from this code what is the problem that i faced because the fft not show

a = arduino;
imu = mpu6050(a);
xlabel(‘Count’);
ylabel(‘Acceleration (m/s^2)’);
title(‘Acceleration values from mpu6050 sensor (Z-axis only)’);
z_val = animatedline(‘Color’,’b’);
axis tight;
legend(‘Acceleration in Z-axis’);
stop_time = 100;
count = 1;
tic;
while(toc <= stop_time)
[accel] = readAcceleration(imu);
if ~isempty(accel)
addpoints(z_val,count,accel(:,3));
% Compute FFT of the Z-axis acceleration
fft_z = fft(accel(:,3));
n = length(fft_z);
freq = (0:n-1)*(100/stop_time)/n; % Assuming sampling rate based on stop_time
% Plot the FFT magnitude
subplot(2,1,2);
plot(freq, abs(fft_z));
xlabel(‘Frequency (Hz)’);
ylabel(‘Magnitude’);
title(‘FFT of Z-axis Acceleration’);
drawnow limitrate;

count = count + 1;
end
endfrom this code what is the problem that i faced because the fft not show

a = arduino;
imu = mpu6050(a);
xlabel(‘Count’);
ylabel(‘Acceleration (m/s^2)’);
title(‘Acceleration values from mpu6050 sensor (Z-axis only)’);
z_val = animatedline(‘Color’,’b’);
axis tight;
legend(‘Acceleration in Z-axis’);
stop_time = 100;
count = 1;
tic;
while(toc <= stop_time)
[accel] = readAcceleration(imu);
if ~isempty(accel)
addpoints(z_val,count,accel(:,3));
% Compute FFT of the Z-axis acceleration
fft_z = fft(accel(:,3));
n = length(fft_z);
freq = (0:n-1)*(100/stop_time)/n; % Assuming sampling rate based on stop_time
% Plot the FFT magnitude
subplot(2,1,2);
plot(freq, abs(fft_z));
xlabel(‘Frequency (Hz)’);
ylabel(‘Magnitude’);
title(‘FFT of Z-axis Acceleration’);
drawnow limitrate;

count = count + 1;
end
end from this code what is the problem that i faced because the fft not show

a = arduino;
imu = mpu6050(a);
xlabel(‘Count’);
ylabel(‘Acceleration (m/s^2)’);
title(‘Acceleration values from mpu6050 sensor (Z-axis only)’);
z_val = animatedline(‘Color’,’b’);
axis tight;
legend(‘Acceleration in Z-axis’);
stop_time = 100;
count = 1;
tic;
while(toc <= stop_time)
[accel] = readAcceleration(imu);
if ~isempty(accel)
addpoints(z_val,count,accel(:,3));
% Compute FFT of the Z-axis acceleration
fft_z = fft(accel(:,3));
n = length(fft_z);
freq = (0:n-1)*(100/stop_time)/n; % Assuming sampling rate based on stop_time
% Plot the FFT magnitude
subplot(2,1,2);
plot(freq, abs(fft_z));
xlabel(‘Frequency (Hz)’);
ylabel(‘Magnitude’);
title(‘FFT of Z-axis Acceleration’);
drawnow limitrate;

count = count + 1;
end
end signal processing, mpu6050 MATLAB Answers — New Questions

​

How can I use writetable to make a table to excel sheet and .csv file which has multi-line headers. Headers can include both per-variable headers like units and description, but also per-table headers like description or comments.
For example, take a table like below.
T = table(["Blue";"Red";"Green"],[50;100;75],[0.1;0.5;0.9],…
VariableNames={‘Color’,’Weight’,’Temp’});
T.Properties.VariableUnits = ["[-]";"[kg]";"°C"];
T.Properties.VariableDescriptions = ["Outside Color";"Gross Weight";"Melting Point"];
T.Properties.Description = "Playdough Properties Chart";
Now I want to make it look like this in excel and .csv files

I know to do this by other means, but I would like to be able to do it with writetable, and also to read it back in with readtable.
Is there a straight-forward way of doing this?How can I use writetable to make a table to excel sheet and .csv file which has multi-line headers. Headers can include both per-variable headers like units and description, but also per-table headers like description or comments.
For example, take a table like below.
T = table(["Blue";"Red";"Green"],[50;100;75],[0.1;0.5;0.9],…
VariableNames={‘Color’,’Weight’,’Temp’});
T.Properties.VariableUnits = ["[-]";"[kg]";"°C"];
T.Properties.VariableDescriptions = ["Outside Color";"Gross Weight";"Melting Point"];
T.Properties.Description = "Playdough Properties Chart";
Now I want to make it look like this in excel and .csv files

I know to do this by other means, but I would like to be able to do it with writetable, and also to read it back in with readtable.
Is there a straight-forward way of doing this? How can I use writetable to make a table to excel sheet and .csv file which has multi-line headers. Headers can include both per-variable headers like units and description, but also per-table headers like description or comments.
For example, take a table like below.
T = table(["Blue";"Red";"Green"],[50;100;75],[0.1;0.5;0.9],…
VariableNames={‘Color’,’Weight’,’Temp’});
T.Properties.VariableUnits = ["[-]";"[kg]";"°C"];
T.Properties.VariableDescriptions = ["Outside Color";"Gross Weight";"Melting Point"];
T.Properties.Description = "Playdough Properties Chart";
Now I want to make it look like this in excel and .csv files

I know to do this by other means, but I would like to be able to do it with writetable, and also to read it back in with readtable.
Is there a straight-forward way of doing this? tables, csv, excel MATLAB Answers — New Questions

​

I have a relatively large Simulink model that I am validating using a data set. This model is using the ECEF 6DOF block with only a gravitational force as the input.
I have observed that the Simulink model output varies considerably depending on the relative tolerance selected (ranging from 1e-5 to 1e-12), with the output approaching the data set values as the relative tolerance passes below 1e-10. Decreasing the relative tolerance has a significant negative impact on model runtime, so I would like to keep the relative tolerance at a reasonable value while maintaining accuracy in the model results.
Is a significant variation with changing relative tolerance (below the obvious candidates such as >= 1e-5) expected for a typical Simulink model?I have a relatively large Simulink model that I am validating using a data set. This model is using the ECEF 6DOF block with only a gravitational force as the input.
I have observed that the Simulink model output varies considerably depending on the relative tolerance selected (ranging from 1e-5 to 1e-12), with the output approaching the data set values as the relative tolerance passes below 1e-10. Decreasing the relative tolerance has a significant negative impact on model runtime, so I would like to keep the relative tolerance at a reasonable value while maintaining accuracy in the model results.
Is a significant variation with changing relative tolerance (below the obvious candidates such as >= 1e-5) expected for a typical Simulink model? I have a relatively large Simulink model that I am validating using a data set. This model is using the ECEF 6DOF block with only a gravitational force as the input.
I have observed that the Simulink model output varies considerably depending on the relative tolerance selected (ranging from 1e-5 to 1e-12), with the output approaching the data set values as the relative tolerance passes below 1e-10. Decreasing the relative tolerance has a significant negative impact on model runtime, so I would like to keep the relative tolerance at a reasonable value while maintaining accuracy in the model results.
Is a significant variation with changing relative tolerance (below the obvious candidates such as >= 1e-5) expected for a typical Simulink model? relative tolerance MATLAB Answers — New Questions

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how can i open .out extension file in matlab ?how can i open .out extension file in matlab ? how can i open .out extension file in matlab ? .out, extension, file MATLAB Answers — New Questions

​

I have both the tstat and the degrees of freedom, is there a quick way to find the P value of a two tailed hypothesis at a significance of 0.05I have both the tstat and the degrees of freedom, is there a quick way to find the P value of a two tailed hypothesis at a significance of 0.05 I have both the tstat and the degrees of freedom, is there a quick way to find the P value of a two tailed hypothesis at a significance of 0.05 matlab MATLAB Answers — New Questions

​

After changing my GitHub username (From Silver-Fang to Ebola-Chan-bot, as shown in the screenshot), I can’t publish my GitHub releases any more. It just says that something went wrong and I don’t know what’s wrong.

I should have correctly set the integration App on GitHub side:After changing my GitHub username (From Silver-Fang to Ebola-Chan-bot, as shown in the screenshot), I can’t publish my GitHub releases any more. It just says that something went wrong and I don’t know what’s wrong.

I should have correctly set the integration App on GitHub side: After changing my GitHub username (From Silver-Fang to Ebola-Chan-bot, as shown in the screenshot), I can’t publish my GitHub releases any more. It just says that something went wrong and I don’t know what’s wrong.

I should have correctly set the integration App on GitHub side: file exchange, github MATLAB Answers — New Questions

​

In Singular Spectrum Analysis (SSA), the goal is to decompose a signal into three main components:
Trend: The overall trend of the signal, which represents long-term changes.
Oscillations: Parts of the signal that change periodically, typically corresponding to medium-term variations.
Noise: Parts of the signal that change randomly, usually representing noise or disturbances in the signal.
In this process, the signal is first transformed into a trajectory matrix, and then its components are extracted via Singular Value Decomposition (SVD).
Main Challenge:
After performing SVD and calculating the entropy of each component, we are looking for a point where the entropy stabilizes. This point is considered where additional components no longer contribute meaningful information to the model.
The problem is that the point of entropy stabilization is not being correctly identified, which results in an improper decomposition of the signal into the three components (trend, oscillations, and noise).
Question: Is there a method in MATLAB that can more accurately identify the point of entropy stabilization and correctly decompose the signal into trend, oscillations, and noise?

clc;
clear;
close all;

% Load the CSV file
[file, path] = uigetfile(‘*.csv’, ‘Please upload your CSV file:’);
if isequal(file, 0)
error(‘No file selected.’);
end
data = readtable(fullfile(path, file));

% Display available columns for the user to choose from
disp(‘Available columns in your dataset:’);
disp(data.Properties.VariableNames’);
column_name = input(‘Enter the column name you want to analyze: ‘, ‘s’);

% Extract the signal (time series) from the chosen column
if ~any(strcmp(data.Properties.VariableNames, column_name))
error([‘Column "’, column_name, ‘" not found in the dataset.’]);
end
signal = data.(column_name);
signal = signal(~isnan(signal)); % Drop NaN values

% SSA Functions
function X = trajectory_matrix(signal, window_length)
N = length(signal);
K = N – window_length + 1;
X = zeros(window_length, K);
for i = 1:K
X(:, i) = signal(i:i + window_length – 1);
end
end

function entropy = singular_entropy(S)
P = S / sum(S);
entropy = -sum(P .* log(P));
end

function reconstructed_signal = reconstruct_signal(U, S, V, indices, original_length)
reconstructed_trajectory = zeros(size(U, 1), size(V, 1));
for i = indices
reconstructed_trajectory = reconstructed_trajectory + S(i) * (U(:, i) * V(i, :));
end

[rows, cols] = size(reconstructed_trajectory);
signal_length = rows + cols – 1;
reconstructed_signal = zeros(signal_length, 1);
counts = zeros(signal_length, 1);

% Extracting anti-diagonals
for k = 1:(rows + cols – 1)
anti_diag_elements = [];
for i = 1:rows
j = k – i + 1;
if j >= 1 && j <= cols
anti_diag_elements = [anti_diag_elements; reconstructed_trajectory(i, j)];
end
end
reconstructed_signal(k) = mean(anti_diag_elements);
counts(k) = length(anti_diag_elements);
end

reconstructed_signal = reconstructed_signal ./ counts; % Normalize the signal
reconstructed_signal = reconstructed_signal(1:original_length);
end

% Perform SSA
window_length = floor(length(signal) / 3); % Adjust window length
X = trajectory_matrix(signal, window_length);

% Perform SVD decomposition
[U, S_mat, V] = svd(X, ‘econ’);
S = diag(S_mat);

% Calculate Singular Entropy for each component cumulatively
cumulative_entropy = zeros(length(S), 1);
for i = 1:length(S)
cumulative_entropy(i) = singular_entropy(S(1:i));
end

% Plot Singular Entropy to observe stabilization point
figure;
plot(1:length(S), cumulative_entropy, ‘-o’, ‘LineWidth’, 1.5);
xlabel(‘Component Index’);
ylabel(‘Cumulative Singular Entropy’);
title(‘Cumulative Singular Entropy vs. Component Index’);
grid on;

% Calculate the point where entropy stabilizes
entropy_diff = diff(cumulative_entropy); % Difference between consecutive entropy values
threshold = 0.01; % Threshold to detect stabilization
stable_point = find(entropy_diff < threshold, 1);

% Mark the stabilization point
hold on;
xline(stable_point + 1, ‘–r’, [‘Stabilization Point: ‘, num2str(stable_point + 1)]);
legend(‘Cumulative Singular Entropy’, ‘Stabilization Point’);

% Number of components based on entropy stabilization
num_components = stable_point + 1;
disp([‘Number of components based on entropy stabilization: ‘, num2str(num_components)]);

% Reconstruct the signal by categorizing components
trend_indices = 1; % Trend corresponds to the first component
oscillation_indices = 2:num_components; % Oscillations from second component to stabilization point
noise_indices = (num_components + 1):length(S); % Noise corresponds to components after stabilization

% Reconstruct signals based on selected indices
trend_signal = reconstruct_signal(U, S, V’, trend_indices, length(signal));
oscillation_signal = reconstruct_signal(U, S, V’, oscillation_indices, length(signal));
noise_signal = reconstruct_signal(U, S, V’, noise_indices, length(signal));

% Plot the original signal, Trend, Oscillations, and Noise
figure;
plot(signal, ‘b’, ‘DisplayName’, ‘Original Signal’, ‘LineWidth’, 1.5);
hold on;
plot(trend_signal, ‘g’, ‘DisplayName’, ‘Trend’, ‘LineWidth’, 1.5);
plot(oscillation_signal, ‘orange’, ‘DisplayName’, ‘Oscillations’, ‘LineWidth’, 1.5);
plot(noise_signal, ‘r’, ‘DisplayName’, ‘Noise’, ‘LineWidth’, 1.5);
title(‘Signal Decomposition’);
xlabel(‘Time’);
ylabel(‘Amplitude’);
legend;
grid on;In Singular Spectrum Analysis (SSA), the goal is to decompose a signal into three main components:
Trend: The overall trend of the signal, which represents long-term changes.
Oscillations: Parts of the signal that change periodically, typically corresponding to medium-term variations.
Noise: Parts of the signal that change randomly, usually representing noise or disturbances in the signal.
In this process, the signal is first transformed into a trajectory matrix, and then its components are extracted via Singular Value Decomposition (SVD).
Main Challenge:
After performing SVD and calculating the entropy of each component, we are looking for a point where the entropy stabilizes. This point is considered where additional components no longer contribute meaningful information to the model.
The problem is that the point of entropy stabilization is not being correctly identified, which results in an improper decomposition of the signal into the three components (trend, oscillations, and noise).
Question: Is there a method in MATLAB that can more accurately identify the point of entropy stabilization and correctly decompose the signal into trend, oscillations, and noise?

clc;
clear;
close all;

% Load the CSV file
[file, path] = uigetfile(‘*.csv’, ‘Please upload your CSV file:’);
if isequal(file, 0)
error(‘No file selected.’);
end
data = readtable(fullfile(path, file));

% Display available columns for the user to choose from
disp(‘Available columns in your dataset:’);
disp(data.Properties.VariableNames’);
column_name = input(‘Enter the column name you want to analyze: ‘, ‘s’);

% Extract the signal (time series) from the chosen column
if ~any(strcmp(data.Properties.VariableNames, column_name))
error([‘Column "’, column_name, ‘" not found in the dataset.’]);
end
signal = data.(column_name);
signal = signal(~isnan(signal)); % Drop NaN values

% SSA Functions
function X = trajectory_matrix(signal, window_length)
N = length(signal);
K = N – window_length + 1;
X = zeros(window_length, K);
for i = 1:K
X(:, i) = signal(i:i + window_length – 1);
end
end

function entropy = singular_entropy(S)
P = S / sum(S);
entropy = -sum(P .* log(P));
end

function reconstructed_signal = reconstruct_signal(U, S, V, indices, original_length)
reconstructed_trajectory = zeros(size(U, 1), size(V, 1));
for i = indices
reconstructed_trajectory = reconstructed_trajectory + S(i) * (U(:, i) * V(i, :));
end

[rows, cols] = size(reconstructed_trajectory);
signal_length = rows + cols – 1;
reconstructed_signal = zeros(signal_length, 1);
counts = zeros(signal_length, 1);

% Extracting anti-diagonals
for k = 1:(rows + cols – 1)
anti_diag_elements = [];
for i = 1:rows
j = k – i + 1;
if j >= 1 && j <= cols
anti_diag_elements = [anti_diag_elements; reconstructed_trajectory(i, j)];
end
end
reconstructed_signal(k) = mean(anti_diag_elements);
counts(k) = length(anti_diag_elements);
end

reconstructed_signal = reconstructed_signal ./ counts; % Normalize the signal
reconstructed_signal = reconstructed_signal(1:original_length);
end

% Perform SSA
window_length = floor(length(signal) / 3); % Adjust window length
X = trajectory_matrix(signal, window_length);

% Perform SVD decomposition
[U, S_mat, V] = svd(X, ‘econ’);
S = diag(S_mat);

% Calculate Singular Entropy for each component cumulatively
cumulative_entropy = zeros(length(S), 1);
for i = 1:length(S)
cumulative_entropy(i) = singular_entropy(S(1:i));
end

% Plot Singular Entropy to observe stabilization point
figure;
plot(1:length(S), cumulative_entropy, ‘-o’, ‘LineWidth’, 1.5);
xlabel(‘Component Index’);
ylabel(‘Cumulative Singular Entropy’);
title(‘Cumulative Singular Entropy vs. Component Index’);
grid on;

% Calculate the point where entropy stabilizes
entropy_diff = diff(cumulative_entropy); % Difference between consecutive entropy values
threshold = 0.01; % Threshold to detect stabilization
stable_point = find(entropy_diff < threshold, 1);

% Mark the stabilization point
hold on;
xline(stable_point + 1, ‘–r’, [‘Stabilization Point: ‘, num2str(stable_point + 1)]);
legend(‘Cumulative Singular Entropy’, ‘Stabilization Point’);

% Number of components based on entropy stabilization
num_components = stable_point + 1;
disp([‘Number of components based on entropy stabilization: ‘, num2str(num_components)]);

% Reconstruct the signal by categorizing components
trend_indices = 1; % Trend corresponds to the first component
oscillation_indices = 2:num_components; % Oscillations from second component to stabilization point
noise_indices = (num_components + 1):length(S); % Noise corresponds to components after stabilization

% Reconstruct signals based on selected indices
trend_signal = reconstruct_signal(U, S, V’, trend_indices, length(signal));
oscillation_signal = reconstruct_signal(U, S, V’, oscillation_indices, length(signal));
noise_signal = reconstruct_signal(U, S, V’, noise_indices, length(signal));

% Plot the original signal, Trend, Oscillations, and Noise
figure;
plot(signal, ‘b’, ‘DisplayName’, ‘Original Signal’, ‘LineWidth’, 1.5);
hold on;
plot(trend_signal, ‘g’, ‘DisplayName’, ‘Trend’, ‘LineWidth’, 1.5);
plot(oscillation_signal, ‘orange’, ‘DisplayName’, ‘Oscillations’, ‘LineWidth’, 1.5);
plot(noise_signal, ‘r’, ‘DisplayName’, ‘Noise’, ‘LineWidth’, 1.5);
title(‘Signal Decomposition’);
xlabel(‘Time’);
ylabel(‘Amplitude’);
legend;
grid on; In Singular Spectrum Analysis (SSA), the goal is to decompose a signal into three main components:
Trend: The overall trend of the signal, which represents long-term changes.
Oscillations: Parts of the signal that change periodically, typically corresponding to medium-term variations.
Noise: Parts of the signal that change randomly, usually representing noise or disturbances in the signal.
In this process, the signal is first transformed into a trajectory matrix, and then its components are extracted via Singular Value Decomposition (SVD).
Main Challenge:
After performing SVD and calculating the entropy of each component, we are looking for a point where the entropy stabilizes. This point is considered where additional components no longer contribute meaningful information to the model.
The problem is that the point of entropy stabilization is not being correctly identified, which results in an improper decomposition of the signal into the three components (trend, oscillations, and noise).
Question: Is there a method in MATLAB that can more accurately identify the point of entropy stabilization and correctly decompose the signal into trend, oscillations, and noise?

clc;
clear;
close all;

% Load the CSV file
[file, path] = uigetfile(‘*.csv’, ‘Please upload your CSV file:’);
if isequal(file, 0)
error(‘No file selected.’);
end
data = readtable(fullfile(path, file));

% Display available columns for the user to choose from
disp(‘Available columns in your dataset:’);
disp(data.Properties.VariableNames’);
column_name = input(‘Enter the column name you want to analyze: ‘, ‘s’);

% Extract the signal (time series) from the chosen column
if ~any(strcmp(data.Properties.VariableNames, column_name))
error([‘Column "’, column_name, ‘" not found in the dataset.’]);
end
signal = data.(column_name);
signal = signal(~isnan(signal)); % Drop NaN values

% SSA Functions
function X = trajectory_matrix(signal, window_length)
N = length(signal);
K = N – window_length + 1;
X = zeros(window_length, K);
for i = 1:K
X(:, i) = signal(i:i + window_length – 1);
end
end

function entropy = singular_entropy(S)
P = S / sum(S);
entropy = -sum(P .* log(P));
end

function reconstructed_signal = reconstruct_signal(U, S, V, indices, original_length)
reconstructed_trajectory = zeros(size(U, 1), size(V, 1));
for i = indices
reconstructed_trajectory = reconstructed_trajectory + S(i) * (U(:, i) * V(i, :));
end

[rows, cols] = size(reconstructed_trajectory);
signal_length = rows + cols – 1;
reconstructed_signal = zeros(signal_length, 1);
counts = zeros(signal_length, 1);

% Extracting anti-diagonals
for k = 1:(rows + cols – 1)
anti_diag_elements = [];
for i = 1:rows
j = k – i + 1;
if j >= 1 && j <= cols
anti_diag_elements = [anti_diag_elements; reconstructed_trajectory(i, j)];
end
end
reconstructed_signal(k) = mean(anti_diag_elements);
counts(k) = length(anti_diag_elements);
end

reconstructed_signal = reconstructed_signal ./ counts; % Normalize the signal
reconstructed_signal = reconstructed_signal(1:original_length);
end

% Perform SSA
window_length = floor(length(signal) / 3); % Adjust window length
X = trajectory_matrix(signal, window_length);

% Perform SVD decomposition
[U, S_mat, V] = svd(X, ‘econ’);
S = diag(S_mat);

% Calculate Singular Entropy for each component cumulatively
cumulative_entropy = zeros(length(S), 1);
for i = 1:length(S)
cumulative_entropy(i) = singular_entropy(S(1:i));
end

% Plot Singular Entropy to observe stabilization point
figure;
plot(1:length(S), cumulative_entropy, ‘-o’, ‘LineWidth’, 1.5);
xlabel(‘Component Index’);
ylabel(‘Cumulative Singular Entropy’);
title(‘Cumulative Singular Entropy vs. Component Index’);
grid on;

% Calculate the point where entropy stabilizes
entropy_diff = diff(cumulative_entropy); % Difference between consecutive entropy values
threshold = 0.01; % Threshold to detect stabilization
stable_point = find(entropy_diff < threshold, 1);

% Mark the stabilization point
hold on;
xline(stable_point + 1, ‘–r’, [‘Stabilization Point: ‘, num2str(stable_point + 1)]);
legend(‘Cumulative Singular Entropy’, ‘Stabilization Point’);

% Number of components based on entropy stabilization
num_components = stable_point + 1;
disp([‘Number of components based on entropy stabilization: ‘, num2str(num_components)]);

% Reconstruct the signal by categorizing components
trend_indices = 1; % Trend corresponds to the first component
oscillation_indices = 2:num_components; % Oscillations from second component to stabilization point
noise_indices = (num_components + 1):length(S); % Noise corresponds to components after stabilization

% Reconstruct signals based on selected indices
trend_signal = reconstruct_signal(U, S, V’, trend_indices, length(signal));
oscillation_signal = reconstruct_signal(U, S, V’, oscillation_indices, length(signal));
noise_signal = reconstruct_signal(U, S, V’, noise_indices, length(signal));

% Plot the original signal, Trend, Oscillations, and Noise
figure;
plot(signal, ‘b’, ‘DisplayName’, ‘Original Signal’, ‘LineWidth’, 1.5);
hold on;
plot(trend_signal, ‘g’, ‘DisplayName’, ‘Trend’, ‘LineWidth’, 1.5);
plot(oscillation_signal, ‘orange’, ‘DisplayName’, ‘Oscillations’, ‘LineWidth’, 1.5);
plot(noise_signal, ‘r’, ‘DisplayName’, ‘Noise’, ‘LineWidth’, 1.5);
title(‘Signal Decomposition’);
xlabel(‘Time’);
ylabel(‘Amplitude’);
legend;
grid on; singular spectrum analysis, ssa, entropy, signal d MATLAB Answers — New Questions

​

Hello,
When updating the channel feed the TCP connection is terminated by the Thingspeak server immediately after each request (doesn’t matter "bad" or "good").
That behavior does not depend on the method I use (reqests from ESP8266 via WiFi, GPRS, ordinary terminal program under Windows etc.)
Is that normal?
From technical point of view this is not a problem but when using GPRS the service provider charges me for 100kB of data each time the new TCP connection is open (even if seding 100 bytes) so I suspect it would be better (cheaper) to keep the connection open between the reqests.

Thank you in advance for any advice.Hello,
When updating the channel feed the TCP connection is terminated by the Thingspeak server immediately after each request (doesn’t matter "bad" or "good").
That behavior does not depend on the method I use (reqests from ESP8266 via WiFi, GPRS, ordinary terminal program under Windows etc.)
Is that normal?
From technical point of view this is not a problem but when using GPRS the service provider charges me for 100kB of data each time the new TCP connection is open (even if seding 100 bytes) so I suspect it would be better (cheaper) to keep the connection open between the reqests.

Thank you in advance for any advice. Hello,
When updating the channel feed the TCP connection is terminated by the Thingspeak server immediately after each request (doesn’t matter "bad" or "good").
That behavior does not depend on the method I use (reqests from ESP8266 via WiFi, GPRS, ordinary terminal program under Windows etc.)
Is that normal?
From technical point of view this is not a problem but when using GPRS the service provider charges me for 100kB of data each time the new TCP connection is open (even if seding 100 bytes) so I suspect it would be better (cheaper) to keep the connection open between the reqests.

Thank you in advance for any advice. tcp, disconnection MATLAB Answers — New Questions

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I just installed a trial version of matlab 2015b and I need to add the Autosar package. I just download it and now trying to install it but I get this error from the Support package installer "Unable to load support package information. This may be duw to network problemas. Verify your network connection and retry the support package installation. If the problem persist, contact MathWorks technical support"
I check my network and I have good connection and speed. I alredy did around 20 retries but the issue persist. I noticed that other users experiences the same issue, one of them said retrying fix the problem, but that is not working for me.I just installed a trial version of matlab 2015b and I need to add the Autosar package. I just download it and now trying to install it but I get this error from the Support package installer "Unable to load support package information. This may be duw to network problemas. Verify your network connection and retry the support package installation. If the problem persist, contact MathWorks technical support"
I check my network and I have good connection and speed. I alredy did around 20 retries but the issue persist. I noticed that other users experiences the same issue, one of them said retrying fix the problem, but that is not working for me. I just installed a trial version of matlab 2015b and I need to add the Autosar package. I just download it and now trying to install it but I get this error from the Support package installer "Unable to load support package information. This may be duw to network problemas. Verify your network connection and retry the support package installation. If the problem persist, contact MathWorks technical support"
I check my network and I have good connection and speed. I alredy did around 20 retries but the issue persist. I noticed that other users experiences the same issue, one of them said retrying fix the problem, but that is not working for me. autosar, package installation MATLAB Answers — New Questions

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Hello all,
Currently I have a Simulink customed block (named as Teq block) for my own project which was built in s-function for simulation and tlc for code generation. Because of some reasons on algorithm, I have to find a way to get the value of a Simulink.Parameter(another block) via my Teq_sf.tlc file. However, my tlc file can only get the name of Simulink.Parameter instead of its value as my expectation.
I tried to define a mdlRTW function in sf.c file and call this input to tlc file via RTW, as followings:
static void mdlRTW(SimStruct *S) % in sf.c file
{
real64 *u1 = (real64 *) ssGetInputPortSignal(S, 0);
if (!ssWriteRTWParamSettings( S, 1, SSWRITE_VALUE_QSTR, "Input_Value", *u1))
{
ssSetErrorStatus(S,"Error writing parameter data to .rtw file");
return;
}
}
and %assign input_value = SFcnParamSettings.Input_Value (in tlc)
However, this way did not work because system can not understand SFcnParamSettings. Currently, this is the big blocker for my progress.
Do you know any valid solutions which I can get a value of Simulink.Parameter into my Teq_sf.tlc file but not the name?
Thank you in advance!Hello all,
Currently I have a Simulink customed block (named as Teq block) for my own project which was built in s-function for simulation and tlc for code generation. Because of some reasons on algorithm, I have to find a way to get the value of a Simulink.Parameter(another block) via my Teq_sf.tlc file. However, my tlc file can only get the name of Simulink.Parameter instead of its value as my expectation.
I tried to define a mdlRTW function in sf.c file and call this input to tlc file via RTW, as followings:
static void mdlRTW(SimStruct *S) % in sf.c file
{
real64 *u1 = (real64 *) ssGetInputPortSignal(S, 0);
if (!ssWriteRTWParamSettings( S, 1, SSWRITE_VALUE_QSTR, "Input_Value", *u1))
{
ssSetErrorStatus(S,"Error writing parameter data to .rtw file");
return;
}
}
and %assign input_value = SFcnParamSettings.Input_Value (in tlc)
However, this way did not work because system can not understand SFcnParamSettings. Currently, this is the big blocker for my progress.
Do you know any valid solutions which I can get a value of Simulink.Parameter into my Teq_sf.tlc file but not the name?
Thank you in advance! Hello all,
Currently I have a Simulink customed block (named as Teq block) for my own project which was built in s-function for simulation and tlc for code generation. Because of some reasons on algorithm, I have to find a way to get the value of a Simulink.Parameter(another block) via my Teq_sf.tlc file. However, my tlc file can only get the name of Simulink.Parameter instead of its value as my expectation.
I tried to define a mdlRTW function in sf.c file and call this input to tlc file via RTW, as followings:
static void mdlRTW(SimStruct *S) % in sf.c file
{
real64 *u1 = (real64 *) ssGetInputPortSignal(S, 0);
if (!ssWriteRTWParamSettings( S, 1, SSWRITE_VALUE_QSTR, "Input_Value", *u1))
{
ssSetErrorStatus(S,"Error writing parameter data to .rtw file");
return;
}
}
and %assign input_value = SFcnParamSettings.Input_Value (in tlc)
However, this way did not work because system can not understand SFcnParamSettings. Currently, this is the big blocker for my progress.
Do you know any valid solutions which I can get a value of Simulink.Parameter into my Teq_sf.tlc file but not the name?
Thank you in advance! matlab, simulink, s-function, tlc MATLAB Answers — New Questions

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