deleted that one before any answers/comments were posted on it. I’m trying to run a ray tracing simulation for a simple ground reflection scenario. I have an STL file with a "floor" that is 1m wide (in y-direction) and 25m long (in the x-direction). I place a transmitter at the location [0.1;0.5;3]. I place 1000 receivers collinear with the transmitter, and at the same height:
clear all;clc;close all;
stlFile = ’25m1m_floor.stl’;
viewer = siteviewer(‘SceneModel’,stlFile); % Required to link STL for ray tracing
tx = txsite(‘cartesian’, ‘AntennaPosition’, [0.1;0.5;3], ‘TransmitterFrequency’, 900e6, ‘TransmitterPower’, 0.001);
numPointsX = 1000;numPointsY = 1;
x = linspace(0.1, 25, numPointsX); y = linspace(0.5, 0.5, numPointsY); [X, Y] = meshgrid(x, y);
Z = 3 * ones(size(X));
for j = 1:numPointsX
for i = 1:numPointsY
rxs(i,j) = rxsite(‘cartesian’, ‘AntennaPosition’, [X(i,j); Y(i,j); Z(i,j)], ‘CoordinateSystem’, ‘cartesian’);
end
end
%To display the tx and rx in siteviewer
%{
show(tx,"ShowAntennaHeight",false);
for i = 1:numel(rxs)
show(rxs(i),"ShowAntennaHeight",false);
end
%}
pm = propagationModel(‘raytracing’, ‘Method’, ‘sbr’, ‘MaxNumReflections’, 1,…
‘SurfaceMaterial’, ‘concrete’, ‘CoordinateSystem’, ‘cartesian’);
ss(1,:) = sigstrength(rxs,tx,pm);
packetLength = 33;
txPower = 0.01;
for j = 1:numPointsX
for i = 1:numPointsY
rx = rxs(i,j);
rays_out = raytrace(tx, rx, pm);
rays = rays_out{:};
[RSS(i,j), rxPower(i,j)] = calcRSSRays(rays,packetLength,txPower);
end
end
figure;plot(x,RSS);
hold;plot(x,ss);
function [RSS,rxPower] = calcRSSRays(rays,packetLength,txPower)
data = randi([0 3], packetLength, 1);
txPacket = sqrt(txPower)*pskmod(data, 4, pi/4);
rxPacket = zeros(1, packetLength);
pathGains = -[rays.PathLoss];
phaseShifts = [rays.PhaseShift];
pathGainsLinear = 10.^(pathGains / 10);
for s = 1:packetLength
E_ray = sqrt(pathGainsLinear) .* exp(-1j .* phaseShifts);
E_total = sum(E_ray * txPacket(s),"all");
rxPacket(s) = E_total;
end
rxPower = mean(abs(rxPacket).^2);
RSS = 10 * log10(rxPower./txPower);
end
The transmitter frequency is 900Mhz so the wavelength is around 0.33m. I’ve specified MaxNumReflections as 1, which I think should capture the ground reflection, and when I plot for one tx-rx pair, I can see that:
Since the tx and rx are all at the same height, I think I should get an RSS profile which decays with distance according to the power rule (i.e. 1/d^2), but with undulations where the ground reflected path interferes constructively and destructively with the line of sight ray.
However, when I plot the output from the sigstrength() function, there are no undulations. I’ve also tried calculating RSS from a packet-level simulation that I wrote, taking the rays output from the raytrace function – I think this is correct, because I do see the interference pattern in it:
Does anyone know if I’ve made a mistake in the ray-tracing/use of sigstrength()? Or is any further calculation required on it?
Or is this something to ask Mathworks support?
NOTE 1: Question editor doesn’t recognize .stl file extension for upload, so I’ve changed the extension to .txt. Extension needs to be changed to .stl to run it.
NOTE 2: I posted this question earlier, but I’ve come up with a better version of the simulation, so I’ve deleted that one before any answers/comments were posted on it. ray tracing, signal processing, communication, simulation MATLAB Answers — New Questions
Simplifying a TDMS reading code
I am trying to read multiple groups and channels of a TDMS file and doing some graphing and calculations at the end. I need to be able to acceses any channel or group that I want to use in the graphs or calcualtions, I would really apperciate it if someone had any advice on sliming the code down it currently takes 6 mins to run which is a really long time.
defaultFolder = ‘C:UserslillypColostateLute,Chris – EM-TRAS internalMatlab’;
addpath(defaultFolder);
fileFilter = ‘*.tdms’;
[fileName, filepath] = uigetfile(fullfile(defaultFolder, fileFilter), ‘Select a TDMS file’);
if fileName ~= 0
fullFilePath = fullfile(filepath, fileName);
disp([‘Selected file: ‘, fullFilePath]);
else
disp(‘No file selected.’);
end
fileName = fullFilePath;
group_Time = "Torques";
channel_Time = "Timestamp";
tdmsreadprop(fileName, "ChannelGroupName",group_Time, "ChannelName", channel_Time);
data = tdmsread(fileName, "ChannelGroupName",group_Time, "ChannelName", channel_Time);
time = data{1};
if istable(time)
time = table2array(time);
elseif isdatetime(time)
time = seconds(time – time(1));
end
% Speed readings%
group_N1 = "Rotary encoders";
channel_N1 = "N1 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N1, "ChannelName", channel_N1);
data = tdmsread(fileName, "ChannelGroupName",group_N1, "ChannelName", channel_N1);
N1 = data{1};
group_N2 = "Rotary encoders";
channel_N2 = "N2 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N2, "ChannelName", channel_N2);
data = tdmsread(fileName, "ChannelGroupName",group_N2, "ChannelName", channel_N2);
N2 = data{1};
group_N3 = "Rotary encoders";
channel_N3 = "N3 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N3, "ChannelName", channel_N3);
data = tdmsread(fileName, "ChannelGroupName",group_N3, "ChannelName", channel_N3);
N3 = data{1};
group_N4 = "Rotary encoders";
channel_N4 = "N4 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N4, "ChannelName", channel_N4);
data = tdmsread(fileName, "ChannelGroupName",group_N4, "ChannelName", channel_N4);
N4 = data{1};
group_N5 = "Rotary encoders";
channel_N5 = "N5 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N5, "ChannelName", channel_N5);
data = tdmsread(fileName, "ChannelGroupName",group_N5, "ChannelName", channel_N5);
N5 = data{1};
group_N6 = "Rotary encoders";
channel_N6 = "N6 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N6, "ChannelName", channel_N6);
data = tdmsread(fileName, "ChannelGroupName",group_N6, "ChannelName", channel_N6);
N6 = data{1};
group_N7 = "Rotary encoders";
channel_N7 = "N7 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N7, "ChannelName", channel_N7);
data = tdmsread(fileName, "ChannelGroupName",group_N7, "ChannelName", channel_N7);
N7 = data{1};
group_N8 = "Rotary encoders";
channel_N8 = "N8 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N8, "ChannelName", channel_N8);
data = tdmsread(fileName, "ChannelGroupName",group_N8, "ChannelName", channel_N8);
N8 = data{1};
% Torque readings%
group_T1 = "Torques";
channel_T1 = "T1 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T1, "ChannelName", channel_T1);
data = tdmsread(fileName, "ChannelGroupName",group_T1, "ChannelName", channel_T1);
T1 = data{1};
group_T2 = "Torques";
channel_T2 = "T2 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T2, "ChannelName", channel_T2);
data = tdmsread(fileName, "ChannelGroupName",group_T2, "ChannelName", channel_T2);
T2 = data{1};
group_T3 = "Torques";
channel_T3 = "T3 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T3, "ChannelName", channel_T3);
data = tdmsread(fileName, "ChannelGroupName",group_T3, "ChannelName", channel_T3);
T3 = data{1};
group_T4 = "Torques";
channel_T4 = "T4 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T4, "ChannelName", channel_T4);
data = tdmsread(fileName, "ChannelGroupName",group_T4, "ChannelName", channel_T4);
T4 = data{1};
group_T5 = "Torques";
channel_T5 = "T5 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T5, "ChannelName", channel_T5);
data = tdmsread(fileName, "ChannelGroupName",group_T5, "ChannelName", channel_T5);
T5 = data{1};
group_T6 = "Torques";
channel_T6 = "T6 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T6, "ChannelName", channel_T6);
data = tdmsread(fileName, "ChannelGroupName",group_T6, "ChannelName", channel_T6);
T6 = data{1};
group_T7 = "Torques";
channel_T7 = "T7 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T7, "ChannelName", channel_T7);
data = tdmsread(fileName, "ChannelGroupName",group_T7, "ChannelName", channel_T7);
T7 = data{1};
group_T8 = "Torques";
channel_T8 = "T8 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T8, "ChannelName", channel_T8);
data = tdmsread(fileName, "ChannelGroupName",group_T8, "ChannelName", channel_T8);
T8 = data{1};
%Current readings%
group_I1a = "Motor voltages and currents";
channel_I1a = "I1a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1a, "ChannelName", channel_I1a);
data = tdmsread(fileName, "ChannelGroupName",group_I1a, "ChannelName", channel_I1a);
I1a = data{1};
group_I1b = "Motor voltages and currents";
channel_I1b = "I1b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1b, "ChannelName", channel_I1b);
data = tdmsread(fileName, "ChannelGroupName",group_I1b, "ChannelName", channel_I1b);
I1b = data{1};
group_I1c = "Motor voltages and currents";
channel_I1c = "I1c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1c, "ChannelName", channel_I1c);
data = tdmsread(fileName, "ChannelGroupName",group_I1c, "ChannelName", channel_I1c);
I1c = data{1};
group_I2a = "Motor voltages and currents";
channel_I2a = "I2a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2a, "ChannelName", channel_I2a);
data = tdmsread(fileName, "ChannelGroupName",group_I2a, "ChannelName", channel_I2a);
I2a = data{1};
group_I2b = "Motor voltages and currents";
channel_I2b = "I2b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2b, "ChannelName", channel_I2b);
data = tdmsread(fileName, "ChannelGroupName",group_I2b, "ChannelName", channel_I2b);
I2b = data{1};
group_I2c = "Motor voltages and currents";
channel_I2c = "I2c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2c, "ChannelName", channel_I2c);
data = tdmsread(fileName, "ChannelGroupName",group_I2c, "ChannelName", channel_I2c);
I2c = data{1};
group_I3a = "Motor voltages and currents";
channel_I3a = "I3a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3a, "ChannelName", channel_I3a);
data = tdmsread(fileName, "ChannelGroupName",group_I3a, "ChannelName", channel_I3a);
I3a = data{1};
group_I3b = "Motor voltages and currents";
channel_I3b = "I3b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3b, "ChannelName", channel_I3b);
data = tdmsread(fileName, "ChannelGroupName",group_I3b, "ChannelName", channel_I3b);
I3b = data{1};
group_I3c = "Motor voltages and currents";
channel_I3c = "I3c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3c, "ChannelName", channel_I3c);
data = tdmsread(fileName, "ChannelGroupName",group_I3c, "ChannelName", channel_I3c);
I3c = data{1};
group_I4a = "Motor voltages and currents";
channel_I4a = "I4a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4a, "ChannelName", channel_I4a);
data = tdmsread(fileName, "ChannelGroupName",group_I4a, "ChannelName", channel_I4a);
I4a = data{1};
group_I4b = "Motor voltages and currents";
channel_I4b = "I4b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4b, "ChannelName", channel_I4b);
data = tdmsread(fileName, "ChannelGroupName",group_I4b, "ChannelName", channel_I4b);
I4b = data{1};
group_I4c = "Motor voltages and currents";
channel_I4c = "I4c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4c, "ChannelName", channel_I4c);
data = tdmsread(fileName, "ChannelGroupName",group_I4c, "ChannelName", channel_I4c);
I4c = data{1};
group_IDC = "Motor voltages and currents";
channel_IDC = "IDC [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_IDC, "ChannelName", channel_IDC);
data = tdmsread(fileName, "ChannelGroupName",group_IDC, "ChannelName", channel_IDC);
IDC = data{1};
%Voltage readings%
group_V1ab = "Motor voltages and currents";
channel_V1ab = "V1ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1ab, "ChannelName", channel_V1ab);
data = tdmsread(fileName, "ChannelGroupName",group_V1ab, "ChannelName", channel_V1ab);
V1ab = data{1};
group_V1bc = "Motor voltages and currents";
channel_V1bc = "V1bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1bc, "ChannelName", channel_V1bc);
data = tdmsread(fileName, "ChannelGroupName",group_V1bc, "ChannelName", channel_V1bc);
V1bc = data{1};
group_V1ca = "Motor voltages and currents";
channel_V1ca = "V1ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1ca, "ChannelName", channel_V1ca);
data = tdmsread(fileName, "ChannelGroupName",group_V1ca, "ChannelName", channel_V1ca);
V1ca = data{1};
group_V2ab = "Motor voltages and currents";
channel_V2ab = "V2ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2ab, "ChannelName", channel_V2ab);
data = tdmsread(fileName, "ChannelGroupName",group_V2ab, "ChannelName", channel_V2ab);
V2ab = data{1};
group_V2bc = "Motor voltages and currents";
channel_V2bc = "V2bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2bc, "ChannelName", channel_V2bc);
data = tdmsread(fileName, "ChannelGroupName",group_V2bc, "ChannelName", channel_V2bc);
V2bc = data{1};
group_V2ca = "Motor voltages and currents";
channel_V2ca = "V2ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2ca, "ChannelName", channel_V2ca);
data = tdmsread(fileName, "ChannelGroupName",group_V2ca, "ChannelName", channel_V2ca);
V2ca = data{1};
group_V3ab = "Motor voltages and currents";
channel_V3ab = "V3ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3ab, "ChannelName", channel_V3ab);
data = tdmsread(fileName, "ChannelGroupName",group_V3ab, "ChannelName", channel_V3ab);
V3ab = data{1};
group_V3bc = "Motor voltages and currents";
channel_V3bc = "V3bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3bc, "ChannelName", channel_V3bc);
data = tdmsread(fileName, "ChannelGroupName",group_V3bc, "ChannelName", channel_V3bc);
V3bc = data{1};
group_V3ca = "Motor voltages and currents";
channel_V3ca = "V3ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3ca, "ChannelName", channel_V3ca);
data = tdmsread(fileName, "ChannelGroupName",group_V3ca, "ChannelName", channel_V3ca);
V3ca = data{1};
group_V4ab = "Motor voltages and currents";
channel_V4ab = "V4ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4ab, "ChannelName", channel_V4ab);
data = tdmsread(fileName, "ChannelGroupName",group_V4ab, "ChannelName", channel_V4ab);
V4ab = data{1};
group_V4bc = "Motor voltages and currents";
channel_V4bc = "V4bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4bc, "ChannelName", channel_V4bc);
data = tdmsread(fileName, "ChannelGroupName",group_V4bc, "ChannelName", channel_V4bc);
V4bc = data{1};
group_V4ca = "Motor voltages and currents";
channel_V4ca = "V4ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4ca, "ChannelName", channel_V4ca);
data = tdmsread(fileName, "ChannelGroupName",group_V4ca, "ChannelName", channel_V4ca);
V4ca = data{1};
group_VDC = "Motor voltages and currents";
channel_VDC = "VDC [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_VDC, "ChannelName", channel_VDC);
data = tdmsread(fileName, "ChannelGroupName",group_VDC, "ChannelName", channel_VDC);
VDC = data{1};
% Control Commands- Speed
group_M5S = "Control commands";
channel_M5S = "M5 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M5S, "ChannelName", channel_M5S);
data = tdmsread(fileName, "ChannelGroupName",group_M5S, "ChannelName", channel_M5S);
M5S = data{1};
group_M6S = "Control commands";
channel_M6S = "M6 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M6S, "ChannelName", channel_M6S);
data = tdmsread(fileName, "ChannelGroupName",group_M6S, "ChannelName", channel_M6S);
M6S = data{1};
group_M7S = "Control commands";
channel_M7S = "M7 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M7S, "ChannelName", channel_M7S);
data = tdmsread(fileName, "ChannelGroupName",group_M7S, "ChannelName", channel_M7S);
M7S = data{1};
group_M8S = "Control commands";
channel_M8S = "M8 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M8S, "ChannelName", channel_M8S);
data = tdmsread(fileName, "ChannelGroupName",group_M8S, "ChannelName", channel_M8S);
M8S = data{1};
% Control Commands- Torque
group_M5T = "Control commands";
channel_M5T = "M5 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M5T, "ChannelName", channel_M5T);
data = tdmsread(fileName, "ChannelGroupName",group_M5T, "ChannelName", channel_M5T);
M5T = data{1};
group_M6T = "Control commands";
channel_M6T = "M6 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M6T, "ChannelName", channel_M6T);
data = tdmsread(fileName, "ChannelGroupName",group_M6T, "ChannelName", channel_M6T);
M6T = data{1};
group_M7T = "Control commands";
channel_M7T = "M7 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M7T, "ChannelName", channel_M7T);
data = tdmsread(fileName, "ChannelGroupName",group_M7T, "ChannelName", channel_M7T);
M7T = data{1};
group_M8T = "Control commands";
channel_M8T = "M8 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M8T, "ChannelName", channel_M8T);
data = tdmsread(fileName, "ChannelGroupName",group_M8T, "ChannelName", channel_M8T);
M8T = data{1};
%ARRAYS
N1 = table2array(N1);
N2 = table2array(N2);
N3 = table2array(N3);
N4 = table2array(N4);
N5 = table2array(N5);
N6 = table2array(N6);
N7 = table2array(N7);
N8 = table2array(N8);
T1 = table2array(T1);
T2 = table2array(T2);
T3 = table2array(T3);
T4 = table2array(T4);
T5 = table2array(T5);
T6 = table2array(T6);
T7 = table2array(T7);
T8 = table2array(T8);
I1a = table2array(I1a);
I2a = table2array(I2a);
I3a = table2array(I3a);
I4a = table2array(I4a);
I1b = table2array(I1b);
I2b = table2array(I2b);
I3b = table2array(I3b);
I4b = table2array(I4b);
I1c = table2array(I1c);
I2c = table2array(I2c);
I3c = table2array(I3c);
I4c = table2array(I4c);
IDC = table2array(IDC);
V1ab = table2array(V1ab);
V2ab = table2array(V2ab);
V3ab = table2array(V3ab);
V4ab = table2array(V4ab);
V1bc = table2array(V1bc);
V2bc = table2array(V2bc);
V3bc = table2array(V3bc);
V4bc = table2array(V4bc);
V1ca = table2array(V1ca);
V2ca = table2array(V2ca);
V3ca = table2array(V3ca);
V4ca = table2array(V4ca);
VDC= table2array(VDC);
M5S = table2array(M5S);
M6S = table2array(M6S);
M7S = table2array(M7S);
M8S = table2array(M8S);
M5T = table2array(M5T);
M6T = table2array(M6T);
M7T = table2array(M7T);
M8T = table2array(M8T);
% GRAPHS AND TIME STAMPS
minLength_1 = min([length(time), length(T1), length(I1a)]);
minLength_2 = min([length(time), length(T1), length(M5T)]);
time = time(1:minLength_1);
T5 = T5(1:minLength_1);
I1a = I1a(1:minLength_1);
M5T = M5T(1:minLength_2);
figure;
hold on;
subplot(2, 1, 1);
plot(time, T5, ‘r-‘, ‘DisplayName’, ‘T5 [Nm]’);
xlabel(‘Time (s)’);
ylabel(‘T5 [Nm]’);
title(‘T5 vs. Time’);
legend(‘show’);
subplot(2, 1, 2);
plot(time, M5T, ‘b-‘, ‘DisplayName’, ‘M5T [Nm]’);
xlabel(‘Time (s)’);
ylabel(‘M5T [Nm]’);
title(‘M5T vs. Time’);
legend(‘show’);
legend;
hold off;
[~, maxIdx] = max(T5);
maxTime = time(maxIdx);
zeroCrossIdx = find(T5(maxIdx:end) <= 0, 1, ‘first’) + maxIdx – 1;
if isempty(zeroCrossIdx)
timeToZero = NaN;
disp(‘Data does not return to zero after maximum.’);
else
timeToZero = time(zeroCrossIdx) – maxTime;
disp(timeToZero);
end
% Find the index where interpolated T5 matches M5
matchIndex = find(T5 == M5T);
if isempty(matchIndex)
disp(‘T5 does not match M5 ‘);
else
matchTime = commonTime(matchIndex);
fprintf(‘T5 matches M5 at t = %.2f seconds (interpolated).n’, matchTime);
endI am trying to read multiple groups and channels of a TDMS file and doing some graphing and calculations at the end. I need to be able to acceses any channel or group that I want to use in the graphs or calcualtions, I would really apperciate it if someone had any advice on sliming the code down it currently takes 6 mins to run which is a really long time.
defaultFolder = ‘C:UserslillypColostateLute,Chris – EM-TRAS internalMatlab’;
addpath(defaultFolder);
fileFilter = ‘*.tdms’;
[fileName, filepath] = uigetfile(fullfile(defaultFolder, fileFilter), ‘Select a TDMS file’);
if fileName ~= 0
fullFilePath = fullfile(filepath, fileName);
disp([‘Selected file: ‘, fullFilePath]);
else
disp(‘No file selected.’);
end
fileName = fullFilePath;
group_Time = "Torques";
channel_Time = "Timestamp";
tdmsreadprop(fileName, "ChannelGroupName",group_Time, "ChannelName", channel_Time);
data = tdmsread(fileName, "ChannelGroupName",group_Time, "ChannelName", channel_Time);
time = data{1};
if istable(time)
time = table2array(time);
elseif isdatetime(time)
time = seconds(time – time(1));
end
% Speed readings%
group_N1 = "Rotary encoders";
channel_N1 = "N1 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N1, "ChannelName", channel_N1);
data = tdmsread(fileName, "ChannelGroupName",group_N1, "ChannelName", channel_N1);
N1 = data{1};
group_N2 = "Rotary encoders";
channel_N2 = "N2 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N2, "ChannelName", channel_N2);
data = tdmsread(fileName, "ChannelGroupName",group_N2, "ChannelName", channel_N2);
N2 = data{1};
group_N3 = "Rotary encoders";
channel_N3 = "N3 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N3, "ChannelName", channel_N3);
data = tdmsread(fileName, "ChannelGroupName",group_N3, "ChannelName", channel_N3);
N3 = data{1};
group_N4 = "Rotary encoders";
channel_N4 = "N4 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N4, "ChannelName", channel_N4);
data = tdmsread(fileName, "ChannelGroupName",group_N4, "ChannelName", channel_N4);
N4 = data{1};
group_N5 = "Rotary encoders";
channel_N5 = "N5 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N5, "ChannelName", channel_N5);
data = tdmsread(fileName, "ChannelGroupName",group_N5, "ChannelName", channel_N5);
N5 = data{1};
group_N6 = "Rotary encoders";
channel_N6 = "N6 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N6, "ChannelName", channel_N6);
data = tdmsread(fileName, "ChannelGroupName",group_N6, "ChannelName", channel_N6);
N6 = data{1};
group_N7 = "Rotary encoders";
channel_N7 = "N7 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N7, "ChannelName", channel_N7);
data = tdmsread(fileName, "ChannelGroupName",group_N7, "ChannelName", channel_N7);
N7 = data{1};
group_N8 = "Rotary encoders";
channel_N8 = "N8 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N8, "ChannelName", channel_N8);
data = tdmsread(fileName, "ChannelGroupName",group_N8, "ChannelName", channel_N8);
N8 = data{1};
% Torque readings%
group_T1 = "Torques";
channel_T1 = "T1 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T1, "ChannelName", channel_T1);
data = tdmsread(fileName, "ChannelGroupName",group_T1, "ChannelName", channel_T1);
T1 = data{1};
group_T2 = "Torques";
channel_T2 = "T2 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T2, "ChannelName", channel_T2);
data = tdmsread(fileName, "ChannelGroupName",group_T2, "ChannelName", channel_T2);
T2 = data{1};
group_T3 = "Torques";
channel_T3 = "T3 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T3, "ChannelName", channel_T3);
data = tdmsread(fileName, "ChannelGroupName",group_T3, "ChannelName", channel_T3);
T3 = data{1};
group_T4 = "Torques";
channel_T4 = "T4 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T4, "ChannelName", channel_T4);
data = tdmsread(fileName, "ChannelGroupName",group_T4, "ChannelName", channel_T4);
T4 = data{1};
group_T5 = "Torques";
channel_T5 = "T5 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T5, "ChannelName", channel_T5);
data = tdmsread(fileName, "ChannelGroupName",group_T5, "ChannelName", channel_T5);
T5 = data{1};
group_T6 = "Torques";
channel_T6 = "T6 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T6, "ChannelName", channel_T6);
data = tdmsread(fileName, "ChannelGroupName",group_T6, "ChannelName", channel_T6);
T6 = data{1};
group_T7 = "Torques";
channel_T7 = "T7 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T7, "ChannelName", channel_T7);
data = tdmsread(fileName, "ChannelGroupName",group_T7, "ChannelName", channel_T7);
T7 = data{1};
group_T8 = "Torques";
channel_T8 = "T8 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T8, "ChannelName", channel_T8);
data = tdmsread(fileName, "ChannelGroupName",group_T8, "ChannelName", channel_T8);
T8 = data{1};
%Current readings%
group_I1a = "Motor voltages and currents";
channel_I1a = "I1a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1a, "ChannelName", channel_I1a);
data = tdmsread(fileName, "ChannelGroupName",group_I1a, "ChannelName", channel_I1a);
I1a = data{1};
group_I1b = "Motor voltages and currents";
channel_I1b = "I1b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1b, "ChannelName", channel_I1b);
data = tdmsread(fileName, "ChannelGroupName",group_I1b, "ChannelName", channel_I1b);
I1b = data{1};
group_I1c = "Motor voltages and currents";
channel_I1c = "I1c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1c, "ChannelName", channel_I1c);
data = tdmsread(fileName, "ChannelGroupName",group_I1c, "ChannelName", channel_I1c);
I1c = data{1};
group_I2a = "Motor voltages and currents";
channel_I2a = "I2a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2a, "ChannelName", channel_I2a);
data = tdmsread(fileName, "ChannelGroupName",group_I2a, "ChannelName", channel_I2a);
I2a = data{1};
group_I2b = "Motor voltages and currents";
channel_I2b = "I2b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2b, "ChannelName", channel_I2b);
data = tdmsread(fileName, "ChannelGroupName",group_I2b, "ChannelName", channel_I2b);
I2b = data{1};
group_I2c = "Motor voltages and currents";
channel_I2c = "I2c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2c, "ChannelName", channel_I2c);
data = tdmsread(fileName, "ChannelGroupName",group_I2c, "ChannelName", channel_I2c);
I2c = data{1};
group_I3a = "Motor voltages and currents";
channel_I3a = "I3a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3a, "ChannelName", channel_I3a);
data = tdmsread(fileName, "ChannelGroupName",group_I3a, "ChannelName", channel_I3a);
I3a = data{1};
group_I3b = "Motor voltages and currents";
channel_I3b = "I3b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3b, "ChannelName", channel_I3b);
data = tdmsread(fileName, "ChannelGroupName",group_I3b, "ChannelName", channel_I3b);
I3b = data{1};
group_I3c = "Motor voltages and currents";
channel_I3c = "I3c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3c, "ChannelName", channel_I3c);
data = tdmsread(fileName, "ChannelGroupName",group_I3c, "ChannelName", channel_I3c);
I3c = data{1};
group_I4a = "Motor voltages and currents";
channel_I4a = "I4a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4a, "ChannelName", channel_I4a);
data = tdmsread(fileName, "ChannelGroupName",group_I4a, "ChannelName", channel_I4a);
I4a = data{1};
group_I4b = "Motor voltages and currents";
channel_I4b = "I4b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4b, "ChannelName", channel_I4b);
data = tdmsread(fileName, "ChannelGroupName",group_I4b, "ChannelName", channel_I4b);
I4b = data{1};
group_I4c = "Motor voltages and currents";
channel_I4c = "I4c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4c, "ChannelName", channel_I4c);
data = tdmsread(fileName, "ChannelGroupName",group_I4c, "ChannelName", channel_I4c);
I4c = data{1};
group_IDC = "Motor voltages and currents";
channel_IDC = "IDC [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_IDC, "ChannelName", channel_IDC);
data = tdmsread(fileName, "ChannelGroupName",group_IDC, "ChannelName", channel_IDC);
IDC = data{1};
%Voltage readings%
group_V1ab = "Motor voltages and currents";
channel_V1ab = "V1ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1ab, "ChannelName", channel_V1ab);
data = tdmsread(fileName, "ChannelGroupName",group_V1ab, "ChannelName", channel_V1ab);
V1ab = data{1};
group_V1bc = "Motor voltages and currents";
channel_V1bc = "V1bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1bc, "ChannelName", channel_V1bc);
data = tdmsread(fileName, "ChannelGroupName",group_V1bc, "ChannelName", channel_V1bc);
V1bc = data{1};
group_V1ca = "Motor voltages and currents";
channel_V1ca = "V1ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1ca, "ChannelName", channel_V1ca);
data = tdmsread(fileName, "ChannelGroupName",group_V1ca, "ChannelName", channel_V1ca);
V1ca = data{1};
group_V2ab = "Motor voltages and currents";
channel_V2ab = "V2ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2ab, "ChannelName", channel_V2ab);
data = tdmsread(fileName, "ChannelGroupName",group_V2ab, "ChannelName", channel_V2ab);
V2ab = data{1};
group_V2bc = "Motor voltages and currents";
channel_V2bc = "V2bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2bc, "ChannelName", channel_V2bc);
data = tdmsread(fileName, "ChannelGroupName",group_V2bc, "ChannelName", channel_V2bc);
V2bc = data{1};
group_V2ca = "Motor voltages and currents";
channel_V2ca = "V2ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2ca, "ChannelName", channel_V2ca);
data = tdmsread(fileName, "ChannelGroupName",group_V2ca, "ChannelName", channel_V2ca);
V2ca = data{1};
group_V3ab = "Motor voltages and currents";
channel_V3ab = "V3ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3ab, "ChannelName", channel_V3ab);
data = tdmsread(fileName, "ChannelGroupName",group_V3ab, "ChannelName", channel_V3ab);
V3ab = data{1};
group_V3bc = "Motor voltages and currents";
channel_V3bc = "V3bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3bc, "ChannelName", channel_V3bc);
data = tdmsread(fileName, "ChannelGroupName",group_V3bc, "ChannelName", channel_V3bc);
V3bc = data{1};
group_V3ca = "Motor voltages and currents";
channel_V3ca = "V3ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3ca, "ChannelName", channel_V3ca);
data = tdmsread(fileName, "ChannelGroupName",group_V3ca, "ChannelName", channel_V3ca);
V3ca = data{1};
group_V4ab = "Motor voltages and currents";
channel_V4ab = "V4ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4ab, "ChannelName", channel_V4ab);
data = tdmsread(fileName, "ChannelGroupName",group_V4ab, "ChannelName", channel_V4ab);
V4ab = data{1};
group_V4bc = "Motor voltages and currents";
channel_V4bc = "V4bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4bc, "ChannelName", channel_V4bc);
data = tdmsread(fileName, "ChannelGroupName",group_V4bc, "ChannelName", channel_V4bc);
V4bc = data{1};
group_V4ca = "Motor voltages and currents";
channel_V4ca = "V4ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4ca, "ChannelName", channel_V4ca);
data = tdmsread(fileName, "ChannelGroupName",group_V4ca, "ChannelName", channel_V4ca);
V4ca = data{1};
group_VDC = "Motor voltages and currents";
channel_VDC = "VDC [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_VDC, "ChannelName", channel_VDC);
data = tdmsread(fileName, "ChannelGroupName",group_VDC, "ChannelName", channel_VDC);
VDC = data{1};
% Control Commands- Speed
group_M5S = "Control commands";
channel_M5S = "M5 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M5S, "ChannelName", channel_M5S);
data = tdmsread(fileName, "ChannelGroupName",group_M5S, "ChannelName", channel_M5S);
M5S = data{1};
group_M6S = "Control commands";
channel_M6S = "M6 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M6S, "ChannelName", channel_M6S);
data = tdmsread(fileName, "ChannelGroupName",group_M6S, "ChannelName", channel_M6S);
M6S = data{1};
group_M7S = "Control commands";
channel_M7S = "M7 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M7S, "ChannelName", channel_M7S);
data = tdmsread(fileName, "ChannelGroupName",group_M7S, "ChannelName", channel_M7S);
M7S = data{1};
group_M8S = "Control commands";
channel_M8S = "M8 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M8S, "ChannelName", channel_M8S);
data = tdmsread(fileName, "ChannelGroupName",group_M8S, "ChannelName", channel_M8S);
M8S = data{1};
% Control Commands- Torque
group_M5T = "Control commands";
channel_M5T = "M5 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M5T, "ChannelName", channel_M5T);
data = tdmsread(fileName, "ChannelGroupName",group_M5T, "ChannelName", channel_M5T);
M5T = data{1};
group_M6T = "Control commands";
channel_M6T = "M6 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M6T, "ChannelName", channel_M6T);
data = tdmsread(fileName, "ChannelGroupName",group_M6T, "ChannelName", channel_M6T);
M6T = data{1};
group_M7T = "Control commands";
channel_M7T = "M7 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M7T, "ChannelName", channel_M7T);
data = tdmsread(fileName, "ChannelGroupName",group_M7T, "ChannelName", channel_M7T);
M7T = data{1};
group_M8T = "Control commands";
channel_M8T = "M8 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M8T, "ChannelName", channel_M8T);
data = tdmsread(fileName, "ChannelGroupName",group_M8T, "ChannelName", channel_M8T);
M8T = data{1};
%ARRAYS
N1 = table2array(N1);
N2 = table2array(N2);
N3 = table2array(N3);
N4 = table2array(N4);
N5 = table2array(N5);
N6 = table2array(N6);
N7 = table2array(N7);
N8 = table2array(N8);
T1 = table2array(T1);
T2 = table2array(T2);
T3 = table2array(T3);
T4 = table2array(T4);
T5 = table2array(T5);
T6 = table2array(T6);
T7 = table2array(T7);
T8 = table2array(T8);
I1a = table2array(I1a);
I2a = table2array(I2a);
I3a = table2array(I3a);
I4a = table2array(I4a);
I1b = table2array(I1b);
I2b = table2array(I2b);
I3b = table2array(I3b);
I4b = table2array(I4b);
I1c = table2array(I1c);
I2c = table2array(I2c);
I3c = table2array(I3c);
I4c = table2array(I4c);
IDC = table2array(IDC);
V1ab = table2array(V1ab);
V2ab = table2array(V2ab);
V3ab = table2array(V3ab);
V4ab = table2array(V4ab);
V1bc = table2array(V1bc);
V2bc = table2array(V2bc);
V3bc = table2array(V3bc);
V4bc = table2array(V4bc);
V1ca = table2array(V1ca);
V2ca = table2array(V2ca);
V3ca = table2array(V3ca);
V4ca = table2array(V4ca);
VDC= table2array(VDC);
M5S = table2array(M5S);
M6S = table2array(M6S);
M7S = table2array(M7S);
M8S = table2array(M8S);
M5T = table2array(M5T);
M6T = table2array(M6T);
M7T = table2array(M7T);
M8T = table2array(M8T);
% GRAPHS AND TIME STAMPS
minLength_1 = min([length(time), length(T1), length(I1a)]);
minLength_2 = min([length(time), length(T1), length(M5T)]);
time = time(1:minLength_1);
T5 = T5(1:minLength_1);
I1a = I1a(1:minLength_1);
M5T = M5T(1:minLength_2);
figure;
hold on;
subplot(2, 1, 1);
plot(time, T5, ‘r-‘, ‘DisplayName’, ‘T5 [Nm]’);
xlabel(‘Time (s)’);
ylabel(‘T5 [Nm]’);
title(‘T5 vs. Time’);
legend(‘show’);
subplot(2, 1, 2);
plot(time, M5T, ‘b-‘, ‘DisplayName’, ‘M5T [Nm]’);
xlabel(‘Time (s)’);
ylabel(‘M5T [Nm]’);
title(‘M5T vs. Time’);
legend(‘show’);
legend;
hold off;
[~, maxIdx] = max(T5);
maxTime = time(maxIdx);
zeroCrossIdx = find(T5(maxIdx:end) <= 0, 1, ‘first’) + maxIdx – 1;
if isempty(zeroCrossIdx)
timeToZero = NaN;
disp(‘Data does not return to zero after maximum.’);
else
timeToZero = time(zeroCrossIdx) – maxTime;
disp(timeToZero);
end
% Find the index where interpolated T5 matches M5
matchIndex = find(T5 == M5T);
if isempty(matchIndex)
disp(‘T5 does not match M5 ‘);
else
matchTime = commonTime(matchIndex);
fprintf(‘T5 matches M5 at t = %.2f seconds (interpolated).n’, matchTime);
end I am trying to read multiple groups and channels of a TDMS file and doing some graphing and calculations at the end. I need to be able to acceses any channel or group that I want to use in the graphs or calcualtions, I would really apperciate it if someone had any advice on sliming the code down it currently takes 6 mins to run which is a really long time.
defaultFolder = ‘C:UserslillypColostateLute,Chris – EM-TRAS internalMatlab’;
addpath(defaultFolder);
fileFilter = ‘*.tdms’;
[fileName, filepath] = uigetfile(fullfile(defaultFolder, fileFilter), ‘Select a TDMS file’);
if fileName ~= 0
fullFilePath = fullfile(filepath, fileName);
disp([‘Selected file: ‘, fullFilePath]);
else
disp(‘No file selected.’);
end
fileName = fullFilePath;
group_Time = "Torques";
channel_Time = "Timestamp";
tdmsreadprop(fileName, "ChannelGroupName",group_Time, "ChannelName", channel_Time);
data = tdmsread(fileName, "ChannelGroupName",group_Time, "ChannelName", channel_Time);
time = data{1};
if istable(time)
time = table2array(time);
elseif isdatetime(time)
time = seconds(time – time(1));
end
% Speed readings%
group_N1 = "Rotary encoders";
channel_N1 = "N1 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N1, "ChannelName", channel_N1);
data = tdmsread(fileName, "ChannelGroupName",group_N1, "ChannelName", channel_N1);
N1 = data{1};
group_N2 = "Rotary encoders";
channel_N2 = "N2 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N2, "ChannelName", channel_N2);
data = tdmsread(fileName, "ChannelGroupName",group_N2, "ChannelName", channel_N2);
N2 = data{1};
group_N3 = "Rotary encoders";
channel_N3 = "N3 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N3, "ChannelName", channel_N3);
data = tdmsread(fileName, "ChannelGroupName",group_N3, "ChannelName", channel_N3);
N3 = data{1};
group_N4 = "Rotary encoders";
channel_N4 = "N4 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N4, "ChannelName", channel_N4);
data = tdmsread(fileName, "ChannelGroupName",group_N4, "ChannelName", channel_N4);
N4 = data{1};
group_N5 = "Rotary encoders";
channel_N5 = "N5 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N5, "ChannelName", channel_N5);
data = tdmsread(fileName, "ChannelGroupName",group_N5, "ChannelName", channel_N5);
N5 = data{1};
group_N6 = "Rotary encoders";
channel_N6 = "N6 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N6, "ChannelName", channel_N6);
data = tdmsread(fileName, "ChannelGroupName",group_N6, "ChannelName", channel_N6);
N6 = data{1};
group_N7 = "Rotary encoders";
channel_N7 = "N7 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N7, "ChannelName", channel_N7);
data = tdmsread(fileName, "ChannelGroupName",group_N7, "ChannelName", channel_N7);
N7 = data{1};
group_N8 = "Rotary encoders";
channel_N8 = "N8 [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_N8, "ChannelName", channel_N8);
data = tdmsread(fileName, "ChannelGroupName",group_N8, "ChannelName", channel_N8);
N8 = data{1};
% Torque readings%
group_T1 = "Torques";
channel_T1 = "T1 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T1, "ChannelName", channel_T1);
data = tdmsread(fileName, "ChannelGroupName",group_T1, "ChannelName", channel_T1);
T1 = data{1};
group_T2 = "Torques";
channel_T2 = "T2 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T2, "ChannelName", channel_T2);
data = tdmsread(fileName, "ChannelGroupName",group_T2, "ChannelName", channel_T2);
T2 = data{1};
group_T3 = "Torques";
channel_T3 = "T3 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T3, "ChannelName", channel_T3);
data = tdmsread(fileName, "ChannelGroupName",group_T3, "ChannelName", channel_T3);
T3 = data{1};
group_T4 = "Torques";
channel_T4 = "T4 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T4, "ChannelName", channel_T4);
data = tdmsread(fileName, "ChannelGroupName",group_T4, "ChannelName", channel_T4);
T4 = data{1};
group_T5 = "Torques";
channel_T5 = "T5 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T5, "ChannelName", channel_T5);
data = tdmsread(fileName, "ChannelGroupName",group_T5, "ChannelName", channel_T5);
T5 = data{1};
group_T6 = "Torques";
channel_T6 = "T6 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T6, "ChannelName", channel_T6);
data = tdmsread(fileName, "ChannelGroupName",group_T6, "ChannelName", channel_T6);
T6 = data{1};
group_T7 = "Torques";
channel_T7 = "T7 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T7, "ChannelName", channel_T7);
data = tdmsread(fileName, "ChannelGroupName",group_T7, "ChannelName", channel_T7);
T7 = data{1};
group_T8 = "Torques";
channel_T8 = "T8 [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_T8, "ChannelName", channel_T8);
data = tdmsread(fileName, "ChannelGroupName",group_T8, "ChannelName", channel_T8);
T8 = data{1};
%Current readings%
group_I1a = "Motor voltages and currents";
channel_I1a = "I1a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1a, "ChannelName", channel_I1a);
data = tdmsread(fileName, "ChannelGroupName",group_I1a, "ChannelName", channel_I1a);
I1a = data{1};
group_I1b = "Motor voltages and currents";
channel_I1b = "I1b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1b, "ChannelName", channel_I1b);
data = tdmsread(fileName, "ChannelGroupName",group_I1b, "ChannelName", channel_I1b);
I1b = data{1};
group_I1c = "Motor voltages and currents";
channel_I1c = "I1c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I1c, "ChannelName", channel_I1c);
data = tdmsread(fileName, "ChannelGroupName",group_I1c, "ChannelName", channel_I1c);
I1c = data{1};
group_I2a = "Motor voltages and currents";
channel_I2a = "I2a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2a, "ChannelName", channel_I2a);
data = tdmsread(fileName, "ChannelGroupName",group_I2a, "ChannelName", channel_I2a);
I2a = data{1};
group_I2b = "Motor voltages and currents";
channel_I2b = "I2b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2b, "ChannelName", channel_I2b);
data = tdmsread(fileName, "ChannelGroupName",group_I2b, "ChannelName", channel_I2b);
I2b = data{1};
group_I2c = "Motor voltages and currents";
channel_I2c = "I2c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I2c, "ChannelName", channel_I2c);
data = tdmsread(fileName, "ChannelGroupName",group_I2c, "ChannelName", channel_I2c);
I2c = data{1};
group_I3a = "Motor voltages and currents";
channel_I3a = "I3a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3a, "ChannelName", channel_I3a);
data = tdmsread(fileName, "ChannelGroupName",group_I3a, "ChannelName", channel_I3a);
I3a = data{1};
group_I3b = "Motor voltages and currents";
channel_I3b = "I3b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3b, "ChannelName", channel_I3b);
data = tdmsread(fileName, "ChannelGroupName",group_I3b, "ChannelName", channel_I3b);
I3b = data{1};
group_I3c = "Motor voltages and currents";
channel_I3c = "I3c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I3c, "ChannelName", channel_I3c);
data = tdmsread(fileName, "ChannelGroupName",group_I3c, "ChannelName", channel_I3c);
I3c = data{1};
group_I4a = "Motor voltages and currents";
channel_I4a = "I4a [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4a, "ChannelName", channel_I4a);
data = tdmsread(fileName, "ChannelGroupName",group_I4a, "ChannelName", channel_I4a);
I4a = data{1};
group_I4b = "Motor voltages and currents";
channel_I4b = "I4b [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4b, "ChannelName", channel_I4b);
data = tdmsread(fileName, "ChannelGroupName",group_I4b, "ChannelName", channel_I4b);
I4b = data{1};
group_I4c = "Motor voltages and currents";
channel_I4c = "I4c [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_I4c, "ChannelName", channel_I4c);
data = tdmsread(fileName, "ChannelGroupName",group_I4c, "ChannelName", channel_I4c);
I4c = data{1};
group_IDC = "Motor voltages and currents";
channel_IDC = "IDC [A]";
tdmsreadprop(fileName, "ChannelGroupName",group_IDC, "ChannelName", channel_IDC);
data = tdmsread(fileName, "ChannelGroupName",group_IDC, "ChannelName", channel_IDC);
IDC = data{1};
%Voltage readings%
group_V1ab = "Motor voltages and currents";
channel_V1ab = "V1ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1ab, "ChannelName", channel_V1ab);
data = tdmsread(fileName, "ChannelGroupName",group_V1ab, "ChannelName", channel_V1ab);
V1ab = data{1};
group_V1bc = "Motor voltages and currents";
channel_V1bc = "V1bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1bc, "ChannelName", channel_V1bc);
data = tdmsread(fileName, "ChannelGroupName",group_V1bc, "ChannelName", channel_V1bc);
V1bc = data{1};
group_V1ca = "Motor voltages and currents";
channel_V1ca = "V1ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V1ca, "ChannelName", channel_V1ca);
data = tdmsread(fileName, "ChannelGroupName",group_V1ca, "ChannelName", channel_V1ca);
V1ca = data{1};
group_V2ab = "Motor voltages and currents";
channel_V2ab = "V2ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2ab, "ChannelName", channel_V2ab);
data = tdmsread(fileName, "ChannelGroupName",group_V2ab, "ChannelName", channel_V2ab);
V2ab = data{1};
group_V2bc = "Motor voltages and currents";
channel_V2bc = "V2bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2bc, "ChannelName", channel_V2bc);
data = tdmsread(fileName, "ChannelGroupName",group_V2bc, "ChannelName", channel_V2bc);
V2bc = data{1};
group_V2ca = "Motor voltages and currents";
channel_V2ca = "V2ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V2ca, "ChannelName", channel_V2ca);
data = tdmsread(fileName, "ChannelGroupName",group_V2ca, "ChannelName", channel_V2ca);
V2ca = data{1};
group_V3ab = "Motor voltages and currents";
channel_V3ab = "V3ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3ab, "ChannelName", channel_V3ab);
data = tdmsread(fileName, "ChannelGroupName",group_V3ab, "ChannelName", channel_V3ab);
V3ab = data{1};
group_V3bc = "Motor voltages and currents";
channel_V3bc = "V3bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3bc, "ChannelName", channel_V3bc);
data = tdmsread(fileName, "ChannelGroupName",group_V3bc, "ChannelName", channel_V3bc);
V3bc = data{1};
group_V3ca = "Motor voltages and currents";
channel_V3ca = "V3ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V3ca, "ChannelName", channel_V3ca);
data = tdmsread(fileName, "ChannelGroupName",group_V3ca, "ChannelName", channel_V3ca);
V3ca = data{1};
group_V4ab = "Motor voltages and currents";
channel_V4ab = "V4ab [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4ab, "ChannelName", channel_V4ab);
data = tdmsread(fileName, "ChannelGroupName",group_V4ab, "ChannelName", channel_V4ab);
V4ab = data{1};
group_V4bc = "Motor voltages and currents";
channel_V4bc = "V4bc [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4bc, "ChannelName", channel_V4bc);
data = tdmsread(fileName, "ChannelGroupName",group_V4bc, "ChannelName", channel_V4bc);
V4bc = data{1};
group_V4ca = "Motor voltages and currents";
channel_V4ca = "V4ca [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_V4ca, "ChannelName", channel_V4ca);
data = tdmsread(fileName, "ChannelGroupName",group_V4ca, "ChannelName", channel_V4ca);
V4ca = data{1};
group_VDC = "Motor voltages and currents";
channel_VDC = "VDC [V]";
tdmsreadprop(fileName, "ChannelGroupName",group_VDC, "ChannelName", channel_VDC);
data = tdmsread(fileName, "ChannelGroupName",group_VDC, "ChannelName", channel_VDC);
VDC = data{1};
% Control Commands- Speed
group_M5S = "Control commands";
channel_M5S = "M5 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M5S, "ChannelName", channel_M5S);
data = tdmsread(fileName, "ChannelGroupName",group_M5S, "ChannelName", channel_M5S);
M5S = data{1};
group_M6S = "Control commands";
channel_M6S = "M6 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M6S, "ChannelName", channel_M6S);
data = tdmsread(fileName, "ChannelGroupName",group_M6S, "ChannelName", channel_M6S);
M6S = data{1};
group_M7S = "Control commands";
channel_M7S = "M7 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M7S, "ChannelName", channel_M7S);
data = tdmsread(fileName, "ChannelGroupName",group_M7S, "ChannelName", channel_M7S);
M7S = data{1};
group_M8S = "Control commands";
channel_M8S = "M8 speed cmd [RPM]";
tdmsreadprop(fileName, "ChannelGroupName",group_M8S, "ChannelName", channel_M8S);
data = tdmsread(fileName, "ChannelGroupName",group_M8S, "ChannelName", channel_M8S);
M8S = data{1};
% Control Commands- Torque
group_M5T = "Control commands";
channel_M5T = "M5 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M5T, "ChannelName", channel_M5T);
data = tdmsread(fileName, "ChannelGroupName",group_M5T, "ChannelName", channel_M5T);
M5T = data{1};
group_M6T = "Control commands";
channel_M6T = "M6 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M6T, "ChannelName", channel_M6T);
data = tdmsread(fileName, "ChannelGroupName",group_M6T, "ChannelName", channel_M6T);
M6T = data{1};
group_M7T = "Control commands";
channel_M7T = "M7 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M7T, "ChannelName", channel_M7T);
data = tdmsread(fileName, "ChannelGroupName",group_M7T, "ChannelName", channel_M7T);
M7T = data{1};
group_M8T = "Control commands";
channel_M8T = "M8 torque cmd [Nm]";
tdmsreadprop(fileName, "ChannelGroupName",group_M8T, "ChannelName", channel_M8T);
data = tdmsread(fileName, "ChannelGroupName",group_M8T, "ChannelName", channel_M8T);
M8T = data{1};
%ARRAYS
N1 = table2array(N1);
N2 = table2array(N2);
N3 = table2array(N3);
N4 = table2array(N4);
N5 = table2array(N5);
N6 = table2array(N6);
N7 = table2array(N7);
N8 = table2array(N8);
T1 = table2array(T1);
T2 = table2array(T2);
T3 = table2array(T3);
T4 = table2array(T4);
T5 = table2array(T5);
T6 = table2array(T6);
T7 = table2array(T7);
T8 = table2array(T8);
I1a = table2array(I1a);
I2a = table2array(I2a);
I3a = table2array(I3a);
I4a = table2array(I4a);
I1b = table2array(I1b);
I2b = table2array(I2b);
I3b = table2array(I3b);
I4b = table2array(I4b);
I1c = table2array(I1c);
I2c = table2array(I2c);
I3c = table2array(I3c);
I4c = table2array(I4c);
IDC = table2array(IDC);
V1ab = table2array(V1ab);
V2ab = table2array(V2ab);
V3ab = table2array(V3ab);
V4ab = table2array(V4ab);
V1bc = table2array(V1bc);
V2bc = table2array(V2bc);
V3bc = table2array(V3bc);
V4bc = table2array(V4bc);
V1ca = table2array(V1ca);
V2ca = table2array(V2ca);
V3ca = table2array(V3ca);
V4ca = table2array(V4ca);
VDC= table2array(VDC);
M5S = table2array(M5S);
M6S = table2array(M6S);
M7S = table2array(M7S);
M8S = table2array(M8S);
M5T = table2array(M5T);
M6T = table2array(M6T);
M7T = table2array(M7T);
M8T = table2array(M8T);
% GRAPHS AND TIME STAMPS
minLength_1 = min([length(time), length(T1), length(I1a)]);
minLength_2 = min([length(time), length(T1), length(M5T)]);
time = time(1:minLength_1);
T5 = T5(1:minLength_1);
I1a = I1a(1:minLength_1);
M5T = M5T(1:minLength_2);
figure;
hold on;
subplot(2, 1, 1);
plot(time, T5, ‘r-‘, ‘DisplayName’, ‘T5 [Nm]’);
xlabel(‘Time (s)’);
ylabel(‘T5 [Nm]’);
title(‘T5 vs. Time’);
legend(‘show’);
subplot(2, 1, 2);
plot(time, M5T, ‘b-‘, ‘DisplayName’, ‘M5T [Nm]’);
xlabel(‘Time (s)’);
ylabel(‘M5T [Nm]’);
title(‘M5T vs. Time’);
legend(‘show’);
legend;
hold off;
[~, maxIdx] = max(T5);
maxTime = time(maxIdx);
zeroCrossIdx = find(T5(maxIdx:end) <= 0, 1, ‘first’) + maxIdx – 1;
if isempty(zeroCrossIdx)
timeToZero = NaN;
disp(‘Data does not return to zero after maximum.’);
else
timeToZero = time(zeroCrossIdx) – maxTime;
disp(timeToZero);
end
% Find the index where interpolated T5 matches M5
matchIndex = find(T5 == M5T);
if isempty(matchIndex)
disp(‘T5 does not match M5 ‘);
else
matchTime = commonTime(matchIndex);
fprintf(‘T5 matches M5 at t = %.2f seconds (interpolated).n’, matchTime);
end #tdms, #tdmsread MATLAB Answers — New Questions
How to remove DC component in FFT?
I succesfully plotted my FFT with MATLAB discussion help. Now I could not remove the DC component at 0Hz. Which shows me a very high amplitude. Can any one suggest me an idea?
data1 = xlsread(‘Reading 1.xlsx’) ; %Loading Sensor data from Excel file
t = data1 (1:512,2); %Selecting Time vector
s = data1 (1:512,3); %Selecting Z axis vibrations
L = numel(t); %Signal length
Ts = mean(diff(t)); %Sampling interval
Fs = 1/Ts; %Sampling frequency
Fn = Fs/2; %Nyquist frequency
FTs = fft(s)/L; %Fast fourier transform (s- data)
Fv = linspace(0,1, fix(L/2)+1)*Fn; %Frequency vector
Iv = 1:numel(Fv); %Index vector
subplot(2, 1, 1); %plotting top pane
plot(t,s); %Acceleration vs time
set(gca,’xlim’,[1 50]); %Scale to fit
grid; %Grids on
title (‘Acceleration vs time’);
xlabel(‘time(s)’);
ylabel(‘Acceleration’);
subplot(2, 1, 2); %Plotting bottom pane
plot(Fv, abs(FTs(Iv))*2,’red’); %FFT – Amplitude vs Frequency
grid
title (‘Fast fourier transform’);
xlabel(‘Frequency (Hz)’);
ylabel (‘Amplitude (m)’);I succesfully plotted my FFT with MATLAB discussion help. Now I could not remove the DC component at 0Hz. Which shows me a very high amplitude. Can any one suggest me an idea?
data1 = xlsread(‘Reading 1.xlsx’) ; %Loading Sensor data from Excel file
t = data1 (1:512,2); %Selecting Time vector
s = data1 (1:512,3); %Selecting Z axis vibrations
L = numel(t); %Signal length
Ts = mean(diff(t)); %Sampling interval
Fs = 1/Ts; %Sampling frequency
Fn = Fs/2; %Nyquist frequency
FTs = fft(s)/L; %Fast fourier transform (s- data)
Fv = linspace(0,1, fix(L/2)+1)*Fn; %Frequency vector
Iv = 1:numel(Fv); %Index vector
subplot(2, 1, 1); %plotting top pane
plot(t,s); %Acceleration vs time
set(gca,’xlim’,[1 50]); %Scale to fit
grid; %Grids on
title (‘Acceleration vs time’);
xlabel(‘time(s)’);
ylabel(‘Acceleration’);
subplot(2, 1, 2); %Plotting bottom pane
plot(Fv, abs(FTs(Iv))*2,’red’); %FFT – Amplitude vs Frequency
grid
title (‘Fast fourier transform’);
xlabel(‘Frequency (Hz)’);
ylabel (‘Amplitude (m)’); I succesfully plotted my FFT with MATLAB discussion help. Now I could not remove the DC component at 0Hz. Which shows me a very high amplitude. Can any one suggest me an idea?
data1 = xlsread(‘Reading 1.xlsx’) ; %Loading Sensor data from Excel file
t = data1 (1:512,2); %Selecting Time vector
s = data1 (1:512,3); %Selecting Z axis vibrations
L = numel(t); %Signal length
Ts = mean(diff(t)); %Sampling interval
Fs = 1/Ts; %Sampling frequency
Fn = Fs/2; %Nyquist frequency
FTs = fft(s)/L; %Fast fourier transform (s- data)
Fv = linspace(0,1, fix(L/2)+1)*Fn; %Frequency vector
Iv = 1:numel(Fv); %Index vector
subplot(2, 1, 1); %plotting top pane
plot(t,s); %Acceleration vs time
set(gca,’xlim’,[1 50]); %Scale to fit
grid; %Grids on
title (‘Acceleration vs time’);
xlabel(‘time(s)’);
ylabel(‘Acceleration’);
subplot(2, 1, 2); %Plotting bottom pane
plot(Fv, abs(FTs(Iv))*2,’red’); %FFT – Amplitude vs Frequency
grid
title (‘Fast fourier transform’);
xlabel(‘Frequency (Hz)’);
ylabel (‘Amplitude (m)’); dc, fft, amplitude MATLAB Answers — New Questions
How do I import my MATLAB path preferences when I install MATLAB on new computers?
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I have been able to import my desktop layouts manually by copying over the ___Layout.xml files that reside in the prefdir. However, I am not sure which prefdir file holds the fonts and keyboard shortcuts, so I have not been able to import those. Does anyone know which prefdir files are the applicable ones? I have tried copying over matlab.prf, but it doesn’t seem to have any effect. I have a new computer with Matlab 2024b newly installed on it and am looking to import preference settings (mainly desktop layouts, font settings, and keyboard shortcuts) that I use in my Matlab installations on other computers. I am aware of this previous thread, but it is not applicable here, because it assumes that a previous release of Matlab resides on the computer. That is not the case for me, as the intended computer is brand new.
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The installer for a compiled (using R2015b) app, that has been distributed to many customers for several years, is suddenly (noticed in last couple of days) throwing the above exception. This has happened from multiple sites, domestic and international. The installer is set up to download the MCR from MathWorks.
Is anyone aware of any MathWorks, Windows (10, 11) updates that might be causing this?
Thanks! It’s a bit urgent!!The installer for a compiled (using R2015b) app, that has been distributed to many customers for several years, is suddenly (noticed in last couple of days) throwing the above exception. This has happened from multiple sites, domestic and international. The installer is set up to download the MCR from MathWorks.
Is anyone aware of any MathWorks, Windows (10, 11) updates that might be causing this?
Thanks! It’s a bit urgent!! The installer for a compiled (using R2015b) app, that has been distributed to many customers for several years, is suddenly (noticed in last couple of days) throwing the above exception. This has happened from multiple sites, domestic and international. The installer is set up to download the MCR from MathWorks.
Is anyone aware of any MathWorks, Windows (10, 11) updates that might be causing this?
Thanks! It’s a bit urgent!! http, code 403, download server MATLAB Answers — New Questions
