Hi everyone!
I’ve been learning MATLAB for about six months, so I apologize if I ask any basic questions. In part of my project, I have a system of six coupled ODEs (all functions of t). You can refer to the attached picture for more details.
To obtain the T profile in terms of t, I decided to define the function similarly to how many others do:
function dYdt = complexSystem(t, Y)
Aa = 1e14; % Pre-exponential factor for xa
Ea = 2.24e-19; % Activation energy for xa
Ac = 4e13; % Pre-exponential factor for xc
Ec = 2.03e-19; % Activation energy for xc
As = 1e15; % Pre-exponential factor for xs
Es = 2.24e-19; % Activation energy for xs
Aec = 1e12; % Pre-exponential factor for xec
Eec = 1.4e-19; % Activation energy for xec
Kb = 1.38e-23; % Boltzmann constant
z0 = 0.033; % Initial value for z
ma = 0.13;
ha = 1.714e6;
mc = 0.29;
hca = 3.14e5;
hs = 2.57e5;
C = -3600 * 1.1 * 3.0 * (1-0.12-0.51);

T = Y(1);
Xa = Y(2);
Xc = Y(3);
Xs = Y(4);
Z = Y(5);
Soc = Y(6);

dXadt = -Aa * Xa * exp(-Ea/(Kb*T));
dXcdt = Ac * Xc * exp(1 – Xc) * exp(-Ec / (Kb * T));
dXsdt = -As * Xs * exp((-Z / z0) * (-Es / (Kb * T)));
dZdt = Aa * Xs * exp((-Z / z0) * (-Es / (Kb * T)));
dSocdt = -Aec * (1 – Xc) * Xa * exp(-Eec / (Kb * T));

Qa = -ma * ha * dXadt;
Qc = -mc * hca * dXcdt;
Qs = -ma * hs * dXsdt;
Qec = C * dSocdt;

Q = Qa + Qc + Qs + Qec;
dTdt = Q;

dYdt = [dTdt; dXadt; dXcdt; dXsdt; dZdt; dSocdt];
end
Then, I defined the initial conditions and solved the ODEs using ode45.
% Initial conditions
Y0 = [298; 0.75; 0.04; 0.015; 0.0331; 0]; % [T0; Xa0; Xc0; Xs0; Z0; Soc0]

% Time span
tspan = [0 100000];

% Solve ODEs
[T, Y] = ode45(@(t, Y) complexSystem(t, Y), tspan, Y0);
Lastly, I attempted to plot the results. However, the code took forever to run, and I didn’t get any figures from it.
Could my initial conditions be incorrect? Or are my ODEs not converging?
Thank you so much for your time and help!
figure;
plot(T, Y)
legend(‘T’, ‘X_a’, ‘X_c’, ‘X_s’, ‘Z’, ‘S_{oc}’)
title(‘Solution of Corrected Complex System’)
xlabel(‘Time t’)
ylabel(‘State Variables’)Hi everyone!
I’ve been learning MATLAB for about six months, so I apologize if I ask any basic questions. In part of my project, I have a system of six coupled ODEs (all functions of t). You can refer to the attached picture for more details.
To obtain the T profile in terms of t, I decided to define the function similarly to how many others do:
function dYdt = complexSystem(t, Y)
Aa = 1e14; % Pre-exponential factor for xa
Ea = 2.24e-19; % Activation energy for xa
Ac = 4e13; % Pre-exponential factor for xc
Ec = 2.03e-19; % Activation energy for xc
As = 1e15; % Pre-exponential factor for xs
Es = 2.24e-19; % Activation energy for xs
Aec = 1e12; % Pre-exponential factor for xec
Eec = 1.4e-19; % Activation energy for xec
Kb = 1.38e-23; % Boltzmann constant
z0 = 0.033; % Initial value for z
ma = 0.13;
ha = 1.714e6;
mc = 0.29;
hca = 3.14e5;
hs = 2.57e5;
C = -3600 * 1.1 * 3.0 * (1-0.12-0.51);

T = Y(1);
Xa = Y(2);
Xc = Y(3);
Xs = Y(4);
Z = Y(5);
Soc = Y(6);

dXadt = -Aa * Xa * exp(-Ea/(Kb*T));
dXcdt = Ac * Xc * exp(1 – Xc) * exp(-Ec / (Kb * T));
dXsdt = -As * Xs * exp((-Z / z0) * (-Es / (Kb * T)));
dZdt = Aa * Xs * exp((-Z / z0) * (-Es / (Kb * T)));
dSocdt = -Aec * (1 – Xc) * Xa * exp(-Eec / (Kb * T));

Qa = -ma * ha * dXadt;
Qc = -mc * hca * dXcdt;
Qs = -ma * hs * dXsdt;
Qec = C * dSocdt;

Q = Qa + Qc + Qs + Qec;
dTdt = Q;

dYdt = [dTdt; dXadt; dXcdt; dXsdt; dZdt; dSocdt];
end
Then, I defined the initial conditions and solved the ODEs using ode45.
% Initial conditions
Y0 = [298; 0.75; 0.04; 0.015; 0.0331; 0]; % [T0; Xa0; Xc0; Xs0; Z0; Soc0]

% Time span
tspan = [0 100000];

% Solve ODEs
[T, Y] = ode45(@(t, Y) complexSystem(t, Y), tspan, Y0);
Lastly, I attempted to plot the results. However, the code took forever to run, and I didn’t get any figures from it.
Could my initial conditions be incorrect? Or are my ODEs not converging?
Thank you so much for your time and help!
figure;
plot(T, Y)
legend(‘T’, ‘X_a’, ‘X_c’, ‘X_s’, ‘Z’, ‘S_{oc}’)
title(‘Solution of Corrected Complex System’)
xlabel(‘Time t’)
ylabel(‘State Variables’) Hi everyone!
I’ve been learning MATLAB for about six months, so I apologize if I ask any basic questions. In part of my project, I have a system of six coupled ODEs (all functions of t). You can refer to the attached picture for more details.
To obtain the T profile in terms of t, I decided to define the function similarly to how many others do:
function dYdt = complexSystem(t, Y)
Aa = 1e14; % Pre-exponential factor for xa
Ea = 2.24e-19; % Activation energy for xa
Ac = 4e13; % Pre-exponential factor for xc
Ec = 2.03e-19; % Activation energy for xc
As = 1e15; % Pre-exponential factor for xs
Es = 2.24e-19; % Activation energy for xs
Aec = 1e12; % Pre-exponential factor for xec
Eec = 1.4e-19; % Activation energy for xec
Kb = 1.38e-23; % Boltzmann constant
z0 = 0.033; % Initial value for z
ma = 0.13;
ha = 1.714e6;
mc = 0.29;
hca = 3.14e5;
hs = 2.57e5;
C = -3600 * 1.1 * 3.0 * (1-0.12-0.51);

T = Y(1);
Xa = Y(2);
Xc = Y(3);
Xs = Y(4);
Z = Y(5);
Soc = Y(6);

dXadt = -Aa * Xa * exp(-Ea/(Kb*T));
dXcdt = Ac * Xc * exp(1 – Xc) * exp(-Ec / (Kb * T));
dXsdt = -As * Xs * exp((-Z / z0) * (-Es / (Kb * T)));
dZdt = Aa * Xs * exp((-Z / z0) * (-Es / (Kb * T)));
dSocdt = -Aec * (1 – Xc) * Xa * exp(-Eec / (Kb * T));

Qa = -ma * ha * dXadt;
Qc = -mc * hca * dXcdt;
Qs = -ma * hs * dXsdt;
Qec = C * dSocdt;

Q = Qa + Qc + Qs + Qec;
dTdt = Q;

dYdt = [dTdt; dXadt; dXcdt; dXsdt; dZdt; dSocdt];
end
Then, I defined the initial conditions and solved the ODEs using ode45.
% Initial conditions
Y0 = [298; 0.75; 0.04; 0.015; 0.0331; 0]; % [T0; Xa0; Xc0; Xs0; Z0; Soc0]

% Time span
tspan = [0 100000];

% Solve ODEs
[T, Y] = ode45(@(t, Y) complexSystem(t, Y), tspan, Y0);
Lastly, I attempted to plot the results. However, the code took forever to run, and I didn’t get any figures from it.
Could my initial conditions be incorrect? Or are my ODEs not converging?
Thank you so much for your time and help!
figure;
plot(T, Y)
legend(‘T’, ‘X_a’, ‘X_c’, ‘X_s’, ‘Z’, ‘S_{oc}’)
title(‘Solution of Corrected Complex System’)
xlabel(‘Time t’)
ylabel(‘State Variables’) ode45 MATLAB Answers — New Questions

​

Hi,everyone.There’re three variables in my function,x,y and w.
I want to integral x and y first,but there were a variable w stuck in my function.Can I left it and integral x,y first? Thank you.
clear

L=0.1;
section=50;
a=L/2;
b=L/section;
v=3e8;
f1=1.2e9;
f2=4.8e9;
f3=7.5e9;
f4=10e9;
w1=(2*pi*f1);
w2=(2*pi*f2);
w3=(2*pi*f3);
w4=(2*pi*f4);
Z01=50;
Z02=75;
a0=(log(Z02/Z01))./(2.*L);
T=a;
Ub=a;
Lb=-a;
ub=a;
lb=-a;
k=6;

syms x y w

l=0:(2*L)/(2*k):L % Approximate sections dvided

sec=numel(l)-1;

% Cm0

for s=1:1:sec

for t=1:1:sec

for m=1:1:k

P1=matlabFunction(((cos((m.*pi.*(y))./a)).*(cos(2.*(x-y).*w./v))));

P2(1,m)=integral2(P1,Lb+l(s),Lb+l(s+1),Lb+l(t),@(x)x);

P3(1,m)=integral2(P1,Lb+l(s),Lb+l(s+1),@(x)x,Lb+l(t+1));

P{s,t}=(P2+P3) % s section multiply t section

end

endHi,everyone.There’re three variables in my function,x,y and w.
I want to integral x and y first,but there were a variable w stuck in my function.Can I left it and integral x,y first? Thank you.
clear

L=0.1;
section=50;
a=L/2;
b=L/section;
v=3e8;
f1=1.2e9;
f2=4.8e9;
f3=7.5e9;
f4=10e9;
w1=(2*pi*f1);
w2=(2*pi*f2);
w3=(2*pi*f3);
w4=(2*pi*f4);
Z01=50;
Z02=75;
a0=(log(Z02/Z01))./(2.*L);
T=a;
Ub=a;
Lb=-a;
ub=a;
lb=-a;
k=6;

syms x y w

l=0:(2*L)/(2*k):L % Approximate sections dvided

sec=numel(l)-1;

% Cm0

for s=1:1:sec

for t=1:1:sec

for m=1:1:k

P1=matlabFunction(((cos((m.*pi.*(y))./a)).*(cos(2.*(x-y).*w./v))));

P2(1,m)=integral2(P1,Lb+l(s),Lb+l(s+1),Lb+l(t),@(x)x);

P3(1,m)=integral2(P1,Lb+l(s),Lb+l(s+1),@(x)x,Lb+l(t+1));

P{s,t}=(P2+P3) % s section multiply t section

end

end Hi,everyone.There’re three variables in my function,x,y and w.
I want to integral x and y first,but there were a variable w stuck in my function.Can I left it and integral x,y first? Thank you.
clear

L=0.1;
section=50;
a=L/2;
b=L/section;
v=3e8;
f1=1.2e9;
f2=4.8e9;
f3=7.5e9;
f4=10e9;
w1=(2*pi*f1);
w2=(2*pi*f2);
w3=(2*pi*f3);
w4=(2*pi*f4);
Z01=50;
Z02=75;
a0=(log(Z02/Z01))./(2.*L);
T=a;
Ub=a;
Lb=-a;
ub=a;
lb=-a;
k=6;

syms x y w

l=0:(2*L)/(2*k):L % Approximate sections dvided

sec=numel(l)-1;

% Cm0

for s=1:1:sec

for t=1:1:sec

for m=1:1:k

P1=matlabFunction(((cos((m.*pi.*(y))./a)).*(cos(2.*(x-y).*w./v))));

P2(1,m)=integral2(P1,Lb+l(s),Lb+l(s+1),Lb+l(t),@(x)x);

P3(1,m)=integral2(P1,Lb+l(s),Lb+l(s+1),@(x)x,Lb+l(t+1));

P{s,t}=(P2+P3) % s section multiply t section

end

end integral2 MATLAB Answers — New Questions

​

There is a MATLAB example that uses minibatchqueue for input date as 4-D (image) and output as categorical. What I need is to update this example to accept output to be three numberical values (through a 3-fully connected layer).

The MATLAB example is:

[XTrain,YTrain] = digitTrain4DArrayData;
dsX = arrayDatastore(XTrain,IterationDimension=4);
dsY = arrayDatastore(YTrain);
dsTrain = combine(dsX,dsY);

classes = categories(YTrain);
numClasses = numel(classes);

net = dlnetwork;

layers = [
imageInputLayer([28 28 1],Mean=mean(XTrain,4))
convolution2dLayer(5,20)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer];

net = addLayers(net,layers);
net = initialize(net);

miniBatchSize = 128;

mbq = minibatchqueue(dsTrain,…
MiniBatchSize=miniBatchSize,…
PartialMiniBatch="discard",…
MiniBatchFcn=@preprocessMiniBatch,…
MiniBatchFormat=["SSCB",""]);

function [X,Y] = preprocessMiniBatch(XCell,YCell)
% Extract image data from the cell array and concatenate over fourth
% dimension to add a third singleton dimension, as the channel
% dimension.
X = cat(4,XCell{:});

% Extract label data from cell and concatenate.
Y = cat(2,YCell{:});

% One-hot encode labels.
Y = onehotencode(Y,1);
end

Again, what I need is to know how to modify the code to accept three regression values at fully connected output layer.

Actually, I tried alot and alot without success. I think the main trick is the update that should be done inside this function: preprocessMiniBatch (defined above).
ThanksThere is a MATLAB example that uses minibatchqueue for input date as 4-D (image) and output as categorical. What I need is to update this example to accept output to be three numberical values (through a 3-fully connected layer).

The MATLAB example is:

[XTrain,YTrain] = digitTrain4DArrayData;
dsX = arrayDatastore(XTrain,IterationDimension=4);
dsY = arrayDatastore(YTrain);
dsTrain = combine(dsX,dsY);

classes = categories(YTrain);
numClasses = numel(classes);

net = dlnetwork;

layers = [
imageInputLayer([28 28 1],Mean=mean(XTrain,4))
convolution2dLayer(5,20)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer];

net = addLayers(net,layers);
net = initialize(net);

miniBatchSize = 128;

mbq = minibatchqueue(dsTrain,…
MiniBatchSize=miniBatchSize,…
PartialMiniBatch="discard",…
MiniBatchFcn=@preprocessMiniBatch,…
MiniBatchFormat=["SSCB",""]);

function [X,Y] = preprocessMiniBatch(XCell,YCell)
% Extract image data from the cell array and concatenate over fourth
% dimension to add a third singleton dimension, as the channel
% dimension.
X = cat(4,XCell{:});

% Extract label data from cell and concatenate.
Y = cat(2,YCell{:});

% One-hot encode labels.
Y = onehotencode(Y,1);
end

Again, what I need is to know how to modify the code to accept three regression values at fully connected output layer.

Actually, I tried alot and alot without success. I think the main trick is the update that should be done inside this function: preprocessMiniBatch (defined above).
Thanks There is a MATLAB example that uses minibatchqueue for input date as 4-D (image) and output as categorical. What I need is to update this example to accept output to be three numberical values (through a 3-fully connected layer).

The MATLAB example is:

[XTrain,YTrain] = digitTrain4DArrayData;
dsX = arrayDatastore(XTrain,IterationDimension=4);
dsY = arrayDatastore(YTrain);
dsTrain = combine(dsX,dsY);

classes = categories(YTrain);
numClasses = numel(classes);

net = dlnetwork;

layers = [
imageInputLayer([28 28 1],Mean=mean(XTrain,4))
convolution2dLayer(5,20)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
convolution2dLayer(3,20,Padding=1)
reluLayer
fullyConnectedLayer(numClasses)
softmaxLayer];

net = addLayers(net,layers);
net = initialize(net);

miniBatchSize = 128;

mbq = minibatchqueue(dsTrain,…
MiniBatchSize=miniBatchSize,…
PartialMiniBatch="discard",…
MiniBatchFcn=@preprocessMiniBatch,…
MiniBatchFormat=["SSCB",""]);

function [X,Y] = preprocessMiniBatch(XCell,YCell)
% Extract image data from the cell array and concatenate over fourth
% dimension to add a third singleton dimension, as the channel
% dimension.
X = cat(4,XCell{:});

% Extract label data from cell and concatenate.
Y = cat(2,YCell{:});

% One-hot encode labels.
Y = onehotencode(Y,1);
end

Again, what I need is to know how to modify the code to accept three regression values at fully connected output layer.

Actually, I tried alot and alot without success. I think the main trick is the update that should be done inside this function: preprocessMiniBatch (defined above).
Thanks deep learning, neural network, optimization, deep learning toolbox, deep learning toolbox model quantization library MATLAB Answers — New Questions

​

These data are already preprocessed so they are already syncronized and they are of the same size. However, when I tried running, MATLAB says: Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN. What seems to be the problem with this and how can this be fixed?
% load data
clear;
clc;
ld1 = load(‘CALT23A.mat’);
ld2 = load(‘CALT23D.mat’);
ld3 = load(‘CALT23M.mat’);
ld4 = load(‘CALT23S.mat’);
ld5 = load(‘CALT23K.mat’);
%%
% Accelerometer
accx = mean([ld1.Acceleration.X ld2.Acceleration.X ld3.Acceleration.X ld4.Acceleration.X],2,’omitnan’);
accy = mean([ld1.Acceleration.Y ld2.Acceleration.Y ld3.Acceleration.Y ld4.Acceleration.Y],2,’omitnan’);
accz = mean([ld1.Acceleration.Z ld2.Acceleration.Z ld3.Acceleration.Z ld4.Acceleration.Z],2,’omitnan’);
accelerometerReadings = [accx accy accz];
% Gyroscope
angx = mean([ld1.AngularVelocity.X ld2.AngularVelocity.X ld3.AngularVelocity.X ld5.AngularVelocity.X],2,’omitnan’);
angy = mean([ld1.AngularVelocity.Y ld2.AngularVelocity.Y ld3.AngularVelocity.Y ld5.AngularVelocity.Y],2,’omitnan’);
angz = mean([ld1.AngularVelocity.Z ld2.AngularVelocity.Z ld3.AngularVelocity.Z ld5.AngularVelocity.Z],2,’omitnan’);
gyroscopeReadings = [angx angy angz];
% Magnetometer
magx = mean([ld1.MagneticField.X ld2.MagneticField.X ld3.MagneticField.X ld4.MagneticField.X],2,’omitnan’);
magy = mean([ld1.MagneticField.Y ld2.MagneticField.Y ld3.MagneticField.Y ld4.MagneticField.Y],2,’omitnan’);
magz = mean([ld1.MagneticField.Z ld2.MagneticField.Z ld3.MagneticField.Z ld4.MagneticField.Z],2,’omitnan’);
magnetometerReadings = [magx magy magz];
%GPS
lat = mean([ld1.Position.latitude ld2.Position.latitude ld3.Position.latitude ld4.Position.latitude],2,’omitnan’);
long = mean([ld1.Position.longitude ld2.Position.longitude ld3.Position.longitude ld4.Position.longitude],2,’omitnan’);
speed = mean([ld1.Position.speed ld2.Position.speed ld3.Position.speed ld4.Position.speed],2,’omitnan’);
gps_speed_combined = speed;
%% IMU Filter (indirect Kalman Filter)
fuse = imufilter(‘SampleRate’,100, ‘DecimationFactor’,1,’AccelerometerNoise’,0.5,’GyroscopeNoise’,0.01);
q1 = fuse(accelerometerReadings, gyroscopeReadings);
orient1 = eulerd(q1, ‘ZYX’,’frame’);
f = figure;
f.Position(1:4) = [100 20 1100 600];
plot(orient1(:,3)); hold off
grid on
axis tight
xlabel(‘Time (sec)’);
ylabel(‘Angle (deg)’);
title(‘Orientation Estimate using IMU Filter’)
saveas(gcf, ‘imufilter.png’)These data are already preprocessed so they are already syncronized and they are of the same size. However, when I tried running, MATLAB says: Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN. What seems to be the problem with this and how can this be fixed?
% load data
clear;
clc;
ld1 = load(‘CALT23A.mat’);
ld2 = load(‘CALT23D.mat’);
ld3 = load(‘CALT23M.mat’);
ld4 = load(‘CALT23S.mat’);
ld5 = load(‘CALT23K.mat’);
%%
% Accelerometer
accx = mean([ld1.Acceleration.X ld2.Acceleration.X ld3.Acceleration.X ld4.Acceleration.X],2,’omitnan’);
accy = mean([ld1.Acceleration.Y ld2.Acceleration.Y ld3.Acceleration.Y ld4.Acceleration.Y],2,’omitnan’);
accz = mean([ld1.Acceleration.Z ld2.Acceleration.Z ld3.Acceleration.Z ld4.Acceleration.Z],2,’omitnan’);
accelerometerReadings = [accx accy accz];
% Gyroscope
angx = mean([ld1.AngularVelocity.X ld2.AngularVelocity.X ld3.AngularVelocity.X ld5.AngularVelocity.X],2,’omitnan’);
angy = mean([ld1.AngularVelocity.Y ld2.AngularVelocity.Y ld3.AngularVelocity.Y ld5.AngularVelocity.Y],2,’omitnan’);
angz = mean([ld1.AngularVelocity.Z ld2.AngularVelocity.Z ld3.AngularVelocity.Z ld5.AngularVelocity.Z],2,’omitnan’);
gyroscopeReadings = [angx angy angz];
% Magnetometer
magx = mean([ld1.MagneticField.X ld2.MagneticField.X ld3.MagneticField.X ld4.MagneticField.X],2,’omitnan’);
magy = mean([ld1.MagneticField.Y ld2.MagneticField.Y ld3.MagneticField.Y ld4.MagneticField.Y],2,’omitnan’);
magz = mean([ld1.MagneticField.Z ld2.MagneticField.Z ld3.MagneticField.Z ld4.MagneticField.Z],2,’omitnan’);
magnetometerReadings = [magx magy magz];
%GPS
lat = mean([ld1.Position.latitude ld2.Position.latitude ld3.Position.latitude ld4.Position.latitude],2,’omitnan’);
long = mean([ld1.Position.longitude ld2.Position.longitude ld3.Position.longitude ld4.Position.longitude],2,’omitnan’);
speed = mean([ld1.Position.speed ld2.Position.speed ld3.Position.speed ld4.Position.speed],2,’omitnan’);
gps_speed_combined = speed;
%% IMU Filter (indirect Kalman Filter)
fuse = imufilter(‘SampleRate’,100, ‘DecimationFactor’,1,’AccelerometerNoise’,0.5,’GyroscopeNoise’,0.01);
q1 = fuse(accelerometerReadings, gyroscopeReadings);
orient1 = eulerd(q1, ‘ZYX’,’frame’);
f = figure;
f.Position(1:4) = [100 20 1100 600];
plot(orient1(:,3)); hold off
grid on
axis tight
xlabel(‘Time (sec)’);
ylabel(‘Angle (deg)’);
title(‘Orientation Estimate using IMU Filter’)
saveas(gcf, ‘imufilter.png’) These data are already preprocessed so they are already syncronized and they are of the same size. However, when I tried running, MATLAB says: Matrix is singular, close to singular or badly scaled. Results may be inaccurate. RCOND = NaN. What seems to be the problem with this and how can this be fixed?
% load data
clear;
clc;
ld1 = load(‘CALT23A.mat’);
ld2 = load(‘CALT23D.mat’);
ld3 = load(‘CALT23M.mat’);
ld4 = load(‘CALT23S.mat’);
ld5 = load(‘CALT23K.mat’);
%%
% Accelerometer
accx = mean([ld1.Acceleration.X ld2.Acceleration.X ld3.Acceleration.X ld4.Acceleration.X],2,’omitnan’);
accy = mean([ld1.Acceleration.Y ld2.Acceleration.Y ld3.Acceleration.Y ld4.Acceleration.Y],2,’omitnan’);
accz = mean([ld1.Acceleration.Z ld2.Acceleration.Z ld3.Acceleration.Z ld4.Acceleration.Z],2,’omitnan’);
accelerometerReadings = [accx accy accz];
% Gyroscope
angx = mean([ld1.AngularVelocity.X ld2.AngularVelocity.X ld3.AngularVelocity.X ld5.AngularVelocity.X],2,’omitnan’);
angy = mean([ld1.AngularVelocity.Y ld2.AngularVelocity.Y ld3.AngularVelocity.Y ld5.AngularVelocity.Y],2,’omitnan’);
angz = mean([ld1.AngularVelocity.Z ld2.AngularVelocity.Z ld3.AngularVelocity.Z ld5.AngularVelocity.Z],2,’omitnan’);
gyroscopeReadings = [angx angy angz];
% Magnetometer
magx = mean([ld1.MagneticField.X ld2.MagneticField.X ld3.MagneticField.X ld4.MagneticField.X],2,’omitnan’);
magy = mean([ld1.MagneticField.Y ld2.MagneticField.Y ld3.MagneticField.Y ld4.MagneticField.Y],2,’omitnan’);
magz = mean([ld1.MagneticField.Z ld2.MagneticField.Z ld3.MagneticField.Z ld4.MagneticField.Z],2,’omitnan’);
magnetometerReadings = [magx magy magz];
%GPS
lat = mean([ld1.Position.latitude ld2.Position.latitude ld3.Position.latitude ld4.Position.latitude],2,’omitnan’);
long = mean([ld1.Position.longitude ld2.Position.longitude ld3.Position.longitude ld4.Position.longitude],2,’omitnan’);
speed = mean([ld1.Position.speed ld2.Position.speed ld3.Position.speed ld4.Position.speed],2,’omitnan’);
gps_speed_combined = speed;
%% IMU Filter (indirect Kalman Filter)
fuse = imufilter(‘SampleRate’,100, ‘DecimationFactor’,1,’AccelerometerNoise’,0.5,’GyroscopeNoise’,0.01);
q1 = fuse(accelerometerReadings, gyroscopeReadings);
orient1 = eulerd(q1, ‘ZYX’,’frame’);
f = figure;
f.Position(1:4) = [100 20 1100 600];
plot(orient1(:,3)); hold off
grid on
axis tight
xlabel(‘Time (sec)’);
ylabel(‘Angle (deg)’);
title(‘Orientation Estimate using IMU Filter’)
saveas(gcf, ‘imufilter.png’) matrix, singular MATLAB Answers — New Questions

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I have a text file with time(1st column) and voltage amplitude (2nd column). How do I plot a spectrogram plotting time vs instantaneous frequency vs amplitude? Could you please help me plot it? I will really appreciate it.
I have attached the text file and a picture of the time domain representation of the signal.I have a text file with time(1st column) and voltage amplitude (2nd column). How do I plot a spectrogram plotting time vs instantaneous frequency vs amplitude? Could you please help me plot it? I will really appreciate it.
I have attached the text file and a picture of the time domain representation of the signal. I have a text file with time(1st column) and voltage amplitude (2nd column). How do I plot a spectrogram plotting time vs instantaneous frequency vs amplitude? Could you please help me plot it? I will really appreciate it.
I have attached the text file and a picture of the time domain representation of the signal. spectrogram, fft MATLAB Answers — New Questions

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Dear Sir/Madam,
I just wanted to know how to create sensor network on matlab with different layers like physical,data link layer and network layer.Dear Sir/Madam,
I just wanted to know how to create sensor network on matlab with different layers like physical,data link layer and network layer. Dear Sir/Madam,
I just wanted to know how to create sensor network on matlab with different layers like physical,data link layer and network layer. sensor network MATLAB Answers — New Questions

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I have a dataset comprising a cube of images, with dimension 256x320x151. I need tu cut it down to a dataset with dimension 256x320x10, while keeping all the other attributes invariate, as they have useful information on the data acquisition parameters.
I have tried the code:
h5disp(‘PCA_cube – Copia.h5’)
fileattrib(‘PCA_cube – Copia.h5′,’+w’);
fid = H5F.open(‘PCA_cube – Copia.h5′,’H5F_ACC_RDWR’,’H5P_DEFAULT’);
dset_id = H5D.open(fid,’/Cube/Images’);
H5D.set_extent(dset_id,[320,256,10]); % C-style indexing
H5D.close(dset_id);
H5F.close(fid);
h5read(PCA_cube – Copia.h5′, ‘/Cube/Images’)
However I get the error message: "Error using hdf5lib2 : The HDF5 library encountered an error and produced the following stack trace information:H5D__set_extent dataset has contiguous storage"
I’m adding also some information on the file structure:

Any help or suggestion is much welcome!I have a dataset comprising a cube of images, with dimension 256x320x151. I need tu cut it down to a dataset with dimension 256x320x10, while keeping all the other attributes invariate, as they have useful information on the data acquisition parameters.
I have tried the code:
h5disp(‘PCA_cube – Copia.h5’)
fileattrib(‘PCA_cube – Copia.h5′,’+w’);
fid = H5F.open(‘PCA_cube – Copia.h5′,’H5F_ACC_RDWR’,’H5P_DEFAULT’);
dset_id = H5D.open(fid,’/Cube/Images’);
H5D.set_extent(dset_id,[320,256,10]); % C-style indexing
H5D.close(dset_id);
H5F.close(fid);
h5read(PCA_cube – Copia.h5′, ‘/Cube/Images’)
However I get the error message: "Error using hdf5lib2 : The HDF5 library encountered an error and produced the following stack trace information:H5D__set_extent dataset has contiguous storage"
I’m adding also some information on the file structure:

Any help or suggestion is much welcome! I have a dataset comprising a cube of images, with dimension 256x320x151. I need tu cut it down to a dataset with dimension 256x320x10, while keeping all the other attributes invariate, as they have useful information on the data acquisition parameters.
I have tried the code:
h5disp(‘PCA_cube – Copia.h5’)
fileattrib(‘PCA_cube – Copia.h5′,’+w’);
fid = H5F.open(‘PCA_cube – Copia.h5′,’H5F_ACC_RDWR’,’H5P_DEFAULT’);
dset_id = H5D.open(fid,’/Cube/Images’);
H5D.set_extent(dset_id,[320,256,10]); % C-style indexing
H5D.close(dset_id);
H5F.close(fid);
h5read(PCA_cube – Copia.h5′, ‘/Cube/Images’)
However I get the error message: "Error using hdf5lib2 : The HDF5 library encountered an error and produced the following stack trace information:H5D__set_extent dataset has contiguous storage"
I’m adding also some information on the file structure:

Any help or suggestion is much welcome! h5, hdf5lib2, dataset, hdf5 MATLAB Answers — New Questions

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I can use the following line
opts = detectImportOptions(workbookFile,’Sheet’,sheetName,’Range’,Range);
containing an excel range name (see name manager) in Range and the corresponding sheet name in sheetName. The thing is, excel already allows me to set range names for the whole workbook. So, no need to specify the sheet name. I’d like my script to be agnostic to changes in sheet names or positions.
How can I do that?
If I launch without sheetName
opts = detectImportOptions(workbookFile,’Range’,Range);
Matlab doesn’t find my range, although the range name in excel is given with "Scope: Workbook".I can use the following line
opts = detectImportOptions(workbookFile,’Sheet’,sheetName,’Range’,Range);
containing an excel range name (see name manager) in Range and the corresponding sheet name in sheetName. The thing is, excel already allows me to set range names for the whole workbook. So, no need to specify the sheet name. I’d like my script to be agnostic to changes in sheet names or positions.
How can I do that?
If I launch without sheetName
opts = detectImportOptions(workbookFile,’Range’,Range);
Matlab doesn’t find my range, although the range name in excel is given with "Scope: Workbook". I can use the following line
opts = detectImportOptions(workbookFile,’Sheet’,sheetName,’Range’,Range);
containing an excel range name (see name manager) in Range and the corresponding sheet name in sheetName. The thing is, excel already allows me to set range names for the whole workbook. So, no need to specify the sheet name. I’d like my script to be agnostic to changes in sheet names or positions.
How can I do that?
If I launch without sheetName
opts = detectImportOptions(workbookFile,’Range’,Range);
Matlab doesn’t find my range, although the range name in excel is given with "Scope: Workbook". excel MATLAB Answers — New Questions

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Im having issue where i think the index is looking for instances of detected circles but it either is not filling the index or maybe there isnt enough to fill the array but im having issue getting this to work. its from the help documentation so i expected it to just work but im still having trouble and thus am having trouble understanding how to use imfindcircles. I tried using edge(canny) and using a binary image to see if that would make it easier to find circles but im still hvaing issues.

A = imread(‘coins.png’);
imshow(A)
[centers, radii, metric] = imfindcircles(A,[15 30]);
centersStrong5 = centers(1:5,:);
radiiStrong5 = radii(1:5);
metricStrong5 = metric(1:5);
viscircles(centersStrong5, radiiStrong5,’EdgeColor’,’b’);Im having issue where i think the index is looking for instances of detected circles but it either is not filling the index or maybe there isnt enough to fill the array but im having issue getting this to work. its from the help documentation so i expected it to just work but im still having trouble and thus am having trouble understanding how to use imfindcircles. I tried using edge(canny) and using a binary image to see if that would make it easier to find circles but im still hvaing issues.

A = imread(‘coins.png’);
imshow(A)
[centers, radii, metric] = imfindcircles(A,[15 30]);
centersStrong5 = centers(1:5,:);
radiiStrong5 = radii(1:5);
metricStrong5 = metric(1:5);
viscircles(centersStrong5, radiiStrong5,’EdgeColor’,’b’); Im having issue where i think the index is looking for instances of detected circles but it either is not filling the index or maybe there isnt enough to fill the array but im having issue getting this to work. its from the help documentation so i expected it to just work but im still having trouble and thus am having trouble understanding how to use imfindcircles. I tried using edge(canny) and using a binary image to see if that would make it easier to find circles but im still hvaing issues.

A = imread(‘coins.png’);
imshow(A)
[centers, radii, metric] = imfindcircles(A,[15 30]);
centersStrong5 = centers(1:5,:);
radiiStrong5 = radii(1:5);
metricStrong5 = metric(1:5);
viscircles(centersStrong5, radiiStrong5,’EdgeColor’,’b’); imfindcircles, matlab, hough transform, edge detection, image processing toolbox MATLAB Answers — New Questions

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I am trying to animate a trajectory following a marker.
Here is the problematic part of my code:
figure(1)
axis([0, ceil(max(x)), 0, ceil(max(y))])
daspect([1 1 1])
grid on
xlabel(‘x (m)’), ylabel(‘y (m)’), title(‘Bouncing Projectile’), subtitle([‘{theta} = ‘, num2str(theta), ‘°, u = ‘, num2str(u), ‘ ms^{-1}, h = ‘, num2str(h), ‘ m, g = ‘, num2str(g), ‘ ms^{-2}, C = ‘, num2str(C), ‘, N = ‘, num2str(N)])

h = animatedline(‘Color’, ‘b’);
p = plot(x(1), y(1), ‘o’, ‘MarkerEdgeColor’, ‘red’, ‘MarkerFaceColor’, ‘red’);

for k = 1:length(x)
p.XData = x(k);
p.YData = y(k);
addpoints(h, x(k), y(k));
drawnow limitrate
pause(dt)
end

Whithout the code for the marker, the program runs well. However, for some strange reason, adding the code for the marker seems to create an error:

Error using matlab.graphics.animation.AnimatedLine/addpoints
Value must be a handle.
Error in file (line 54)
addpoints(h, x(k), y(k));

A graph is produced when I run the program, however the axes are not labeled, there is no title, and only one point is plotted.
What mistake have I made?I am trying to animate a trajectory following a marker.
Here is the problematic part of my code:
figure(1)
axis([0, ceil(max(x)), 0, ceil(max(y))])
daspect([1 1 1])
grid on
xlabel(‘x (m)’), ylabel(‘y (m)’), title(‘Bouncing Projectile’), subtitle([‘{theta} = ‘, num2str(theta), ‘°, u = ‘, num2str(u), ‘ ms^{-1}, h = ‘, num2str(h), ‘ m, g = ‘, num2str(g), ‘ ms^{-2}, C = ‘, num2str(C), ‘, N = ‘, num2str(N)])

h = animatedline(‘Color’, ‘b’);
p = plot(x(1), y(1), ‘o’, ‘MarkerEdgeColor’, ‘red’, ‘MarkerFaceColor’, ‘red’);

for k = 1:length(x)
p.XData = x(k);
p.YData = y(k);
addpoints(h, x(k), y(k));
drawnow limitrate
pause(dt)
end

Whithout the code for the marker, the program runs well. However, for some strange reason, adding the code for the marker seems to create an error:

Error using matlab.graphics.animation.AnimatedLine/addpoints
Value must be a handle.
Error in file (line 54)
addpoints(h, x(k), y(k));

A graph is produced when I run the program, however the axes are not labeled, there is no title, and only one point is plotted.
What mistake have I made? I am trying to animate a trajectory following a marker.
Here is the problematic part of my code:
figure(1)
axis([0, ceil(max(x)), 0, ceil(max(y))])
daspect([1 1 1])
grid on
xlabel(‘x (m)’), ylabel(‘y (m)’), title(‘Bouncing Projectile’), subtitle([‘{theta} = ‘, num2str(theta), ‘°, u = ‘, num2str(u), ‘ ms^{-1}, h = ‘, num2str(h), ‘ m, g = ‘, num2str(g), ‘ ms^{-2}, C = ‘, num2str(C), ‘, N = ‘, num2str(N)])

h = animatedline(‘Color’, ‘b’);
p = plot(x(1), y(1), ‘o’, ‘MarkerEdgeColor’, ‘red’, ‘MarkerFaceColor’, ‘red’);

for k = 1:length(x)
p.XData = x(k);
p.YData = y(k);
addpoints(h, x(k), y(k));
drawnow limitrate
pause(dt)
end

Whithout the code for the marker, the program runs well. However, for some strange reason, adding the code for the marker seems to create an error:

Error using matlab.graphics.animation.AnimatedLine/addpoints
Value must be a handle.
Error in file (line 54)
addpoints(h, x(k), y(k));

A graph is produced when I run the program, however the axes are not labeled, there is no title, and only one point is plotted.
What mistake have I made? matlab MATLAB Answers — New Questions

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