Hi, I have more than 200 ‘.txt’ files to process the data. Each file has same number of coloumns, i.e 10, but number of rows vary. Imported data to array variables and created a structure of each file. And further processing data of individual file for analysis.
Hy=importdata(‘hyde0710p1c1.15txt’);
Ii=importdata(‘iisc0710p1c1.15txt’);
save(‘Hy710.txt’,’Hy’); save(‘Ii710.txt’,’Ii’);
hyde_org=struct(‘GPSTOW’,Hy(:,1),’PRN’,Hy(:,2),’EL’,Hy(:,3),’AZ’,Hy(:,4),’IPPMLat’,Hy(:,5),’IPPGeoLong’,Hy(:,6),’IPPGeoLat’,Hy(:,7),’SlantCodeTEC’,Hy(:,8),’SlantSmoothTEC’,Hy(:,9),’VTEC’,Hy(:,10));
iisc_org=struct(‘GPSTOW’,Ii(:,1),’PRN’,Ii(:,2),’EL’,Ii(:,3),’AZ’,Ii(:,4),’IPPMLat’,Ii(:,5),’IPPGeoLong’,Ii(:,6),’IPPGeoLat’,Ii(:,7),’SlantCodeTEC’,Ii(:,8),’SlantSmoothTEC’,Ii(:,9),’VTEC’,Ii(:,10));
But,now I need to append all the data(to append rows one after the other file’s) of 200 files in ta single new file for processing.
Can anyone help please?Hi, I have more than 200 ‘.txt’ files to process the data. Each file has same number of coloumns, i.e 10, but number of rows vary. Imported data to array variables and created a structure of each file. And further processing data of individual file for analysis.
Hy=importdata(‘hyde0710p1c1.15txt’);
Ii=importdata(‘iisc0710p1c1.15txt’);
save(‘Hy710.txt’,’Hy’); save(‘Ii710.txt’,’Ii’);
hyde_org=struct(‘GPSTOW’,Hy(:,1),’PRN’,Hy(:,2),’EL’,Hy(:,3),’AZ’,Hy(:,4),’IPPMLat’,Hy(:,5),’IPPGeoLong’,Hy(:,6),’IPPGeoLat’,Hy(:,7),’SlantCodeTEC’,Hy(:,8),’SlantSmoothTEC’,Hy(:,9),’VTEC’,Hy(:,10));
iisc_org=struct(‘GPSTOW’,Ii(:,1),’PRN’,Ii(:,2),’EL’,Ii(:,3),’AZ’,Ii(:,4),’IPPMLat’,Ii(:,5),’IPPGeoLong’,Ii(:,6),’IPPGeoLat’,Ii(:,7),’SlantCodeTEC’,Ii(:,8),’SlantSmoothTEC’,Ii(:,9),’VTEC’,Ii(:,10));
But,now I need to append all the data(to append rows one after the other file’s) of 200 files in ta single new file for processing.
Can anyone help please? Hi, I have more than 200 ‘.txt’ files to process the data. Each file has same number of coloumns, i.e 10, but number of rows vary. Imported data to array variables and created a structure of each file. And further processing data of individual file for analysis.
Hy=importdata(‘hyde0710p1c1.15txt’);
Ii=importdata(‘iisc0710p1c1.15txt’);
save(‘Hy710.txt’,’Hy’); save(‘Ii710.txt’,’Ii’);
hyde_org=struct(‘GPSTOW’,Hy(:,1),’PRN’,Hy(:,2),’EL’,Hy(:,3),’AZ’,Hy(:,4),’IPPMLat’,Hy(:,5),’IPPGeoLong’,Hy(:,6),’IPPGeoLat’,Hy(:,7),’SlantCodeTEC’,Hy(:,8),’SlantSmoothTEC’,Hy(:,9),’VTEC’,Hy(:,10));
iisc_org=struct(‘GPSTOW’,Ii(:,1),’PRN’,Ii(:,2),’EL’,Ii(:,3),’AZ’,Ii(:,4),’IPPMLat’,Ii(:,5),’IPPGeoLong’,Ii(:,6),’IPPGeoLat’,Ii(:,7),’SlantCodeTEC’,Ii(:,8),’SlantSmoothTEC’,Ii(:,9),’VTEC’,Ii(:,10));
But,now I need to append all the data(to append rows one after the other file’s) of 200 files in ta single new file for processing.
Can anyone help please? batch file .txt file processing MATLAB Answers — New Questions

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I need the visualizations of the HOG descriptor as an image that i can extract regions for my further analysis.I need the visualizations of the HOG descriptor as an image that i can extract regions for my further analysis. I need the visualizations of the HOG descriptor as an image that i can extract regions for my further analysis. hog, hog features, hog visualization MATLAB Answers — New Questions

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I am trying a sample example to glow LED using MCU C2000 LaunchPad F28379D using example simulink model. After compilation program builds and run without any error but there is no pulse at the configured PIN of the MCU. How do i rectify this.I am trying a sample example to glow LED using MCU C2000 LaunchPad F28379D using example simulink model. After compilation program builds and run without any error but there is no pulse at the configured PIN of the MCU. How do i rectify this. I am trying a sample example to glow LED using MCU C2000 LaunchPad F28379D using example simulink model. After compilation program builds and run without any error but there is no pulse at the configured PIN of the MCU. How do i rectify this. c2000, f28379dlaunchpad, simulation MATLAB Answers — New Questions

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One is to compare two solutions and study the error by taking their difference. The two solutions are two surf figures generated by surf(x1,y1,z1) and surf(x2,y2,z2) but x1 and x2 are different in size and so are y1, y2, z1, z2. One can think of this as two solutions z1, z2 obtained on 2 meshes. Is there a smart way of taking z1-z2 and surf’ing it?One is to compare two solutions and study the error by taking their difference. The two solutions are two surf figures generated by surf(x1,y1,z1) and surf(x2,y2,z2) but x1 and x2 are different in size and so are y1, y2, z1, z2. One can think of this as two solutions z1, z2 obtained on 2 meshes. Is there a smart way of taking z1-z2 and surf’ing it? One is to compare two solutions and study the error by taking their difference. The two solutions are two surf figures generated by surf(x1,y1,z1) and surf(x2,y2,z2) but x1 and x2 are different in size and so are y1, y2, z1, z2. One can think of this as two solutions z1, z2 obtained on 2 meshes. Is there a smart way of taking z1-z2 and surf’ing it? surf MATLAB Answers — New Questions

​

I am getting the error flag -2 for my nonlinear mpc design despite their being no constraints. If there is no constraints, how is there infeasibility? The only way I can get it to be feasible is to make the intial condition the exact same as the reference. If there is deviation the problem is infeasible

clear all
nx = 4;
ny = 2;
nu = 3;

nlobj = nlmpc(nx,ny,nu);

nlobj.Ts = 0.1;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 10;

nlobj.Model.StateFcn = @mystateFunctionREDUCED;
nlobj.Model.IsContinuousTime = true;

nlobj.Model.NumberOfParameters = 5;
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;

nlobj.Model.OutputFcn = @OutputFcn
;

x0 = [1;0;pi/8;0];
mv = [m*g+1; 0; 0];
lastMV = mv;
%validateFcns(nlobj, x0, u0, [], {rho,l,g,J,m});
nloptions = nlmpcmoveopt;
nloptions.Parameters = {rho,l,g,J,m};
Duration = 1;
Ts = 0.01;
xHistory = x0′;
yref = [1;pi/9];

%nlobj.Weights.OutputVariables = [3 3];
%nlobj.Weights.ManipulatedVariablesRate = 0.1;
% nlobj.States(1).Min = 0;
% nlobj.States(1).Max = 20;
% nlobj.States(3).Min = 0;
% nlobj.States(3).Max = pi;

%nlobj.Optimization.CustomIneqConFcn = "myIneqConFunctionreduced";
nlobj.Weights.ManipulatedVariablesRate = [0 0 0];
nlobj.Weights.OutputVariables = [1 1];
%nlobj.Weights.ECR = 0;
Tarray = [];
Uarray = [];
for k = 1:(Duration/Ts)

xk = xHistory(k,:);
% Compute optimal control moves
[mv,nloptions,info] = nlmpcmove(nlobj,xk,lastMV,yref’,[],nloptions);
info
%disp(mv)
% Implement first optimal control move
ODEFUN = @(t,xk) statefcn(xk,mv);
[TOUT,XOUT] = ode45(ODEFUN,[0 Ts], xHistory(k,:)’);
x0 = mystateFunctionREDUCED(x0,mv,rho,l,g,J,m)*Ts+x0;
uHistory(k+1,:) = mv’;
lastMV = mv;
%[t,y] = ode15s(@mystateFunction,[0 Ts],[2 0]);
%T = (m*x0(1)*x0(4)^2-m*g*sin(x0(3))+mv(1)*sin(x0(3)+x0(5))-m*rho/l*mv(3))/(1+m*rho^2/l);
%Tarray = [Tarray T];
Uarray = [Uarray mv];
xHistory(k+1,:) = XOUT(end,:);
%opt.Slack0
%T
%par = m*x0(1).*x0(4).^2-m*g*sin(x0(3))
% Save plant states
%xHistory = [xHistory x0];

end

function z = mystateFunctionREDUCED(x,u,rho,l,g,J,m)
z = zeros(4,1);
% rho = params(1);
% l = params(2);
% %T = params(3);
% g = params(3);
% J = params(4);
% m = params(5);
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
end

function y = OutputFcn(x,u,rho,l,g,J,m)
y = zeros(2,1);
y(1) = x(1);
y(2) = x(3);
end

function f = statefcn(in1,in2)
%QuadrotorStateFcn
% F = QuadrotorStateFcn(IN1,IN2)

% This function was generated by the Symbolic Math Toolbox version 24.1.
% 09-Jan-2025 19:28:44
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;
x = in1;
u = in2;
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
f = [z(1);z(2);z(3);z(4)];
endI am getting the error flag -2 for my nonlinear mpc design despite their being no constraints. If there is no constraints, how is there infeasibility? The only way I can get it to be feasible is to make the intial condition the exact same as the reference. If there is deviation the problem is infeasible

clear all
nx = 4;
ny = 2;
nu = 3;

nlobj = nlmpc(nx,ny,nu);

nlobj.Ts = 0.1;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 10;

nlobj.Model.StateFcn = @mystateFunctionREDUCED;
nlobj.Model.IsContinuousTime = true;

nlobj.Model.NumberOfParameters = 5;
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;

nlobj.Model.OutputFcn = @OutputFcn
;

x0 = [1;0;pi/8;0];
mv = [m*g+1; 0; 0];
lastMV = mv;
%validateFcns(nlobj, x0, u0, [], {rho,l,g,J,m});
nloptions = nlmpcmoveopt;
nloptions.Parameters = {rho,l,g,J,m};
Duration = 1;
Ts = 0.01;
xHistory = x0′;
yref = [1;pi/9];

%nlobj.Weights.OutputVariables = [3 3];
%nlobj.Weights.ManipulatedVariablesRate = 0.1;
% nlobj.States(1).Min = 0;
% nlobj.States(1).Max = 20;
% nlobj.States(3).Min = 0;
% nlobj.States(3).Max = pi;

%nlobj.Optimization.CustomIneqConFcn = "myIneqConFunctionreduced";
nlobj.Weights.ManipulatedVariablesRate = [0 0 0];
nlobj.Weights.OutputVariables = [1 1];
%nlobj.Weights.ECR = 0;
Tarray = [];
Uarray = [];
for k = 1:(Duration/Ts)

xk = xHistory(k,:);
% Compute optimal control moves
[mv,nloptions,info] = nlmpcmove(nlobj,xk,lastMV,yref’,[],nloptions);
info
%disp(mv)
% Implement first optimal control move
ODEFUN = @(t,xk) statefcn(xk,mv);
[TOUT,XOUT] = ode45(ODEFUN,[0 Ts], xHistory(k,:)’);
x0 = mystateFunctionREDUCED(x0,mv,rho,l,g,J,m)*Ts+x0;
uHistory(k+1,:) = mv’;
lastMV = mv;
%[t,y] = ode15s(@mystateFunction,[0 Ts],[2 0]);
%T = (m*x0(1)*x0(4)^2-m*g*sin(x0(3))+mv(1)*sin(x0(3)+x0(5))-m*rho/l*mv(3))/(1+m*rho^2/l);
%Tarray = [Tarray T];
Uarray = [Uarray mv];
xHistory(k+1,:) = XOUT(end,:);
%opt.Slack0
%T
%par = m*x0(1).*x0(4).^2-m*g*sin(x0(3))
% Save plant states
%xHistory = [xHistory x0];

end

function z = mystateFunctionREDUCED(x,u,rho,l,g,J,m)
z = zeros(4,1);
% rho = params(1);
% l = params(2);
% %T = params(3);
% g = params(3);
% J = params(4);
% m = params(5);
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
end

function y = OutputFcn(x,u,rho,l,g,J,m)
y = zeros(2,1);
y(1) = x(1);
y(2) = x(3);
end

function f = statefcn(in1,in2)
%QuadrotorStateFcn
% F = QuadrotorStateFcn(IN1,IN2)

% This function was generated by the Symbolic Math Toolbox version 24.1.
% 09-Jan-2025 19:28:44
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;
x = in1;
u = in2;
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
f = [z(1);z(2);z(3);z(4)];
end I am getting the error flag -2 for my nonlinear mpc design despite their being no constraints. If there is no constraints, how is there infeasibility? The only way I can get it to be feasible is to make the intial condition the exact same as the reference. If there is deviation the problem is infeasible

clear all
nx = 4;
ny = 2;
nu = 3;

nlobj = nlmpc(nx,ny,nu);

nlobj.Ts = 0.1;
nlobj.PredictionHorizon = 10;
nlobj.ControlHorizon = 10;

nlobj.Model.StateFcn = @mystateFunctionREDUCED;
nlobj.Model.IsContinuousTime = true;

nlobj.Model.NumberOfParameters = 5;
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;

nlobj.Model.OutputFcn = @OutputFcn
;

x0 = [1;0;pi/8;0];
mv = [m*g+1; 0; 0];
lastMV = mv;
%validateFcns(nlobj, x0, u0, [], {rho,l,g,J,m});
nloptions = nlmpcmoveopt;
nloptions.Parameters = {rho,l,g,J,m};
Duration = 1;
Ts = 0.01;
xHistory = x0′;
yref = [1;pi/9];

%nlobj.Weights.OutputVariables = [3 3];
%nlobj.Weights.ManipulatedVariablesRate = 0.1;
% nlobj.States(1).Min = 0;
% nlobj.States(1).Max = 20;
% nlobj.States(3).Min = 0;
% nlobj.States(3).Max = pi;

%nlobj.Optimization.CustomIneqConFcn = "myIneqConFunctionreduced";
nlobj.Weights.ManipulatedVariablesRate = [0 0 0];
nlobj.Weights.OutputVariables = [1 1];
%nlobj.Weights.ECR = 0;
Tarray = [];
Uarray = [];
for k = 1:(Duration/Ts)

xk = xHistory(k,:);
% Compute optimal control moves
[mv,nloptions,info] = nlmpcmove(nlobj,xk,lastMV,yref’,[],nloptions);
info
%disp(mv)
% Implement first optimal control move
ODEFUN = @(t,xk) statefcn(xk,mv);
[TOUT,XOUT] = ode45(ODEFUN,[0 Ts], xHistory(k,:)’);
x0 = mystateFunctionREDUCED(x0,mv,rho,l,g,J,m)*Ts+x0;
uHistory(k+1,:) = mv’;
lastMV = mv;
%[t,y] = ode15s(@mystateFunction,[0 Ts],[2 0]);
%T = (m*x0(1)*x0(4)^2-m*g*sin(x0(3))+mv(1)*sin(x0(3)+x0(5))-m*rho/l*mv(3))/(1+m*rho^2/l);
%Tarray = [Tarray T];
Uarray = [Uarray mv];
xHistory(k+1,:) = XOUT(end,:);
%opt.Slack0
%T
%par = m*x0(1).*x0(4).^2-m*g*sin(x0(3))
% Save plant states
%xHistory = [xHistory x0];

end

function z = mystateFunctionREDUCED(x,u,rho,l,g,J,m)
z = zeros(4,1);
% rho = params(1);
% l = params(2);
% %T = params(3);
% g = params(3);
% J = params(4);
% m = params(5);
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
end

function y = OutputFcn(x,u,rho,l,g,J,m)
y = zeros(2,1);
y(1) = x(1);
y(2) = x(3);
end

function f = statefcn(in1,in2)
%QuadrotorStateFcn
% F = QuadrotorStateFcn(IN1,IN2)

% This function was generated by the Symbolic Math Toolbox version 24.1.
% 09-Jan-2025 19:28:44
rho = 0.1;
l = 1/2*1*rho^2;
%T = params(3);
g = 9.81;
J = 0.015;
m = 2;
x = in1;
u = in2;
z(1) = x(2);
z(2) = (rho/l*u(3)+(rho^2/l)*(m*x(1)*x(4)^2-m*g*sin(x(3)+u(1)*sin(x(3)+u(2)))))/(1+rho^2*m/l);
z(3) = x(4);
z(4) = (-1/x(1))*(2*x(2)*x(4)+g*cos(x(3)))+1/(m*x(1))*u(1)*cos(x(3)+u(2));
f = [z(1);z(2);z(3);z(4)];
end control, mpc MATLAB Answers — New Questions

​

Hi all,
I am trying to use the Stereo Camera Calibrator app in MATLAB 2024a, specifically with the ‘Symmetric circle grid’ pattern. But when I go to select this option, it is not available in the drop down menu (See below)

I was also using this app in late 2024 using 2023b and 2024a and the option was available to use but it has since disappeared. I have also tried to access it on 3 different devices and 5 different MATLAB releases (2016b, 2023a, 2023b, 2024a and 2024b) but it seems to have disappeared from all of them. Has it been removed as an option recently? I didn’t find any indication of this in release notes or bug reports.
Any advice on how to fix this problem would be very welcome.
Thank you!Hi all,
I am trying to use the Stereo Camera Calibrator app in MATLAB 2024a, specifically with the ‘Symmetric circle grid’ pattern. But when I go to select this option, it is not available in the drop down menu (See below)

I was also using this app in late 2024 using 2023b and 2024a and the option was available to use but it has since disappeared. I have also tried to access it on 3 different devices and 5 different MATLAB releases (2016b, 2023a, 2023b, 2024a and 2024b) but it seems to have disappeared from all of them. Has it been removed as an option recently? I didn’t find any indication of this in release notes or bug reports.
Any advice on how to fix this problem would be very welcome.
Thank you! Hi all,
I am trying to use the Stereo Camera Calibrator app in MATLAB 2024a, specifically with the ‘Symmetric circle grid’ pattern. But when I go to select this option, it is not available in the drop down menu (See below)

I was also using this app in late 2024 using 2023b and 2024a and the option was available to use but it has since disappeared. I have also tried to access it on 3 different devices and 5 different MATLAB releases (2016b, 2023a, 2023b, 2024a and 2024b) but it seems to have disappeared from all of them. Has it been removed as an option recently? I didn’t find any indication of this in release notes or bug reports.
Any advice on how to fix this problem would be very welcome.
Thank you! computer vision, camera-calibration MATLAB Answers — New Questions

​

vpa(4503599627370491.5)
produces
4503599627370496.0
in 2017b.
Why?

Further,
sym(4503599627370491.5)-sym(4503599627370496)
produces
0.vpa(4503599627370491.5)
produces
4503599627370496.0
in 2017b.
Why?

Further,
sym(4503599627370491.5)-sym(4503599627370496)
produces
0. vpa(4503599627370491.5)
produces
4503599627370496.0
in 2017b.
Why?

Further,
sym(4503599627370491.5)-sym(4503599627370496)
produces
0. vpa, sym MATLAB Answers — New Questions

​

I am trying to access files in my Microsoft OneDrive while using MATLAB Online. How do I enable MATLAB Online to access Microsoft OneDrive?I am trying to access files in my Microsoft OneDrive while using MATLAB Online. How do I enable MATLAB Online to access Microsoft OneDrive? I am trying to access files in my Microsoft OneDrive while using MATLAB Online. How do I enable MATLAB Online to access Microsoft OneDrive? microsoft, onedrive, matlab-online MATLAB Answers — New Questions

​

How to Configure your Student Competition LicenseHow to Configure your Student Competition License How to Configure your Student Competition License student, competition MATLAB Answers — New Questions

​

Hello,I encountered an error while running the example code from the MathWorks tutorial "Train Voice Activity Detection in Noise Model Using Deep Learning". I am using MATLAB Online and did not modify the original code.The error occurs at line 150 when calling the trainnet function:
noisyvadnet = trainnet(XTrain, TTrain, net, "mse", options);

The error message is:
Error using trainnet (line 54)
Execution failed during layer(s) "cnn2.norm1".
Caused by:
Arrays have incompatible sizes for this operation.

Error in deep.internal.recording.operations.TimesBroadcastOp/forward (line 31)
x = x .* y;
^
Error in .* (line 39)
zdata = matlab.lang.internal.move(xdata) .* matlab.lang.internal.move(ydata);
^
Error in audio.ai.vadnet.layers.layerNormalizationElementwiseLayer/predict (line 59)
Z = layer.Scale.*Xn + layer.Bias;
^
Error in nnet.layer.Layer/forward (line 129)
[varargout{1:layer.NumOutputs+layer.PrivateNumStates}] = predict( layer, varargin{:} );
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Since many functions related to this error are read-only, I cannot insert debugging code to investigate further.Could you please help me understand why this error occurs and how to resolve it?Thank you!Hello,I encountered an error while running the example code from the MathWorks tutorial "Train Voice Activity Detection in Noise Model Using Deep Learning". I am using MATLAB Online and did not modify the original code.The error occurs at line 150 when calling the trainnet function:
noisyvadnet = trainnet(XTrain, TTrain, net, "mse", options);

The error message is:
Error using trainnet (line 54)
Execution failed during layer(s) "cnn2.norm1".
Caused by:
Arrays have incompatible sizes for this operation.

Error in deep.internal.recording.operations.TimesBroadcastOp/forward (line 31)
x = x .* y;
^
Error in .* (line 39)
zdata = matlab.lang.internal.move(xdata) .* matlab.lang.internal.move(ydata);
^
Error in audio.ai.vadnet.layers.layerNormalizationElementwiseLayer/predict (line 59)
Z = layer.Scale.*Xn + layer.Bias;
^
Error in nnet.layer.Layer/forward (line 129)
[varargout{1:layer.NumOutputs+layer.PrivateNumStates}] = predict( layer, varargin{:} );
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Since many functions related to this error are read-only, I cannot insert debugging code to investigate further.Could you please help me understand why this error occurs and how to resolve it?Thank you! Hello,I encountered an error while running the example code from the MathWorks tutorial "Train Voice Activity Detection in Noise Model Using Deep Learning". I am using MATLAB Online and did not modify the original code.The error occurs at line 150 when calling the trainnet function:
noisyvadnet = trainnet(XTrain, TTrain, net, "mse", options);

The error message is:
Error using trainnet (line 54)
Execution failed during layer(s) "cnn2.norm1".
Caused by:
Arrays have incompatible sizes for this operation.

Error in deep.internal.recording.operations.TimesBroadcastOp/forward (line 31)
x = x .* y;
^
Error in .* (line 39)
zdata = matlab.lang.internal.move(xdata) .* matlab.lang.internal.move(ydata);
^
Error in audio.ai.vadnet.layers.layerNormalizationElementwiseLayer/predict (line 59)
Z = layer.Scale.*Xn + layer.Bias;
^
Error in nnet.layer.Layer/forward (line 129)
[varargout{1:layer.NumOutputs+layer.PrivateNumStates}] = predict( layer, varargin{:} );
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Since many functions related to this error are read-only, I cannot insert debugging code to investigate further.Could you please help me understand why this error occurs and how to resolve it?Thank you! incompatible sizes, tutorial code MATLAB Answers — New Questions

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