June 2024 | Application Package Repository Telkom University

Month: June 2024

The input was too complicated or too big for MATLAB to parse.

/ 2024-06-30 / Matlab

hi, i’ve a long code and i receive this error..
To fix it i want to reduce code using vectorialization
for i=1:c
if STR_type(i)==1 %%se sono aggregate
SumCTR_color(b(i),:)= col(i,:); %%cosi nel draw del SumCTR_strum gli do il colore uguale
end
legend_lines(i)=plot(Ax_Eq,XDates,TE_strum(:,i)’,’DisplayName’,STR_name{i},’Color’,col(i,:),’LineWidth’,width_arr(i));
text(Ax_Eq,r-2,TE_strum(end,i),strcat({‘ ‘},num2str(i)),’Color’,col(i,:),’Interpreter’,’none’);
%%******** COLORA OOS *********
if app.OOScolorCheckBox.Value %%mi colora di nero da dove inizia solo l’OOS (se ci sono 4 sistemi prende l’OOS piu’ recente!!)!
if isdatetime(OOS_strum(i))
[~,xx_]=find(OOS_strum(i)<=XDates,1,’first’); %%max perche prendo l’OOS piu’ recente!
plot(Ax_Eq,XDates(xx_:end),TE_strum(xx_:end,i)’,’Color’,"black",’LineWidth’,width_arr(i));
end
end
%%******************************
end

how can i do it? this is a small piece of a big codehi, i’ve a long code and i receive this error..
To fix it i want to reduce code using vectorialization
for i=1:c
if STR_type(i)==1 %%se sono aggregate
SumCTR_color(b(i),:)= col(i,:); %%cosi nel draw del SumCTR_strum gli do il colore uguale
end
legend_lines(i)=plot(Ax_Eq,XDates,TE_strum(:,i)’,’DisplayName’,STR_name{i},’Color’,col(i,:),’LineWidth’,width_arr(i));
text(Ax_Eq,r-2,TE_strum(end,i),strcat({‘ ‘},num2str(i)),’Color’,col(i,:),’Interpreter’,’none’);
%%******** COLORA OOS *********
if app.OOScolorCheckBox.Value %%mi colora di nero da dove inizia solo l’OOS (se ci sono 4 sistemi prende l’OOS piu’ recente!!)!
if isdatetime(OOS_strum(i))
[~,xx_]=find(OOS_strum(i)<=XDates,1,’first’); %%max perche prendo l’OOS piu’ recente!
plot(Ax_Eq,XDates(xx_:end),TE_strum(xx_:end,i)’,’Color’,"black",’LineWidth’,width_arr(i));
end
end
%%******************************
end

how can i do it? this is a small piece of a big code hi, i’ve a long code and i receive this error..
To fix it i want to reduce code using vectorialization
for i=1:c
if STR_type(i)==1 %%se sono aggregate
SumCTR_color(b(i),:)= col(i,:); %%cosi nel draw del SumCTR_strum gli do il colore uguale
end
legend_lines(i)=plot(Ax_Eq,XDates,TE_strum(:,i)’,’DisplayName’,STR_name{i},’Color’,col(i,:),’LineWidth’,width_arr(i));
text(Ax_Eq,r-2,TE_strum(end,i),strcat({‘ ‘},num2str(i)),’Color’,col(i,:),’Interpreter’,’none’);
%%******** COLORA OOS *********
if app.OOScolorCheckBox.Value %%mi colora di nero da dove inizia solo l’OOS (se ci sono 4 sistemi prende l’OOS piu’ recente!!)!
if isdatetime(OOS_strum(i))
[~,xx_]=find(OOS_strum(i)<=XDates,1,’first’); %%max perche prendo l’OOS piu’ recente!
plot(Ax_Eq,XDates(xx_:end),TE_strum(xx_:end,i)’,’Color’,"black",’LineWidth’,width_arr(i));
end
end
%%******************************
end

how can i do it? this is a small piece of a big code the input was too complicated or too big for matla MATLAB Answers — New Questions

Set Column width of Excel through Matlab

/ 2024-06-30 / Matlab

Hi, I tried to set the width of a column but i don’t try..

i found a hint here:

http://www.mathworks.com/matlabcentral/fileexchange/23270-set-columns-in-excel-to-auto-fit/content/xlsAutoFitCol.m

and i simply call it (in another file which I open excel) with xlsAutoFitCol(‘myfile.xls’,’Sheet1′,’A:S’); but nothing change…
that’s what appears to me…

http://imageshack.us/photo/my-images/402/catturahi.png/

and what i want…

http://imageshack.us/photo/my-images/689/cattura1yw.png/

Someone can help me?Hi, I tried to set the width of a column but i don’t try..

i found a hint here:

http://www.mathworks.com/matlabcentral/fileexchange/23270-set-columns-in-excel-to-auto-fit/content/xlsAutoFitCol.m

and i simply call it (in another file which I open excel) with xlsAutoFitCol(‘myfile.xls’,’Sheet1′,’A:S’); but nothing change…
that’s what appears to me…

http://imageshack.us/photo/my-images/402/catturahi.png/

and what i want…

http://imageshack.us/photo/my-images/689/cattura1yw.png/

Someone can help me? Hi, I tried to set the width of a column but i don’t try..

i found a hint here:

http://www.mathworks.com/matlabcentral/fileexchange/23270-set-columns-in-excel-to-auto-fit/content/xlsAutoFitCol.m

and i simply call it (in another file which I open excel) with xlsAutoFitCol(‘myfile.xls’,’Sheet1′,’A:S’); but nothing change…
that’s what appears to me…

http://imageshack.us/photo/my-images/402/catturahi.png/

and what i want…

http://imageshack.us/photo/my-images/689/cattura1yw.png/

Someone can help me? column, excel MATLAB Answers — New Questions

Poll Pay Referral Code {D2QQSUPD3R}: Get $25 Sign up Bonus And 15% Referral Commission

/ 2024-06-30 / Microsoft

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How can I accelerate deep learning training using GPU?

/ 2024-06-30 / Matlab

I’ve made a simple neural network
It classifies MNIST handwritten digit using fully-connected layers

lgraph_2 = [ …
imageInputLayer([28 28 1])
fullyConnectedLayer(512)
reluLayer
fullyConnectedLayer(256)
reluLayer
fullyConnectedLayer(128)
reluLayer
fullyConnectedLayer(10)
softmaxLayer
classificationLayer];

And the options in the neural network is

miniBatchSize = 10;
valFrequency = 5;
options = trainingOptions(‘sgdm’, …
‘MiniBatchSize’,miniBatchSize, …
‘MaxEpochs’,5, …
‘InitialLearnRate’,3e-4, …
‘Shuffle’,’every-epoch’, …
‘ValidationData’,augimdsValidation, …
‘ValidationFrequency’,valFrequency, …
‘Verbose’,true, …
‘Plots’,’training-progress’, …
‘ExecutionEnvironment’, ‘parallel’);

I expected when i use a GPU, it’s training speed will be high

But when I train this network using Macbook(sigle CPU)

it takes 1 hour for around 2500 iterations

And when I use my desktop using RTX 2080Ti,

It takes much longer time to train.

MATLAB detects my GPU properly(I checked the GPU information using gpuDevice)

I don’t know how can I accelerate the training proess.

Thank you in advanceI’ve made a simple neural network
It classifies MNIST handwritten digit using fully-connected layers

And the options in the neural network is

I expected when i use a GPU, it’s training speed will be high

But when I train this network using Macbook(sigle CPU)

it takes 1 hour for around 2500 iterations

And when I use my desktop using RTX 2080Ti,

It takes much longer time to train.

MATLAB detects my GPU properly(I checked the GPU information using gpuDevice)

I don’t know how can I accelerate the training proess.

Thank you in advance I’ve made a simple neural network
It classifies MNIST handwritten digit using fully-connected layers

And the options in the neural network is

I expected when i use a GPU, it’s training speed will be high

But when I train this network using Macbook(sigle CPU)

it takes 1 hour for around 2500 iterations

And when I use my desktop using RTX 2080Ti,

It takes much longer time to train.

MATLAB detects my GPU properly(I checked the GPU information using gpuDevice)

I don’t know how can I accelerate the training proess.

Thank you in advance deep learning MATLAB Answers — New Questions

how to change the font size in simulink?

/ 2024-06-30 / Matlab

how to change the font size of task pane while i am doing the onramp course for simulink?
The font size of Task Pane (where we are assigned various tasks to do) is very small. How to change it .
Zooming options?how to change the font size of task pane while i am doing the onramp course for simulink?
The font size of Task Pane (where we are assigned various tasks to do) is very small. How to change it .
Zooming options? how to change the font size of task pane while i am doing the onramp course for simulink?
The font size of Task Pane (where we are assigned various tasks to do) is very small. How to change it .
Zooming options? font size in simulink task pane MATLAB Answers — New Questions

MDE DeviceNetworkEvents missing full URL for HTTPS traffic

/ 2024-06-30 / Microsoft

Hiall ,

I’ve integrated Sentinel with some external TI feeds (like Phishtank, etc) and collected MDE DeviceNetworkEvents.

It seems that most (if not all) HTTPS traffic (URL) is not fully logged.

Example: https://cloudflare-ipfs.com instead of https://cloudflare-ipfs.com/xxx.dat.

PS: with HTTP traffic I got the full URL with path, etc.

It means that the URL doesn’t match when trying to compare URL TI source (full URL) and URL (partial) generated by the browser.

The goal is to push the IOC (in this case the URL) into the Indicators list.

I don’t want to populate the indicator with domain list because it can blacklist a full domain.

Example :

https://docs.google.com/presentation/d/e/2

It could be a phishing URL, but don’t want to blacklist docs.google.com domain because it can contains valid URL…

Any idea ?

Regards,

Hiall , I’ve integrated Sentinel with some external TI feeds (like Phishtank, etc) and collected MDE DeviceNetworkEvents.It seems that most (if not all) HTTPS traffic (URL) is not fully logged.Example: https://cloudflare-ipfs.com instead of https://cloudflare-ipfs.com/xxx.dat.PS: with HTTP traffic I got the full URL with path, etc. It means that the URL doesn’t match when trying to compare URL TI source (full URL) and URL (partial) generated by the browser.The goal is to push the IOC (in this case the URL) into the Indicators list.I don’t want to populate the indicator with domain list because it can blacklist a full domain.Example :https://docs.google.com/presentation/d/e/2It could be a phishing URL, but don’t want to blacklist docs.google.com domain because it can contains valid URL… Any idea ? Regards, HA Read More

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/ 2024-06-30 / Microsoft

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Can the MATLAB Engine API implement interactive operations in the MATLAB Command Window？

/ 2024-06-30 / Matlab

In the MATLAB interface window, when running some programs or scripts, some interactive instructions are required, such as giving a prompt in the command window and asking the user to enter some numbers ，Y or N, and pressing the enter key, then the program will continue to execute based on the input content. Can the above operation be achieved using Python through the Matlab Engine API interface without opening the Matlab interface now?The following is a function that requires interactive operations in the command window of Matlab：
function my_function()
disp(‘Step 1: Please enter a number:’);
x = input(”);
disp([‘You entered: ‘, num2str(x)]);
disp(‘Step 2: Please enter another number:’);
y = input(”);
disp([‘You entered: ‘, num2str(y)]);
end
How to use Python to call the above function through the Matlab Engine API and achieve its normal operation? The prompt words and input content are completed through programming or input in Python.In the MATLAB interface window, when running some programs or scripts, some interactive instructions are required, such as giving a prompt in the command window and asking the user to enter some numbers ，Y or N, and pressing the enter key, then the program will continue to execute based on the input content. Can the above operation be achieved using Python through the Matlab Engine API interface without opening the Matlab interface now?The following is a function that requires interactive operations in the command window of Matlab：
function my_function()
disp(‘Step 1: Please enter a number:’);
x = input(”);
disp([‘You entered: ‘, num2str(x)]);
disp(‘Step 2: Please enter another number:’);
y = input(”);
disp([‘You entered: ‘, num2str(y)]);
end
How to use Python to call the above function through the Matlab Engine API and achieve its normal operation? The prompt words and input content are completed through programming or input in Python. In the MATLAB interface window, when running some programs or scripts, some interactive instructions are required, such as giving a prompt in the command window and asking the user to enter some numbers ，Y or N, and pressing the enter key, then the program will continue to execute based on the input content. Can the above operation be achieved using Python through the Matlab Engine API interface without opening the Matlab interface now?The following is a function that requires interactive operations in the command window of Matlab：
function my_function()
disp(‘Step 1: Please enter a number:’);
x = input(”);
disp([‘You entered: ‘, num2str(x)]);
disp(‘Step 2: Please enter another number:’);
y = input(”);
disp([‘You entered: ‘, num2str(y)]);
end
How to use Python to call the above function through the Matlab Engine API and achieve its normal operation? The prompt words and input content are completed through programming or input in Python. matlab engine api for python, interactive operations, command window MATLAB Answers — New Questions

Taylor Series Derivative Approximations

/ 2024-06-30 / Matlab

This is what I am trying to achieve:

Write a MATLAB algorithm that evaluates

y′(t) = f(y,t)

y(a) = yo

using a first order Taylor series to approximate the derivative. Test your code by setting

f(y,t) = y − t^2 + 1, with y(0) = 0.5,

and comparing your results to the analytical solution. For your records, in 150 words or less as
comments in the code, report the key steps in the code and the step size
used to achieve a reasonably accurate answer.

This is what I have:

syms x y t;
a=input(‘central point: ‘);
f=input(‘f(x)=’);
v1=input(‘variable 1: ‘);
v2=input(‘variable 2: ‘);
taylor(f,[v1,v2],a,’Order’,2)

So I’ve written an algorithm that gives the first order taylor series of an input function. I don’t know how to use this to approximate the derivative. Any direction would be helpful.This is what I am trying to achieve:

Write a MATLAB algorithm that evaluates

y′(t) = f(y,t)

y(a) = yo

using a first order Taylor series to approximate the derivative. Test your code by setting

f(y,t) = y − t^2 + 1, with y(0) = 0.5,

This is what I have:

syms x y t;
a=input(‘central point: ‘);
f=input(‘f(x)=’);
v1=input(‘variable 1: ‘);
v2=input(‘variable 2: ‘);
taylor(f,[v1,v2],a,’Order’,2)

Write a MATLAB algorithm that evaluates

y′(t) = f(y,t)

y(a) = yo

using a first order Taylor series to approximate the derivative. Test your code by setting

f(y,t) = y − t^2 + 1, with y(0) = 0.5,

This is what I have:

syms x y t;
a=input(‘central point: ‘);
f=input(‘f(x)=’);
v1=input(‘variable 1: ‘);
v2=input(‘variable 2: ‘);
taylor(f,[v1,v2],a,’Order’,2)

I have an error in this code and the error is () Is there anyone who can help me I would be grateful for that

/ 2024-06-30 / Matlab

% إضافة مسار مكتبة WFDB Toolbox إلى Matlab
addpath("C:wfdbwfdb-app-toolbox-0-10-0mcode"); % استبدل هذا المسار بموقع مكتبة WFDB على جهازك

% قائمة بالتسجيلات من قاعدة البيانات
records = {‘100’, ‘101’, ‘102’, ‘103’, ‘104’, ‘105’, ‘106’, ‘107’, ‘108’, ‘109’, …
‘111’, ‘112’, ‘113’, ‘114’, ‘115’, ‘116’, ‘117’, ‘118’, ‘119’, ‘121’, …
‘122’, ‘123’, ‘124’, ‘200’, ‘201’, ‘202’, ‘203’, ‘205’, ‘207’, ‘208’, …
‘209’, ‘210’, ‘212’, ‘213’, ‘214’, ‘215’, ‘217’, ‘219’, ‘220’, ‘221’, …
‘222’, ‘223’, ‘228’, ‘230’, ‘231’, ‘232’, ‘233’, ‘234’};

% تحديد نسبة البيانات للتدريب
train_ratio = 0.7;

% ترتيب السجلات عشوائيًا لضمان توزيع عشوائي
shuffled_indices = randperm(length(records));
records = records(shuffled_indices);

% حساب عدد السجلات للتدريب والاختبار
num_train = round(train_ratio * length(records));
num_test = length(records) – num_train;

% تقسيم السجلات إلى مجموعتي التدريب والاختبار
train_records = records(1:num_train);
test_records = records(num_train+1:end);

% دالة لقراءة ومعالجة بيانات ECG
function data = read_and_process_ecg(recordName)
[ECG, Fs] = rdsamp(recordName, 1); % قراءة البيانات
ECG = ECG – mean(ECG); % إزالة ضوضاء التيار المستمر
[b, a] = butter(4, [0.5 45] / (Fs / 2), ‘bandpass’); % مرشح رقمي
filtered_ECG = filtfilt(b, a, ECG); % تطبيق المرشح بتمرير صفري
normalized_ECG = 2 * (filtered_ECG – min(filtered_ECG)) / (max(filtered_ECG) – min(filtered_ECG)) – 1; % تطبيع
qrs_peaks = panTompkinsQRS(normalized_ECG, Fs); % اكتشاف قمم QRS
data.segments = segmentHeartBeats(normalized_ECG, qrs_peaks, Fs); % تجزئة نبضات القلب
data.Fs = Fs;
end

% معالجة بيانات التدريب
train_data = cell(length(train_records), 1);
for i = 1:length(train_records)
recordName = train_records{i};
fprintf(‘Processing training record %s…n’, recordName);
train_data{i} = read_and_process_ecg(recordName);
end

% معالجة بيانات الاختبار
test_data = cell(length(test_records), 1);
for i = 1:length(test_records)
recordName = test_records{i};
fprintf(‘Processing test record %s…n’, recordName);
test_data{i} = read_and_process_ecg(recordName);
end

% توحيد البيانات في مصفوفة 3D (عينات، ميزات، قنوات)
function [X, Y] = prepare_data(data, fixed_segment_length)

% حساب عدد العينات
num_samples = sum(cellfun(@(d) length(d.segments), data));

% تهيئة مصفوفة البيانات
X = zeros(num_samples, fixed_segment_length, 1); % إضافة بُعد القنوات
Y = zeros(num_samples, 1);

sample_idx = 1;
for i = 1:length(data)
for j = 1:length(data{i}.segments)
segment = data{i}.segments{j};
segment_length = length(segment);

% إذا كان المقطع أطول من الطول الثابت، نقوم بتقليمه
if segment_length > fixed_segment_length
segment = segment(1:fixed_segment_length);
% إذا كان المقطع أقصر، نقوم بتعبئته بالأصفار
elseif segment_length < fixed_segment_length
segment = [segment; zeros(fixed_segment_length – segment_length, 1)];
end

% تعيين المقطع إلى مصفوفة X
X(sample_idx, :, 1) = segment’; % تعيين مع إضافة بُعد القناة
% تعيين التسمية
Y(sample_idx) = i; % استخدام رقم السجل كتصنيف مبدئي
sample_idx = sample_idx + 1;
end
end

% تحويل X إلى الشكل المطلوب (طول السلسلة، عدد العينات، عدد القنوات)
% X = permute(X, [2, 3, 1]); % ليس هناك حاجة لإعادة الترتيب هنا
end

% تعيين الطول الثابت للمقاطع
fixed_segment_length = 625; % يمكنك ضبط هذا الطول بناءً على تحليل البيانات

% تجهيز بيانات التدريب والاختبار
[xTrain, yTrain] = prepare_data(train_data, fixed_segment_length);
[xTest, yTest] = prepare_data(test_data, fixed_segment_length);

disp([‘Size of xTrain: ‘, num2str(size(xTrain))]);
disp([‘Size of xTest: ‘, num2str(size(xTest))]);

% تحويل التسميات إلى مصفوفات دلالية (Categorical)
numClasses = numel(unique(yTrain));
yTrain = categorical(yTrain);
yTest = categorical(yTest);

% % تحويل التسميات إلى مصفوفات دلالية (Categorical)
% yTrain = cellfun(@(x) categorical(x, 1:length(train_records)), num2cell(yTrain), ‘UniformOutput’, false);
% yTest = cellfun(@(x) categorical(x, 1:length(test_records)), num2cell(yTest), ‘UniformOutput’, false);
% حصول على القيم الممكنة للتصنيفات
labels = unique(yTrain);

% تحويل `yTrain` و `yTest` إلى مصفوفة خلية من المتجهات النمطية
yTrain = cellfun(@(x) categorical(x, labels), num2cell(yTrain), ‘UniformOutput’, false);
yTest = cellfun(@(x) categorical(x, labels), num2cell(yTest), ‘UniformOutput’, false);

% تحويل الأبعاد إلى الصيغة الصحيحة إذا لزم الأمر
xTrain = permute(xTrain, [2, 3, 1]);
xTest = permute(xTest, [2, 3, 1]);

% 2. بناء نموذج 1D-CNN
layers = [
sequenceInputLayer([fixed_segment_length 1]) % طبقة الإدخال مع حجم البيانات وعدد القنوات
convolution1dLayer(3, 32, ‘Padding’, ‘same’) % طبقة الالتفاف
batchNormalizationLayer % طبقة تطبيع الدفعات
reluLayer % طبقة التنشيط ReLU
maxPooling1dLayer(2, ‘Stride’, 2) % طبقة التجميع
dropoutLayer(0.5) % طبقة الإسقاط لتقليل الإفراط في التكيف
convolution1dLayer(3, 64, ‘Padding’, ‘same’)
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2, ‘Stride’, 2)
dropoutLayer(0.5)
flattenLayer % تحويل البيانات إلى مصفوفة مسطحة
fullyConnectedLayer(128) % طبقة الاتصال الكاملة
reluLayer
dropoutLayer(0.5)
fullyConnectedLayer(numClasses) % طبقة الخرج بعدد الفئات
softmaxLayer % طبقة سوفت ماكس
classificationLayer]; % طبقة التصنيف

% 3. تجميع النموذج
options = trainingOptions(‘adam’, …
‘InitialLearnRate’, 0.001, …
‘MaxEpochs’, 20, …
‘MiniBatchSize’, 32, …
‘ValidationData’, {xTest, yTest}, …
‘Plots’, ‘training-progress’, …
‘Verbose’, false);

% 4. تدريب النموذج
net = trainNetwork(xTrain, yTrain, layers, options);

% 5. تقييم النموذج
yPred = classify(net, xTest);
accuracy = sum(yPred == yTest) / numel(yTest);
disp([‘Test Accuracy: ‘, num2str(accuracy * 100), ‘%’]);

% طباعة تقرير التصنيف
confusionchart(yTest, yPred);

% الدالة: تطبيق خوارزمية Pan-Tompkins لاكتشاف قمم QRS
function qrs_peaks = panTompkinsQRS(ecg, fs)
% تطبيق خوارزمية Pan-Tompkins لاكتشاف قمم QRS في إشارات ECG
% سيتم تنفيذ عدة خطوات: الاشتقاق، التربيع، التنعيم، وإيجاد القمم

% المشتقة
diff_ecg = diff(ecg);

% التربيع
squared_ecg = diff_ecg .^ 2;

% التنعيم باستخدام نافذة متحركة
window_size = round(0.12 * fs); % 120ms نافذة
smoothed_ecg = conv(squared_ecg, ones(1, window_size) / window_size, ‘same’);

% إيجاد القمم
max_value = max(smoothed_ecg); % الحصول على القيمة العظمى للإشارة المربعة المنعمة
min_peak_height = 0.02 * max_value; % تعيين MinPeakHeight كنسبة مئوية من القيمة العظمى

[~, qrs_peaks] = findpeaks(smoothed_ecg, ‘MinPeakHeight’, min_peak_height, ‘MinPeakDistance’, round(0.6 * fs));
end

% الدالة: تجزئة إشارات نبضات القلب الفردية بناءً على مواقع قمم QRS
function segments = segmentHeartBeats(ecg, qrs_peaks, fs)
% تجزئة إشارات نبضات القلب الفردية بناءً على مواقع قمم QRS
% سنقوم بتحديد أجزاء الإشارة حول كل قمة QRS

num_beats = length(qrs_peaks);
segments = cell(num_beats, 1);

% طول كل مقطع حول QRS (مثلاً 0.6 ثانية)
segment_length = round(0.6 * fs);

for i = 1:num_beats
start_idx = max(qrs_peaks(i) – segment_length, 1);
end_idx = min(qrs_peaks(i) + segment_length, length(ecg));
segments{i} = ecg(start_idx:end_idx);
end
end% إضافة مسار مكتبة WFDB Toolbox إلى Matlab
addpath("C:wfdbwfdb-app-toolbox-0-10-0mcode"); % استبدل هذا المسار بموقع مكتبة WFDB على جهازك