## Month: June 2024

## Windows 11 will add two new snap layouts in the future

I hope that subsequent updates to Windows 11 will bring two new snap layouts.

I hope that subsequent updates to Windows 11 will bring two new snap layouts.Two new snap layouts Read More

## Dynamic JSON for Web activity

Hi all

I’m new to ADF.

I want to create a pipeline that executes a stored procedure from an Azure SQL database and sends an email with the result from the SP.

I followed these instructions to create a Logic App to send an email using a Web activity: How to send email – Azure Data Factory & Azure Synapse | Microsoft Learn

But now I’m struggling with sending the dynamic JSON the the Web Activity.

I have two variables: TextForMessage and Receiver.

Both are set in the Pipeline by a Set variable activities.

I added the Web activity, pointed to the Logic App, and added this for the Body, as explained in the link above:

“error”: {

“code”: “InvalidRequestContent”,

“message”: “The request content is not valid and could not be deserialized: ‘After parsing a value an unexpected character was encountered: “. Path ‘message’, line 2, position 17.’.”

},

“A … <truncated>

“name”: “URLBody”,

“value”: “json({n “message”:”{“”:”Text to send in the email”}”,n “receiver”:”mailaddress@domain”n})”

}

Hi all I’m new to ADF.I want to create a pipeline that executes a stored procedure from an Azure SQL database and sends an email with the result from the SP. I followed these instructions to create a Logic App to send an email using a Web activity: How to send email – Azure Data Factory & Azure Synapse | Microsoft Learn But now I’m struggling with sending the dynamic JSON the the Web Activity.I have two variables: TextForMessage and Receiver.Both are set in the Pipeline by a Set variable activities. I added the Web activity, pointed to the Logic App, and added this for the Body, as explained in the link above:{ “message”:”@{variables(‘TextForMessage’)}”, “receiver”:”@{variables(‘Receiver’)}”} I get an error, that the JSON is not formatted correctly:{“error”: {“code”: “InvalidRequestContent”,”message”: “The request content is not valid and could not be deserialized: ‘After parsing a value an unexpected character was encountered: “. Path ‘message’, line 2, position 17.’.”},”A … <truncated> To get the JOSN, I added another Set Variable activity and added the same expression to put together the JSON:{ “message”:”@{variables(‘TextForMessage’)}”, “receiver”:”@{variables(‘Receiver’)}”} This is the result (I replaced the content):{“name”: “URLBody”,”value”: “json({n “message”:”{“”:”Text to send in the email”}”,n “receiver”:”mailaddress@domain”n})”} I suppose that the expression in the Web activity generates the same output. What can I do to get a properly formatted JSON out of it? Any help is greatly appreciated. RegardsSalvatore Read More

## MDE Enrollment management

Hello

I enrolled my device through Intune for endpoint security , and everything is functioning correctly. However, I have a question regarding the MDE Enrollment status showing as N/A, despite it being managed by Intune. when i select the device

All my devices were enrolled in Intune using Autopilot. Please refer to the attached photo for more details.

in this case there no issue or i need to add some setting

thanks

Hello I enrolled my device through Intune for endpoint security , and everything is functioning correctly. However, I have a question regarding the MDE Enrollment status showing as N/A, despite it being managed by Intune. when i select the device All my devices were enrolled in Intune using Autopilot. Please refer to the attached photo for more details.in this case there no issue or i need to add some setting thanks Read More

## I’m looking for a best & bulk PDF converter for Mac and Windows 11

Hello, I’ve recently been looking for a best and batch PDF converter that works smoothly on both Mac and Windows 11. I need to convert PDF files to Word or Excel frequently, but the few programs I’ve tried are either not full-featured or the formatting converted is messy.

Since I use both Mac and Windows 11 computers, I especially need a **PDF converter for Mac and Windows 11** . So that no matter which computer I work on, I can easily work with PDF files without worrying about compatibility issues or differences in conversion quality. I wonder if anyone can recommend a PDF converter software that can maintain the original formatting and support both Mac and Windows?

Hello, I’ve recently been looking for a best and batch PDF converter that works smoothly on both Mac and Windows 11. I need to convert PDF files to Word or Excel frequently, but the few programs I’ve tried are either not full-featured or the formatting converted is messy. Since I use both Mac and Windows 11 computers, I especially need a PDF converter for Mac and Windows 11 . So that no matter which computer I work on, I can easily work with PDF files without worrying about compatibility issues or differences in conversion quality. I wonder if anyone can recommend a PDF converter software that can maintain the original formatting and support both Mac and Windows? Read More

## How can I implement Dual LSTM in matlab?

I want to implement dual LSTM network in matlab. How can i do it ? When I run this code, i get error as :

"Network: Invalid input layers. Network must have at most one sequence input layer".

How can i solve it? I would be grateful for your quick solution.

My objective is to train different types of features with seperate LSTM models and concatenate the outputs for fully connected layer to get single classification output.

Is it possible in matlab ?

inputSize1 = 4;

inputSize2 = 20;

numClasses = 5;

layers1 = [ …

sequenceInputLayer(inputSize1, ‘Name’, ‘input1’)

lstmLayer(64, ‘OutputMode’, ‘last’, ‘Name’, ‘lstm1’)

dropoutLayer(0.2, ‘Name’, ‘dropout1’)

fullyConnectedLayer(64, ‘Name’, ‘fc1’)];

layers2 = [ …

sequenceInputLayer(inputSize2, ‘Name’, ‘input2’)

lstmLayer(64, ‘OutputMode’, ‘last’, ‘Name’, ‘lstm2’)

dropoutLayer(0.2, ‘Name’, ‘dropout2’)

fullyConnectedLayer(64, ‘Name’, ‘fc2’)];

combinedLayers = [ …

concatenationLayer(1, 2, ‘Name’, ‘concat’)

fullyConnectedLayer(64, ‘Name’, ‘fc_combined’)

reluLayer(‘Name’, ‘relu’)

fullyConnectedLayer(numClasses, ‘Name’, ‘fc_final’)

softmaxLayer(‘Name’, ‘softmax’)

classificationLayer(‘Name’, ‘classification’)];

lgraph = layerGraph();

lgraph = addLayers(lgraph, layers1);

lgraph = addLayers(lgraph, layers2);

lgraph = addLayers(lgraph, combinedLayers);

lgraph = connectLayers(lgraph, ‘fc1’, ‘concat/in1’);

lgraph = connectLayers(lgraph, ‘fc2’, ‘concat/in2’);

plot(lgraph);

options = trainingOptions(‘adam’, …

‘InitialLearnRate’, 0.001, …

‘MaxEpochs’, 10, …

‘MiniBatchSize’, 32, …

‘Shuffle’, ‘once’, …

‘Plots’, ‘training-progress’, …

‘Verbose’, false);

net = trainNetwork(Normalized_data, y_train, lgraph, options);I want to implement dual LSTM network in matlab. How can i do it ? When I run this code, i get error as :

"Network: Invalid input layers. Network must have at most one sequence input layer".

How can i solve it? I would be grateful for your quick solution.

My objective is to train different types of features with seperate LSTM models and concatenate the outputs for fully connected layer to get single classification output.

Is it possible in matlab ?

inputSize1 = 4;

inputSize2 = 20;

numClasses = 5;

layers1 = [ …

sequenceInputLayer(inputSize1, ‘Name’, ‘input1’)

lstmLayer(64, ‘OutputMode’, ‘last’, ‘Name’, ‘lstm1’)

dropoutLayer(0.2, ‘Name’, ‘dropout1’)

fullyConnectedLayer(64, ‘Name’, ‘fc1’)];

layers2 = [ …

sequenceInputLayer(inputSize2, ‘Name’, ‘input2’)

lstmLayer(64, ‘OutputMode’, ‘last’, ‘Name’, ‘lstm2’)

dropoutLayer(0.2, ‘Name’, ‘dropout2’)

fullyConnectedLayer(64, ‘Name’, ‘fc2’)];

combinedLayers = [ …

concatenationLayer(1, 2, ‘Name’, ‘concat’)

fullyConnectedLayer(64, ‘Name’, ‘fc_combined’)

reluLayer(‘Name’, ‘relu’)

fullyConnectedLayer(numClasses, ‘Name’, ‘fc_final’)

softmaxLayer(‘Name’, ‘softmax’)

classificationLayer(‘Name’, ‘classification’)];

lgraph = layerGraph();

lgraph = addLayers(lgraph, layers1);

lgraph = addLayers(lgraph, layers2);

lgraph = addLayers(lgraph, combinedLayers);

lgraph = connectLayers(lgraph, ‘fc1’, ‘concat/in1’);

lgraph = connectLayers(lgraph, ‘fc2’, ‘concat/in2’);

plot(lgraph);

options = trainingOptions(‘adam’, …

‘InitialLearnRate’, 0.001, …

‘MaxEpochs’, 10, …

‘MiniBatchSize’, 32, …

‘Shuffle’, ‘once’, …

‘Plots’, ‘training-progress’, …

‘Verbose’, false);

net = trainNetwork(Normalized_data, y_train, lgraph, options); I want to implement dual LSTM network in matlab. How can i do it ? When I run this code, i get error as :

"Network: Invalid input layers. Network must have at most one sequence input layer".

How can i solve it? I would be grateful for your quick solution.

My objective is to train different types of features with seperate LSTM models and concatenate the outputs for fully connected layer to get single classification output.

Is it possible in matlab ?

inputSize1 = 4;

inputSize2 = 20;

numClasses = 5;

layers1 = [ …

sequenceInputLayer(inputSize1, ‘Name’, ‘input1’)

lstmLayer(64, ‘OutputMode’, ‘last’, ‘Name’, ‘lstm1’)

dropoutLayer(0.2, ‘Name’, ‘dropout1’)

fullyConnectedLayer(64, ‘Name’, ‘fc1’)];

layers2 = [ …

sequenceInputLayer(inputSize2, ‘Name’, ‘input2’)

lstmLayer(64, ‘OutputMode’, ‘last’, ‘Name’, ‘lstm2’)

dropoutLayer(0.2, ‘Name’, ‘dropout2’)

fullyConnectedLayer(64, ‘Name’, ‘fc2’)];

combinedLayers = [ …

concatenationLayer(1, 2, ‘Name’, ‘concat’)

fullyConnectedLayer(64, ‘Name’, ‘fc_combined’)

reluLayer(‘Name’, ‘relu’)

fullyConnectedLayer(numClasses, ‘Name’, ‘fc_final’)

softmaxLayer(‘Name’, ‘softmax’)

classificationLayer(‘Name’, ‘classification’)];

lgraph = layerGraph();

lgraph = addLayers(lgraph, layers1);

lgraph = addLayers(lgraph, layers2);

lgraph = addLayers(lgraph, combinedLayers);

lgraph = connectLayers(lgraph, ‘fc1’, ‘concat/in1’);

lgraph = connectLayers(lgraph, ‘fc2’, ‘concat/in2’);

plot(lgraph);

options = trainingOptions(‘adam’, …

‘InitialLearnRate’, 0.001, …

‘MaxEpochs’, 10, …

‘MiniBatchSize’, 32, …

‘Shuffle’, ‘once’, …

‘Plots’, ‘training-progress’, …

‘Verbose’, false);

net = trainNetwork(Normalized_data, y_train, lgraph, options); lstm, dual, concatenation of layers MATLAB Answers — New Questions

## Documentation for HDL code generated

Is there any way i generate documentation or comments along with the hdl code using hdl coder?Is there any way i generate documentation or comments along with the hdl code using hdl coder? Is there any way i generate documentation or comments along with the hdl code using hdl coder? hdlcoder, dsp hdl toolbox, hdlverifier MATLAB Answers — New Questions

## fail to start parallel pool

picturepicture picture transferred MATLAB Answers — New Questions

## i would like to know why my code doesn’t work please, the data load factors is supposed to be taken from a .txt file from DASL website

function load_factors_analysis()

% Load the data (assuming ‘load_factors.csv’ is in the working directory)

data = load(‘load_factors.csv’);

% Get central and dispersion measurements for each variable

[mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data);

% Create histograms and/or bar graphs for each variable

histograms_and_bar_graphs(data);

% Create a boxplot (assuming data(:,1) is continuous and data(:,2) is categorical)

boxplot_categorical(data(:, 1), data(:, 2));

% Perform a regression analysis (assuming data(:,1) and data(:,2) are continuous)

regression_analysis(data(:, 1), data(:, 2));

% Discuss the results (replace with your actual analysis)

discussion(data, mean, median, stddev, min_val, max_val);

end

function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)

% Get central and dispersion statistics

mean = mean(data);

median = median(data);

stddev = std(data);

min_val = min(data);

max_val = max(data);

% Return the calculated values

return (mean, median; stddev; min_val; max_val);

end

function histograms_and_bar_graphs(data)

for i = 1:size(data, 2)

if isnumeric(data(:, i))

h = histogram(data(:, i));

title(strcat(‘Histogram of ‘, data(1, i)));

show(h);

else

b = bar(unique(data(:, i)), count(data(:, i)));

title(strcat(‘Bar graph of ‘, data(1, i)));

show(b);

end

end

end

function boxplot_categorical(data_continuous, data_categorical)

% Create a boxplot for continuous data divided by categorical classes

boxplot(data_continuous, data_categorical);

end

function regression_analysis(data1, data2)

% Perform linear regression

[b, a, rsq, pval, se] = regress(data1, data2);

% Print the results

disp(‘Coefficients:’);

disp([b, a]);

disp(‘R-squared:’);

disp(rsq);

disp(‘P-value:’);

disp(pval);

disp(‘Standard error:’);

disp(se);

end

function discussion(~, ~, ~, ~, ~, ~)

% Replace this with your analysis of the data and calculated statistics

disp(‘This is a placeholder for your data analysis discussion.’);

disp(‘Here, you would discuss insights from the central tendency’);

disp(‘(mean, median), dispersion (standard deviation),’);

disp(‘minimum and maximum values, and any relationships found’);

disp(‘between variables using the boxplot and regression analysis.’);

endfunction load_factors_analysis()

% Load the data (assuming ‘load_factors.csv’ is in the working directory)

data = load(‘load_factors.csv’);

% Get central and dispersion measurements for each variable

[mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data);

% Create histograms and/or bar graphs for each variable

histograms_and_bar_graphs(data);

% Create a boxplot (assuming data(:,1) is continuous and data(:,2) is categorical)

boxplot_categorical(data(:, 1), data(:, 2));

% Perform a regression analysis (assuming data(:,1) and data(:,2) are continuous)

regression_analysis(data(:, 1), data(:, 2));

% Discuss the results (replace with your actual analysis)

discussion(data, mean, median, stddev, min_val, max_val);

end

function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)

% Get central and dispersion statistics

mean = mean(data);

median = median(data);

stddev = std(data);

min_val = min(data);

max_val = max(data);

% Return the calculated values

return (mean, median; stddev; min_val; max_val);

end

function histograms_and_bar_graphs(data)

for i = 1:size(data, 2)

if isnumeric(data(:, i))

h = histogram(data(:, i));

title(strcat(‘Histogram of ‘, data(1, i)));

show(h);

else

b = bar(unique(data(:, i)), count(data(:, i)));

title(strcat(‘Bar graph of ‘, data(1, i)));

show(b);

end

end

end

function boxplot_categorical(data_continuous, data_categorical)

% Create a boxplot for continuous data divided by categorical classes

boxplot(data_continuous, data_categorical);

end

function regression_analysis(data1, data2)

% Perform linear regression

[b, a, rsq, pval, se] = regress(data1, data2);

% Print the results

disp(‘Coefficients:’);

disp([b, a]);

disp(‘R-squared:’);

disp(rsq);

disp(‘P-value:’);

disp(pval);

disp(‘Standard error:’);

disp(se);

end

function discussion(~, ~, ~, ~, ~, ~)

% Replace this with your analysis of the data and calculated statistics

disp(‘This is a placeholder for your data analysis discussion.’);

disp(‘Here, you would discuss insights from the central tendency’);

disp(‘(mean, median), dispersion (standard deviation),’);

disp(‘minimum and maximum values, and any relationships found’);

disp(‘between variables using the boxplot and regression analysis.’);

end function load_factors_analysis()

% Load the data (assuming ‘load_factors.csv’ is in the working directory)

data = load(‘load_factors.csv’);

% Get central and dispersion measurements for each variable

[mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data);

% Create histograms and/or bar graphs for each variable

histograms_and_bar_graphs(data);

% Create a boxplot (assuming data(:,1) is continuous and data(:,2) is categorical)

boxplot_categorical(data(:, 1), data(:, 2));

% Perform a regression analysis (assuming data(:,1) and data(:,2) are continuous)

regression_analysis(data(:, 1), data(:, 2));

% Discuss the results (replace with your actual analysis)

discussion(data, mean, median, stddev, min_val, max_val);

end

function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)

% Get central and dispersion statistics

mean = mean(data);

median = median(data);

stddev = std(data);

min_val = min(data);

max_val = max(data);

% Return the calculated values

return (mean, median; stddev; min_val; max_val);

end

function histograms_and_bar_graphs(data)

for i = 1:size(data, 2)

if isnumeric(data(:, i))

h = histogram(data(:, i));

title(strcat(‘Histogram of ‘, data(1, i)));

show(h);

else

b = bar(unique(data(:, i)), count(data(:, i)));

title(strcat(‘Bar graph of ‘, data(1, i)));

show(b);

end

end

end

function boxplot_categorical(data_continuous, data_categorical)

% Create a boxplot for continuous data divided by categorical classes

boxplot(data_continuous, data_categorical);

end

function regression_analysis(data1, data2)

% Perform linear regression

[b, a, rsq, pval, se] = regress(data1, data2);

% Print the results

disp(‘Coefficients:’);

disp([b, a]);

disp(‘R-squared:’);

disp(rsq);

disp(‘P-value:’);

disp(pval);

disp(‘Standard error:’);

disp(se);

end

function discussion(~, ~, ~, ~, ~, ~)

% Replace this with your analysis of the data and calculated statistics

disp(‘This is a placeholder for your data analysis discussion.’);

disp(‘Here, you would discuss insights from the central tendency’);

disp(‘(mean, median), dispersion (standard deviation),’);

disp(‘minimum and maximum values, and any relationships found’);

disp(‘between variables using the boxplot and regression analysis.’);

end statistics, code issues MATLAB Answers — New Questions

## Insufficient number of outputs from right hand side of equal sign to satisfy assignment.

[app.Sis.Slippage]

ans =

Columns 1 through 17

0 9 9 9 6 9 6 6 6 0 0 0 0 0 0 0 0

Columns 18 through 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column 35

0

b1 =

Columns 1 through 17

0 90 90 90 60 90 60 60 60 0 0 0 0 0 0 0 0

Columns 18 through 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column 35

0

[app.Sis.Slippage]=b1

Insufficient number of outputs from right hand side of equal sign to satisfy assignment.[app.Sis.Slippage]

ans =

Columns 1 through 17

0 9 9 9 6 9 6 6 6 0 0 0 0 0 0 0 0

Columns 18 through 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column 35

0

b1 =

Columns 1 through 17

0 90 90 90 60 90 60 60 60 0 0 0 0 0 0 0 0

Columns 18 through 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column 35

0

[app.Sis.Slippage]=b1

Insufficient number of outputs from right hand side of equal sign to satisfy assignment. [app.Sis.Slippage]

ans =

Columns 1 through 17

0 9 9 9 6 9 6 6 6 0 0 0 0 0 0 0 0

Columns 18 through 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column 35

0

b1 =

Columns 1 through 17

0 90 90 90 60 90 60 60 60 0 0 0 0 0 0 0 0

Columns 18 through 34

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Column 35

0

[app.Sis.Slippage]=b1

Insufficient number of outputs from right hand side of equal sign to satisfy assignment. insufficient number of outputs from right hand sid MATLAB Answers — New Questions

## Error when click “Send to simulink” Button on Bikesim

When click "Send to simulink" Button on Bikesim, i receive and signal like this. How can I solve this problem?

Thanks for your helps.When click "Send to simulink" Button on Bikesim, i receive and signal like this. How can I solve this problem?

Thanks for your helps. When click "Send to simulink" Button on Bikesim, i receive and signal like this. How can I solve this problem?

Thanks for your helps. bikesim, cannot find matlab MATLAB Answers — New Questions

## multiplying polynomials with conv

use matlab to carry out the following multiplication of polynomials

(x+1.4)(x-0.4)x(x+0.6)(x-1.4)

heres what i did—

a = [1,1.4];

b = [1, -.4];

c = [1, 0];

d = [1, .6];

e = [1, -1.4];

p = conv(a, conv(b, conv(c, conv(d, e))))

it gave me the correct result but its tagging an extra zero onto the end of my result. i assume this means its coming out as one higher power then it is supposed to be with a "0" constant. anyone see any problems here?

my answer is supposed to come out as

p = [1, 0.2, -2.2, -3.92, .4704]

while my work is giving me

p = [1, 0.2, -2.2, -3.92, .4704, 0]

so somewhere it is giving me an extra power of x and im not sure whyuse matlab to carry out the following multiplication of polynomials

(x+1.4)(x-0.4)x(x+0.6)(x-1.4)

heres what i did—

a = [1,1.4];

b = [1, -.4];

c = [1, 0];

d = [1, .6];

e = [1, -1.4];

p = conv(a, conv(b, conv(c, conv(d, e))))

it gave me the correct result but its tagging an extra zero onto the end of my result. i assume this means its coming out as one higher power then it is supposed to be with a "0" constant. anyone see any problems here?

my answer is supposed to come out as

p = [1, 0.2, -2.2, -3.92, .4704]

while my work is giving me

p = [1, 0.2, -2.2, -3.92, .4704, 0]

so somewhere it is giving me an extra power of x and im not sure why use matlab to carry out the following multiplication of polynomials

(x+1.4)(x-0.4)x(x+0.6)(x-1.4)

heres what i did—

a = [1,1.4];

b = [1, -.4];

c = [1, 0];

d = [1, .6];

e = [1, -1.4];

p = conv(a, conv(b, conv(c, conv(d, e))))

it gave me the correct result but its tagging an extra zero onto the end of my result. i assume this means its coming out as one higher power then it is supposed to be with a "0" constant. anyone see any problems here?

my answer is supposed to come out as

p = [1, 0.2, -2.2, -3.92, .4704]

while my work is giving me

p = [1, 0.2, -2.2, -3.92, .4704, 0]

so somewhere it is giving me an extra power of x and im not sure why polynomials MATLAB Answers — New Questions

## ECG Segmentation and Classification using PQRST

Hello MATLAB community,

I’m working on ECG classification using MATLAB, and my data is stored in CSV format with 13 columns (time + 12 leads). I’m interested in extracting PQRST segments using the segmentation method described in this MATLAB code: ECG Segmentation and Filtering.

I have a few questions regarding preprocessing:

Should I apply low-pass filters to all recordings to enhance signal quality?

I intend to retain various types of noise in the data to ensure model robustness. What are your recommendations on managing noise while preserving the integrity of PQRST segments?

Additionally, I’m curious about segmentation approach:

Should I perform segmentation separately for each of the 12 leads, or should I merge them before segmentation? What would be the pros and cons of each approach?

Here an example of an ECG data:

Any insights or suggestions on adapting the segmentation method and handling data preprocessing would be greatly appreciated. Thank you!Hello MATLAB community,

I’m working on ECG classification using MATLAB, and my data is stored in CSV format with 13 columns (time + 12 leads). I’m interested in extracting PQRST segments using the segmentation method described in this MATLAB code: ECG Segmentation and Filtering.

I have a few questions regarding preprocessing:

Should I apply low-pass filters to all recordings to enhance signal quality?

I intend to retain various types of noise in the data to ensure model robustness. What are your recommendations on managing noise while preserving the integrity of PQRST segments?

Additionally, I’m curious about segmentation approach:

Should I perform segmentation separately for each of the 12 leads, or should I merge them before segmentation? What would be the pros and cons of each approach?

Here an example of an ECG data:

Any insights or suggestions on adapting the segmentation method and handling data preprocessing would be greatly appreciated. Thank you! Hello MATLAB community,

I’m working on ECG classification using MATLAB, and my data is stored in CSV format with 13 columns (time + 12 leads). I’m interested in extracting PQRST segments using the segmentation method described in this MATLAB code: ECG Segmentation and Filtering.

I have a few questions regarding preprocessing:

Should I apply low-pass filters to all recordings to enhance signal quality?

I intend to retain various types of noise in the data to ensure model robustness. What are your recommendations on managing noise while preserving the integrity of PQRST segments?

Additionally, I’m curious about segmentation approach:

Should I perform segmentation separately for each of the 12 leads, or should I merge them before segmentation? What would be the pros and cons of each approach?

Here an example of an ECG data:

Any insights or suggestions on adapting the segmentation method and handling data preprocessing would be greatly appreciated. Thank you! ecg, ptb-xl, signal processing, pqrst, segmentation MATLAB Answers — New Questions

## How to solve Simultaneous equations

3x+2y=0

x+y=43x+2y=0

x+y=4 3x+2y=0

x+y=4 sim MATLAB Answers — New Questions

## What could be the reason why my model does not give accurate results as I planned?

Hi everyone. First of all, thank you for your time. This will be my first question on the matlab platform. Please excuse me if I have any mistakes. If you understand the problem, you can already find the necessary files in the zip folder. If you want to view images in pgm format, you can use the GIMP application.

I am planning to design a MLP image processing model without using any toolbox.

I plan to train my model by reading one by one 32×30 scale images in the CMU face images dataset I obtained from the internet and then continue with testing process.

(I use imread function that is provided by MATLAB)

INPUT is a cell vector which contains image matrixes in each element. So each element represents an image actually. While processing samples one by one I get its images as column vector.

Here is the code for file operations and image reading:

clc;clear;close;

%*****************Reading Images**************

myFolder = ”; %% Images folder path

if ~isfolder(myFolder) %% Checking if the folder doesn’t exist

errorMessage = sprintf(‘Error: The following folder does not exist:n%snPlease specify a new folder.’, myFolder);

uiwait(warndlg(errorMessage));

myFolder = uigetdir(); % Ask for a new one.

if myFolder == 0

% User clicked Cancel

return;

end

end

filePattern = fullfile(myFolder, ‘*.pgm’);

theFiles = dir(filePattern);

% Define the number of image files in the folder

numImages = length(theFiles);

% Initialize a cell array to store the images

INPUT = cell(numImages, 1);

for k = 1 : length(theFiles)

baseFileName = theFiles(k).name;

fullFileName = fullfile(theFiles(k).folder, baseFileName);

INPUT{k} = imread(fullFileName);

imshow(INPUT{k}); % Display image.

drawnow; % Force display to update immediately.

end

%***********************************************

It is a user-interactive model, and firstly I get the number of:

Hidden layers:

Neurons in each hidden layer: (neuron numbers will be the same for each hidden layer)

Max iteration:

of my model from user.

Other definitions are shown in below code. I define NumberOfInput as 960 which comes from 32×30 because Weight matrix’s size between input layer and first hidden layer needed to adjusted in that way.

Weight matrix values are assigned randomly.

My model should return 0 if person doesn’t wear sunglasses and 1 if person wears with high accuracy. So it is scaler and there will be 1 output obviously.

I studied about MLP models and I found that finding perfect variables is a hard subject in machine learning and it depends on application and tests. So I defined my ETA with various values: 0.01,0.02,0.05,0.2,0.5….

In this type of model input to neurons are defined as netH and output of these neurons are defined as H except last connections. In there they become netO and O.

Also sigma size is defined in order to use after forward state (backward state starts).

My model is an example of Supervised Learning and it needs some outputs for training images like mentioned DESIRED as below. Images inside Model_Training are located as open–>sunglasses–>open–>sunglasses… so I decided to define desired with this order as shown below.

Here is the code:

%*******************VARİABLES*******************

NumberOfPatterns=numImages;

NumberOfInput=960;

NumberOfOutput=1;

LearningRate_ETA=0.5;

while true

NofLayers=input("Layer number: "); % Hidden layer number

Nofneurons=input("Neuron number: "); % Neuron number of each hidden layer

Max_iteration=input("Max iteration number: "); % Max iteration

if(Nofneurons<=0 || NofLayers<=0 || Max_iteration<=0)

fprintf("These values can’t be accepted !");

fprintf("nPlease enter again");

else

break;

end

end

W = cell(NofLayers+1,1);

H=cell(NofLayers,1);

sigma=cell(NofLayers+1,1);

%***********************************************

% Random values are assigned to Weights

for i=1:NofLayers+1

if i==1

W{i}=rand(NumberOfInput,Nofneurons);

elseif i==NofLayers+1

W{i}=rand(Nofneurons,NumberOfOutput);

else

W{i}=rand(Nofneurons,Nofneurons);

end

end

%***********************************************

DESIRED=zeros(NumberOfPatterns,1);

%****************Adjusting Desired Results******

%Training images are located in order. Ex:

%A_open.pgn

%A_sunglasses.pgn

for i=1:NumberOfPatterns

if(mod(i,2)==1)

DESIRED(i)=0;

else

DESIRED(i)=1;

end

end

%************************************************

Right now processing starts. I need to mention that I use sigmoid function as activation function.

In order not to prolong the topic further I will share directly code in here.

%***********************Processing***************

for a=1:Max_iteration

totalerr=0;

for i = 1:NumberOfPatterns

ImageVector = reshape(INPUT{i}, [], 1);

X = double(ImageVector);

for lay=1:NofLayers+1

if(lay==1) %First connections

netH=W{lay}’*X;

H{lay}=sigmoid(netH);%%%

elseif (lay==NofLayers+1) %Last connections

netO=W{lay}’*H{lay-1};

O=sigmoid(netO);

else % between connections layers

netH=W{lay}’*H{lay-1}; %

H{lay}=sigmoid(netH);%

end

end

err=DESIRED(i)-O;

for j=1:NumberOfOutput

sigma{NofLayers+1}=err*O(j)*(1-O(j)); %Last sigma value

end

for l=1:NofLayers

for k=1:Nofneurons

[rowsigma colsigma]=size(sigma{NofLayers-l+2});

[rowW colsW]=size(W{NofLayers-l+2}(k,:));

%These conditions satisfies proper matrix multiplciation

if(colsigma==rowW)

sigma{NofLayers-l+1}=sigma{NofLayers-l+2}*W{NofLayers-l+2}(k,:) *H{NofLayers+1-l}(k)*(1-H{NofLayers+1-l}(k));

else

sigma{NofLayers-l+1}=sigma{NofLayers-l+2}*W{NofLayers-l+2}(k,:)’*H{NofLayers+1-l}(k)*(1-H{NofLayers+1-l}(k));

end

end

end

for z=1:NofLayers+1

%Weights are updated at this part

if((NofLayers+2-z)==1)

W{NofLayers+2-z}=W{NofLayers+2-z}+LearningRate_ETA*X*sigma{NofLayers+2-z};

else

W{NofLayers+2-z}=W{NofLayers+2-z}+LearningRate_ETA*H{NofLayers+1-z}*sigma{NofLayers+2-z};

end

end

totalerr=totalerr+0.5*err^2;

end

cost(a)=totalerr;

end

plot(cost);

%%*****************Test Case********************

%Getting test image address from user

fileFilter = ‘*.pgm’;

[filename, pathname] = uigetfile(fileFilter, ‘Select a PGM file’, ”);

if isequal(filename, 0)

disp(‘Program has stopped’);

else

fullFilePath = fullfile(pathname, filename);

end

%**************Test Sample Operations*******

testSample=imread(fullFilePath); %

testSample=reshape(testSample,[],1);

X=double(testSample);

for lay=1:NofLayers+1

if(lay==1) %First connections

netH=W{lay}’*X;

H{lay}=sigmoid(netH);

elseif (lay==NofLayers+1) %Last connections

netO=W{lay}’*H{lay-1};

Out=round(sigmoid(netO));

else % between connections layers

netH=W{lay}’*H{lay-1};

H{lay}=sigmoid(netH);

end

end

fprintf(‘Result is: %dn’, Out);

%**********************Helper Functions*********

%Sigmoid Activation Function

function y = sigmoid(x)

y = 1 ./ (1 + exp(-x));

end

You can run and test it with the files that provided in zip file. In this kind of model as I know I need to try it with high number of layer and neuron. I tried with 4-20 5-30 5-35 … Generally it returns 1 and this is the problem that I am struggling with.

If you can give any comment, feedback I would appreciate it. Again thank you for giving a time.Hi everyone. First of all, thank you for your time. This will be my first question on the matlab platform. Please excuse me if I have any mistakes. If you understand the problem, you can already find the necessary files in the zip folder. If you want to view images in pgm format, you can use the GIMP application.

I am planning to design a MLP image processing model without using any toolbox.

I plan to train my model by reading one by one 32×30 scale images in the CMU face images dataset I obtained from the internet and then continue with testing process.

(I use imread function that is provided by MATLAB)

INPUT is a cell vector which contains image matrixes in each element. So each element represents an image actually. While processing samples one by one I get its images as column vector.

Here is the code for file operations and image reading:

clc;clear;close;

%*****************Reading Images**************

myFolder = ”; %% Images folder path

if ~isfolder(myFolder) %% Checking if the folder doesn’t exist

errorMessage = sprintf(‘Error: The following folder does not exist:n%snPlease specify a new folder.’, myFolder);

uiwait(warndlg(errorMessage));

myFolder = uigetdir(); % Ask for a new one.

if myFolder == 0

% User clicked Cancel

return;

end

end

filePattern = fullfile(myFolder, ‘*.pgm’);

theFiles = dir(filePattern);

% Define the number of image files in the folder

numImages = length(theFiles);

% Initialize a cell array to store the images

INPUT = cell(numImages, 1);

for k = 1 : length(theFiles)

baseFileName = theFiles(k).name;

fullFileName = fullfile(theFiles(k).folder, baseFileName);

INPUT{k} = imread(fullFileName);

imshow(INPUT{k}); % Display image.

drawnow; % Force display to update immediately.

end

%***********************************************

It is a user-interactive model, and firstly I get the number of:

Hidden layers:

Neurons in each hidden layer: (neuron numbers will be the same for each hidden layer)

Max iteration:

of my model from user.

Other definitions are shown in below code. I define NumberOfInput as 960 which comes from 32×30 because Weight matrix’s size between input layer and first hidden layer needed to adjusted in that way.

Weight matrix values are assigned randomly.

My model should return 0 if person doesn’t wear sunglasses and 1 if person wears with high accuracy. So it is scaler and there will be 1 output obviously.

I studied about MLP models and I found that finding perfect variables is a hard subject in machine learning and it depends on application and tests. So I defined my ETA with various values: 0.01,0.02,0.05,0.2,0.5….

In this type of model input to neurons are defined as netH and output of these neurons are defined as H except last connections. In there they become netO and O.

Also sigma size is defined in order to use after forward state (backward state starts).

My model is an example of Supervised Learning and it needs some outputs for training images like mentioned DESIRED as below. Images inside Model_Training are located as open–>sunglasses–>open–>sunglasses… so I decided to define desired with this order as shown below.

Here is the code:

%*******************VARİABLES*******************

NumberOfPatterns=numImages;

NumberOfInput=960;

NumberOfOutput=1;

LearningRate_ETA=0.5;

while true

NofLayers=input("Layer number: "); % Hidden layer number

Nofneurons=input("Neuron number: "); % Neuron number of each hidden layer

Max_iteration=input("Max iteration number: "); % Max iteration

if(Nofneurons<=0 || NofLayers<=0 || Max_iteration<=0)

fprintf("These values can’t be accepted !");

fprintf("nPlease enter again");

else

break;

end

end

W = cell(NofLayers+1,1);

H=cell(NofLayers,1);

sigma=cell(NofLayers+1,1);

%***********************************************

% Random values are assigned to Weights

for i=1:NofLayers+1

if i==1

W{i}=rand(NumberOfInput,Nofneurons);

elseif i==NofLayers+1

W{i}=rand(Nofneurons,NumberOfOutput);

else

W{i}=rand(Nofneurons,Nofneurons);

end

end

%***********************************************

DESIRED=zeros(NumberOfPatterns,1);

%****************Adjusting Desired Results******

%Training images are located in order. Ex:

%A_open.pgn

%A_sunglasses.pgn

for i=1:NumberOfPatterns

if(mod(i,2)==1)

DESIRED(i)=0;

else

DESIRED(i)=1;

end

end

%************************************************

Right now processing starts. I need to mention that I use sigmoid function as activation function.

In order not to prolong the topic further I will share directly code in here.

%***********************Processing***************

for a=1:Max_iteration

totalerr=0;

for i = 1:NumberOfPatterns

ImageVector = reshape(INPUT{i}, [], 1);

X = double(ImageVector);

for lay=1:NofLayers+1

if(lay==1) %First connections

netH=W{lay}’*X;

H{lay}=sigmoid(netH);%%%

elseif (lay==NofLayers+1) %Last connections

netO=W{lay}’*H{lay-1};

O=sigmoid(netO);

else % between connections layers

netH=W{lay}’*H{lay-1}; %

H{lay}=sigmoid(netH);%

end

end

err=DESIRED(i)-O;

for j=1:NumberOfOutput

sigma{NofLayers+1}=err*O(j)*(1-O(j)); %Last sigma value

end

for l=1:NofLayers

for k=1:Nofneurons

[rowsigma colsigma]=size(sigma{NofLayers-l+2});

[rowW colsW]=size(W{NofLayers-l+2}(k,:));

%These conditions satisfies proper matrix multiplciation

if(colsigma==rowW)

sigma{NofLayers-l+1}=sigma{NofLayers-l+2}*W{NofLayers-l+2}(k,:) *H{NofLayers+1-l}(k)*(1-H{NofLayers+1-l}(k));

else

sigma{NofLayers-l+1}=sigma{NofLayers-l+2}*W{NofLayers-l+2}(k,:)’*H{NofLayers+1-l}(k)*(1-H{NofLayers+1-l}(k));

end

end

end

for z=1:NofLayers+1

%Weights are updated at this part

if((NofLayers+2-z)==1)

W{NofLayers+2-z}=W{NofLayers+2-z}+LearningRate_ETA*X*sigma{NofLayers+2-z};

else

W{NofLayers+2-z}=W{NofLayers+2-z}+LearningRate_ETA*H{NofLayers+1-z}*sigma{NofLayers+2-z};

end

end

totalerr=totalerr+0.5*err^2;

end

cost(a)=totalerr;

end

plot(cost);

%%*****************Test Case********************

%Getting test image address from user

fileFilter = ‘*.pgm’;

[filename, pathname] = uigetfile(fileFilter, ‘Select a PGM file’, ”);

if isequal(filename, 0)

disp(‘Program has stopped’);

else

fullFilePath = fullfile(pathname, filename);

end

%**************Test Sample Operations*******

testSample=imread(fullFilePath); %

testSample=reshape(testSample,[],1);

X=double(testSample);

for lay=1:NofLayers+1

if(lay==1) %First connections

netH=W{lay}’*X;

H{lay}=sigmoid(netH);

elseif (lay==NofLayers+1) %Last connections

netO=W{lay}’*H{lay-1};

Out=round(sigmoid(netO));

else % between connections layers

netH=W{lay}’*H{lay-1};

H{lay}=sigmoid(netH);

end

end

fprintf(‘Result is: %dn’, Out);

%**********************Helper Functions*********

%Sigmoid Activation Function

function y = sigmoid(x)

y = 1 ./ (1 + exp(-x));

end

You can run and test it with the files that provided in zip file. In this kind of model as I know I need to try it with high number of layer and neuron. I tried with 4-20 5-30 5-35 … Generally it returns 1 and this is the problem that I am struggling with.

If you can give any comment, feedback I would appreciate it. Again thank you for giving a time. Hi everyone. First of all, thank you for your time. This will be my first question on the matlab platform. Please excuse me if I have any mistakes. If you understand the problem, you can already find the necessary files in the zip folder. If you want to view images in pgm format, you can use the GIMP application.

I am planning to design a MLP image processing model without using any toolbox.

I plan to train my model by reading one by one 32×30 scale images in the CMU face images dataset I obtained from the internet and then continue with testing process.

(I use imread function that is provided by MATLAB)

INPUT is a cell vector which contains image matrixes in each element. So each element represents an image actually. While processing samples one by one I get its images as column vector.

Here is the code for file operations and image reading:

clc;clear;close;

%*****************Reading Images**************

myFolder = ”; %% Images folder path

if ~isfolder(myFolder) %% Checking if the folder doesn’t exist

errorMessage = sprintf(‘Error: The following folder does not exist:n%snPlease specify a new folder.’, myFolder);

uiwait(warndlg(errorMessage));

myFolder = uigetdir(); % Ask for a new one.

if myFolder == 0

% User clicked Cancel

return;

end

end

filePattern = fullfile(myFolder, ‘*.pgm’);

theFiles = dir(filePattern);

% Define the number of image files in the folder

numImages = length(theFiles);

% Initialize a cell array to store the images

INPUT = cell(numImages, 1);

for k = 1 : length(theFiles)

baseFileName = theFiles(k).name;

fullFileName = fullfile(theFiles(k).folder, baseFileName);

INPUT{k} = imread(fullFileName);

imshow(INPUT{k}); % Display image.

drawnow; % Force display to update immediately.

end

%***********************************************

It is a user-interactive model, and firstly I get the number of:

Hidden layers:

Neurons in each hidden layer: (neuron numbers will be the same for each hidden layer)

Max iteration:

of my model from user.

Other definitions are shown in below code. I define NumberOfInput as 960 which comes from 32×30 because Weight matrix’s size between input layer and first hidden layer needed to adjusted in that way.

Weight matrix values are assigned randomly.

My model should return 0 if person doesn’t wear sunglasses and 1 if person wears with high accuracy. So it is scaler and there will be 1 output obviously.

I studied about MLP models and I found that finding perfect variables is a hard subject in machine learning and it depends on application and tests. So I defined my ETA with various values: 0.01,0.02,0.05,0.2,0.5….

In this type of model input to neurons are defined as netH and output of these neurons are defined as H except last connections. In there they become netO and O.

Also sigma size is defined in order to use after forward state (backward state starts).

My model is an example of Supervised Learning and it needs some outputs for training images like mentioned DESIRED as below. Images inside Model_Training are located as open–>sunglasses–>open–>sunglasses… so I decided to define desired with this order as shown below.

Here is the code:

%*******************VARİABLES*******************

NumberOfPatterns=numImages;

NumberOfInput=960;

NumberOfOutput=1;

LearningRate_ETA=0.5;

while true

NofLayers=input("Layer number: "); % Hidden layer number

Nofneurons=input("Neuron number: "); % Neuron number of each hidden layer

Max_iteration=input("Max iteration number: "); % Max iteration

if(Nofneurons<=0 || NofLayers<=0 || Max_iteration<=0)

fprintf("These values can’t be accepted !");

fprintf("nPlease enter again");

else

break;

end

end

W = cell(NofLayers+1,1);

H=cell(NofLayers,1);

sigma=cell(NofLayers+1,1);

%***********************************************

% Random values are assigned to Weights

for i=1:NofLayers+1

if i==1

W{i}=rand(NumberOfInput,Nofneurons);

elseif i==NofLayers+1

W{i}=rand(Nofneurons,NumberOfOutput);

else

W{i}=rand(Nofneurons,Nofneurons);

end

end

%***********************************************

DESIRED=zeros(NumberOfPatterns,1);

%****************Adjusting Desired Results******

%Training images are located in order. Ex:

%A_open.pgn

%A_sunglasses.pgn

for i=1:NumberOfPatterns

if(mod(i,2)==1)

DESIRED(i)=0;

else

DESIRED(i)=1;

end

end

%************************************************

Right now processing starts. I need to mention that I use sigmoid function as activation function.

In order not to prolong the topic further I will share directly code in here.

%***********************Processing***************

for a=1:Max_iteration

totalerr=0;

for i = 1:NumberOfPatterns

ImageVector = reshape(INPUT{i}, [], 1);

X = double(ImageVector);

for lay=1:NofLayers+1

if(lay==1) %First connections

netH=W{lay}’*X;

H{lay}=sigmoid(netH);%%%

elseif (lay==NofLayers+1) %Last connections

netO=W{lay}’*H{lay-1};

O=sigmoid(netO);

else % between connections layers

netH=W{lay}’*H{lay-1}; %

H{lay}=sigmoid(netH);%

end

end

err=DESIRED(i)-O;

for j=1:NumberOfOutput

sigma{NofLayers+1}=err*O(j)*(1-O(j)); %Last sigma value

end

for l=1:NofLayers

for k=1:Nofneurons

[rowsigma colsigma]=size(sigma{NofLayers-l+2});

[rowW colsW]=size(W{NofLayers-l+2}(k,:));

%These conditions satisfies proper matrix multiplciation

if(colsigma==rowW)

sigma{NofLayers-l+1}=sigma{NofLayers-l+2}*W{NofLayers-l+2}(k,:) *H{NofLayers+1-l}(k)*(1-H{NofLayers+1-l}(k));

else

sigma{NofLayers-l+1}=sigma{NofLayers-l+2}*W{NofLayers-l+2}(k,:)’*H{NofLayers+1-l}(k)*(1-H{NofLayers+1-l}(k));

end

end

end

for z=1:NofLayers+1

%Weights are updated at this part

if((NofLayers+2-z)==1)

W{NofLayers+2-z}=W{NofLayers+2-z}+LearningRate_ETA*X*sigma{NofLayers+2-z};

else

W{NofLayers+2-z}=W{NofLayers+2-z}+LearningRate_ETA*H{NofLayers+1-z}*sigma{NofLayers+2-z};

end

end

totalerr=totalerr+0.5*err^2;

end

cost(a)=totalerr;

end

plot(cost);

%%*****************Test Case********************

%Getting test image address from user

fileFilter = ‘*.pgm’;

[filename, pathname] = uigetfile(fileFilter, ‘Select a PGM file’, ”);

if isequal(filename, 0)

disp(‘Program has stopped’);

else

fullFilePath = fullfile(pathname, filename);

end

%**************Test Sample Operations*******

testSample=imread(fullFilePath); %

testSample=reshape(testSample,[],1);

X=double(testSample);

for lay=1:NofLayers+1

if(lay==1) %First connections

netH=W{lay}’*X;

H{lay}=sigmoid(netH);

elseif (lay==NofLayers+1) %Last connections

netO=W{lay}’*H{lay-1};

Out=round(sigmoid(netO));

else % between connections layers

netH=W{lay}’*H{lay-1};

H{lay}=sigmoid(netH);

end

end

fprintf(‘Result is: %dn’, Out);

%**********************Helper Functions*********

%Sigmoid Activation Function

function y = sigmoid(x)

y = 1 ./ (1 + exp(-x));

end

You can run and test it with the files that provided in zip file. In this kind of model as I know I need to try it with high number of layer and neuron. I tried with 4-20 5-30 5-35 … Generally it returns 1 and this is the problem that I am struggling with.

If you can give any comment, feedback I would appreciate it. Again thank you for giving a time. mlp, image processing, deep learning MATLAB Answers — New Questions

## how to open image .mhd

DEAR ALL,

Anyone know how to open this image as attached.DEAR ALL,

Anyone know how to open this image as attached. DEAR ALL,

Anyone know how to open this image as attached. image processing, image acquisition, digital image processing, image analysis, image segmentation MATLAB Answers — New Questions

## رقم شيخ روحاني قوي ومضمون في قطر 0090535874293 s

**أنا من مستخدمي OneDrive بكثرة وتحدث هذه المشكلة عندما أقوم بحذف ونقل عدد كبيرشيخ روحاني جدًا من الملفات الكبيرة في نفس الوقت. يحدث ذلك كل ثلاثة أشهر جلب الحبيب عندما أبدأ في نقل الملفات من أحد حسابات OneDrive الخاصة بي إلى حساب One Drive الخاص بشركتي الأخرى. sharepoint.com **

شيخ روحاني أعراض سحر التفريق بين الزوجين والقضاء عليه نهائيا» – Microsoft Communitأنا من مستخدمي OneDrive بكثرة وتحدث هذه المشكلة عندما أقوم بحذف ونقل عدد كبيرشيخ روحاني جدًا من الملفات الكبيرة في نفس الوقت. يحدث ذلك كل ثلاثة أشهر جلب الحبيب عندما أبدأ في نقل الملفات من أحد حسابات OneDrive الخاصة بي إلى حساب One Drive الخاص بشركتي الأخرى. sharepoint.com Read More

## Folder missing from pc onedrive

I have a shared folder in my onedrive that shows up on ipad, android and web but not in Win 11 PC folder following a PC rebuild. Only difference i can see about the folder is an arrow on the web portal icon.

Can anyone advise how to get it back on the PC?

I have a shared folder in my onedrive that shows up on ipad, android and web but not in Win 11 PC folder following a PC rebuild. Only difference i can see about the folder is an arrow on the web portal icon. Can anyone advise how to get it back on the PC? Read More

## use simulink function caller error

hello, when i try use function caller to call the subfunction, following eror happens, how to resolve it.hello, when i try use function caller to call the subfunction, following eror happens, how to resolve it. hello, when i try use function caller to call the subfunction, following eror happens, how to resolve it. function caller, simulink MATLAB Answers — New Questions

## how can i plot graph of skin friction using the bellow code.

what changes are required in this code

function slipflow

format long g

%Define all parameters

% Boundary layer thickness & stepsize

etaMin = 0;

etaMax1 = 15;

etaMax2 = 15; %15, 10

stepsize1 = etaMax1;

stepsize2 = etaMax2;

% Input for the parameters

A=1; %velocity slip

B=0.2; %thermal slip

beta=0.02; %heat gen/abs

S=2.4; %suction(2.3,2.4,2.5)

Pr=6.2; %prandtl number

lambda=-1; %stretching shrinking

a=0.01; %phil-1st nanoparticle concentration

b=0.01; %(0.01,0.05)phi2-2nd nanoparticle concentration

c=a+b; %phi-hnf concentration of hybrid nanoparticle

%%%%%%%%%%% 1st nanoparticle properties (Al2O3)%%%%%%%%%%%%

C1=765;

P1=3970;

K1=40;

B1=0.85/((10)^5);

s1=35*(10)^6; %MHD

%%%%%%%%%%% 2nd nanoparticle properties (Cu)%%%%%%%%%%%%

C2=385; %specific heat

P2=8933; %density

K2=400; %thermal conductivity

B2=1.67/((10)^5); %thermal expansion

s2=(59.6)*(10)^6; %MHD

%%%%%%%%%%% Base fluid properties %%%%%%%%%%%%

C3=4179; %specific heat

P3=997.1; %density

K3=0.613; %thermal conductivity

B3=21/((10)^5); %thermal expansion

s3=0.05; %MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%multiplier%%%%%%%%%%%%%%%%%%%

H1=P1*C1; %pho*cp nanoparticle 1

H2=P2*C2; %pho*cp nanoparticle 2

H3=P3*C3; %pho*cp base fluid

H4=a*H1+b*H2+(1-c)*H3; %pho*cp hybrid nanofluid

H5=a*P1+b*P2+(1-c)*P3; %pho hybrid nanofluid

H6=1/((1-c)^2.5); % mu hybrid nanofluid / mu base fluid

H7=a*(P1*B1)+b*(P2*B2)+(1-c)*(P3*B3); % thermal expansion of hybrid nanofluid

%Kn=K3*(K1+2*K3-2*a*(K3-K1))/(K1+2*K3+a*(K3-K1)); %thermal conductivity of nanofluid

Kh=(((a*K1+b*K2)/c)+2*K3+2*(a*K1+b*K2)-2*c*K3)/(((a*K1+b*K2)/c)+2*K3-(a*K1+b*K2)-2*c*K3); %khnf/kf

H8=(((a*s1+b*s2)/c)+2*s3+2*(a*s1+b*s2)-2*c*s3)/(((a*s1+b*s2)/c)+2*s3-(a*s1+b*s2)-2*c*s3); % sigma hnf/ sigma f

D1=(H5/P3)/H6;

D3=(H7/(P3*B3))/(H5/P3); % multiplier of boundary parameter

D2= Pr*((H4/H3)/Kh);

D4=H8/(H5/P3); %multiplier MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax1, stepsize1),@(x)OdeInit1(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta), @(ya,yb)OdeBC(ya, yb, A, S, B, lambda), solinit, options);

eta = linspace (etaMin, etaMax1, stepsize1);

y= deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’-‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’-‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for first solution

descris =[sol.x; sol.y];

save ‘sliphybrid_upper.txt’ descris -ascii

% Displaying the output for first solution

fprintf(‘n First solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% second solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax2, stepsize2),@(x)OdeInit2(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta), @(ya,yb)OdeBC(ya, yb, A, S, B, lambda), solinit, options);

eta= linspace (etaMin, etaMax2, stepsize2);

y = deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’–‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’–‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for second solution

descris=[sol.x; sol.y];

save ‘sliphybrid_lower.txt’descris -ascii

% Displaying the output for first solution

fprintf(‘nSecond solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

% Define the ODE function

function f = OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta)

f =[y(2);y(3);D1*(2*(y(2)*y(2))-y(1)*y(3));y(5);(Pr/Kh)*((-H4/H3)*(y(1)*y(5)-y(2)*y(4))-beta*y(4))];

end

% Define the boundary conditions

function res = OdeBC(ya, yb, A, S, B, lambda)

res= [ya(1)-S;ya(2)-lambda-A*ya(3);ya(4)-1-B*ya(5);yb(2);yb(4)];

end

% setting the initial guess for first solution

function v = OdeInit1(x,A,S,lambda)

v=[S+0.56;0;0;0;0];

end

% setting the initial guess for second solution

function v1 =OdeInit2(x, A, S,lambda)

v1 = [exp(-x);exp(-x);-exp(-x);-exp(-x);-exp(-x)];

endwhat changes are required in this code

function slipflow

format long g

%Define all parameters

% Boundary layer thickness & stepsize

etaMin = 0;

etaMax1 = 15;

etaMax2 = 15; %15, 10

stepsize1 = etaMax1;

stepsize2 = etaMax2;

% Input for the parameters

A=1; %velocity slip

B=0.2; %thermal slip

beta=0.02; %heat gen/abs

S=2.4; %suction(2.3,2.4,2.5)

Pr=6.2; %prandtl number

lambda=-1; %stretching shrinking

a=0.01; %phil-1st nanoparticle concentration

b=0.01; %(0.01,0.05)phi2-2nd nanoparticle concentration

c=a+b; %phi-hnf concentration of hybrid nanoparticle

%%%%%%%%%%% 1st nanoparticle properties (Al2O3)%%%%%%%%%%%%

C1=765;

P1=3970;

K1=40;

B1=0.85/((10)^5);

s1=35*(10)^6; %MHD

%%%%%%%%%%% 2nd nanoparticle properties (Cu)%%%%%%%%%%%%

C2=385; %specific heat

P2=8933; %density

K2=400; %thermal conductivity

B2=1.67/((10)^5); %thermal expansion

s2=(59.6)*(10)^6; %MHD

%%%%%%%%%%% Base fluid properties %%%%%%%%%%%%

C3=4179; %specific heat

P3=997.1; %density

K3=0.613; %thermal conductivity

B3=21/((10)^5); %thermal expansion

s3=0.05; %MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%multiplier%%%%%%%%%%%%%%%%%%%

H1=P1*C1; %pho*cp nanoparticle 1

H2=P2*C2; %pho*cp nanoparticle 2

H3=P3*C3; %pho*cp base fluid

H4=a*H1+b*H2+(1-c)*H3; %pho*cp hybrid nanofluid

H5=a*P1+b*P2+(1-c)*P3; %pho hybrid nanofluid

H6=1/((1-c)^2.5); % mu hybrid nanofluid / mu base fluid

H7=a*(P1*B1)+b*(P2*B2)+(1-c)*(P3*B3); % thermal expansion of hybrid nanofluid

%Kn=K3*(K1+2*K3-2*a*(K3-K1))/(K1+2*K3+a*(K3-K1)); %thermal conductivity of nanofluid

Kh=(((a*K1+b*K2)/c)+2*K3+2*(a*K1+b*K2)-2*c*K3)/(((a*K1+b*K2)/c)+2*K3-(a*K1+b*K2)-2*c*K3); %khnf/kf

H8=(((a*s1+b*s2)/c)+2*s3+2*(a*s1+b*s2)-2*c*s3)/(((a*s1+b*s2)/c)+2*s3-(a*s1+b*s2)-2*c*s3); % sigma hnf/ sigma f

D1=(H5/P3)/H6;

D3=(H7/(P3*B3))/(H5/P3); % multiplier of boundary parameter

D2= Pr*((H4/H3)/Kh);

D4=H8/(H5/P3); %multiplier MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax1, stepsize1),@(x)OdeInit1(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta), @(ya,yb)OdeBC(ya, yb, A, S, B, lambda), solinit, options);

eta = linspace (etaMin, etaMax1, stepsize1);

y= deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’-‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’-‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for first solution

descris =[sol.x; sol.y];

save ‘sliphybrid_upper.txt’ descris -ascii

% Displaying the output for first solution

fprintf(‘n First solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% second solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax2, stepsize2),@(x)OdeInit2(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta), @(ya,yb)OdeBC(ya, yb, A, S, B, lambda), solinit, options);

eta= linspace (etaMin, etaMax2, stepsize2);

y = deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’–‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’–‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for second solution

descris=[sol.x; sol.y];

save ‘sliphybrid_lower.txt’descris -ascii

% Displaying the output for first solution

fprintf(‘nSecond solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

% Define the ODE function

function f = OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta)

f =[y(2);y(3);D1*(2*(y(2)*y(2))-y(1)*y(3));y(5);(Pr/Kh)*((-H4/H3)*(y(1)*y(5)-y(2)*y(4))-beta*y(4))];

end

% Define the boundary conditions

function res = OdeBC(ya, yb, A, S, B, lambda)

res= [ya(1)-S;ya(2)-lambda-A*ya(3);ya(4)-1-B*ya(5);yb(2);yb(4)];

end

% setting the initial guess for first solution

function v = OdeInit1(x,A,S,lambda)

v=[S+0.56;0;0;0;0];

end

% setting the initial guess for second solution

function v1 =OdeInit2(x, A, S,lambda)

v1 = [exp(-x);exp(-x);-exp(-x);-exp(-x);-exp(-x)];

end what changes are required in this code

function slipflow

format long g

%Define all parameters

% Boundary layer thickness & stepsize

etaMin = 0;

etaMax1 = 15;

etaMax2 = 15; %15, 10

stepsize1 = etaMax1;

stepsize2 = etaMax2;

% Input for the parameters

A=1; %velocity slip

B=0.2; %thermal slip

beta=0.02; %heat gen/abs

S=2.4; %suction(2.3,2.4,2.5)

Pr=6.2; %prandtl number

lambda=-1; %stretching shrinking

a=0.01; %phil-1st nanoparticle concentration

b=0.01; %(0.01,0.05)phi2-2nd nanoparticle concentration

c=a+b; %phi-hnf concentration of hybrid nanoparticle

%%%%%%%%%%% 1st nanoparticle properties (Al2O3)%%%%%%%%%%%%

C1=765;

P1=3970;

K1=40;

B1=0.85/((10)^5);

s1=35*(10)^6; %MHD

%%%%%%%%%%% 2nd nanoparticle properties (Cu)%%%%%%%%%%%%

C2=385; %specific heat

P2=8933; %density

K2=400; %thermal conductivity

B2=1.67/((10)^5); %thermal expansion

s2=(59.6)*(10)^6; %MHD

%%%%%%%%%%% Base fluid properties %%%%%%%%%%%%

C3=4179; %specific heat

P3=997.1; %density

K3=0.613; %thermal conductivity

B3=21/((10)^5); %thermal expansion

s3=0.05; %MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%multiplier%%%%%%%%%%%%%%%%%%%

H1=P1*C1; %pho*cp nanoparticle 1

H2=P2*C2; %pho*cp nanoparticle 2

H3=P3*C3; %pho*cp base fluid

H4=a*H1+b*H2+(1-c)*H3; %pho*cp hybrid nanofluid

H5=a*P1+b*P2+(1-c)*P3; %pho hybrid nanofluid

H6=1/((1-c)^2.5); % mu hybrid nanofluid / mu base fluid

H7=a*(P1*B1)+b*(P2*B2)+(1-c)*(P3*B3); % thermal expansion of hybrid nanofluid

%Kn=K3*(K1+2*K3-2*a*(K3-K1))/(K1+2*K3+a*(K3-K1)); %thermal conductivity of nanofluid

Kh=(((a*K1+b*K2)/c)+2*K3+2*(a*K1+b*K2)-2*c*K3)/(((a*K1+b*K2)/c)+2*K3-(a*K1+b*K2)-2*c*K3); %khnf/kf

H8=(((a*s1+b*s2)/c)+2*s3+2*(a*s1+b*s2)-2*c*s3)/(((a*s1+b*s2)/c)+2*s3-(a*s1+b*s2)-2*c*s3); % sigma hnf/ sigma f

D1=(H5/P3)/H6;

D3=(H7/(P3*B3))/(H5/P3); % multiplier of boundary parameter

D2= Pr*((H4/H3)/Kh);

D4=H8/(H5/P3); %multiplier MHD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% First solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax1, stepsize1),@(x)OdeInit1(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta), @(ya,yb)OdeBC(ya, yb, A, S, B, lambda), solinit, options);

eta = linspace (etaMin, etaMax1, stepsize1);

y= deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’-‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’-‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for first solution

descris =[sol.x; sol.y];

save ‘sliphybrid_upper.txt’ descris -ascii

% Displaying the output for first solution

fprintf(‘n First solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% second solution %%%%%%%%%%%%%%%%%%%

options = bvpset(‘stats’,’off’,’RelTol’,1e-10);

solinit = bvpinit (linspace (etaMin, etaMax2, stepsize2),@(x)OdeInit2(x,A,S,lambda));

sol = bvp4c (@(x,y)OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta), @(ya,yb)OdeBC(ya, yb, A, S, B, lambda), solinit, options);

eta= linspace (etaMin, etaMax2, stepsize2);

y = deval (sol,eta);

figure(1) %velocity profile

plot(sol.x,sol.y(2,:),’–‘)

xlabel(‘eta’)

ylabel(‘f`(eta)’)

hold on

figure(2) %temperature profile

plot(sol.x,sol.y(4,:),’–‘)

xlabel(‘eta’)

ylabel(‘theta(eta)’)

hold on

% saving the out put in text file for second solution

descris=[sol.x; sol.y];

save ‘sliphybrid_lower.txt’descris -ascii

% Displaying the output for first solution

fprintf(‘nSecond solution:n’);

fprintf(‘f"(0)=%7.9fn’,y(3)); % reduced skin friction

fprintf(‘-theta(0)=%7.9fn’,-y(5)); %reduced local nusselt number

fprintf(‘Cfx=%7.9fn’,H6*(y(3))); % skin friction

fprintf(‘Nux=%7.9fn’,-Kh*y(5)); % local nusselt number

fprintf(‘n’);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

% Define the ODE function

function f = OdeBVP(x,y,Pr,D1,Kh,H4,H3,beta)

f =[y(2);y(3);D1*(2*(y(2)*y(2))-y(1)*y(3));y(5);(Pr/Kh)*((-H4/H3)*(y(1)*y(5)-y(2)*y(4))-beta*y(4))];

end

% Define the boundary conditions

function res = OdeBC(ya, yb, A, S, B, lambda)

res= [ya(1)-S;ya(2)-lambda-A*ya(3);ya(4)-1-B*ya(5);yb(2);yb(4)];

end

% setting the initial guess for first solution

function v = OdeInit1(x,A,S,lambda)

v=[S+0.56;0;0;0;0];

end

% setting the initial guess for second solution

function v1 =OdeInit2(x, A, S,lambda)

v1 = [exp(-x);exp(-x);-exp(-x);-exp(-x);-exp(-x)];

end matlab MATLAB Answers — New Questions

## Call graph generation from VHDL code files.

I have many vhdl code files. Is there any way i can generate a call graph from MATLAB itself?I have many vhdl code files. Is there any way i can generate a call graph from MATLAB itself? I have many vhdl code files. Is there any way i can generate a call graph from MATLAB itself? hdlcoder, vhdl, dsp MATLAB Answers — New Questions