function ProjectOzone
clc
ki = 7e-3;
kj = 2e-5;
k1 = 1e3;
k2 = 1e3;
k3 = 5e7;
OH = 1e-6;
%constants
t0=0;
tn=1000000;
% time iteration interval is set to a high value to reach steady state
Ca0 = 0.002292;
Cb0 = 0;
Cc0 = 0;
% initial concentrations
[tsoln, Csoln] = ode45(@Ozone,[t0 tn],[Ca0 Cb0 Cc0]);
plot(tsoln, Csoln)
xlabel(‘Time’)
ylabel(‘Concentration’)
legend(‘Cb’)
function dC = Ozone(~, C)
dC = zeros(3,1);
dC(1) = -C(1)*(3*ki + kj*OH + 2*k2*sqrt((C(1)/(2*k3))*(2*ki+kj*OH)));
dC(2) = (2*ki)*C(1) – k1*C(1)*C(2) + k2*C(1)*C(3);
dC(3) = kj*C(1)*OH + k1*C(1)*C(2) – k2*C(1)*C(3) – (2*k3)*(C(3)^2);
end %dC(1) is evaluated by steady state approximation method
end
%HERE IS THE CODE, I POST IT IF YOU WANT TO CHECKfunction ProjectOzone
clc
ki = 7e-3;
kj = 2e-5;
k1 = 1e3;
k2 = 1e3;
k3 = 5e7;
OH = 1e-6;
%constants
t0=0;
tn=1000000;
% time iteration interval is set to a high value to reach steady state
Ca0 = 0.002292;
Cb0 = 0;
Cc0 = 0;
% initial concentrations
[tsoln, Csoln] = ode45(@Ozone,[t0 tn],[Ca0 Cb0 Cc0]);
plot(tsoln, Csoln)
xlabel(‘Time’)
ylabel(‘Concentration’)
legend(‘Cb’)
function dC = Ozone(~, C)
dC = zeros(3,1);
dC(1) = -C(1)*(3*ki + kj*OH + 2*k2*sqrt((C(1)/(2*k3))*(2*ki+kj*OH)));
dC(2) = (2*ki)*C(1) – k1*C(1)*C(2) + k2*C(1)*C(3);
dC(3) = kj*C(1)*OH + k1*C(1)*C(2) – k2*C(1)*C(3) – (2*k3)*(C(3)^2);
end %dC(1) is evaluated by steady state approximation method
end
%HERE IS THE CODE, I POST IT IF YOU WANT TO CHECK function ProjectOzone
clc
ki = 7e-3;
kj = 2e-5;
k1 = 1e3;
k2 = 1e3;
k3 = 5e7;
OH = 1e-6;
%constants
t0=0;
tn=1000000;
% time iteration interval is set to a high value to reach steady state
Ca0 = 0.002292;
Cb0 = 0;
Cc0 = 0;
% initial concentrations
[tsoln, Csoln] = ode45(@Ozone,[t0 tn],[Ca0 Cb0 Cc0]);
plot(tsoln, Csoln)
xlabel(‘Time’)
ylabel(‘Concentration’)
legend(‘Cb’)
function dC = Ozone(~, C)
dC = zeros(3,1);
dC(1) = -C(1)*(3*ki + kj*OH + 2*k2*sqrt((C(1)/(2*k3))*(2*ki+kj*OH)));
dC(2) = (2*ki)*C(1) – k1*C(1)*C(2) + k2*C(1)*C(3);
dC(3) = kj*C(1)*OH + k1*C(1)*C(2) – k2*C(1)*C(3) – (2*k3)*(C(3)^2);
end %dC(1) is evaluated by steady state approximation method
end
%HERE IS THE CODE, I POST IT IF YOU WANT TO CHECK ozone-degradation, mathematical modelling, ode45, plotting, plot MATLAB Answers — New Questions

​

Hi everyone, i’m experimenting with using a DNN for modeling an electrical circuit. I have done many tries and until now i always used one sample for input( sequenceInputLayer(1) ) a one for output( since it is a regression task). Now, since the circuit i am modeling has memory, i would like to try to extend input lenght to count for more input sample at once but keeping my output as one single sample. I am facing problems in this beacuse i have my input_data vector that is 1167×1 so 1 column. This means that sequenceInputLayer expect always 1 as dimension and i am limited to this.
On the other hand i can transpose my vectors and use 1167 as input and output dimension, but I would like more freedom and try for example a NN with an input layer of size 300, then 2 hidden layes with size 50/100 (using tanh ecc..) and then my final output layer with output size 1 ( again, since this is a regression task). I already read MATLAB documentstion but i didn’t find what i am searching for. This is the code i am using now, if you have suggestions i would be really happy!!!

Maybe i have to use mini batches? What changes i have to do in order to realize the network i said before?

(Yes, i know that for dynamical systems with memory CNN and RNN neural nets are better but in literature there are also papaer that use DNN for this type of modeling, i.e. using more input(past sample) in a DNN for taking into account memory effects).
%load Vout.mat %LTspice data
t = (0:5.145e-5:60e-3)’; %creates 1167 samples
input_data=10*sin(200*2*pi*t);
output_data=Vout;
% plot(input_data);hold on
% plot(output_data);
%B = reshape(input_data,[],4); %ottengo numero di colonne specifiche,4

% Creazione della rete neurale profonda
layers = […
sequenceInputLayer(1)
fullyConnectedLayer(10)
reluLayer
%lstmLayer(100)
fullyConnectedLayer(10)
sigmoidLayer
fullyConnectedLayer(10)
tanhLayer
fullyConnectedLayer(1) % Output layer con una sola uscita
%tanhLayer
];

% Creazione della dlnetwork
net = dlnetwork(layers);

% Impostazione delle opzioni di addestramento
options = trainingOptions(‘adam’, …
‘MaxEpochs’,1000,…
‘InitialLearnRate’,1e-2, …
‘Verbose’,false, …
‘Plots’,’training-progress’);

% Addestramento della rete neurale
net = trainnet(input_data, output_data, net,"huber" , options);
%Predict
output_test = predict(net,input_data);Hi everyone, i’m experimenting with using a DNN for modeling an electrical circuit. I have done many tries and until now i always used one sample for input( sequenceInputLayer(1) ) a one for output( since it is a regression task). Now, since the circuit i am modeling has memory, i would like to try to extend input lenght to count for more input sample at once but keeping my output as one single sample. I am facing problems in this beacuse i have my input_data vector that is 1167×1 so 1 column. This means that sequenceInputLayer expect always 1 as dimension and i am limited to this.
On the other hand i can transpose my vectors and use 1167 as input and output dimension, but I would like more freedom and try for example a NN with an input layer of size 300, then 2 hidden layes with size 50/100 (using tanh ecc..) and then my final output layer with output size 1 ( again, since this is a regression task). I already read MATLAB documentstion but i didn’t find what i am searching for. This is the code i am using now, if you have suggestions i would be really happy!!!

Maybe i have to use mini batches? What changes i have to do in order to realize the network i said before?

(Yes, i know that for dynamical systems with memory CNN and RNN neural nets are better but in literature there are also papaer that use DNN for this type of modeling, i.e. using more input(past sample) in a DNN for taking into account memory effects).
%load Vout.mat %LTspice data
t = (0:5.145e-5:60e-3)’; %creates 1167 samples
input_data=10*sin(200*2*pi*t);
output_data=Vout;
% plot(input_data);hold on
% plot(output_data);
%B = reshape(input_data,[],4); %ottengo numero di colonne specifiche,4

% Creazione della rete neurale profonda
layers = […
sequenceInputLayer(1)
fullyConnectedLayer(10)
reluLayer
%lstmLayer(100)
fullyConnectedLayer(10)
sigmoidLayer
fullyConnectedLayer(10)
tanhLayer
fullyConnectedLayer(1) % Output layer con una sola uscita
%tanhLayer
];

% Creazione della dlnetwork
net = dlnetwork(layers);

% Impostazione delle opzioni di addestramento
options = trainingOptions(‘adam’, …
‘MaxEpochs’,1000,…
‘InitialLearnRate’,1e-2, …
‘Verbose’,false, …
‘Plots’,’training-progress’);

% Addestramento della rete neurale
net = trainnet(input_data, output_data, net,"huber" , options);
%Predict
output_test = predict(net,input_data); Hi everyone, i’m experimenting with using a DNN for modeling an electrical circuit. I have done many tries and until now i always used one sample for input( sequenceInputLayer(1) ) a one for output( since it is a regression task). Now, since the circuit i am modeling has memory, i would like to try to extend input lenght to count for more input sample at once but keeping my output as one single sample. I am facing problems in this beacuse i have my input_data vector that is 1167×1 so 1 column. This means that sequenceInputLayer expect always 1 as dimension and i am limited to this.
On the other hand i can transpose my vectors and use 1167 as input and output dimension, but I would like more freedom and try for example a NN with an input layer of size 300, then 2 hidden layes with size 50/100 (using tanh ecc..) and then my final output layer with output size 1 ( again, since this is a regression task). I already read MATLAB documentstion but i didn’t find what i am searching for. This is the code i am using now, if you have suggestions i would be really happy!!!

Maybe i have to use mini batches? What changes i have to do in order to realize the network i said before?

(Yes, i know that for dynamical systems with memory CNN and RNN neural nets are better but in literature there are also papaer that use DNN for this type of modeling, i.e. using more input(past sample) in a DNN for taking into account memory effects).
%load Vout.mat %LTspice data
t = (0:5.145e-5:60e-3)’; %creates 1167 samples
input_data=10*sin(200*2*pi*t);
output_data=Vout;
% plot(input_data);hold on
% plot(output_data);
%B = reshape(input_data,[],4); %ottengo numero di colonne specifiche,4

% Creazione della rete neurale profonda
layers = […
sequenceInputLayer(1)
fullyConnectedLayer(10)
reluLayer
%lstmLayer(100)
fullyConnectedLayer(10)
sigmoidLayer
fullyConnectedLayer(10)
tanhLayer
fullyConnectedLayer(1) % Output layer con una sola uscita
%tanhLayer
];

% Creazione della dlnetwork
net = dlnetwork(layers);

% Impostazione delle opzioni di addestramento
options = trainingOptions(‘adam’, …
‘MaxEpochs’,1000,…
‘InitialLearnRate’,1e-2, …
‘Verbose’,false, …
‘Plots’,’training-progress’);

% Addestramento della rete neurale
net = trainnet(input_data, output_data, net,"huber" , options);
%Predict
output_test = predict(net,input_data); neural networks, circuit modeling, feedforward neural networks MATLAB Answers — New Questions

​

Hi, I’m doing a research about appliying virtual synchronous machine in tidal energy.
Do anyone has a simulink model of Virtual Synchronous Machine (VSM)?
Many thanks.Hi, I’m doing a research about appliying virtual synchronous machine in tidal energy.
Do anyone has a simulink model of Virtual Synchronous Machine (VSM)?
Many thanks. Hi, I’m doing a research about appliying virtual synchronous machine in tidal energy.
Do anyone has a simulink model of Virtual Synchronous Machine (VSM)?
Many thanks. virtual synchronous generator MATLAB Answers — New Questions

​

I am new in use of MATLAB_. I want to do a nonlinear optimization with genetic algorithm in Solver-based application. My problem is that I failed, but if I start again, it still includes the things and choices I entered before. How can I start with a clean sheet?I am new in use of MATLAB_. I want to do a nonlinear optimization with genetic algorithm in Solver-based application. My problem is that I failed, but if I start again, it still includes the things and choices I entered before. How can I start with a clean sheet? I am new in use of MATLAB_. I want to do a nonlinear optimization with genetic algorithm in Solver-based application. My problem is that I failed, but if I start again, it still includes the things and choices I entered before. How can I start with a clean sheet? use optimize leve editor task MATLAB Answers — New Questions

​

Post Content Post Content horzcat MATLAB Answers — New Questions

​

Hello,
I am trying to build a Bus (struct) (<Quiver>) that has an array of a nested bus type (<Arrow>). Later, I want to dissect this array (<arrows>), loop over it and use the members (<point>, <head>) of that bus object.
However, I get the following error:
Selected signal ‘arrows.origin.x’ in the Bus Selector block ‘Demo/Inport’ is invalid since it refers to a bus element within an array of sub-buses. The path to the array of sub-buses is ‘arrows’. Please select the appropriate array element using a Selector block before using the Bus Selector block to access an element within the bus.

This is very annoying. Programatically this is totally normal, nested stucts. Why can I specify the dimension of that struct array, if it wont compile?

How can I do what I intend to do?

This is what I am doing, basically:Hello,
I am trying to build a Bus (struct) (<Quiver>) that has an array of a nested bus type (<Arrow>). Later, I want to dissect this array (<arrows>), loop over it and use the members (<point>, <head>) of that bus object.
However, I get the following error:
Selected signal ‘arrows.origin.x’ in the Bus Selector block ‘Demo/Inport’ is invalid since it refers to a bus element within an array of sub-buses. The path to the array of sub-buses is ‘arrows’. Please select the appropriate array element using a Selector block before using the Bus Selector block to access an element within the bus.

This is very annoying. Programatically this is totally normal, nested stucts. Why can I specify the dimension of that struct array, if it wont compile?

How can I do what I intend to do?

This is what I am doing, basically: Hello,
I am trying to build a Bus (struct) (<Quiver>) that has an array of a nested bus type (<Arrow>). Later, I want to dissect this array (<arrows>), loop over it and use the members (<point>, <head>) of that bus object.
However, I get the following error:
Selected signal ‘arrows.origin.x’ in the Bus Selector block ‘Demo/Inport’ is invalid since it refers to a bus element within an array of sub-buses. The path to the array of sub-buses is ‘arrows’. Please select the appropriate array element using a Selector block before using the Bus Selector block to access an element within the bus.

This is very annoying. Programatically this is totally normal, nested stucts. Why can I specify the dimension of that struct array, if it wont compile?

How can I do what I intend to do?

This is what I am doing, basically: array, bus, simulink, matlab, error MATLAB Answers — New Questions

​

Hi all,
I have been having issues setting my sampling rate/ DAQ.Rate to the values 2048 and 120. I am using a start function that is set for x amount of seconds. This then triggers the scansavailablecallback and within this callback it will readout about 1 tenth of the values to be graphed out over time. When setting the DAQ’s Rate to 2048 is collects in sample batches of 205, this leaves the sample batch for 0.9s to 1.0s missing 2 samples and does not end up triggering final 0.1s of the callback. I am not too sure why it does this and am wondering if using the scansavailable callback is part of the issue. I have tried to change the DAQ rate for the 0.9-1.0s time to then activate the callback again but this does not seem to work. A similiar issue happens for 120 where it’s sample batch seems to be 13 which I am quite confused by. Any ideas or guidance on how to go about solving these issues would be greatly appreciated.

This is the callback function
function scansAvailable_Callback(app, src, ~)

% Callback function executed on DAQ object ScansAvailable event
% runs after the start button pushed event is complete
if ~isvalid(app)
return
end
disp (app.numscan)
% Read data from the DAQ object

% Read data from the DAQ object
[app.data, timestamps] = read(src,app.DAQ.ScansAvailableFcnCount, ‘OutputFormat’,’Matrix’);

test = [timestamps,app.data];

assignin("base","test",test)

% Calibration
if app.calibrate == true
% sends data to function to be calibrated appropriately
app.calibratedData = calibrateData(app,app.data, app.sl, app.int);
else
% if calibration is not turned off, just take the voltage values
for jj = app.SelectedChannels
app.calibratedData(:,jj) = (app.data(:,jj));
end
end

% checking too put the app back into configuration @ the end of the

% Store continuous acquisition data in FIFO data buffers
buffersize = round(app.DAQ.Rate * app.TimewindowEditField.Value) + 1;

app.TimestampsFIFOBuffer = storeDataInFIFO(app, app.TimestampsFIFOBuffer, buffersize, timestamps);
app.DataFIFOBufferch1 = storeDataInFIFO(app, app.DataFIFOBufferch1, buffersize, app.calibratedData(:,app.SelectedChannels));

% Plotting (Update the plot every N data points received
updatePlotInterval = 1; % Adjust this value based on your preference
if mod(src.ScansAvailableFcnCount, updatePlotInterval) == 0

% runs function that is responsible for plotting a singular
% graph
updateLivePlot(app);
end

% for data that goes to the workspace and
for ii = 1:(round(app.RateEdit.Value/10))

% keep count of the sample#/time
app.fws_x = app.fws_x + 1;

% initialize data to be written into the workspace
app.fwsdata(app.fws_x,(app.SelectedChannels)) = app.calibratedData(ii,app.SelectedChannels);

end
disp(app.fws_x)

if app.ContinuousSwitch.Value == "Off"
lengthcheck = (app.TrialLengthsEditField.Value* app.RateEdit.Value);
disp(lengthcheck)

if app.fws_x >= lengthcheck

if app.LogRequested
try
% Change the data to a timetable
WSData = array2timetable(app.fwsdata(1:app.TrialLengthsEditField.Value*app.DAQ.Rate,app.SelectedChannels),’SampleRate’,app.RateEdit.Value);
varname = table2array(app.UITable.Data(:,1));

% renames the variable names to match the channel names
for i = app.SelectedChannels
WSData = renamevars(WSData,i,varname(i));
end

% write into base workspace with the name stated in the UI
assignin(‘base’, app.VariableNameEditField.Value, WSData);
assignin("base","variable_name",app.VariableNameEditField.Value)
catch exception
uialert(app.LiveDataAcquisitionUIFigure,exception.message,’You cannot start the variable name with a number’)
end
end
setAppViewState(app, ‘configuration’);

end

end

app.numscan = app.numscan + 1;

end
And the following is what initiates the callback
function StartButtonPushed(app, event)
% so the app can go back into "configuration state"
app.fws_x = 0;
app.fwsdata = 0;
app.wstimestamps= 0;

% clearing the graphs just helps with mainly tab 2 when the
% user is switching where a channels goes (between the 2
% graphs)

cla(app.LiveAxes)

if numel(app.SelectedChannels) == 0
warndlg(‘Please select Channels to record’,’No Channels to Record from Selected’)
else

% dur = isinteger(app.DAQ.Rate/10);

% Disable DAQ device, channel properties, and start acquisition UI components
setAppViewState(app, ‘acquisition’);

% Reset FIFO buffer data
app.calibratedData = [];
app.DataFIFOBufferch1 = [];
app.TimestampsFIFOBuffer = [];

% Checks to see if the app is set to continuous or has a the
% User selected to use a recording window instead
if app.continuous == true

% preallocate for the data that is to be written for
% the workspace
app.fwsdata = zeros(400000,numel(app.numchan));
try
% starts data collection
start(app.DAQ,’continuous’);

catch exception
% In case of error show it and revert the change
uialert(app.LiveDataAcquisitionUIFigure, exception.message, ‘Start error, you may have selected channels too quick!’);
setAppViewState(app, ‘configuration’);

end
else
% preallocate for the data that is to be written for
% the workspace based on the trial length and the
% sample rate
app.fwsdata = zeros((app.RateEdit.Value*app.TrialLengthsEditField.Value),numel(app.numchan));
try
start(app.DAQ,"Duration",app.TrialLengthsEditField.Value)

catch exception
% In case of error show it and revert the change
uialert(app.LiveDataAcquisitionUIFigure, exception.message, ‘Start error’);
setAppViewState(app, ‘configuration’);
end

end
% Clear Log status text
app.LogStatusText.Text = ”;
endHi all,
I have been having issues setting my sampling rate/ DAQ.Rate to the values 2048 and 120. I am using a start function that is set for x amount of seconds. This then triggers the scansavailablecallback and within this callback it will readout about 1 tenth of the values to be graphed out over time. When setting the DAQ’s Rate to 2048 is collects in sample batches of 205, this leaves the sample batch for 0.9s to 1.0s missing 2 samples and does not end up triggering final 0.1s of the callback. I am not too sure why it does this and am wondering if using the scansavailable callback is part of the issue. I have tried to change the DAQ rate for the 0.9-1.0s time to then activate the callback again but this does not seem to work. A similiar issue happens for 120 where it’s sample batch seems to be 13 which I am quite confused by. Any ideas or guidance on how to go about solving these issues would be greatly appreciated.

This is the callback function
function scansAvailable_Callback(app, src, ~)

% Callback function executed on DAQ object ScansAvailable event
% runs after the start button pushed event is complete
if ~isvalid(app)
return
end
disp (app.numscan)
% Read data from the DAQ object

% Read data from the DAQ object
[app.data, timestamps] = read(src,app.DAQ.ScansAvailableFcnCount, ‘OutputFormat’,’Matrix’);

test = [timestamps,app.data];

assignin("base","test",test)

% Calibration
if app.calibrate == true
% sends data to function to be calibrated appropriately
app.calibratedData = calibrateData(app,app.data, app.sl, app.int);
else
% if calibration is not turned off, just take the voltage values
for jj = app.SelectedChannels
app.calibratedData(:,jj) = (app.data(:,jj));
end
end

% checking too put the app back into configuration @ the end of the

% Store continuous acquisition data in FIFO data buffers
buffersize = round(app.DAQ.Rate * app.TimewindowEditField.Value) + 1;

app.TimestampsFIFOBuffer = storeDataInFIFO(app, app.TimestampsFIFOBuffer, buffersize, timestamps);
app.DataFIFOBufferch1 = storeDataInFIFO(app, app.DataFIFOBufferch1, buffersize, app.calibratedData(:,app.SelectedChannels));

% Plotting (Update the plot every N data points received
updatePlotInterval = 1; % Adjust this value based on your preference
if mod(src.ScansAvailableFcnCount, updatePlotInterval) == 0

% runs function that is responsible for plotting a singular
% graph
updateLivePlot(app);
end

% for data that goes to the workspace and
for ii = 1:(round(app.RateEdit.Value/10))

% keep count of the sample#/time
app.fws_x = app.fws_x + 1;

% initialize data to be written into the workspace
app.fwsdata(app.fws_x,(app.SelectedChannels)) = app.calibratedData(ii,app.SelectedChannels);

end
disp(app.fws_x)

if app.ContinuousSwitch.Value == "Off"
lengthcheck = (app.TrialLengthsEditField.Value* app.RateEdit.Value);
disp(lengthcheck)

if app.fws_x >= lengthcheck

if app.LogRequested
try
% Change the data to a timetable
WSData = array2timetable(app.fwsdata(1:app.TrialLengthsEditField.Value*app.DAQ.Rate,app.SelectedChannels),’SampleRate’,app.RateEdit.Value);
varname = table2array(app.UITable.Data(:,1));

% renames the variable names to match the channel names
for i = app.SelectedChannels
WSData = renamevars(WSData,i,varname(i));
end

% write into base workspace with the name stated in the UI
assignin(‘base’, app.VariableNameEditField.Value, WSData);
assignin("base","variable_name",app.VariableNameEditField.Value)
catch exception
uialert(app.LiveDataAcquisitionUIFigure,exception.message,’You cannot start the variable name with a number’)
end
end
setAppViewState(app, ‘configuration’);

end

end

app.numscan = app.numscan + 1;

end
And the following is what initiates the callback
function StartButtonPushed(app, event)
% so the app can go back into "configuration state"
app.fws_x = 0;
app.fwsdata = 0;
app.wstimestamps= 0;

% clearing the graphs just helps with mainly tab 2 when the
% user is switching where a channels goes (between the 2
% graphs)

cla(app.LiveAxes)

if numel(app.SelectedChannels) == 0
warndlg(‘Please select Channels to record’,’No Channels to Record from Selected’)
else

% dur = isinteger(app.DAQ.Rate/10);

% Disable DAQ device, channel properties, and start acquisition UI components
setAppViewState(app, ‘acquisition’);

% Reset FIFO buffer data
app.calibratedData = [];
app.DataFIFOBufferch1 = [];
app.TimestampsFIFOBuffer = [];

% Checks to see if the app is set to continuous or has a the
% User selected to use a recording window instead
if app.continuous == true

% preallocate for the data that is to be written for
% the workspace
app.fwsdata = zeros(400000,numel(app.numchan));
try
% starts data collection
start(app.DAQ,’continuous’);

catch exception
% In case of error show it and revert the change
uialert(app.LiveDataAcquisitionUIFigure, exception.message, ‘Start error, you may have selected channels too quick!’);
setAppViewState(app, ‘configuration’);

end
else
% preallocate for the data that is to be written for
% the workspace based on the trial length and the
% sample rate
app.fwsdata = zeros((app.RateEdit.Value*app.TrialLengthsEditField.Value),numel(app.numchan));
try
start(app.DAQ,"Duration",app.TrialLengthsEditField.Value)

catch exception
% In case of error show it and revert the change
uialert(app.LiveDataAcquisitionUIFigure, exception.message, ‘Start error’);
setAppViewState(app, ‘configuration’);
end

end
% Clear Log status text
app.LogStatusText.Text = ”;
end Hi all,
I have been having issues setting my sampling rate/ DAQ.Rate to the values 2048 and 120. I am using a start function that is set for x amount of seconds. This then triggers the scansavailablecallback and within this callback it will readout about 1 tenth of the values to be graphed out over time. When setting the DAQ’s Rate to 2048 is collects in sample batches of 205, this leaves the sample batch for 0.9s to 1.0s missing 2 samples and does not end up triggering final 0.1s of the callback. I am not too sure why it does this and am wondering if using the scansavailable callback is part of the issue. I have tried to change the DAQ rate for the 0.9-1.0s time to then activate the callback again but this does not seem to work. A similiar issue happens for 120 where it’s sample batch seems to be 13 which I am quite confused by. Any ideas or guidance on how to go about solving these issues would be greatly appreciated.

This is the callback function
function scansAvailable_Callback(app, src, ~)

% Callback function executed on DAQ object ScansAvailable event
% runs after the start button pushed event is complete
if ~isvalid(app)
return
end
disp (app.numscan)
% Read data from the DAQ object

% Read data from the DAQ object
[app.data, timestamps] = read(src,app.DAQ.ScansAvailableFcnCount, ‘OutputFormat’,’Matrix’);

test = [timestamps,app.data];

assignin("base","test",test)

% Calibration
if app.calibrate == true
% sends data to function to be calibrated appropriately
app.calibratedData = calibrateData(app,app.data, app.sl, app.int);
else
% if calibration is not turned off, just take the voltage values
for jj = app.SelectedChannels
app.calibratedData(:,jj) = (app.data(:,jj));
end
end

% checking too put the app back into configuration @ the end of the

% Store continuous acquisition data in FIFO data buffers
buffersize = round(app.DAQ.Rate * app.TimewindowEditField.Value) + 1;

app.TimestampsFIFOBuffer = storeDataInFIFO(app, app.TimestampsFIFOBuffer, buffersize, timestamps);
app.DataFIFOBufferch1 = storeDataInFIFO(app, app.DataFIFOBufferch1, buffersize, app.calibratedData(:,app.SelectedChannels));

% Plotting (Update the plot every N data points received
updatePlotInterval = 1; % Adjust this value based on your preference
if mod(src.ScansAvailableFcnCount, updatePlotInterval) == 0

% runs function that is responsible for plotting a singular
% graph
updateLivePlot(app);
end

% for data that goes to the workspace and
for ii = 1:(round(app.RateEdit.Value/10))

% keep count of the sample#/time
app.fws_x = app.fws_x + 1;

% initialize data to be written into the workspace
app.fwsdata(app.fws_x,(app.SelectedChannels)) = app.calibratedData(ii,app.SelectedChannels);

end
disp(app.fws_x)

if app.ContinuousSwitch.Value == "Off"
lengthcheck = (app.TrialLengthsEditField.Value* app.RateEdit.Value);
disp(lengthcheck)

if app.fws_x >= lengthcheck

if app.LogRequested
try
% Change the data to a timetable
WSData = array2timetable(app.fwsdata(1:app.TrialLengthsEditField.Value*app.DAQ.Rate,app.SelectedChannels),’SampleRate’,app.RateEdit.Value);
varname = table2array(app.UITable.Data(:,1));

% renames the variable names to match the channel names
for i = app.SelectedChannels
WSData = renamevars(WSData,i,varname(i));
end

% write into base workspace with the name stated in the UI
assignin(‘base’, app.VariableNameEditField.Value, WSData);
assignin("base","variable_name",app.VariableNameEditField.Value)
catch exception
uialert(app.LiveDataAcquisitionUIFigure,exception.message,’You cannot start the variable name with a number’)
end
end
setAppViewState(app, ‘configuration’);

end

end

app.numscan = app.numscan + 1;

end
And the following is what initiates the callback
function StartButtonPushed(app, event)
% so the app can go back into "configuration state"
app.fws_x = 0;
app.fwsdata = 0;
app.wstimestamps= 0;

% clearing the graphs just helps with mainly tab 2 when the
% user is switching where a channels goes (between the 2
% graphs)

cla(app.LiveAxes)

if numel(app.SelectedChannels) == 0
warndlg(‘Please select Channels to record’,’No Channels to Record from Selected’)
else

% dur = isinteger(app.DAQ.Rate/10);

% Disable DAQ device, channel properties, and start acquisition UI components
setAppViewState(app, ‘acquisition’);

% Reset FIFO buffer data
app.calibratedData = [];
app.DataFIFOBufferch1 = [];
app.TimestampsFIFOBuffer = [];

% Checks to see if the app is set to continuous or has a the
% User selected to use a recording window instead
if app.continuous == true

% preallocate for the data that is to be written for
% the workspace
app.fwsdata = zeros(400000,numel(app.numchan));
try
% starts data collection
start(app.DAQ,’continuous’);

catch exception
% In case of error show it and revert the change
uialert(app.LiveDataAcquisitionUIFigure, exception.message, ‘Start error, you may have selected channels too quick!’);
setAppViewState(app, ‘configuration’);

end
else
% preallocate for the data that is to be written for
% the workspace based on the trial length and the
% sample rate
app.fwsdata = zeros((app.RateEdit.Value*app.TrialLengthsEditField.Value),numel(app.numchan));
try
start(app.DAQ,"Duration",app.TrialLengthsEditField.Value)

catch exception
% In case of error show it and revert the change
uialert(app.LiveDataAcquisitionUIFigure, exception.message, ‘Start error’);
setAppViewState(app, ‘configuration’);
end

end
% Clear Log status text
app.LogStatusText.Text = ”;
end data acquisition, toolbox, graph, callback, appdesigner, app designer MATLAB Answers — New Questions

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idler_1 = 17;
idler_2 = 17;
idler_3 = 17;
Z_gears(1) = 17;
% Z_gear(1) is defined same holds for the idler’s
% Number of Z_gears is determined by N and gear_ratio_combo goes from (1,1)
% to (1,(columlength)
% idlers are on Z_gear place Z_gear(3),Z_gear(5),Z_gear(7) etc
% but if Z_gear(end) is uneven then do not place idler there
Z_gears(2) = Z_gears(1)*gear_ratio_combo(1,1);
Z_gears(3) = idler_1;
Z_gears(4) = Z_gears(3)*gear_ratio_combo(1,2);
Z_gears(5) = idler_2;
Z_gears(6) = Z_gears(5)*gear_ratio_combo(1,3);
Z_gears(7) = idler_3;
Z_gears(8) = Z_gears(7)*gear_ratio_combo(1,4);idler_1 = 17;
idler_2 = 17;
idler_3 = 17;
Z_gears(1) = 17;
% Z_gear(1) is defined same holds for the idler’s
% Number of Z_gears is determined by N and gear_ratio_combo goes from (1,1)
% to (1,(columlength)
% idlers are on Z_gear place Z_gear(3),Z_gear(5),Z_gear(7) etc
% but if Z_gear(end) is uneven then do not place idler there
Z_gears(2) = Z_gears(1)*gear_ratio_combo(1,1);
Z_gears(3) = idler_1;
Z_gears(4) = Z_gears(3)*gear_ratio_combo(1,2);
Z_gears(5) = idler_2;
Z_gears(6) = Z_gears(5)*gear_ratio_combo(1,3);
Z_gears(7) = idler_3;
Z_gears(8) = Z_gears(7)*gear_ratio_combo(1,4); idler_1 = 17;
idler_2 = 17;
idler_3 = 17;
Z_gears(1) = 17;
% Z_gear(1) is defined same holds for the idler’s
% Number of Z_gears is determined by N and gear_ratio_combo goes from (1,1)
% to (1,(columlength)
% idlers are on Z_gear place Z_gear(3),Z_gear(5),Z_gear(7) etc
% but if Z_gear(end) is uneven then do not place idler there
Z_gears(2) = Z_gears(1)*gear_ratio_combo(1,1);
Z_gears(3) = idler_1;
Z_gears(4) = Z_gears(3)*gear_ratio_combo(1,2);
Z_gears(5) = idler_2;
Z_gears(6) = Z_gears(5)*gear_ratio_combo(1,3);
Z_gears(7) = idler_3;
Z_gears(8) = Z_gears(7)*gear_ratio_combo(1,4); sequences, for loop MATLAB Answers — New Questions

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Hi,
it’s a timetable and i want to sum all duration equal:
example:
04/01/2010 2202 1.23
05/01/2010 2202 1.26
……
i want this:
duration value
22:02 2.49 (1.23+1.26)Hi,
it’s a timetable and i want to sum all duration equal:
example:
04/01/2010 2202 1.23
05/01/2010 2202 1.26
……
i want this:
duration value
22:02 2.49 (1.23+1.26) Hi,
it’s a timetable and i want to sum all duration equal:
example:
04/01/2010 2202 1.23
05/01/2010 2202 1.26
……
i want this:
duration value
22:02 2.49 (1.23+1.26) how aggregate duraration and sum it in timetable MATLAB Answers — New Questions

​

Matlab Predict function is not efficient…Perhaps because it is general suitable for all models. Is there a way to speed it? I am working with incremental regression kernel. Predict takes more time than updatemetricsandfit!Matlab Predict function is not efficient…Perhaps because it is general suitable for all models. Is there a way to speed it? I am working with incremental regression kernel. Predict takes more time than updatemetricsandfit! Matlab Predict function is not efficient…Perhaps because it is general suitable for all models. Is there a way to speed it? I am working with incremental regression kernel. Predict takes more time than updatemetricsandfit! efficient MATLAB Answers — New Questions

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