Hello! I want to ask how to draw some pie charts in a map by using the m_map package? And move the pie charts to the corresponding latitude and longitude position? Thanks!Hello! I want to ask how to draw some pie charts in a map by using the m_map package? And move the pie charts to the corresponding latitude and longitude position? Thanks! Hello! I want to ask how to draw some pie charts in a map by using the m_map package? And move the pie charts to the corresponding latitude and longitude position? Thanks! pie chart, m_map MATLAB Answers — New Questions

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Just as the title says, I type all my requirements for HB in the command section but the editor doesn’t show any of my commands, when I try to save the page it just shows a blank command and editor sections, please helpJust as the title says, I type all my requirements for HB in the command section but the editor doesn’t show any of my commands, when I try to save the page it just shows a blank command and editor sections, please help Just as the title says, I type all my requirements for HB in the command section but the editor doesn’t show any of my commands, when I try to save the page it just shows a blank command and editor sections, please help help, homework MATLAB Answers — New Questions

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Please, what is the memory consumption of the recursive quad tree decomposition procedure [S = qtdecomp(I)] with respect to the input set I?Please, what is the memory consumption of the recursive quad tree decomposition procedure [S = qtdecomp(I)] with respect to the input set I? Please, what is the memory consumption of the recursive quad tree decomposition procedure [S = qtdecomp(I)] with respect to the input set I? image segmentation MATLAB Answers — New Questions

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I want to calculate the hour by hour average values of the FIT column of the attached file, I have tried the following code but I could not solve the problem, then I want to graph the average data vs time
data1 = readtable(‘datos.csv’, ‘VariableNamingRule’,’preserve’);
MyDateTime = data1.Date + data1.Time;
MyDateTime.Format = ‘yyyy-MM-dd HH:mm:ss’;

data2 = [data1(:,1) table(MyDateTime) data1(:,[3:end])];

%%
% Create a new column for the time (round up to the start of the hour)
data.Hour = dateshift(data2.MyDateTime, ‘start’, ‘hour’);

% Group by new time column and calculate average
averageData = groupsummary(data2.MyDateTime, ‘Hour’, ‘mean’, ‘value’);

% Show results
disp(averageData);

% Graph average hourly data
figure;
plot(averageData.Hour, averageData.mean_value, ‘-o’);
xlabel(‘Date & Time’);
ylabel(‘Average value Pressure’);
title(‘Average Hour by Hour’);
grid on;

% Save results to a new CSV file
writetable(averageData, ‘promedios_hora_a_hora.csv’)I want to calculate the hour by hour average values of the FIT column of the attached file, I have tried the following code but I could not solve the problem, then I want to graph the average data vs time
data1 = readtable(‘datos.csv’, ‘VariableNamingRule’,’preserve’);
MyDateTime = data1.Date + data1.Time;
MyDateTime.Format = ‘yyyy-MM-dd HH:mm:ss’;

data2 = [data1(:,1) table(MyDateTime) data1(:,[3:end])];

%%
% Create a new column for the time (round up to the start of the hour)
data.Hour = dateshift(data2.MyDateTime, ‘start’, ‘hour’);

% Group by new time column and calculate average
averageData = groupsummary(data2.MyDateTime, ‘Hour’, ‘mean’, ‘value’);

% Show results
disp(averageData);

% Graph average hourly data
figure;
plot(averageData.Hour, averageData.mean_value, ‘-o’);
xlabel(‘Date & Time’);
ylabel(‘Average value Pressure’);
title(‘Average Hour by Hour’);
grid on;

% Save results to a new CSV file
writetable(averageData, ‘promedios_hora_a_hora.csv’) I want to calculate the hour by hour average values of the FIT column of the attached file, I have tried the following code but I could not solve the problem, then I want to graph the average data vs time
data1 = readtable(‘datos.csv’, ‘VariableNamingRule’,’preserve’);
MyDateTime = data1.Date + data1.Time;
MyDateTime.Format = ‘yyyy-MM-dd HH:mm:ss’;

data2 = [data1(:,1) table(MyDateTime) data1(:,[3:end])];

%%
% Create a new column for the time (round up to the start of the hour)
data.Hour = dateshift(data2.MyDateTime, ‘start’, ‘hour’);

% Group by new time column and calculate average
averageData = groupsummary(data2.MyDateTime, ‘Hour’, ‘mean’, ‘value’);

% Show results
disp(averageData);

% Graph average hourly data
figure;
plot(averageData.Hour, averageData.mean_value, ‘-o’);
xlabel(‘Date & Time’);
ylabel(‘Average value Pressure’);
title(‘Average Hour by Hour’);
grid on;

% Save results to a new CSV file
writetable(averageData, ‘promedios_hora_a_hora.csv’) average data MATLAB Answers — New Questions

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File Exchange offers several procedures for Inverse Laplace Transfrom but – if I am not mistaken – all are for the case when F(s) is provided in an analitic form. This actually restricts their use to the input data in a noise-free form, close to 64 bits. Adding noise – which is always present in experimental data – usually makes Inverse Transfrom unstable even at quite high S/N ratio. Are there any published Matlab procedures to overcome the damaging presence of noise?
P.S. Historically, Povencher published a program, Contin, to perform this task, and there was (I can’t recall the link now) a Matlab implementation but when I tried it with a 1:500 S/N ratio it failed fro me.File Exchange offers several procedures for Inverse Laplace Transfrom but – if I am not mistaken – all are for the case when F(s) is provided in an analitic form. This actually restricts their use to the input data in a noise-free form, close to 64 bits. Adding noise – which is always present in experimental data – usually makes Inverse Transfrom unstable even at quite high S/N ratio. Are there any published Matlab procedures to overcome the damaging presence of noise?
P.S. Historically, Povencher published a program, Contin, to perform this task, and there was (I can’t recall the link now) a Matlab implementation but when I tried it with a 1:500 S/N ratio it failed fro me. File Exchange offers several procedures for Inverse Laplace Transfrom but – if I am not mistaken – all are for the case when F(s) is provided in an analitic form. This actually restricts their use to the input data in a noise-free form, close to 64 bits. Adding noise – which is always present in experimental data – usually makes Inverse Transfrom unstable even at quite high S/N ratio. Are there any published Matlab procedures to overcome the damaging presence of noise?
P.S. Historically, Povencher published a program, Contin, to perform this task, and there was (I can’t recall the link now) a Matlab implementation but when I tried it with a 1:500 S/N ratio it failed fro me. inverse laplace transform MATLAB Answers — New Questions

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I have a question the following: When i build the project (using SoC blockset) matlab 2022a without any erros. In addition, when i build it in Matlab 2024a, one big erros in block FIR is showed as the picture. Please, help me to resolve this errors.I have a question the following: When i build the project (using SoC blockset) matlab 2022a without any erros. In addition, when i build it in Matlab 2024a, one big erros in block FIR is showed as the picture. Please, help me to resolve this errors. I have a question the following: When i build the project (using SoC blockset) matlab 2022a without any erros. In addition, when i build it in Matlab 2024a, one big erros in block FIR is showed as the picture. Please, help me to resolve this errors. building fitter MATLAB Answers — New Questions

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I have picked the title but don’t know which direction to take it. Looking for any and all inspiration. I took the project as it sounded interesting when reading into it, but I’m a satellite novice, and my degree is in electronics.I have picked the title but don’t know which direction to take it. Looking for any and all inspiration. I took the project as it sounded interesting when reading into it, but I’m a satellite novice, and my degree is in electronics. I have picked the title but don’t know which direction to take it. Looking for any and all inspiration. I took the project as it sounded interesting when reading into it, but I’m a satellite novice, and my degree is in electronics. dissertation, satellite, communication, simulation MATLAB Answers — New Questions

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I have been at it for awhile but cannot figure it out, and I have also gotten lost in documentation for a few days now. I am attemping to train a Faster R-CNN model with a pretrained ResNet backbone. So far I have found documentation stating not to use lgraph and to use dlnetwork instead, so I attemtped it that way also and got the same error. More documentation stated not to use net=resnet50 either, and to use [net,classNames] = imagePretrainedNetwork instead. The issue is that I cannot figure out how to fit all of these pieces together. The dataset can be found here and downlaoded for free: https://www.flir.com/oem/adas/adas-dataset-form/
When the model attempts to run, it appears that it detects only 3 classes. I also used analyzeNetwork and network designer to look at the layers and it appears that the boxdeltas and R-CNN classification layers have the correct number of outputs. Any help is greatly appreciated!!

Here is the code so far (some parts generated by chatgpt and others taken from official documentation), but I have several versions of this with slight variations:
%% Define the custom read function
function imgOut = ensureRGB(imgIn)
[~, ~, numChannels] = size(imgIn);
if numChannels == 1
imgOut = repmat(imgIn, [1 1 3]);
else
imgOut = imgIn;
end
end
%% Define the paths
imageFolder = "C:UsersUserDesktopFLIR_Thermal_DatasetFLIR_ADAS_v2images_thermal_train";
annotationFolder = "C:UsersUserDocumentsMATLABtrainingData.mat";
matFile = "C:UsersUserDocumentsMATLABtrainingData.mat"; % MATLAB format annotations(there is a function to convert the original data into this .mat file if anyone needs it)
%% Load the training data from the MAT-file
load(matFile, ‘trainingData’);
% Shuffle the training data
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
%% Create image datastore with custom read function and specify file extensions
imds = imageDatastore(trainingData.imageFilename, …
‘ReadFcn’, @(filename) ensureRGB(imread(filename)), …
‘FileExtensions’, {‘.jpg’, ‘.jpeg’, ‘.png’, ‘.bmp’});
%% Create box label datastore
blds = boxLabelDatastore(trainingData(:, {‘bbox’, ‘label’}));
%% Combine the datastores
ds = combine(imds, blds);
%% Verify with a sample image
sampleImg = readimage(imds, 1);
[height, width, numChannels] = size(sampleImg);
disp([‘Sample Image Number of Channels: ‘, num2str(numChannels)]);
%% Define number of classes
numClasses = 16;
%% Define input image size and anchor boxes
inputImageSize = [512 640 3];
anchorBoxes = [32 32; 64 64; 128 128];
%% Load the ResNet-50 network
lgraph = layerGraph(resnet50);
% Specify the feature extraction layer
featureLayer = ‘activation_40_relu’;
% Create Faster R-CNN layers
dlnetwork = fasterRCNNLayers(inputImageSize, numClasses, anchorBoxes, lgraph, featureLayer);
%% Analyze the network to ensure all layers are correct
analyzeNetwork(dlnetwork);
%% Define training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 1e-4, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Shuffle’, ‘every-epoch’, …
‘Plots’, ‘training-progress’);
% Train the network
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

ERROR:
Training a Faster R-CNN Object Detector for the following object classes:
* car
* light
* person
Error using trainFasterRCNNObjectDetector (line 33)
Invalid network.

Error in
untitled (line 74)
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

Caused by:
Layer ‘boxDeltas’: The input size must be 1×1×12. This R-CNN box regression layer expects the third input dimension to be 4 times the number of object classes
the network should detect (3 classes). See the
documentation for more details about creating Fast or Faster R-CNN networks.
Layer ‘rcnnClassification’: The input size must be 1×1×4. The classification layer expects the third input dimension to be the number of object classes the
network should detect (3 classes) plus 1. The additional class is required for the "background" class. See the
documentation for more details about creating
Fast or Faster R-CNN networks.

So far I have tried:
1) using dlnetwork instead of lgraph
2) using [net,classNames] = imagePretrainedNetwork instead of net=resnet50
3) manually changing the layers in the designer
4) changing the channels from 1 to 3. (when loaded into my python environment the images had three channels, in MATLAB they showed 1)
5) resizing the imagesI have been at it for awhile but cannot figure it out, and I have also gotten lost in documentation for a few days now. I am attemping to train a Faster R-CNN model with a pretrained ResNet backbone. So far I have found documentation stating not to use lgraph and to use dlnetwork instead, so I attemtped it that way also and got the same error. More documentation stated not to use net=resnet50 either, and to use [net,classNames] = imagePretrainedNetwork instead. The issue is that I cannot figure out how to fit all of these pieces together. The dataset can be found here and downlaoded for free: https://www.flir.com/oem/adas/adas-dataset-form/
When the model attempts to run, it appears that it detects only 3 classes. I also used analyzeNetwork and network designer to look at the layers and it appears that the boxdeltas and R-CNN classification layers have the correct number of outputs. Any help is greatly appreciated!!

Here is the code so far (some parts generated by chatgpt and others taken from official documentation), but I have several versions of this with slight variations:
%% Define the custom read function
function imgOut = ensureRGB(imgIn)
[~, ~, numChannels] = size(imgIn);
if numChannels == 1
imgOut = repmat(imgIn, [1 1 3]);
else
imgOut = imgIn;
end
end
%% Define the paths
imageFolder = "C:UsersUserDesktopFLIR_Thermal_DatasetFLIR_ADAS_v2images_thermal_train";
annotationFolder = "C:UsersUserDocumentsMATLABtrainingData.mat";
matFile = "C:UsersUserDocumentsMATLABtrainingData.mat"; % MATLAB format annotations(there is a function to convert the original data into this .mat file if anyone needs it)
%% Load the training data from the MAT-file
load(matFile, ‘trainingData’);
% Shuffle the training data
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
%% Create image datastore with custom read function and specify file extensions
imds = imageDatastore(trainingData.imageFilename, …
‘ReadFcn’, @(filename) ensureRGB(imread(filename)), …
‘FileExtensions’, {‘.jpg’, ‘.jpeg’, ‘.png’, ‘.bmp’});
%% Create box label datastore
blds = boxLabelDatastore(trainingData(:, {‘bbox’, ‘label’}));
%% Combine the datastores
ds = combine(imds, blds);
%% Verify with a sample image
sampleImg = readimage(imds, 1);
[height, width, numChannels] = size(sampleImg);
disp([‘Sample Image Number of Channels: ‘, num2str(numChannels)]);
%% Define number of classes
numClasses = 16;
%% Define input image size and anchor boxes
inputImageSize = [512 640 3];
anchorBoxes = [32 32; 64 64; 128 128];
%% Load the ResNet-50 network
lgraph = layerGraph(resnet50);
% Specify the feature extraction layer
featureLayer = ‘activation_40_relu’;
% Create Faster R-CNN layers
dlnetwork = fasterRCNNLayers(inputImageSize, numClasses, anchorBoxes, lgraph, featureLayer);
%% Analyze the network to ensure all layers are correct
analyzeNetwork(dlnetwork);
%% Define training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 1e-4, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Shuffle’, ‘every-epoch’, …
‘Plots’, ‘training-progress’);
% Train the network
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

ERROR:
Training a Faster R-CNN Object Detector for the following object classes:
* car
* light
* person
Error using trainFasterRCNNObjectDetector (line 33)
Invalid network.

Error in
untitled (line 74)
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

Caused by:
Layer ‘boxDeltas’: The input size must be 1×1×12. This R-CNN box regression layer expects the third input dimension to be 4 times the number of object classes
the network should detect (3 classes). See the
documentation for more details about creating Fast or Faster R-CNN networks.
Layer ‘rcnnClassification’: The input size must be 1×1×4. The classification layer expects the third input dimension to be the number of object classes the
network should detect (3 classes) plus 1. The additional class is required for the "background" class. See the
documentation for more details about creating
Fast or Faster R-CNN networks.

So far I have tried:
1) using dlnetwork instead of lgraph
2) using [net,classNames] = imagePretrainedNetwork instead of net=resnet50
3) manually changing the layers in the designer
4) changing the channels from 1 to 3. (when loaded into my python environment the images had three channels, in MATLAB they showed 1)
5) resizing the images I have been at it for awhile but cannot figure it out, and I have also gotten lost in documentation for a few days now. I am attemping to train a Faster R-CNN model with a pretrained ResNet backbone. So far I have found documentation stating not to use lgraph and to use dlnetwork instead, so I attemtped it that way also and got the same error. More documentation stated not to use net=resnet50 either, and to use [net,classNames] = imagePretrainedNetwork instead. The issue is that I cannot figure out how to fit all of these pieces together. The dataset can be found here and downlaoded for free: https://www.flir.com/oem/adas/adas-dataset-form/
When the model attempts to run, it appears that it detects only 3 classes. I also used analyzeNetwork and network designer to look at the layers and it appears that the boxdeltas and R-CNN classification layers have the correct number of outputs. Any help is greatly appreciated!!

Here is the code so far (some parts generated by chatgpt and others taken from official documentation), but I have several versions of this with slight variations:
%% Define the custom read function
function imgOut = ensureRGB(imgIn)
[~, ~, numChannels] = size(imgIn);
if numChannels == 1
imgOut = repmat(imgIn, [1 1 3]);
else
imgOut = imgIn;
end
end
%% Define the paths
imageFolder = "C:UsersUserDesktopFLIR_Thermal_DatasetFLIR_ADAS_v2images_thermal_train";
annotationFolder = "C:UsersUserDocumentsMATLABtrainingData.mat";
matFile = "C:UsersUserDocumentsMATLABtrainingData.mat"; % MATLAB format annotations(there is a function to convert the original data into this .mat file if anyone needs it)
%% Load the training data from the MAT-file
load(matFile, ‘trainingData’);
% Shuffle the training data
rng(0);
shuffledIdx = randperm(height(trainingData));
trainingData = trainingData(shuffledIdx,:);
%% Create image datastore with custom read function and specify file extensions
imds = imageDatastore(trainingData.imageFilename, …
‘ReadFcn’, @(filename) ensureRGB(imread(filename)), …
‘FileExtensions’, {‘.jpg’, ‘.jpeg’, ‘.png’, ‘.bmp’});
%% Create box label datastore
blds = boxLabelDatastore(trainingData(:, {‘bbox’, ‘label’}));
%% Combine the datastores
ds = combine(imds, blds);
%% Verify with a sample image
sampleImg = readimage(imds, 1);
[height, width, numChannels] = size(sampleImg);
disp([‘Sample Image Number of Channels: ‘, num2str(numChannels)]);
%% Define number of classes
numClasses = 16;
%% Define input image size and anchor boxes
inputImageSize = [512 640 3];
anchorBoxes = [32 32; 64 64; 128 128];
%% Load the ResNet-50 network
lgraph = layerGraph(resnet50);
% Specify the feature extraction layer
featureLayer = ‘activation_40_relu’;
% Create Faster R-CNN layers
dlnetwork = fasterRCNNLayers(inputImageSize, numClasses, anchorBoxes, lgraph, featureLayer);
%% Analyze the network to ensure all layers are correct
analyzeNetwork(dlnetwork);
%% Define training options
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’, 16, …
‘InitialLearnRate’, 1e-4, …
‘MaxEpochs’, 10, …
‘Verbose’, true, …
‘Shuffle’, ‘every-epoch’, …
‘Plots’, ‘training-progress’);
% Train the network
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

ERROR:
Training a Faster R-CNN Object Detector for the following object classes:
* car
* light
* person
Error using trainFasterRCNNObjectDetector (line 33)
Invalid network.

Error in
untitled (line 74)
detector = trainFasterRCNNObjectDetector(ds, dlnetwork, options);

Caused by:
Layer ‘boxDeltas’: The input size must be 1×1×12. This R-CNN box regression layer expects the third input dimension to be 4 times the number of object classes
the network should detect (3 classes). See the
documentation for more details about creating Fast or Faster R-CNN networks.
Layer ‘rcnnClassification’: The input size must be 1×1×4. The classification layer expects the third input dimension to be the number of object classes the
network should detect (3 classes) plus 1. The additional class is required for the "background" class. See the
documentation for more details about creating
Fast or Faster R-CNN networks.

So far I have tried:
1) using dlnetwork instead of lgraph
2) using [net,classNames] = imagePretrainedNetwork instead of net=resnet50
3) manually changing the layers in the designer
4) changing the channels from 1 to 3. (when loaded into my python environment the images had three channels, in MATLAB they showed 1)
5) resizing the images faster r-cnn, deep learning, flir adas, resnet50 MATLAB Answers — New Questions

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Is there a way to extract the breast density of a mammography through Matlab code?Is there a way to extract the breast density of a mammography through Matlab code? Is there a way to extract the breast density of a mammography through Matlab code? density, breast, mammography MATLAB Answers — New Questions

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My mathlab online unable to open, everytime login it will automatically return to the login pageMy mathlab online unable to open, everytime login it will automatically return to the login page My mathlab online unable to open, everytime login it will automatically return to the login page #errorinmathlabonline MATLAB Answers — New Questions

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