I encountered errors while running a Simulink Coder build, Rapid Accelerator build, or FMU Export using the Visual Studio C++ compiler on Windows. The errors were related to missing C Standard Library headers like "limits.h", "string.h", "stdlib.h", or "stddef.h":

fatal error C1083: Cannot open include file: ‘xxx.h’: No such file or directory
The same build works with the MinGW64 compiler. I verified that my Visual Studio setup is correct by successfully compiling a "Hello World" C program and creating a MEX file from MATLAB.I encountered errors while running a Simulink Coder build, Rapid Accelerator build, or FMU Export using the Visual Studio C++ compiler on Windows. The errors were related to missing C Standard Library headers like "limits.h", "string.h", "stdlib.h", or "stddef.h":

fatal error C1083: Cannot open include file: ‘xxx.h’: No such file or directory
The same build works with the MinGW64 compiler. I verified that my Visual Studio setup is correct by successfully compiling a "Hello World" C program and creating a MEX file from MATLAB. I encountered errors while running a Simulink Coder build, Rapid Accelerator build, or FMU Export using the Visual Studio C++ compiler on Windows. The errors were related to missing C Standard Library headers like "limits.h", "string.h", "stdlib.h", or "stddef.h":

fatal error C1083: Cannot open include file: ‘xxx.h’: No such file or directory
The same build works with the MinGW64 compiler. I verified that my Visual Studio setup is correct by successfully compiling a "Hello World" C program and creating a MEX file from MATLAB. raccel, rapid, accel, stdlib, cstdlib, cstd MATLAB Answers — New Questions

​

Hi folks, I have a simple boxplot and I can’t figure out how to make a legend like the one shown in the photograph below. Ideally, the symbols and line specs would all match the associated text.
Perhaps doing it using the annotation or note tool? Was wondering if anyone has done this before. This type of formatting is a requirement for a journal paper.
For example (see example.png) I’ve gotten this far:
data = [1 2 3 4 4 5 5 6 6 7 8 9 13]
figure; boxplot(data);
a = get(get(gca,’children’),’children’); % Get the handles of all the objects
legend([a(1) a(2) a(3) a(4)],{‘Outliers’,’Median’,’25-75%’,’+/-1.5 IQR’})
But am wondering if there are alternative or better ways, and perhaps a way to show the blue bounding box? Just wanted to hear y’alls thoughts. Cheers.Hi folks, I have a simple boxplot and I can’t figure out how to make a legend like the one shown in the photograph below. Ideally, the symbols and line specs would all match the associated text.
Perhaps doing it using the annotation or note tool? Was wondering if anyone has done this before. This type of formatting is a requirement for a journal paper.
For example (see example.png) I’ve gotten this far:
data = [1 2 3 4 4 5 5 6 6 7 8 9 13]
figure; boxplot(data);
a = get(get(gca,’children’),’children’); % Get the handles of all the objects
legend([a(1) a(2) a(3) a(4)],{‘Outliers’,’Median’,’25-75%’,’+/-1.5 IQR’})
But am wondering if there are alternative or better ways, and perhaps a way to show the blue bounding box? Just wanted to hear y’alls thoughts. Cheers. Hi folks, I have a simple boxplot and I can’t figure out how to make a legend like the one shown in the photograph below. Ideally, the symbols and line specs would all match the associated text.
Perhaps doing it using the annotation or note tool? Was wondering if anyone has done this before. This type of formatting is a requirement for a journal paper.
For example (see example.png) I’ve gotten this far:
data = [1 2 3 4 4 5 5 6 6 7 8 9 13]
figure; boxplot(data);
a = get(get(gca,’children’),’children’); % Get the handles of all the objects
legend([a(1) a(2) a(3) a(4)],{‘Outliers’,’Median’,’25-75%’,’+/-1.5 IQR’})
But am wondering if there are alternative or better ways, and perhaps a way to show the blue bounding box? Just wanted to hear y’alls thoughts. Cheers. boxplot, legend, figure, labels MATLAB Answers — New Questions

​

Hi,

I could not compile my app with SAM due to the following error.
Where can I find the specific LICENSE that states why and how SAM cannot be packaged?
Or am I misunderstanding anything about specifying parameters for packaging? I’m very new to compiling app in Matlab.

Thanks,
Lam

mcc -o v9 -W ‘WinMain:v9,version=1.0’ -T link:exe -d ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedv9for_testing’ -v ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedv9.m’ -a ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedLICENSE’ -a C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessamdatapreTrainedSAM.mat -a C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessam+matlabshared+supportpkg+internal+sppkglegacySAM.m -r ‘C:Program FilesMATLABR2024atoolboxcompilerpackagingResourcesdefault_icon.ico’ -Z ‘Deep Learning Toolbox Converter for ONNX Model Format’
Compiler version: 24.1 (R2024a)

Analyzing file dependencies.

Warning: Removed http proxy service credentials from preference settings.

Warning: In "C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessam+matlabshared+supportpkg+internal+sppkglegacySAM.m", "matlabshared.supportpkg.internal.sppkglegacy.SupportPackageRegistryPluginBase" are excluded from packaging for the MATLAB Runtime environment according to the MATLAB Compiler license. Either remove the file or function from your code, or use the MATLAB function "isdeployed" to ensure the function is not invoked in the deployed component.
foundation::storage::vfs::Exception

mcc failed.Hi,

I could not compile my app with SAM due to the following error.
Where can I find the specific LICENSE that states why and how SAM cannot be packaged?
Or am I misunderstanding anything about specifying parameters for packaging? I’m very new to compiling app in Matlab.

Thanks,
Lam

mcc -o v9 -W ‘WinMain:v9,version=1.0’ -T link:exe -d ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedv9for_testing’ -v ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedv9.m’ -a ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedLICENSE’ -a C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessamdatapreTrainedSAM.mat -a C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessam+matlabshared+supportpkg+internal+sppkglegacySAM.m -r ‘C:Program FilesMATLABR2024atoolboxcompilerpackagingResourcesdefault_icon.ico’ -Z ‘Deep Learning Toolbox Converter for ONNX Model Format’
Compiler version: 24.1 (R2024a)

Analyzing file dependencies.

Warning: Removed http proxy service credentials from preference settings.

Warning: In "C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessam+matlabshared+supportpkg+internal+sppkglegacySAM.m", "matlabshared.supportpkg.internal.sppkglegacy.SupportPackageRegistryPluginBase" are excluded from packaging for the MATLAB Runtime environment according to the MATLAB Compiler license. Either remove the file or function from your code, or use the MATLAB function "isdeployed" to ensure the function is not invoked in the deployed component.
foundation::storage::vfs::Exception

mcc failed. Hi,

I could not compile my app with SAM due to the following error.
Where can I find the specific LICENSE that states why and how SAM cannot be packaged?
Or am I misunderstanding anything about specifying parameters for packaging? I’m very new to compiling app in Matlab.

Thanks,
Lam

mcc -o v9 -W ‘WinMain:v9,version=1.0’ -T link:exe -d ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedv9for_testing’ -v ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedv9.m’ -a ‘D:OneDrive – ZeonCorporation1.WORK24_MatlabSAMworkedLICENSE’ -a C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessamdatapreTrainedSAM.mat -a C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessam+matlabshared+supportpkg+internal+sppkglegacySAM.m -r ‘C:Program FilesMATLABR2024atoolboxcompilerpackagingResourcesdefault_icon.ico’ -Z ‘Deep Learning Toolbox Converter for ONNX Model Format’
Compiler version: 24.1 (R2024a)

Analyzing file dependencies.

Warning: Removed http proxy service credentials from preference settings.

Warning: In "C:ProgramDataMATLABSupportPackagesR2024atoolboximagessupportpackagessam+matlabshared+supportpkg+internal+sppkglegacySAM.m", "matlabshared.supportpkg.internal.sppkglegacy.SupportPackageRegistryPluginBase" are excluded from packaging for the MATLAB Runtime environment according to the MATLAB Compiler license. Either remove the file or function from your code, or use the MATLAB function "isdeployed" to ensure the function is not invoked in the deployed component.
foundation::storage::vfs::Exception

mcc failed. segment anything model, sam, compiler, matlab compiler MATLAB Answers — New Questions

​

While using hdlsetuptoolpath the system path is being prepended twice.
First the correct path and second time a non existent path.
Due to this I can’t validate the model it says Xilinx Vivado not found.While using hdlsetuptoolpath the system path is being prepended twice.
First the correct path and second time a non existent path.
Due to this I can’t validate the model it says Xilinx Vivado not found. While using hdlsetuptoolpath the system path is being prepended twice.
First the correct path and second time a non existent path.
Due to this I can’t validate the model it says Xilinx Vivado not found. hdlsetuptoolpath, soc builder MATLAB Answers — New Questions

​

Hi,
I tried to run the yolo code , but i get the error below :
‘validateInputData’ is used in the following examples:
Object Detection Using YOLO v3 Deep Learning
Object Detection Using YOLO v4 Deep Learning
Prune Filters in a Detection Network Using Taylor Scores
Error in yolo4Test (line 45)
validateInputData(trainingData);
Could you help please ?
Iam trying to run the Mathworks code for yolo4 ?Hi,
I tried to run the yolo code , but i get the error below :
‘validateInputData’ is used in the following examples:
Object Detection Using YOLO v3 Deep Learning
Object Detection Using YOLO v4 Deep Learning
Prune Filters in a Detection Network Using Taylor Scores
Error in yolo4Test (line 45)
validateInputData(trainingData);
Could you help please ?
Iam trying to run the Mathworks code for yolo4 ? Hi,
I tried to run the yolo code , but i get the error below :
‘validateInputData’ is used in the following examples:
Object Detection Using YOLO v3 Deep Learning
Object Detection Using YOLO v4 Deep Learning
Prune Filters in a Detection Network Using Taylor Scores
Error in yolo4Test (line 45)
validateInputData(trainingData);
Could you help please ?
Iam trying to run the Mathworks code for yolo4 ? deep learning, yolo4 MATLAB Answers — New Questions

​

I am trying to solve a quadratic optimization problem but quadprog keeps telling me that my problem is non-convex.
After several experiments, I found that the problem comes from the equation constraints matrix A, which is a 57250*57441 matrix.
For the following code,
[m, n] = size(A);
assert(m < n);
options = optimoptions(‘quadprog’,’Display’,’off’);
[Pwp,fval,exitflag,output] = quadprog(speye(n), zeros(n,1), [], [], A, zeros(m, 1), [], [], [], options);
obviously the solution should be the all-zero vector. But the output still said that this is a nonconvex problem.I am trying to solve a quadratic optimization problem but quadprog keeps telling me that my problem is non-convex.
After several experiments, I found that the problem comes from the equation constraints matrix A, which is a 57250*57441 matrix.
For the following code,
[m, n] = size(A);
assert(m < n);
options = optimoptions(‘quadprog’,’Display’,’off’);
[Pwp,fval,exitflag,output] = quadprog(speye(n), zeros(n,1), [], [], A, zeros(m, 1), [], [], [], options);
obviously the solution should be the all-zero vector. But the output still said that this is a nonconvex problem. I am trying to solve a quadratic optimization problem but quadprog keeps telling me that my problem is non-convex.
After several experiments, I found that the problem comes from the equation constraints matrix A, which is a 57250*57441 matrix.
For the following code,
[m, n] = size(A);
assert(m < n);
options = optimoptions(‘quadprog’,’Display’,’off’);
[Pwp,fval,exitflag,output] = quadprog(speye(n), zeros(n,1), [], [], A, zeros(m, 1), [], [], [], options);
obviously the solution should be the all-zero vector. But the output still said that this is a nonconvex problem. optimization MATLAB Answers — New Questions

​

I am trying to compute this quantity here H grad(H) where H is the mean curvture

The code breaks at the last line because there is a dimension mismatch between H and grad(H). size(grad(H))= [960 3] and size(V)=[162 3] and size(mean_curvture)=[162 3]. Why is the grad(H) has that dimension? where is the mistake?

% Load a mesh
[V, F] = load_mesh(‘sphere.off’);

% Compute Mean Curvature and Normal Vector

% 2. Compute Cotangent Laplacian (L) and Mass Matrix (M)
L = cotmatrix(V, F);
M = massmatrix(V, F, ‘barycentric’);

% 3. Compute Mean Curvature Vector (H)
H = -inv(M) * (L * V);

% 4. Compute the Magnitude of Mean Curvature (mean curvature at each vertex)
mean_curvature = sqrt(sum(H.^2, 2));

% Compute the Normal Vector
normals = per_vertex_normals(V, F);

% Compute Gaussian Curvature
gaussian_curvature = discrete_gaussian_curvature(V,F);

% Compute Laplacian of Mean Curvature
laplacian_H = L * mean_curvature;

% Compute the Gradient of Mean Curvature
% Use gptoolbox’s gradient operator for scalar fields
grad_H = grad(V, F) * mean_curvature;

% Compute the new term: newthing
%H_squared = mean_curvature .^ 2;
%newthing = laplacian_H + ((H_squared – 2 * gaussian_curvature) .* mean_curvature) .* normals + mean_curvature .* grad_H;
% Compute the new term: newthing
H_squared = mean_curvature .^ 2;
newthing = laplacian_H + ((H_squared – 2 * gaussian_curvature) .* mean_curvature) .* normals + mean_curvature .* grad_H;

% Define small arc equation
h = 0.1; % Small parameter h
vertex = V; % V contains the vertices

% Compute the small arc
f_h = vertex + (mean_curvature .* normals) * h + newthing * (h^2 / 2);

% Visualization of small arc
figure;
trisurf(F, V(:,1), V(:,2), V(:,3), ‘FaceColor’, ‘cyan’, ‘EdgeColor’, ‘none’);
hold on;
plot3(f_h(:,1), f_h(:,2), f_h(:,3), ‘r.’, ‘MarkerSize’, 10);
axis equal;
title(‘Small Arc Visualization’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
the grad(H) script
% Step 1: Load a Mesh
[V, F] = load_mesh(‘sphere.off’);

% Step 2: Compute Laplace-Beltrami Operator
L = cotmatrix(V, F); % cotangent Laplace-Beltrami operator

% Step 3: Compute the Mean Curvature Vector (H)
H = -L * V; % Mean curvature normal vector

% Step 4: Compute the Mean Curvature Magnitude
mean_curvature = sqrt(sum(H.^2, 2));

% Step 5: Compute the Gradient of Mean Curvature
% Use gptoolbox’s gradient operator for scalar fields
grad_H = grad(V, F) * mean_curvature;

% Step 6: Visualization or Further Processing
figure;
trisurf(F, V(:,1), V(:,2), V(:,3), mean_curvature, ‘EdgeColor’, ‘none’);
axis equal;
lighting gouraud;
camlight;
colorbar;
title(‘Mean Curvature Gradient’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
grad(V, F) from gp toolbox
function [G] = grad(V,F)
% GRAD Compute the numerical gradient operator for triangle or tet meshes
%
% G = grad(V,F)
%
% Inputs:
% V #vertices by dim list of mesh vertex positions
% F #faces by simplex-size list of mesh face indices
% Outputs:
% G #faces*dim by #V Gradient operator
%
% Example:
% L = cotmatrix(V,F);
% G = grad(V,F);
% dblA = doublearea(V,F);
% GMG = -G’*repdiag(diag(sparse(dblA)/2),size(V,2))*G;
%
% % Columns of W are scalar fields
% G = grad(V,F);
% % Compute gradient magnitude for each column in W
% GM = squeeze(sqrt(sum(reshape(G*W,size(F,1),size(V,2),size(W,2)).^2,2)));
%

dim = size(V,2);
ss = size(F,2);
switch ss
case 2
% Edge lengths
len = normrow(V(F(:,2),:)-V(F(:,1),:));
% Gradient is just staggered grid finite difference
G = sparse(repmat(1:size(F,1),2,1)’,F,[1 -1]./len, size(F,1),size(V,1));
case 3
% append with 0s for convenience
if size(V,2) == 2
V = [V zeros(size(V,1),1)];
end

% Gradient of a scalar function defined on piecewise linear elements (mesh)
% is constant on each triangle i,j,k:
% grad(Xijk) = (Xj-Xi) * (Vi – Vk)^R90 / 2A + (Xk-Xi) * (Vj – Vi)^R90 / 2A
% grad(Xijk) = Xj * (Vi – Vk)^R90 / 2A + Xk * (Vj – Vi)^R90 / 2A +
% -Xi * (Vi – Vk)^R90 / 2A – Xi * (Vj – Vi)^R90 / 2A
% where Xi is the scalar value at vertex i, Vi is the 3D position of vertex
% i, and A is the area of triangle (i,j,k). ^R90 represent a rotation of
% 90 degrees
%
% renaming indices of vertices of triangles for convenience
i1 = F(:,1); i2 = F(:,2); i3 = F(:,3);
% #F x 3 matrices of triangle edge vectors, named after opposite vertices
v32 = V(i3,:) – V(i2,:); v13 = V(i1,:) – V(i3,:); v21 = V(i2,:) – V(i1,:);

% area of parallelogram is twice area of triangle
% area of parallelogram is || v1 x v2 ||
n = cross(v32,v13,2);

% This does correct l2 norm of rows, so that it contains #F list of twice
% triangle areas
dblA = normrow(n);

% now normalize normals to get unit normals
u = normalizerow(n);

% rotate each vector 90 degrees around normal
%eperp21 = bsxfun(@times,normalizerow(cross(u,v21)),normrow(v21)./dblA);
%eperp13 = bsxfun(@times,normalizerow(cross(u,v13)),normrow(v13)./dblA);
eperp21 = bsxfun(@times,cross(u,v21),1./dblA);
eperp13 = bsxfun(@times,cross(u,v13),1./dblA);

%g = …
% ( …
% repmat(X(F(:,2)) – X(F(:,1)),1,3).*eperp13 + …
% repmat(X(F(:,3)) – X(F(:,1)),1,3).*eperp21 …
% );

GI = …
[0*size(F,1)+repmat(1:size(F,1),1,4) …
1*size(F,1)+repmat(1:size(F,1),1,4) …
2*size(F,1)+repmat(1:size(F,1),1,4)]’;
GJ = repmat([F(:,2);F(:,1);F(:,3);F(:,1)],3,1);
GV = [eperp13(:,1);-eperp13(:,1);eperp21(:,1);-eperp21(:,1); …
eperp13(:,2);-eperp13(:,2);eperp21(:,2);-eperp21(:,2); …
eperp13(:,3);-eperp13(:,3);eperp21(:,3);-eperp21(:,3)];
G = sparse(GI,GJ,GV, 3*size(F,1), size(V,1));

%% Alternatively
%%
%% f(x) is piecewise-linear function:
%%
%% f(x) = ∑ φi(x) fi, f(x ∈ T) = φi(x) fi + φj(x) fj + φk(x) fk
%% ∇f(x) = … = ∇φi(x) fi + ∇φj(x) fj + ∇φk(x) fk
%% = ∇φi fi + ∇φj fj + ∇φk) fk
%%
%% ∇φi = 1/hjk ((Vj-Vk)/||Vj-Vk||)^perp =
%% = ||Vj-Vk|| /(2 Aijk) * ((Vj-Vk)/||Vj-Vk||)^perp
%% = 1/(2 Aijk) * (Vj-Vk)^perp
%%
%m = size(F,1);
%eperp32 = bsxfun(@times,cross(u,v32),1./dblA);
%G = sparse( …
% [0*m + repmat(1:m,1,3) …
% 1*m + repmat(1:m,1,3) …
% 2*m + repmat(1:m,1,3)]’, …
% repmat([F(:,1);F(:,2);F(:,3)],3,1), …
% [eperp32(:,1);eperp13(:,1);eperp21(:,1); …
% eperp32(:,2);eperp13(:,2);eperp21(:,2); …
% eperp32(:,3);eperp13(:,3);eperp21(:,3)], …
% 3*m,size(V,1));

if dim == 2
G = G(1:(size(F,1)*dim),:);
end

% Should be the same as:
% g = …
% bsxfun(@times,X(F(:,1)),cross(u,v32)) + …
% bsxfun(@times,X(F(:,2)),cross(u,v13)) + …
% bsxfun(@times,X(F(:,3)),cross(u,v21));
% g = bsxfun(@rdivide,g,dblA);

case 4
% really dealing with tets
T = F;
% number of dimensions
assert(dim == 3);
% number of vertices
n = size(V,1);
% number of elements
m = size(T,1);
% simplex size
assert(size(T,2) == 4);

if m == 1 && ~isnumeric(V)
simple_volume = @(ad,r) -sum(ad.*r,2)./6;
simple_volume = @(ad,bd,cd) simple_volume(ad, …
[bd(:,2).*cd(:,3)-bd(:,3).*cd(:,2), …
bd(:,3).*cd(:,1)-bd(:,1).*cd(:,3), …
bd(:,1).*cd(:,2)-bd(:,2).*cd(:,1)]);
P = sym(‘P’,[1 3]);
V1 = V(T(:,1),:);
V2 = V(T(:,2),:);
V3 = V(T(:,3),:);
V4 = V(T(:,4),:);
V1P = V1-P;
V2P = V2-P;
V3P = V3-P;
V4P = V4-P;
A1 = simple_volume(V2P,V4P,V3P);
A2 = simple_volume(V1P,V3P,V4P);
A3 = simple_volume(V1P,V4P,V2P);
A4 = simple_volume(V1P,V2P,V3P);
B = [A1 A2 A3 A4]./(A1+A2+A3+A4);
G = simplify(jacobian(B,P).’);
return
end

% f(x) is piecewise-linear function:
%
% f(x) = ∑ φi(x) fi, f(x ∈ T) = φi(x) fi + φj(x) fj + φk(x) fk + φl(x) fl
% ∇f(x) = … = ∇φi(x) fi + ∇φj(x) fj + ∇φk(x) fk + ∇φl(x) fl
% = ∇φi fi + ∇φj fj + ∇φk fk + ∇φl fl
%
% ∇φi = 1/hjk = Ajkl / 3V * (Facejkl)^perp
% = Ajkl / 3V * (Vj-Vk)x(Vl-Vk)
% = Ajkl / 3V * Njkl / ||Njkl||
%

% get all faces
F = [ …
T(:,1) T(:,2) T(:,3); …
T(:,1) T(:,3) T(:,4); …
T(:,1) T(:,4) T(:,2); …
T(:,2) T(:,4) T(:,3)];
% compute areas of each face
A = doublearea(V,F)/2;
N = normalizerow(normals(V,F));

% compute volume of each tet
vol = volume(V,T);

GI = …
[0*m + repmat(1:m,1,4) …
1*m + repmat(1:m,1,4) …
2*m + repmat(1:m,1,4)];
GJ = repmat([T(:,4);T(:,2);T(:,3);T(:,1)],3,1);
GV = repmat(A./(3*repmat(vol,4,1)),3,1).*N(:);
G = sparse(GI,GJ,GV, 3*m,n);

end
end
the sphere.off file (mesh data)I am trying to compute this quantity here H grad(H) where H is the mean curvture

The code breaks at the last line because there is a dimension mismatch between H and grad(H). size(grad(H))= [960 3] and size(V)=[162 3] and size(mean_curvture)=[162 3]. Why is the grad(H) has that dimension? where is the mistake?

% Load a mesh
[V, F] = load_mesh(‘sphere.off’);

% Compute Mean Curvature and Normal Vector

% 2. Compute Cotangent Laplacian (L) and Mass Matrix (M)
L = cotmatrix(V, F);
M = massmatrix(V, F, ‘barycentric’);

% 3. Compute Mean Curvature Vector (H)
H = -inv(M) * (L * V);

% 4. Compute the Magnitude of Mean Curvature (mean curvature at each vertex)
mean_curvature = sqrt(sum(H.^2, 2));

% Compute the Normal Vector
normals = per_vertex_normals(V, F);

% Compute Gaussian Curvature
gaussian_curvature = discrete_gaussian_curvature(V,F);

% Compute Laplacian of Mean Curvature
laplacian_H = L * mean_curvature;

% Compute the Gradient of Mean Curvature
% Use gptoolbox’s gradient operator for scalar fields
grad_H = grad(V, F) * mean_curvature;

% Compute the new term: newthing
%H_squared = mean_curvature .^ 2;
%newthing = laplacian_H + ((H_squared – 2 * gaussian_curvature) .* mean_curvature) .* normals + mean_curvature .* grad_H;
% Compute the new term: newthing
H_squared = mean_curvature .^ 2;
newthing = laplacian_H + ((H_squared – 2 * gaussian_curvature) .* mean_curvature) .* normals + mean_curvature .* grad_H;

% Define small arc equation
h = 0.1; % Small parameter h
vertex = V; % V contains the vertices

% Compute the small arc
f_h = vertex + (mean_curvature .* normals) * h + newthing * (h^2 / 2);

% Visualization of small arc
figure;
trisurf(F, V(:,1), V(:,2), V(:,3), ‘FaceColor’, ‘cyan’, ‘EdgeColor’, ‘none’);
hold on;
plot3(f_h(:,1), f_h(:,2), f_h(:,3), ‘r.’, ‘MarkerSize’, 10);
axis equal;
title(‘Small Arc Visualization’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
the grad(H) script
% Step 1: Load a Mesh
[V, F] = load_mesh(‘sphere.off’);

% Step 2: Compute Laplace-Beltrami Operator
L = cotmatrix(V, F); % cotangent Laplace-Beltrami operator

% Step 3: Compute the Mean Curvature Vector (H)
H = -L * V; % Mean curvature normal vector

% Step 4: Compute the Mean Curvature Magnitude
mean_curvature = sqrt(sum(H.^2, 2));

% Step 5: Compute the Gradient of Mean Curvature
% Use gptoolbox’s gradient operator for scalar fields
grad_H = grad(V, F) * mean_curvature;

% Step 6: Visualization or Further Processing
figure;
trisurf(F, V(:,1), V(:,2), V(:,3), mean_curvature, ‘EdgeColor’, ‘none’);
axis equal;
lighting gouraud;
camlight;
colorbar;
title(‘Mean Curvature Gradient’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
grad(V, F) from gp toolbox
function [G] = grad(V,F)
% GRAD Compute the numerical gradient operator for triangle or tet meshes
%
% G = grad(V,F)
%
% Inputs:
% V #vertices by dim list of mesh vertex positions
% F #faces by simplex-size list of mesh face indices
% Outputs:
% G #faces*dim by #V Gradient operator
%
% Example:
% L = cotmatrix(V,F);
% G = grad(V,F);
% dblA = doublearea(V,F);
% GMG = -G’*repdiag(diag(sparse(dblA)/2),size(V,2))*G;
%
% % Columns of W are scalar fields
% G = grad(V,F);
% % Compute gradient magnitude for each column in W
% GM = squeeze(sqrt(sum(reshape(G*W,size(F,1),size(V,2),size(W,2)).^2,2)));
%

dim = size(V,2);
ss = size(F,2);
switch ss
case 2
% Edge lengths
len = normrow(V(F(:,2),:)-V(F(:,1),:));
% Gradient is just staggered grid finite difference
G = sparse(repmat(1:size(F,1),2,1)’,F,[1 -1]./len, size(F,1),size(V,1));
case 3
% append with 0s for convenience
if size(V,2) == 2
V = [V zeros(size(V,1),1)];
end

% Gradient of a scalar function defined on piecewise linear elements (mesh)
% is constant on each triangle i,j,k:
% grad(Xijk) = (Xj-Xi) * (Vi – Vk)^R90 / 2A + (Xk-Xi) * (Vj – Vi)^R90 / 2A
% grad(Xijk) = Xj * (Vi – Vk)^R90 / 2A + Xk * (Vj – Vi)^R90 / 2A +
% -Xi * (Vi – Vk)^R90 / 2A – Xi * (Vj – Vi)^R90 / 2A
% where Xi is the scalar value at vertex i, Vi is the 3D position of vertex
% i, and A is the area of triangle (i,j,k). ^R90 represent a rotation of
% 90 degrees
%
% renaming indices of vertices of triangles for convenience
i1 = F(:,1); i2 = F(:,2); i3 = F(:,3);
% #F x 3 matrices of triangle edge vectors, named after opposite vertices
v32 = V(i3,:) – V(i2,:); v13 = V(i1,:) – V(i3,:); v21 = V(i2,:) – V(i1,:);

% area of parallelogram is twice area of triangle
% area of parallelogram is || v1 x v2 ||
n = cross(v32,v13,2);

% This does correct l2 norm of rows, so that it contains #F list of twice
% triangle areas
dblA = normrow(n);

% now normalize normals to get unit normals
u = normalizerow(n);

% rotate each vector 90 degrees around normal
%eperp21 = bsxfun(@times,normalizerow(cross(u,v21)),normrow(v21)./dblA);
%eperp13 = bsxfun(@times,normalizerow(cross(u,v13)),normrow(v13)./dblA);
eperp21 = bsxfun(@times,cross(u,v21),1./dblA);
eperp13 = bsxfun(@times,cross(u,v13),1./dblA);

%g = …
% ( …
% repmat(X(F(:,2)) – X(F(:,1)),1,3).*eperp13 + …
% repmat(X(F(:,3)) – X(F(:,1)),1,3).*eperp21 …
% );

GI = …
[0*size(F,1)+repmat(1:size(F,1),1,4) …
1*size(F,1)+repmat(1:size(F,1),1,4) …
2*size(F,1)+repmat(1:size(F,1),1,4)]’;
GJ = repmat([F(:,2);F(:,1);F(:,3);F(:,1)],3,1);
GV = [eperp13(:,1);-eperp13(:,1);eperp21(:,1);-eperp21(:,1); …
eperp13(:,2);-eperp13(:,2);eperp21(:,2);-eperp21(:,2); …
eperp13(:,3);-eperp13(:,3);eperp21(:,3);-eperp21(:,3)];
G = sparse(GI,GJ,GV, 3*size(F,1), size(V,1));

%% Alternatively
%%
%% f(x) is piecewise-linear function:
%%
%% f(x) = ∑ φi(x) fi, f(x ∈ T) = φi(x) fi + φj(x) fj + φk(x) fk
%% ∇f(x) = … = ∇φi(x) fi + ∇φj(x) fj + ∇φk(x) fk
%% = ∇φi fi + ∇φj fj + ∇φk) fk
%%
%% ∇φi = 1/hjk ((Vj-Vk)/||Vj-Vk||)^perp =
%% = ||Vj-Vk|| /(2 Aijk) * ((Vj-Vk)/||Vj-Vk||)^perp
%% = 1/(2 Aijk) * (Vj-Vk)^perp
%%
%m = size(F,1);
%eperp32 = bsxfun(@times,cross(u,v32),1./dblA);
%G = sparse( …
% [0*m + repmat(1:m,1,3) …
% 1*m + repmat(1:m,1,3) …
% 2*m + repmat(1:m,1,3)]’, …
% repmat([F(:,1);F(:,2);F(:,3)],3,1), …
% [eperp32(:,1);eperp13(:,1);eperp21(:,1); …
% eperp32(:,2);eperp13(:,2);eperp21(:,2); …
% eperp32(:,3);eperp13(:,3);eperp21(:,3)], …
% 3*m,size(V,1));

if dim == 2
G = G(1:(size(F,1)*dim),:);
end

% Should be the same as:
% g = …
% bsxfun(@times,X(F(:,1)),cross(u,v32)) + …
% bsxfun(@times,X(F(:,2)),cross(u,v13)) + …
% bsxfun(@times,X(F(:,3)),cross(u,v21));
% g = bsxfun(@rdivide,g,dblA);

case 4
% really dealing with tets
T = F;
% number of dimensions
assert(dim == 3);
% number of vertices
n = size(V,1);
% number of elements
m = size(T,1);
% simplex size
assert(size(T,2) == 4);

if m == 1 && ~isnumeric(V)
simple_volume = @(ad,r) -sum(ad.*r,2)./6;
simple_volume = @(ad,bd,cd) simple_volume(ad, …
[bd(:,2).*cd(:,3)-bd(:,3).*cd(:,2), …
bd(:,3).*cd(:,1)-bd(:,1).*cd(:,3), …
bd(:,1).*cd(:,2)-bd(:,2).*cd(:,1)]);
P = sym(‘P’,[1 3]);
V1 = V(T(:,1),:);
V2 = V(T(:,2),:);
V3 = V(T(:,3),:);
V4 = V(T(:,4),:);
V1P = V1-P;
V2P = V2-P;
V3P = V3-P;
V4P = V4-P;
A1 = simple_volume(V2P,V4P,V3P);
A2 = simple_volume(V1P,V3P,V4P);
A3 = simple_volume(V1P,V4P,V2P);
A4 = simple_volume(V1P,V2P,V3P);
B = [A1 A2 A3 A4]./(A1+A2+A3+A4);
G = simplify(jacobian(B,P).’);
return
end

% f(x) is piecewise-linear function:
%
% f(x) = ∑ φi(x) fi, f(x ∈ T) = φi(x) fi + φj(x) fj + φk(x) fk + φl(x) fl
% ∇f(x) = … = ∇φi(x) fi + ∇φj(x) fj + ∇φk(x) fk + ∇φl(x) fl
% = ∇φi fi + ∇φj fj + ∇φk fk + ∇φl fl
%
% ∇φi = 1/hjk = Ajkl / 3V * (Facejkl)^perp
% = Ajkl / 3V * (Vj-Vk)x(Vl-Vk)
% = Ajkl / 3V * Njkl / ||Njkl||
%

% get all faces
F = [ …
T(:,1) T(:,2) T(:,3); …
T(:,1) T(:,3) T(:,4); …
T(:,1) T(:,4) T(:,2); …
T(:,2) T(:,4) T(:,3)];
% compute areas of each face
A = doublearea(V,F)/2;
N = normalizerow(normals(V,F));

% compute volume of each tet
vol = volume(V,T);

GI = …
[0*m + repmat(1:m,1,4) …
1*m + repmat(1:m,1,4) …
2*m + repmat(1:m,1,4)];
GJ = repmat([T(:,4);T(:,2);T(:,3);T(:,1)],3,1);
GV = repmat(A./(3*repmat(vol,4,1)),3,1).*N(:);
G = sparse(GI,GJ,GV, 3*m,n);

end
end
the sphere.off file (mesh data) I am trying to compute this quantity here H grad(H) where H is the mean curvture

The code breaks at the last line because there is a dimension mismatch between H and grad(H). size(grad(H))= [960 3] and size(V)=[162 3] and size(mean_curvture)=[162 3]. Why is the grad(H) has that dimension? where is the mistake?

% Load a mesh
[V, F] = load_mesh(‘sphere.off’);

% Compute Mean Curvature and Normal Vector

% 2. Compute Cotangent Laplacian (L) and Mass Matrix (M)
L = cotmatrix(V, F);
M = massmatrix(V, F, ‘barycentric’);

% 3. Compute Mean Curvature Vector (H)
H = -inv(M) * (L * V);

% 4. Compute the Magnitude of Mean Curvature (mean curvature at each vertex)
mean_curvature = sqrt(sum(H.^2, 2));

% Compute the Normal Vector
normals = per_vertex_normals(V, F);

% Compute Gaussian Curvature
gaussian_curvature = discrete_gaussian_curvature(V,F);

% Compute Laplacian of Mean Curvature
laplacian_H = L * mean_curvature;

% Compute the Gradient of Mean Curvature
% Use gptoolbox’s gradient operator for scalar fields
grad_H = grad(V, F) * mean_curvature;

% Compute the new term: newthing
%H_squared = mean_curvature .^ 2;
%newthing = laplacian_H + ((H_squared – 2 * gaussian_curvature) .* mean_curvature) .* normals + mean_curvature .* grad_H;
% Compute the new term: newthing
H_squared = mean_curvature .^ 2;
newthing = laplacian_H + ((H_squared – 2 * gaussian_curvature) .* mean_curvature) .* normals + mean_curvature .* grad_H;

% Define small arc equation
h = 0.1; % Small parameter h
vertex = V; % V contains the vertices

% Compute the small arc
f_h = vertex + (mean_curvature .* normals) * h + newthing * (h^2 / 2);

% Visualization of small arc
figure;
trisurf(F, V(:,1), V(:,2), V(:,3), ‘FaceColor’, ‘cyan’, ‘EdgeColor’, ‘none’);
hold on;
plot3(f_h(:,1), f_h(:,2), f_h(:,3), ‘r.’, ‘MarkerSize’, 10);
axis equal;
title(‘Small Arc Visualization’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
the grad(H) script
% Step 1: Load a Mesh
[V, F] = load_mesh(‘sphere.off’);

% Step 2: Compute Laplace-Beltrami Operator
L = cotmatrix(V, F); % cotangent Laplace-Beltrami operator

% Step 3: Compute the Mean Curvature Vector (H)
H = -L * V; % Mean curvature normal vector

% Step 4: Compute the Mean Curvature Magnitude
mean_curvature = sqrt(sum(H.^2, 2));

% Step 5: Compute the Gradient of Mean Curvature
% Use gptoolbox’s gradient operator for scalar fields
grad_H = grad(V, F) * mean_curvature;

% Step 6: Visualization or Further Processing
figure;
trisurf(F, V(:,1), V(:,2), V(:,3), mean_curvature, ‘EdgeColor’, ‘none’);
axis equal;
lighting gouraud;
camlight;
colorbar;
title(‘Mean Curvature Gradient’);
xlabel(‘X’);
ylabel(‘Y’);
zlabel(‘Z’);
grad(V, F) from gp toolbox
function [G] = grad(V,F)
% GRAD Compute the numerical gradient operator for triangle or tet meshes
%
% G = grad(V,F)
%
% Inputs:
% V #vertices by dim list of mesh vertex positions
% F #faces by simplex-size list of mesh face indices
% Outputs:
% G #faces*dim by #V Gradient operator
%
% Example:
% L = cotmatrix(V,F);
% G = grad(V,F);
% dblA = doublearea(V,F);
% GMG = -G’*repdiag(diag(sparse(dblA)/2),size(V,2))*G;
%
% % Columns of W are scalar fields
% G = grad(V,F);
% % Compute gradient magnitude for each column in W
% GM = squeeze(sqrt(sum(reshape(G*W,size(F,1),size(V,2),size(W,2)).^2,2)));
%

dim = size(V,2);
ss = size(F,2);
switch ss
case 2
% Edge lengths
len = normrow(V(F(:,2),:)-V(F(:,1),:));
% Gradient is just staggered grid finite difference
G = sparse(repmat(1:size(F,1),2,1)’,F,[1 -1]./len, size(F,1),size(V,1));
case 3
% append with 0s for convenience
if size(V,2) == 2
V = [V zeros(size(V,1),1)];
end

% Gradient of a scalar function defined on piecewise linear elements (mesh)
% is constant on each triangle i,j,k:
% grad(Xijk) = (Xj-Xi) * (Vi – Vk)^R90 / 2A + (Xk-Xi) * (Vj – Vi)^R90 / 2A
% grad(Xijk) = Xj * (Vi – Vk)^R90 / 2A + Xk * (Vj – Vi)^R90 / 2A +
% -Xi * (Vi – Vk)^R90 / 2A – Xi * (Vj – Vi)^R90 / 2A
% where Xi is the scalar value at vertex i, Vi is the 3D position of vertex
% i, and A is the area of triangle (i,j,k). ^R90 represent a rotation of
% 90 degrees
%
% renaming indices of vertices of triangles for convenience
i1 = F(:,1); i2 = F(:,2); i3 = F(:,3);
% #F x 3 matrices of triangle edge vectors, named after opposite vertices
v32 = V(i3,:) – V(i2,:); v13 = V(i1,:) – V(i3,:); v21 = V(i2,:) – V(i1,:);

% area of parallelogram is twice area of triangle
% area of parallelogram is || v1 x v2 ||
n = cross(v32,v13,2);

% This does correct l2 norm of rows, so that it contains #F list of twice
% triangle areas
dblA = normrow(n);

% now normalize normals to get unit normals
u = normalizerow(n);

% rotate each vector 90 degrees around normal
%eperp21 = bsxfun(@times,normalizerow(cross(u,v21)),normrow(v21)./dblA);
%eperp13 = bsxfun(@times,normalizerow(cross(u,v13)),normrow(v13)./dblA);
eperp21 = bsxfun(@times,cross(u,v21),1./dblA);
eperp13 = bsxfun(@times,cross(u,v13),1./dblA);

%g = …
% ( …
% repmat(X(F(:,2)) – X(F(:,1)),1,3).*eperp13 + …
% repmat(X(F(:,3)) – X(F(:,1)),1,3).*eperp21 …
% );

GI = …
[0*size(F,1)+repmat(1:size(F,1),1,4) …
1*size(F,1)+repmat(1:size(F,1),1,4) …
2*size(F,1)+repmat(1:size(F,1),1,4)]’;
GJ = repmat([F(:,2);F(:,1);F(:,3);F(:,1)],3,1);
GV = [eperp13(:,1);-eperp13(:,1);eperp21(:,1);-eperp21(:,1); …
eperp13(:,2);-eperp13(:,2);eperp21(:,2);-eperp21(:,2); …
eperp13(:,3);-eperp13(:,3);eperp21(:,3);-eperp21(:,3)];
G = sparse(GI,GJ,GV, 3*size(F,1), size(V,1));

%% Alternatively
%%
%% f(x) is piecewise-linear function:
%%
%% f(x) = ∑ φi(x) fi, f(x ∈ T) = φi(x) fi + φj(x) fj + φk(x) fk
%% ∇f(x) = … = ∇φi(x) fi + ∇φj(x) fj + ∇φk(x) fk
%% = ∇φi fi + ∇φj fj + ∇φk) fk
%%
%% ∇φi = 1/hjk ((Vj-Vk)/||Vj-Vk||)^perp =
%% = ||Vj-Vk|| /(2 Aijk) * ((Vj-Vk)/||Vj-Vk||)^perp
%% = 1/(2 Aijk) * (Vj-Vk)^perp
%%
%m = size(F,1);
%eperp32 = bsxfun(@times,cross(u,v32),1./dblA);
%G = sparse( …
% [0*m + repmat(1:m,1,3) …
% 1*m + repmat(1:m,1,3) …
% 2*m + repmat(1:m,1,3)]’, …
% repmat([F(:,1);F(:,2);F(:,3)],3,1), …
% [eperp32(:,1);eperp13(:,1);eperp21(:,1); …
% eperp32(:,2);eperp13(:,2);eperp21(:,2); …
% eperp32(:,3);eperp13(:,3);eperp21(:,3)], …
% 3*m,size(V,1));

if dim == 2
G = G(1:(size(F,1)*dim),:);
end

% Should be the same as:
% g = …
% bsxfun(@times,X(F(:,1)),cross(u,v32)) + …
% bsxfun(@times,X(F(:,2)),cross(u,v13)) + …
% bsxfun(@times,X(F(:,3)),cross(u,v21));
% g = bsxfun(@rdivide,g,dblA);

case 4
% really dealing with tets
T = F;
% number of dimensions
assert(dim == 3);
% number of vertices
n = size(V,1);
% number of elements
m = size(T,1);
% simplex size
assert(size(T,2) == 4);

if m == 1 && ~isnumeric(V)
simple_volume = @(ad,r) -sum(ad.*r,2)./6;
simple_volume = @(ad,bd,cd) simple_volume(ad, …
[bd(:,2).*cd(:,3)-bd(:,3).*cd(:,2), …
bd(:,3).*cd(:,1)-bd(:,1).*cd(:,3), …
bd(:,1).*cd(:,2)-bd(:,2).*cd(:,1)]);
P = sym(‘P’,[1 3]);
V1 = V(T(:,1),:);
V2 = V(T(:,2),:);
V3 = V(T(:,3),:);
V4 = V(T(:,4),:);
V1P = V1-P;
V2P = V2-P;
V3P = V3-P;
V4P = V4-P;
A1 = simple_volume(V2P,V4P,V3P);
A2 = simple_volume(V1P,V3P,V4P);
A3 = simple_volume(V1P,V4P,V2P);
A4 = simple_volume(V1P,V2P,V3P);
B = [A1 A2 A3 A4]./(A1+A2+A3+A4);
G = simplify(jacobian(B,P).’);
return
end

% f(x) is piecewise-linear function:
%
% f(x) = ∑ φi(x) fi, f(x ∈ T) = φi(x) fi + φj(x) fj + φk(x) fk + φl(x) fl
% ∇f(x) = … = ∇φi(x) fi + ∇φj(x) fj + ∇φk(x) fk + ∇φl(x) fl
% = ∇φi fi + ∇φj fj + ∇φk fk + ∇φl fl
%
% ∇φi = 1/hjk = Ajkl / 3V * (Facejkl)^perp
% = Ajkl / 3V * (Vj-Vk)x(Vl-Vk)
% = Ajkl / 3V * Njkl / ||Njkl||
%

% get all faces
F = [ …
T(:,1) T(:,2) T(:,3); …
T(:,1) T(:,3) T(:,4); …
T(:,1) T(:,4) T(:,2); …
T(:,2) T(:,4) T(:,3)];
% compute areas of each face
A = doublearea(V,F)/2;
N = normalizerow(normals(V,F));

% compute volume of each tet
vol = volume(V,T);

GI = …
[0*m + repmat(1:m,1,4) …
1*m + repmat(1:m,1,4) …
2*m + repmat(1:m,1,4)];
GJ = repmat([T(:,4);T(:,2);T(:,3);T(:,1)],3,1);
GV = repmat(A./(3*repmat(vol,4,1)),3,1).*N(:);
G = sparse(GI,GJ,GV, 3*m,n);

end
end
the sphere.off file (mesh data) image processing, image segmentation, digital image processing, geometry processing, toolbox, matrix MATLAB Answers — New Questions

​

Hello to all,

We need to buy a Matlab license for a project and we already have a working collection of scripts. However, there are so many functions used that it is very difficult to find out what toolboxes we need to buy (it will take a lot of time to check each function’s origin). Is there a tool created by Mathworks which will allow us to find out what toolboxes or Matlab packages are used for a given script?

Best regards,
JeanHello to all,

We need to buy a Matlab license for a project and we already have a working collection of scripts. However, there are so many functions used that it is very difficult to find out what toolboxes we need to buy (it will take a lot of time to check each function’s origin). Is there a tool created by Mathworks which will allow us to find out what toolboxes or Matlab packages are used for a given script?

Best regards,
Jean Hello to all,

We need to buy a Matlab license for a project and we already have a working collection of scripts. However, there are so many functions used that it is very difficult to find out what toolboxes we need to buy (it will take a lot of time to check each function’s origin). Is there a tool created by Mathworks which will allow us to find out what toolboxes or Matlab packages are used for a given script?

Best regards,
Jean toolbox, package, matlab, script, functions MATLAB Answers — New Questions

​

Hello,
I’m currently experimenting with the Satellite communication toolbox. I noticed that there seems to be a difference in the output between creating a satellite scenario and populating it using TLE data from a file (so using satellite(sc, tlefile)), versus defining the satellite scenario and adding the satellites’ parameters (which are extracted from the same TLE file using tleread() function) manually, despite having the same initial conditions (startTime, stopTime, and samplingTime)
For instance, with MATLAB’s TLE file ‘leoSatelliteConstellation.tle’, the following output is shown on the SatelliteViewer.

using the manual method based on tleread() and satellite(scenario,semimajoraxis,eccentricity,inclination,RAAN,argofperiapsis,trueanomaly):

and using satellite(scenario,file):

shouldn’t they have the same output?
When using actual TLE data of Iridium satellites, for instance, loading the constellation using the manual method produced an erroneous output!Hello,
I’m currently experimenting with the Satellite communication toolbox. I noticed that there seems to be a difference in the output between creating a satellite scenario and populating it using TLE data from a file (so using satellite(sc, tlefile)), versus defining the satellite scenario and adding the satellites’ parameters (which are extracted from the same TLE file using tleread() function) manually, despite having the same initial conditions (startTime, stopTime, and samplingTime)
For instance, with MATLAB’s TLE file ‘leoSatelliteConstellation.tle’, the following output is shown on the SatelliteViewer.

using the manual method based on tleread() and satellite(scenario,semimajoraxis,eccentricity,inclination,RAAN,argofperiapsis,trueanomaly):

and using satellite(scenario,file):

shouldn’t they have the same output?
When using actual TLE data of Iridium satellites, for instance, loading the constellation using the manual method produced an erroneous output! Hello,
I’m currently experimenting with the Satellite communication toolbox. I noticed that there seems to be a difference in the output between creating a satellite scenario and populating it using TLE data from a file (so using satellite(sc, tlefile)), versus defining the satellite scenario and adding the satellites’ parameters (which are extracted from the same TLE file using tleread() function) manually, despite having the same initial conditions (startTime, stopTime, and samplingTime)
For instance, with MATLAB’s TLE file ‘leoSatelliteConstellation.tle’, the following output is shown on the SatelliteViewer.

using the manual method based on tleread() and satellite(scenario,semimajoraxis,eccentricity,inclination,RAAN,argofperiapsis,trueanomaly):

and using satellite(scenario,file):

shouldn’t they have the same output?
When using actual TLE data of Iridium satellites, for instance, loading the constellation using the manual method produced an erroneous output! satellite, tle data, satellitescenario MATLAB Answers — New Questions

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Hi，I’m trying to maximize a function with genetic algorithm or patternsearch using Optimization Live Editor task. But it confuses me that an Error "Your objective function must return a scalar value" always occurs, and I have alreay checked out the output of my objective function. Can somebody tell me how to fix this problem? Would appreciate any help!

I checked out the the output of my objective function as follows:
input = [0 0.5];
MaxSidelobe = FindBestPlacingGA(input);
TF = isscalar(MaxSidelobe);
disp(TF);

The objective function and other functions needed：
function MaxSidelobe= FindBestPlacingGA(input)
input(1) = deg2rad(input(1));
mic_pos = [0 0.24 0
-0.2078 -0.12 0
0.2078 -0.12 0];
mic_pos = [Array3N(input(1),input(2));mic_pos];
MaxSidelobe= FPSF_Function(mic_pos,500,0:1:80);
end

function mic_pos = Array3N(theta,rho)
theta3N = [theta+pi/2;theta+pi*7/6;theta+pi*11/6];
mic_pos = zeros(3,3);
mic_pos(:,3) = 0;
[mic_pos(:,1),mic_pos(:,2)] = pol2cart(theta3N,rho);
end

function MSL= FPSF_Function(mic_pos,f,El)

Num_mic = size(mic_pos,1);
Az = -180:1: 180;
c = 343;
k0 = [0 0 -1];
numAz = length(Az);
numEl = length(El);
K = zeros(3, numAz, numEl);

for i = 1:numAz
for j = 1:numEl
az_rad = deg2rad(Az(i));
el_rad = deg2rad(El(j));
x = cos(az_rad) * sin(el_rad);
y = sin(az_rad) * sin(el_rad);
z = cos(el_rad);
K(:, i, j) = [x; y; z];
end
end
W = zeros(numAz,numEl);
for p = 1:numAz
for q = 1:numEl
for n = 1:Num_mic
W(p,q) = exp(-1i*dot(K(:,p,q)’-k0,mic_pos(n,:))*2*pi*f/c) + W(p,q);
end
end
end
W = W/Num_mic;
Y = 10*log10((abs(W)).^2);
local_max = imregionalmax(Y);
max_values = Y(local_max);
Mainlobe = max(max_values(:));
sidelobes = max_values(max_values~=Mainlobe);
MSL = Mainlobe – max(sidelobes(:));
endHi，I’m trying to maximize a function with genetic algorithm or patternsearch using Optimization Live Editor task. But it confuses me that an Error "Your objective function must return a scalar value" always occurs, and I have alreay checked out the output of my objective function. Can somebody tell me how to fix this problem? Would appreciate any help!

I checked out the the output of my objective function as follows:
input = [0 0.5];
MaxSidelobe = FindBestPlacingGA(input);
TF = isscalar(MaxSidelobe);
disp(TF);

The objective function and other functions needed：
function MaxSidelobe= FindBestPlacingGA(input)
input(1) = deg2rad(input(1));
mic_pos = [0 0.24 0
-0.2078 -0.12 0
0.2078 -0.12 0];
mic_pos = [Array3N(input(1),input(2));mic_pos];
MaxSidelobe= FPSF_Function(mic_pos,500,0:1:80);
end

function mic_pos = Array3N(theta,rho)
theta3N = [theta+pi/2;theta+pi*7/6;theta+pi*11/6];
mic_pos = zeros(3,3);
mic_pos(:,3) = 0;
[mic_pos(:,1),mic_pos(:,2)] = pol2cart(theta3N,rho);
end

function MSL= FPSF_Function(mic_pos,f,El)

Num_mic = size(mic_pos,1);
Az = -180:1: 180;
c = 343;
k0 = [0 0 -1];
numAz = length(Az);
numEl = length(El);
K = zeros(3, numAz, numEl);

for i = 1:numAz
for j = 1:numEl
az_rad = deg2rad(Az(i));
el_rad = deg2rad(El(j));
x = cos(az_rad) * sin(el_rad);
y = sin(az_rad) * sin(el_rad);
z = cos(el_rad);
K(:, i, j) = [x; y; z];
end
end
W = zeros(numAz,numEl);
for p = 1:numAz
for q = 1:numEl
for n = 1:Num_mic
W(p,q) = exp(-1i*dot(K(:,p,q)’-k0,mic_pos(n,:))*2*pi*f/c) + W(p,q);
end
end
end
W = W/Num_mic;
Y = 10*log10((abs(W)).^2);
local_max = imregionalmax(Y);
max_values = Y(local_max);
Mainlobe = max(max_values(:));
sidelobes = max_values(max_values~=Mainlobe);
MSL = Mainlobe – max(sidelobes(:));
end Hi，I’m trying to maximize a function with genetic algorithm or patternsearch using Optimization Live Editor task. But it confuses me that an Error "Your objective function must return a scalar value" always occurs, and I have alreay checked out the output of my objective function. Can somebody tell me how to fix this problem? Would appreciate any help!

I checked out the the output of my objective function as follows:
input = [0 0.5];
MaxSidelobe = FindBestPlacingGA(input);
TF = isscalar(MaxSidelobe);
disp(TF);

The objective function and other functions needed：
function MaxSidelobe= FindBestPlacingGA(input)
input(1) = deg2rad(input(1));
mic_pos = [0 0.24 0
-0.2078 -0.12 0
0.2078 -0.12 0];
mic_pos = [Array3N(input(1),input(2));mic_pos];
MaxSidelobe= FPSF_Function(mic_pos,500,0:1:80);
end

function mic_pos = Array3N(theta,rho)
theta3N = [theta+pi/2;theta+pi*7/6;theta+pi*11/6];
mic_pos = zeros(3,3);
mic_pos(:,3) = 0;
[mic_pos(:,1),mic_pos(:,2)] = pol2cart(theta3N,rho);
end

function MSL= FPSF_Function(mic_pos,f,El)

Num_mic = size(mic_pos,1);
Az = -180:1: 180;
c = 343;
k0 = [0 0 -1];
numAz = length(Az);
numEl = length(El);
K = zeros(3, numAz, numEl);

for i = 1:numAz
for j = 1:numEl
az_rad = deg2rad(Az(i));
el_rad = deg2rad(El(j));
x = cos(az_rad) * sin(el_rad);
y = sin(az_rad) * sin(el_rad);
z = cos(el_rad);
K(:, i, j) = [x; y; z];
end
end
W = zeros(numAz,numEl);
for p = 1:numAz
for q = 1:numEl
for n = 1:Num_mic
W(p,q) = exp(-1i*dot(K(:,p,q)’-k0,mic_pos(n,:))*2*pi*f/c) + W(p,q);
end
end
end
W = W/Num_mic;
Y = 10*log10((abs(W)).^2);
local_max = imregionalmax(Y);
max_values = Y(local_max);
Mainlobe = max(max_values(:));
sidelobes = max_values(max_values~=Mainlobe);
MSL = Mainlobe – max(sidelobes(:));
end optimization live editor task, error, scalar value MATLAB Answers — New Questions

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