I am trying to operate a GigE vision camera manufactured by IDS through MATLAB. I installed Image Aquisition Toolbox and the support package for GigE but the gigecamlist function won’t return me anything. I followed the configuration guide on https://www.mathworks.com/help/imaq/configure-gigabit-ethernet-network-adapter-on-windows.html, but it didn’t solve the issue. I tried turing off firewall and that didn’t solve either. The camera itself does work and can be operated with the software from IDS. What could be the issue?I am trying to operate a GigE vision camera manufactured by IDS through MATLAB. I installed Image Aquisition Toolbox and the support package for GigE but the gigecamlist function won’t return me anything. I followed the configuration guide on https://www.mathworks.com/help/imaq/configure-gigabit-ethernet-network-adapter-on-windows.html, but it didn’t solve the issue. I tried turing off firewall and that didn’t solve either. The camera itself does work and can be operated with the software from IDS. What could be the issue? I am trying to operate a GigE vision camera manufactured by IDS through MATLAB. I installed Image Aquisition Toolbox and the support package for GigE but the gigecamlist function won’t return me anything. I followed the configuration guide on https://www.mathworks.com/help/imaq/configure-gigabit-ethernet-network-adapter-on-windows.html, but it didn’t solve the issue. I tried turing off firewall and that didn’t solve either. The camera itself does work and can be operated with the software from IDS. What could be the issue? image acquisition, computer vision MATLAB Answers — New Questions

​

I am trying to use CAT12 to make do surface based morphometry. I am trying to load my cortical thickness maps in the resample and smooth function, but when doing that I get this error when trying to load the files clicking on ‘specify’:
Index exceeds the number of array elements. Index must not exceed 0.

Error in cat_surf_info (line 241)
sinfo(i).side = ff2(hemi_ind(1):hemi_ind(1)+1);

Error in cat_conf_stools>vout_surf_resamp (line 2245)
sinfo = cat_surf_info(job.data_surf{i});

Error in cat_conf_stools>@(job)vout_surf_resamp(job) (line 1275)
surfresamp.vout = @(job) vout_surf_resamp(job);

Error in cfg_exbranch/harvest (line 99)
item.sout = feval(item.vout, val);

Error in cfg_util (line 704)
[tag, val, u3, u4, u5, jobs(cjob).cj] = harvest(subsref(jobs(cjob).cj, …

Error in cfg_ui_util>local_setvaledit (line 661)
cfg_util(‘harvest’, ciid{1:end-1});

Error in cfg_ui_util>@(nval)local_setvaledit(ciid,nval,dflag) (line 275)
udvalshow.setvalcb = @(nval)local_setvaledit(ciid, nval, dflag);

Error in cfg_ui_util (line 454)
feval(udvalshow.setvalcb, val);

Error in cfg_ui>local_valedit_EditValue (line 404)
cfg_ui_util(‘valedit_EditValue’, ciid, itemname, val);

Error in cfg_ui>module_Callback (line 978)
local_valedit_EditValue(hObject);

Error in gui_mainfcn (line 95)
feval(varargin{:});

Error in cfg_ui (line 53)
gui_mainfcn(gui_State, varargin{:});

Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)cfg_ui(‘module_Callback’,hObject,eventdata,guidata(hObject))

Error while evaluating UIControl Callback.

What can I do to fix this error? I am using Matlab 2022a with SPM12 and CAT12.I am trying to use CAT12 to make do surface based morphometry. I am trying to load my cortical thickness maps in the resample and smooth function, but when doing that I get this error when trying to load the files clicking on ‘specify’:
Index exceeds the number of array elements. Index must not exceed 0.

Error in cat_surf_info (line 241)
sinfo(i).side = ff2(hemi_ind(1):hemi_ind(1)+1);

Error in cat_conf_stools>vout_surf_resamp (line 2245)
sinfo = cat_surf_info(job.data_surf{i});

Error in cat_conf_stools>@(job)vout_surf_resamp(job) (line 1275)
surfresamp.vout = @(job) vout_surf_resamp(job);

Error in cfg_exbranch/harvest (line 99)
item.sout = feval(item.vout, val);

Error in cfg_util (line 704)
[tag, val, u3, u4, u5, jobs(cjob).cj] = harvest(subsref(jobs(cjob).cj, …

Error in cfg_ui_util>local_setvaledit (line 661)
cfg_util(‘harvest’, ciid{1:end-1});

Error in cfg_ui_util>@(nval)local_setvaledit(ciid,nval,dflag) (line 275)
udvalshow.setvalcb = @(nval)local_setvaledit(ciid, nval, dflag);

Error in cfg_ui_util (line 454)
feval(udvalshow.setvalcb, val);

Error in cfg_ui>local_valedit_EditValue (line 404)
cfg_ui_util(‘valedit_EditValue’, ciid, itemname, val);

Error in cfg_ui>module_Callback (line 978)
local_valedit_EditValue(hObject);

Error in gui_mainfcn (line 95)
feval(varargin{:});

Error in cfg_ui (line 53)
gui_mainfcn(gui_State, varargin{:});

Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)cfg_ui(‘module_Callback’,hObject,eventdata,guidata(hObject))

Error while evaluating UIControl Callback.

What can I do to fix this error? I am using Matlab 2022a with SPM12 and CAT12. I am trying to use CAT12 to make do surface based morphometry. I am trying to load my cortical thickness maps in the resample and smooth function, but when doing that I get this error when trying to load the files clicking on ‘specify’:
Index exceeds the number of array elements. Index must not exceed 0.

Error in cat_surf_info (line 241)
sinfo(i).side = ff2(hemi_ind(1):hemi_ind(1)+1);

Error in cat_conf_stools>vout_surf_resamp (line 2245)
sinfo = cat_surf_info(job.data_surf{i});

Error in cat_conf_stools>@(job)vout_surf_resamp(job) (line 1275)
surfresamp.vout = @(job) vout_surf_resamp(job);

Error in cfg_exbranch/harvest (line 99)
item.sout = feval(item.vout, val);

Error in cfg_util (line 704)
[tag, val, u3, u4, u5, jobs(cjob).cj] = harvest(subsref(jobs(cjob).cj, …

Error in cfg_ui_util>local_setvaledit (line 661)
cfg_util(‘harvest’, ciid{1:end-1});

Error in cfg_ui_util>@(nval)local_setvaledit(ciid,nval,dflag) (line 275)
udvalshow.setvalcb = @(nval)local_setvaledit(ciid, nval, dflag);

Error in cfg_ui_util (line 454)
feval(udvalshow.setvalcb, val);

Error in cfg_ui>local_valedit_EditValue (line 404)
cfg_ui_util(‘valedit_EditValue’, ciid, itemname, val);

Error in cfg_ui>module_Callback (line 978)
local_valedit_EditValue(hObject);

Error in gui_mainfcn (line 95)
feval(varargin{:});

Error in cfg_ui (line 53)
gui_mainfcn(gui_State, varargin{:});

Error in matlab.graphics.internal.figfile.FigFile/read>@(hObject,eventdata)cfg_ui(‘module_Callback’,hObject,eventdata,guidata(hObject))

Error while evaluating UIControl Callback.

What can I do to fix this error? I am using Matlab 2022a with SPM12 and CAT12. cat12, spm MATLAB Answers — New Questions

​

Good day
I have been working on a semantic segmentation problem using the Deep Learning Toolbox, with the unetLayers method.
But I now need to tune some parameters that I trust are not accessible through the toolbox. I would like to:
Use a custom loss function, sum of a weighted focal loss and a dice loss.
Augment data over each epoch, and if possible, use a custom augmentation method, called RLR (Random Local Rotation).
Implement a learning rate based on the ‘One cycle’ method.
Track the training progress with as much details as possible.
Here is my base code; that worked for training the Unet:
[imds,pxds] = create_ds(folders);

indicesTest = 1:floor(length(imds.Files)/10);
testImds = subset(imds,indicesTest);
testPxds = subset(pxds,indicesTest);

dsTest = combine(testImds,testPxds);

indicesTraining = floor(length(imds.Files)/10):length(imds.Files);
trainingImds = subset(imds,indicesTraining);
trainingPxds = subset(pxds,indicesTraining);

dsTraining = combine(trainingImds,trainingPxds);

imageSize = [128 128 3];
numClasses = 2;
unetNetwork = unetLayers(imageSize,numClasses,EncoderDepth = 3);

opts = trainingOptions ("rmsprop", …
InitialLearnRate = 1e-3, …
MaxEpochs = 40, …
MiniBatchSize = 32, …
VerboseFrequency = 10, …
Plots = "training-progress", …
Shuffle="every-epoch", …
ValidationData = dsTest, …
ValidationFrequency=10, …
OutputNetwork = "best-validation-loss" )

currentNet = trainNetwork(dsTraining,unetNetwork,opts)

create_ds is a function that returns two datastores. imds contains the RGB images and pxds contains categorical images with two classes, that are the masks to each image from imds.

The RLR function returns [image,label], the RGB and categorical images that have been geometrically modified (the dimensions and type are conserved).

Here is the function that returns the custom loss:

function loss = combinedLoss(Y, T, alpha, gamma)
epsilon = 1e-6;
p = sigmoid(Y);
lossPos = -alpha * (1 – p).^gamma .* T .* log(p + epsilon);
lossNeg = -(1 – alpha) * p.^gamma .* (1 – T) .* log(1 – p + epsilon);
weightedFocalLoss = mean(lossPos + lossNeg, ‘all’);
intersection = sum(Y .* T, ‘all’);
union = sum(Y, ‘all’) + sum(T, ‘all’);
diceCoeff = (2 * intersection + epsilon) / (union + epsilon);
diceLoss = 1 – diceCoeff;
loss = weightedFocalLoss + diceLoss;
end

I have reviewed the guides that explain how to create a custom training loop:
‘ Train network using custom training loop’ and ‘Monitor custom training loop progress’ -and some others- several times, but I still don’t get how to adapt the examples to my semantic segmentation problem, and with the correct behaviour for my input data.
I don’t think sharing the fragments of code I have tried to compose would be helpful, as they are very far from functional.

Any help on the matter, whether contributing to answer my question partially, or completely, would be greatly appreciated.

Have a nice week-end!Good day
I have been working on a semantic segmentation problem using the Deep Learning Toolbox, with the unetLayers method.
But I now need to tune some parameters that I trust are not accessible through the toolbox. I would like to:
Use a custom loss function, sum of a weighted focal loss and a dice loss.
Augment data over each epoch, and if possible, use a custom augmentation method, called RLR (Random Local Rotation).
Implement a learning rate based on the ‘One cycle’ method.
Track the training progress with as much details as possible.
Here is my base code; that worked for training the Unet:
[imds,pxds] = create_ds(folders);

indicesTest = 1:floor(length(imds.Files)/10);
testImds = subset(imds,indicesTest);
testPxds = subset(pxds,indicesTest);

dsTest = combine(testImds,testPxds);

indicesTraining = floor(length(imds.Files)/10):length(imds.Files);
trainingImds = subset(imds,indicesTraining);
trainingPxds = subset(pxds,indicesTraining);

dsTraining = combine(trainingImds,trainingPxds);

imageSize = [128 128 3];
numClasses = 2;
unetNetwork = unetLayers(imageSize,numClasses,EncoderDepth = 3);

opts = trainingOptions ("rmsprop", …
InitialLearnRate = 1e-3, …
MaxEpochs = 40, …
MiniBatchSize = 32, …
VerboseFrequency = 10, …
Plots = "training-progress", …
Shuffle="every-epoch", …
ValidationData = dsTest, …
ValidationFrequency=10, …
OutputNetwork = "best-validation-loss" )

currentNet = trainNetwork(dsTraining,unetNetwork,opts)

create_ds is a function that returns two datastores. imds contains the RGB images and pxds contains categorical images with two classes, that are the masks to each image from imds.

The RLR function returns [image,label], the RGB and categorical images that have been geometrically modified (the dimensions and type are conserved).

Here is the function that returns the custom loss:

function loss = combinedLoss(Y, T, alpha, gamma)
epsilon = 1e-6;
p = sigmoid(Y);
lossPos = -alpha * (1 – p).^gamma .* T .* log(p + epsilon);
lossNeg = -(1 – alpha) * p.^gamma .* (1 – T) .* log(1 – p + epsilon);
weightedFocalLoss = mean(lossPos + lossNeg, ‘all’);
intersection = sum(Y .* T, ‘all’);
union = sum(Y, ‘all’) + sum(T, ‘all’);
diceCoeff = (2 * intersection + epsilon) / (union + epsilon);
diceLoss = 1 – diceCoeff;
loss = weightedFocalLoss + diceLoss;
end

I have reviewed the guides that explain how to create a custom training loop:
‘ Train network using custom training loop’ and ‘Monitor custom training loop progress’ -and some others- several times, but I still don’t get how to adapt the examples to my semantic segmentation problem, and with the correct behaviour for my input data.
I don’t think sharing the fragments of code I have tried to compose would be helpful, as they are very far from functional.

Any help on the matter, whether contributing to answer my question partially, or completely, would be greatly appreciated.

Have a nice week-end! Good day
I have been working on a semantic segmentation problem using the Deep Learning Toolbox, with the unetLayers method.
But I now need to tune some parameters that I trust are not accessible through the toolbox. I would like to:
Use a custom loss function, sum of a weighted focal loss and a dice loss.
Augment data over each epoch, and if possible, use a custom augmentation method, called RLR (Random Local Rotation).
Implement a learning rate based on the ‘One cycle’ method.
Track the training progress with as much details as possible.
Here is my base code; that worked for training the Unet:
[imds,pxds] = create_ds(folders);

indicesTest = 1:floor(length(imds.Files)/10);
testImds = subset(imds,indicesTest);
testPxds = subset(pxds,indicesTest);

dsTest = combine(testImds,testPxds);

indicesTraining = floor(length(imds.Files)/10):length(imds.Files);
trainingImds = subset(imds,indicesTraining);
trainingPxds = subset(pxds,indicesTraining);

dsTraining = combine(trainingImds,trainingPxds);

imageSize = [128 128 3];
numClasses = 2;
unetNetwork = unetLayers(imageSize,numClasses,EncoderDepth = 3);

opts = trainingOptions ("rmsprop", …
InitialLearnRate = 1e-3, …
MaxEpochs = 40, …
MiniBatchSize = 32, …
VerboseFrequency = 10, …
Plots = "training-progress", …
Shuffle="every-epoch", …
ValidationData = dsTest, …
ValidationFrequency=10, …
OutputNetwork = "best-validation-loss" )

currentNet = trainNetwork(dsTraining,unetNetwork,opts)

create_ds is a function that returns two datastores. imds contains the RGB images and pxds contains categorical images with two classes, that are the masks to each image from imds.

The RLR function returns [image,label], the RGB and categorical images that have been geometrically modified (the dimensions and type are conserved).

Here is the function that returns the custom loss:

function loss = combinedLoss(Y, T, alpha, gamma)
epsilon = 1e-6;
p = sigmoid(Y);
lossPos = -alpha * (1 – p).^gamma .* T .* log(p + epsilon);
lossNeg = -(1 – alpha) * p.^gamma .* (1 – T) .* log(1 – p + epsilon);
weightedFocalLoss = mean(lossPos + lossNeg, ‘all’);
intersection = sum(Y .* T, ‘all’);
union = sum(Y, ‘all’) + sum(T, ‘all’);
diceCoeff = (2 * intersection + epsilon) / (union + epsilon);
diceLoss = 1 – diceCoeff;
loss = weightedFocalLoss + diceLoss;
end

I have reviewed the guides that explain how to create a custom training loop:
‘ Train network using custom training loop’ and ‘Monitor custom training loop progress’ -and some others- several times, but I still don’t get how to adapt the examples to my semantic segmentation problem, and with the correct behaviour for my input data.
I don’t think sharing the fragments of code I have tried to compose would be helpful, as they are very far from functional.

Any help on the matter, whether contributing to answer my question partially, or completely, would be greatly appreciated.

Have a nice week-end! image segmentation, deep learning, unet MATLAB Answers — New Questions

​

I have two signals, T_para and T_per (5*17*20) loaded from directory, I coded this program to do the following tasks:
Calculate the cost function
Apply interpolation to increase the density of a and r
Minimize the cost function using optimiztion method (Grid search method)
Plot figures with minimum cost value
Please check if there is a mistake technically or in the structure. My program code is:
clear all; clc; close all;
cloud = ‘Homo’;
T_para = zeros(5, 17, 20);
T_per = zeros(5, 17, 20);

% Initialize matrices
T_para_noisy = zeros(5, 17, 20);
T_per_noisy = zeros(5, 17, 20);

% Function to add AWGN
add_noise = @(signal, snr) awgn(signal, snr, ‘measured’);

% Define the cost function
% cost_function = @(original, noisy) sum((original(:) – noisy(:)).^2) / numel(original);
cost_function = @(original, noisy, variance) sum(((original(:) – noisy(:))./ (var(original(:)))).^2);

% Initialize arrays to store the optimal values and global minimum cost
lookup_table = [];
lookup_table_interp = [];

% Initialize global minimum cost values
global_min_cost = Inf;
optimal_a= 0;
optimal_r = 0;

% Define ranges for a and r
a_range = 0.01:0.01:0.05;
r_range = 4:20;

% Initialize cost matrix to store costs for all combinations of a and r
cost_matrix = zeros(length(a_range), length(r_range));

for h = 1000:1000:4000
for fov = [0.2, 0.5, 1, 2, 5, 10]

for a_idx = 1:length(a_range)
for r_idx = 1:length(r_range)
a = a_range(a_idx);
r = r_range(r_idx);

load([‘./Tabledata_’, cloud, ‘/’, num2str(h), ‘m-‘, num2str(fov), ‘mrad/TABLE.mat’]);

% Add noise to the signals
T_para_noisy = add_noise(T_para, 30); % 20dB SNR
T_per_noisy = add_noise(T_per, 30);

% Calculate the variance of the original signals
var_T_para = var(T_para, 0, ‘all’);
var_T_per = var(T_per, 0, ‘all’);

% Calculate the cost for each pair of original and noisy signals
cost_para = cost_function(T_para, T_para_noisy, var_T_para);
cost_per = cost_function(T_per, T_per_noisy, var_T_per);

% Sum the costs
total_cost = cost_para + cost_per;

% Store the cost for plotting
cost_matrix(a_idx, r_idx) = total_cost;

% Check if this is the new global minimum
if total_cost < global_min_cost
global_min_cost= total_cost;
optimal_a = a;
optimal_r = r;
end

end
end
% Store the optimal values and global minimum cost in the lookup table
lookup_table = [lookup_table; optimal_a, optimal_r, global_min_cost];

end
end

% Save and display the look-up table for optimal interpolated values
save(‘LookupTable.mat’, ‘lookup_table’);

% Initialize global minimum cost for interpolated values
global_min_cost_interp = Inf;
optimal_a_interp = 0;
optimal_r_interp = 0;

% Define finer gridded interpolant for cost_matrix
a_interp = 0.01:0.001:0.05;
r_interp = 4:0.5:20;

% Define the original and interpolated grids
[a_grid, r_grid] = meshgrid(a_range, r_range);
[a_interp_grid, r_interp_grid] = meshgrid(a_interp, r_interp);

% Transpose the cost_matrix to match the grid dimensions
cost_matrix = cost_matrix’;

% Apply gridded interpolation using spline method
cost_matrix_interp = interp2(a_grid, r_grid, cost_matrix, a_interp_grid, r_interp_grid, ‘spline’);

% Loop through the interpolated grid to find the global minimum
for a_interp_idx = 1:length(a_interp)
for r_interp_idx = 1:length(r_interp)
interpolated_cost = cost_matrix_interp(r_interp_idx, a_interp_idx); % Note the order of indices
% Check if this is the new global minimum
if interpolated_cost < global_min_cost_interp
global_min_cost_interp = interpolated_cost;
optimal_a_interp = a_interp(a_interp_idx);
optimal_r_interp = r_interp(r_interp_idx);
end
end
end

% Add to the lookup table
lookup_table_interp = [lookup_table_interp; optimal_a_interp, optimal_r_interp, global_min_cost_interp];

% Save and display the look-up table for optimal interpolated values
save(‘LookupTable_Interp.mat’, ‘lookup_table_interp’);

disp(‘Global minimum interpolated cost:’);
disp(global_min_cost_interp);

% Normalize the cost_matrix_interp values
min_cost = min(cost_matrix_interp(:));
max_cost = max(cost_matrix_interp(:));
normalized_cost_matrix_interp = (cost_matrix_interp – min_cost) / (max_cost – min_cost);

%%
% Plot the normalized interpolated cost function
figure(1);
surf(a_interp_grid, r_interp_grid, cost_matrix_interp);
shading interp;
colormap parula;
colorbar;
set(gca, ‘color’, ‘w’, ‘FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
ylabel(‘R_{e} interp (mum)’);
xlabel(‘alpha_{e} interp (m^{-1})’);
zlabel(‘Normalized interpolated cost function’);
title(‘Normalized interpolated CF over alpha_{e} interp and R_{e} interp’);
set(gca, ‘box’, ‘on’, ‘LineWidth’, 1, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘xlim’, [0.01 0.05], ‘xtick’, [0.01 0.02 0.03 0.04 0.05], ‘ylim’, [4 20], ‘ytick’, [4 8 12 16 20]);
grid on;
hold on;

% Create a meshgrid for plotting
[A, R] = meshgrid(a_interp, r_interp);

% Reshape the matrices to vectors for plotting
A_vector = reshape(A, [], 1);
R_vector = reshape(R, [], 1);
Re_cost_matrix_interp = reshape(cost_matrix_interp, [], 1);

% Find the global minimum in the interpolated cost matrix
[global_min_cost_interp, min_idx] = min(Re_cost_matrix_interp);
optimal_a_interp = A_vector(min_idx);
optimal_r_interp = R_vector(min_idx);

% Mark the minimum point
hold on;
surf_handle=plot3(optimal_a_interp, optimal_r_interp, global_min_cost_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’);
view(3); % Set the viewing angle to 3D
% Bring the red point to the front
uistack(surf_handle, ‘top’); % Bring the red point to the front
hold off;

% Plot the 2D Contour Plot of the interpolated cost function
figure(2);
contourf(a_interp_grid, r_interp_grid, normalized_cost_matrix_interp, 20);
colorbar;
xlabel(‘alpha_{e} interp (m^{-1})’);
ylabel(‘R_{e} interp (mum)’);
title(‘2D Contour Plot of interpolated normalized CF’);
set(gca,’FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
set(gca, ‘box’, ‘on’, ‘LineWidth’, 1, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘xlim’, [0.01 0.05], ‘xtick’, [0.01 0.02 0.03 0.04 0.05], ‘ylim’, [4 20], ‘ytick’, [4 8 12 16 20]);
hold on;
plot(optimal_a_interp, optimal_r_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’); % Mark the minimum point from interpolated grid search
% plot(optimal_a, optimal_r, ‘bo’, ‘MarkerSize’, 10, ‘LineWidth’, 2); % Mark the minimum point from original grid search
hold off;

% Plot the normalized cost values
figure(3);
scatter3(R_vector, A_vector, Re_cost_matrix_interp, ‘filled’);
xlabel(‘R_{e} interp (mum)’);
ylabel(‘alpha_{e} interp (m^{-1})’);
zlabel(‘Interpolated cost function’);
title(‘Normalized interpolated CF over alpha_{e} interp and R_{e} interp’);
set(gca, ‘color’, ‘w’, ‘FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
set(gca, ‘box’, ‘on’, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘ylim’, [0.01 0.05], ‘ytick’, [0.01 0.02 0.03 0.04 0.05], ‘xlim’, [4 20], ‘xtick’, [4 8 12 16 20]);
grid on;

hold on;

% Set the view angle
view(60, 40); % Adjust the view angles to see the plot clearly
% Mark the minimum point with a circle
surf_handle = plot3(optimal_r_interp, optimal_a_interp, global_min_cost_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’);

% Bring the red point to the front
uistack(surf_handle, ‘top’); % Bring the red point to the front
hold off;I have two signals, T_para and T_per (5*17*20) loaded from directory, I coded this program to do the following tasks:
Calculate the cost function
Apply interpolation to increase the density of a and r
Minimize the cost function using optimiztion method (Grid search method)
Plot figures with minimum cost value
Please check if there is a mistake technically or in the structure. My program code is:
clear all; clc; close all;
cloud = ‘Homo’;
T_para = zeros(5, 17, 20);
T_per = zeros(5, 17, 20);

% Initialize matrices
T_para_noisy = zeros(5, 17, 20);
T_per_noisy = zeros(5, 17, 20);

% Function to add AWGN
add_noise = @(signal, snr) awgn(signal, snr, ‘measured’);

% Define the cost function
% cost_function = @(original, noisy) sum((original(:) – noisy(:)).^2) / numel(original);
cost_function = @(original, noisy, variance) sum(((original(:) – noisy(:))./ (var(original(:)))).^2);

% Initialize arrays to store the optimal values and global minimum cost
lookup_table = [];
lookup_table_interp = [];

% Initialize global minimum cost values
global_min_cost = Inf;
optimal_a= 0;
optimal_r = 0;

% Define ranges for a and r
a_range = 0.01:0.01:0.05;
r_range = 4:20;

% Initialize cost matrix to store costs for all combinations of a and r
cost_matrix = zeros(length(a_range), length(r_range));

for h = 1000:1000:4000
for fov = [0.2, 0.5, 1, 2, 5, 10]

for a_idx = 1:length(a_range)
for r_idx = 1:length(r_range)
a = a_range(a_idx);
r = r_range(r_idx);

load([‘./Tabledata_’, cloud, ‘/’, num2str(h), ‘m-‘, num2str(fov), ‘mrad/TABLE.mat’]);

% Add noise to the signals
T_para_noisy = add_noise(T_para, 30); % 20dB SNR
T_per_noisy = add_noise(T_per, 30);

% Calculate the variance of the original signals
var_T_para = var(T_para, 0, ‘all’);
var_T_per = var(T_per, 0, ‘all’);

% Calculate the cost for each pair of original and noisy signals
cost_para = cost_function(T_para, T_para_noisy, var_T_para);
cost_per = cost_function(T_per, T_per_noisy, var_T_per);

% Sum the costs
total_cost = cost_para + cost_per;

% Store the cost for plotting
cost_matrix(a_idx, r_idx) = total_cost;

% Check if this is the new global minimum
if total_cost < global_min_cost
global_min_cost= total_cost;
optimal_a = a;
optimal_r = r;
end

end
end
% Store the optimal values and global minimum cost in the lookup table
lookup_table = [lookup_table; optimal_a, optimal_r, global_min_cost];

end
end

% Save and display the look-up table for optimal interpolated values
save(‘LookupTable.mat’, ‘lookup_table’);

% Initialize global minimum cost for interpolated values
global_min_cost_interp = Inf;
optimal_a_interp = 0;
optimal_r_interp = 0;

% Define finer gridded interpolant for cost_matrix
a_interp = 0.01:0.001:0.05;
r_interp = 4:0.5:20;

% Define the original and interpolated grids
[a_grid, r_grid] = meshgrid(a_range, r_range);
[a_interp_grid, r_interp_grid] = meshgrid(a_interp, r_interp);

% Transpose the cost_matrix to match the grid dimensions
cost_matrix = cost_matrix’;

% Apply gridded interpolation using spline method
cost_matrix_interp = interp2(a_grid, r_grid, cost_matrix, a_interp_grid, r_interp_grid, ‘spline’);

% Loop through the interpolated grid to find the global minimum
for a_interp_idx = 1:length(a_interp)
for r_interp_idx = 1:length(r_interp)
interpolated_cost = cost_matrix_interp(r_interp_idx, a_interp_idx); % Note the order of indices
% Check if this is the new global minimum
if interpolated_cost < global_min_cost_interp
global_min_cost_interp = interpolated_cost;
optimal_a_interp = a_interp(a_interp_idx);
optimal_r_interp = r_interp(r_interp_idx);
end
end
end

% Add to the lookup table
lookup_table_interp = [lookup_table_interp; optimal_a_interp, optimal_r_interp, global_min_cost_interp];

% Save and display the look-up table for optimal interpolated values
save(‘LookupTable_Interp.mat’, ‘lookup_table_interp’);

disp(‘Global minimum interpolated cost:’);
disp(global_min_cost_interp);

% Normalize the cost_matrix_interp values
min_cost = min(cost_matrix_interp(:));
max_cost = max(cost_matrix_interp(:));
normalized_cost_matrix_interp = (cost_matrix_interp – min_cost) / (max_cost – min_cost);

%%
% Plot the normalized interpolated cost function
figure(1);
surf(a_interp_grid, r_interp_grid, cost_matrix_interp);
shading interp;
colormap parula;
colorbar;
set(gca, ‘color’, ‘w’, ‘FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
ylabel(‘R_{e} interp (mum)’);
xlabel(‘alpha_{e} interp (m^{-1})’);
zlabel(‘Normalized interpolated cost function’);
title(‘Normalized interpolated CF over alpha_{e} interp and R_{e} interp’);
set(gca, ‘box’, ‘on’, ‘LineWidth’, 1, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘xlim’, [0.01 0.05], ‘xtick’, [0.01 0.02 0.03 0.04 0.05], ‘ylim’, [4 20], ‘ytick’, [4 8 12 16 20]);
grid on;
hold on;

% Create a meshgrid for plotting
[A, R] = meshgrid(a_interp, r_interp);

% Reshape the matrices to vectors for plotting
A_vector = reshape(A, [], 1);
R_vector = reshape(R, [], 1);
Re_cost_matrix_interp = reshape(cost_matrix_interp, [], 1);

% Find the global minimum in the interpolated cost matrix
[global_min_cost_interp, min_idx] = min(Re_cost_matrix_interp);
optimal_a_interp = A_vector(min_idx);
optimal_r_interp = R_vector(min_idx);

% Mark the minimum point
hold on;
surf_handle=plot3(optimal_a_interp, optimal_r_interp, global_min_cost_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’);
view(3); % Set the viewing angle to 3D
% Bring the red point to the front
uistack(surf_handle, ‘top’); % Bring the red point to the front
hold off;

% Plot the 2D Contour Plot of the interpolated cost function
figure(2);
contourf(a_interp_grid, r_interp_grid, normalized_cost_matrix_interp, 20);
colorbar;
xlabel(‘alpha_{e} interp (m^{-1})’);
ylabel(‘R_{e} interp (mum)’);
title(‘2D Contour Plot of interpolated normalized CF’);
set(gca,’FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
set(gca, ‘box’, ‘on’, ‘LineWidth’, 1, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘xlim’, [0.01 0.05], ‘xtick’, [0.01 0.02 0.03 0.04 0.05], ‘ylim’, [4 20], ‘ytick’, [4 8 12 16 20]);
hold on;
plot(optimal_a_interp, optimal_r_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’); % Mark the minimum point from interpolated grid search
% plot(optimal_a, optimal_r, ‘bo’, ‘MarkerSize’, 10, ‘LineWidth’, 2); % Mark the minimum point from original grid search
hold off;

% Plot the normalized cost values
figure(3);
scatter3(R_vector, A_vector, Re_cost_matrix_interp, ‘filled’);
xlabel(‘R_{e} interp (mum)’);
ylabel(‘alpha_{e} interp (m^{-1})’);
zlabel(‘Interpolated cost function’);
title(‘Normalized interpolated CF over alpha_{e} interp and R_{e} interp’);
set(gca, ‘color’, ‘w’, ‘FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
set(gca, ‘box’, ‘on’, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘ylim’, [0.01 0.05], ‘ytick’, [0.01 0.02 0.03 0.04 0.05], ‘xlim’, [4 20], ‘xtick’, [4 8 12 16 20]);
grid on;

hold on;

% Set the view angle
view(60, 40); % Adjust the view angles to see the plot clearly
% Mark the minimum point with a circle
surf_handle = plot3(optimal_r_interp, optimal_a_interp, global_min_cost_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’);

% Bring the red point to the front
uistack(surf_handle, ‘top’); % Bring the red point to the front
hold off; I have two signals, T_para and T_per (5*17*20) loaded from directory, I coded this program to do the following tasks:
Calculate the cost function
Apply interpolation to increase the density of a and r
Minimize the cost function using optimiztion method (Grid search method)
Plot figures with minimum cost value
Please check if there is a mistake technically or in the structure. My program code is:
clear all; clc; close all;
cloud = ‘Homo’;
T_para = zeros(5, 17, 20);
T_per = zeros(5, 17, 20);

% Initialize matrices
T_para_noisy = zeros(5, 17, 20);
T_per_noisy = zeros(5, 17, 20);

% Function to add AWGN
add_noise = @(signal, snr) awgn(signal, snr, ‘measured’);

% Define the cost function
% cost_function = @(original, noisy) sum((original(:) – noisy(:)).^2) / numel(original);
cost_function = @(original, noisy, variance) sum(((original(:) – noisy(:))./ (var(original(:)))).^2);

% Initialize arrays to store the optimal values and global minimum cost
lookup_table = [];
lookup_table_interp = [];

% Initialize global minimum cost values
global_min_cost = Inf;
optimal_a= 0;
optimal_r = 0;

% Define ranges for a and r
a_range = 0.01:0.01:0.05;
r_range = 4:20;

% Initialize cost matrix to store costs for all combinations of a and r
cost_matrix = zeros(length(a_range), length(r_range));

for h = 1000:1000:4000
for fov = [0.2, 0.5, 1, 2, 5, 10]

for a_idx = 1:length(a_range)
for r_idx = 1:length(r_range)
a = a_range(a_idx);
r = r_range(r_idx);

load([‘./Tabledata_’, cloud, ‘/’, num2str(h), ‘m-‘, num2str(fov), ‘mrad/TABLE.mat’]);

% Add noise to the signals
T_para_noisy = add_noise(T_para, 30); % 20dB SNR
T_per_noisy = add_noise(T_per, 30);

% Calculate the variance of the original signals
var_T_para = var(T_para, 0, ‘all’);
var_T_per = var(T_per, 0, ‘all’);

% Calculate the cost for each pair of original and noisy signals
cost_para = cost_function(T_para, T_para_noisy, var_T_para);
cost_per = cost_function(T_per, T_per_noisy, var_T_per);

% Sum the costs
total_cost = cost_para + cost_per;

% Store the cost for plotting
cost_matrix(a_idx, r_idx) = total_cost;

% Check if this is the new global minimum
if total_cost < global_min_cost
global_min_cost= total_cost;
optimal_a = a;
optimal_r = r;
end

end
end
% Store the optimal values and global minimum cost in the lookup table
lookup_table = [lookup_table; optimal_a, optimal_r, global_min_cost];

end
end

% Save and display the look-up table for optimal interpolated values
save(‘LookupTable.mat’, ‘lookup_table’);

% Initialize global minimum cost for interpolated values
global_min_cost_interp = Inf;
optimal_a_interp = 0;
optimal_r_interp = 0;

% Define finer gridded interpolant for cost_matrix
a_interp = 0.01:0.001:0.05;
r_interp = 4:0.5:20;

% Define the original and interpolated grids
[a_grid, r_grid] = meshgrid(a_range, r_range);
[a_interp_grid, r_interp_grid] = meshgrid(a_interp, r_interp);

% Transpose the cost_matrix to match the grid dimensions
cost_matrix = cost_matrix’;

% Apply gridded interpolation using spline method
cost_matrix_interp = interp2(a_grid, r_grid, cost_matrix, a_interp_grid, r_interp_grid, ‘spline’);

% Loop through the interpolated grid to find the global minimum
for a_interp_idx = 1:length(a_interp)
for r_interp_idx = 1:length(r_interp)
interpolated_cost = cost_matrix_interp(r_interp_idx, a_interp_idx); % Note the order of indices
% Check if this is the new global minimum
if interpolated_cost < global_min_cost_interp
global_min_cost_interp = interpolated_cost;
optimal_a_interp = a_interp(a_interp_idx);
optimal_r_interp = r_interp(r_interp_idx);
end
end
end

% Add to the lookup table
lookup_table_interp = [lookup_table_interp; optimal_a_interp, optimal_r_interp, global_min_cost_interp];

% Save and display the look-up table for optimal interpolated values
save(‘LookupTable_Interp.mat’, ‘lookup_table_interp’);

disp(‘Global minimum interpolated cost:’);
disp(global_min_cost_interp);

% Normalize the cost_matrix_interp values
min_cost = min(cost_matrix_interp(:));
max_cost = max(cost_matrix_interp(:));
normalized_cost_matrix_interp = (cost_matrix_interp – min_cost) / (max_cost – min_cost);

%%
% Plot the normalized interpolated cost function
figure(1);
surf(a_interp_grid, r_interp_grid, cost_matrix_interp);
shading interp;
colormap parula;
colorbar;
set(gca, ‘color’, ‘w’, ‘FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
ylabel(‘R_{e} interp (mum)’);
xlabel(‘alpha_{e} interp (m^{-1})’);
zlabel(‘Normalized interpolated cost function’);
title(‘Normalized interpolated CF over alpha_{e} interp and R_{e} interp’);
set(gca, ‘box’, ‘on’, ‘LineWidth’, 1, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘xlim’, [0.01 0.05], ‘xtick’, [0.01 0.02 0.03 0.04 0.05], ‘ylim’, [4 20], ‘ytick’, [4 8 12 16 20]);
grid on;
hold on;

% Create a meshgrid for plotting
[A, R] = meshgrid(a_interp, r_interp);

% Reshape the matrices to vectors for plotting
A_vector = reshape(A, [], 1);
R_vector = reshape(R, [], 1);
Re_cost_matrix_interp = reshape(cost_matrix_interp, [], 1);

% Find the global minimum in the interpolated cost matrix
[global_min_cost_interp, min_idx] = min(Re_cost_matrix_interp);
optimal_a_interp = A_vector(min_idx);
optimal_r_interp = R_vector(min_idx);

% Mark the minimum point
hold on;
surf_handle=plot3(optimal_a_interp, optimal_r_interp, global_min_cost_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’);
view(3); % Set the viewing angle to 3D
% Bring the red point to the front
uistack(surf_handle, ‘top’); % Bring the red point to the front
hold off;

% Plot the 2D Contour Plot of the interpolated cost function
figure(2);
contourf(a_interp_grid, r_interp_grid, normalized_cost_matrix_interp, 20);
colorbar;
xlabel(‘alpha_{e} interp (m^{-1})’);
ylabel(‘R_{e} interp (mum)’);
title(‘2D Contour Plot of interpolated normalized CF’);
set(gca,’FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
set(gca, ‘box’, ‘on’, ‘LineWidth’, 1, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘xlim’, [0.01 0.05], ‘xtick’, [0.01 0.02 0.03 0.04 0.05], ‘ylim’, [4 20], ‘ytick’, [4 8 12 16 20]);
hold on;
plot(optimal_a_interp, optimal_r_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’); % Mark the minimum point from interpolated grid search
% plot(optimal_a, optimal_r, ‘bo’, ‘MarkerSize’, 10, ‘LineWidth’, 2); % Mark the minimum point from original grid search
hold off;

% Plot the normalized cost values
figure(3);
scatter3(R_vector, A_vector, Re_cost_matrix_interp, ‘filled’);
xlabel(‘R_{e} interp (mum)’);
ylabel(‘alpha_{e} interp (m^{-1})’);
zlabel(‘Interpolated cost function’);
title(‘Normalized interpolated CF over alpha_{e} interp and R_{e} interp’);
set(gca, ‘color’, ‘w’, ‘FontSize’, 12, ‘LineWidth’, 1, ‘FontWeight’, ‘normal’);
set(gca, ‘box’, ‘on’, ‘FontName’, ‘Arial’, ‘FontSmoothing’, ‘on’);
set(gca, ‘ylim’, [0.01 0.05], ‘ytick’, [0.01 0.02 0.03 0.04 0.05], ‘xlim’, [4 20], ‘xtick’, [4 8 12 16 20]);
grid on;

hold on;

% Set the view angle
view(60, 40); % Adjust the view angles to see the plot clearly
% Mark the minimum point with a circle
surf_handle = plot3(optimal_r_interp, optimal_a_interp, global_min_cost_interp, ‘ro’, ‘MarkerSize’, 12, ‘MarkerFaceColor’, ‘r’);

% Bring the red point to the front
uistack(surf_handle, ‘top’); % Bring the red point to the front
hold off; cost_function, optimization MATLAB Answers — New Questions

​

In the process of co-simulating GT-SUITE and SIMULINK, I am creating a model that receives two input values ​​of GT and outputs one output value in simulink. At this time, which block should I use if the two inputs are continuously changing?
Let me explain using the picture as an example. The values ​​of Differential_Efficiency and FDR are continuously changing.In the process of co-simulating GT-SUITE and SIMULINK, I am creating a model that receives two input values ​​of GT and outputs one output value in simulink. At this time, which block should I use if the two inputs are continuously changing?
Let me explain using the picture as an example. The values ​​of Differential_Efficiency and FDR are continuously changing. In the process of co-simulating GT-SUITE and SIMULINK, I am creating a model that receives two input values ​​of GT and outputs one output value in simulink. At this time, which block should I use if the two inputs are continuously changing?
Let me explain using the picture as an example. The values ​​of Differential_Efficiency and FDR are continuously changing. co-simualtion, gt-suite MATLAB Answers — New Questions

​

Which Polyspace version is compatible to run MISRA C 2020 STANDARDWhich Polyspace version is compatible to run MISRA C 2020 STANDARD Which Polyspace version is compatible to run MISRA C 2020 STANDARD polyspace MATLAB Answers — New Questions

​

Now I have got images in the proper colour, I am wondering as to how plot a graph to show the Bit Error Rate to EbN0 in dB. I have found an example plot in another report but how can I plot this myself?

I know that semilogy is required for this but I am unsure where to start if anyone could point me to a MATLAB example or give any guidance on where to start with this?

Thanks in advanceNow I have got images in the proper colour, I am wondering as to how plot a graph to show the Bit Error Rate to EbN0 in dB. I have found an example plot in another report but how can I plot this myself?

I know that semilogy is required for this but I am unsure where to start if anyone could point me to a MATLAB example or give any guidance on where to start with this?

Thanks in advance Now I have got images in the proper colour, I am wondering as to how plot a graph to show the Bit Error Rate to EbN0 in dB. I have found an example plot in another report but how can I plot this myself?

I know that semilogy is required for this but I am unsure where to start if anyone could point me to a MATLAB example or give any guidance on where to start with this?

Thanks in advance awgn, ldpc, bit error rate, ebn0 MATLAB Answers — New Questions

​

Hi, I get the following error when trying to plot
Unrecognized function or variable ‘spharm’.

Error in FTyD (line 35)
Ylm = spharm(l, m, Y, X); % Evaluate spherical harmonic Y_lm(x,y)

% Define the function f(t)
f = @(t) 139.85 + (15.8404 + 4.76022i) * exp(-1i * t) + (15.8404 – 4.76022i) * exp(1i * t) + …
(4.64917 – 3.3024i) * exp(-2i * t) + (4.64917 + 3.3024i) * exp(2i * t) + …
(7.42191 – 0.300123i) * exp(-3i * t) + (7.42191 + 0.300123i) * exp(3i * t) + …
(0.340877 – 2.54665i) * exp(-4i * t) + (0.340877 + 2.54665i) * exp(4i * t) + …
(7.72422 + 6.71332i) * exp(-5i * t) + (7.72422 – 6.71332i) * exp(5i * t) + …
(3.16511 – 9.14479i) * exp(-6i * t) + (3.16511 + 9.14479i) * exp(6i * t) + …
(3.31502 + 2.18874i) * exp(-7i * t) + (3.31502 – 2.18874i) * exp(7i * t) + …
(9.31827 + 6.94538i) * exp(-8i * t) + (9.31827 – 6.94538i) * exp(8i * t) + …
(5.87173 + 18.8341i) * exp(-9i * t) + (5.87173 – 18.8341i) * exp(9i * t) + …
(8.75949 + 14.7107i) * exp(-10i * t) + (8.75949 – 14.7107i) * exp(10i * t) + …
(19.2903 + 7.78329i) * exp(-11i * t) + (19.2903 – 7.78329i) * exp(11i * t) + …
(-3.39063 – 18.5502i) * exp(-12i * t) + (-3.39063 + 18.5502i) * exp(12i * t) + …
(3.58427 + 18.7438i) * exp(-13i * t) + (3.58427 – 18.7438i) * exp(13i * t) + …
(2.66129 + 20.0781i) * exp(-14i * t) + (2.66129 – 20.0781i) * exp(14i * t) + …
(-8.46335 – 9.96867i) * exp(-15i * t) + (-8.46335 + 9.96867i) * exp(15i * t) + …
(-8.50037 – 1.20377i) * exp(-16i * t) + (-8.50037 + 1.20377i) * exp(16i * t) + …
(-1.36102 – 16.9315i) * exp(-17i * t) + (-1.36102 + 16.9315i) * exp(17i * t) + …
(-5.78964 – 2.96094i) * exp(-18i * t) + (-5.78964 + 2.96094i) * exp(18i * t) + …
(2.14681 + 8.29635i) * exp(-19i * t) + (2.14681 – 8.29635i) * exp(19i * t) + …
(-3.91145 – 10.7712i) * exp(-20i * t) + (-3.91145 + 10.7712i) * exp(20i * t);

% Parameters
lmax = 10; % Maximum degree for spherical harmonics expansion

% Create grid in x and y
x = linspace(-10, 10, 100);
y = linspace(-10, 10, 100);
[X, Y] = meshgrid(x, y);

% Compute f(x,y)
Fxy = zeros(size(X));
for l = 0:lmax
for m = -l:l
Ylm = spharm(l, m, Y, X); % Evaluate spherical harmonic Y_lm(x,y)
integral_value = integral(@(t) f(t) .* conj(Ylm), -pi, pi); % Compute integral
Fxy = Fxy + integral_value * Ylm; % Accumulate to f(x,y)
end
end

% Plot
figure;
surf(X, Y, real(Fxy), ‘EdgeColor’, ‘none’);
xlabel(‘x’);
ylabel(‘y’);
zlabel(‘Re(f(x,y))’);
title(‘Method 3: Expansion in Spherical Harmonics’);
colorbar;

Cant figure out why…Hi, I get the following error when trying to plot
Unrecognized function or variable ‘spharm’.

Error in FTyD (line 35)
Ylm = spharm(l, m, Y, X); % Evaluate spherical harmonic Y_lm(x,y)

% Define the function f(t)
f = @(t) 139.85 + (15.8404 + 4.76022i) * exp(-1i * t) + (15.8404 – 4.76022i) * exp(1i * t) + …
(4.64917 – 3.3024i) * exp(-2i * t) + (4.64917 + 3.3024i) * exp(2i * t) + …
(7.42191 – 0.300123i) * exp(-3i * t) + (7.42191 + 0.300123i) * exp(3i * t) + …
(0.340877 – 2.54665i) * exp(-4i * t) + (0.340877 + 2.54665i) * exp(4i * t) + …
(7.72422 + 6.71332i) * exp(-5i * t) + (7.72422 – 6.71332i) * exp(5i * t) + …
(3.16511 – 9.14479i) * exp(-6i * t) + (3.16511 + 9.14479i) * exp(6i * t) + …
(3.31502 + 2.18874i) * exp(-7i * t) + (3.31502 – 2.18874i) * exp(7i * t) + …
(9.31827 + 6.94538i) * exp(-8i * t) + (9.31827 – 6.94538i) * exp(8i * t) + …
(5.87173 + 18.8341i) * exp(-9i * t) + (5.87173 – 18.8341i) * exp(9i * t) + …
(8.75949 + 14.7107i) * exp(-10i * t) + (8.75949 – 14.7107i) * exp(10i * t) + …
(19.2903 + 7.78329i) * exp(-11i * t) + (19.2903 – 7.78329i) * exp(11i * t) + …
(-3.39063 – 18.5502i) * exp(-12i * t) + (-3.39063 + 18.5502i) * exp(12i * t) + …
(3.58427 + 18.7438i) * exp(-13i * t) + (3.58427 – 18.7438i) * exp(13i * t) + …
(2.66129 + 20.0781i) * exp(-14i * t) + (2.66129 – 20.0781i) * exp(14i * t) + …
(-8.46335 – 9.96867i) * exp(-15i * t) + (-8.46335 + 9.96867i) * exp(15i * t) + …
(-8.50037 – 1.20377i) * exp(-16i * t) + (-8.50037 + 1.20377i) * exp(16i * t) + …
(-1.36102 – 16.9315i) * exp(-17i * t) + (-1.36102 + 16.9315i) * exp(17i * t) + …
(-5.78964 – 2.96094i) * exp(-18i * t) + (-5.78964 + 2.96094i) * exp(18i * t) + …
(2.14681 + 8.29635i) * exp(-19i * t) + (2.14681 – 8.29635i) * exp(19i * t) + …
(-3.91145 – 10.7712i) * exp(-20i * t) + (-3.91145 + 10.7712i) * exp(20i * t);

% Parameters
lmax = 10; % Maximum degree for spherical harmonics expansion

% Create grid in x and y
x = linspace(-10, 10, 100);
y = linspace(-10, 10, 100);
[X, Y] = meshgrid(x, y);

% Compute f(x,y)
Fxy = zeros(size(X));
for l = 0:lmax
for m = -l:l
Ylm = spharm(l, m, Y, X); % Evaluate spherical harmonic Y_lm(x,y)
integral_value = integral(@(t) f(t) .* conj(Ylm), -pi, pi); % Compute integral
Fxy = Fxy + integral_value * Ylm; % Accumulate to f(x,y)
end
end

% Plot
figure;
surf(X, Y, real(Fxy), ‘EdgeColor’, ‘none’);
xlabel(‘x’);
ylabel(‘y’);
zlabel(‘Re(f(x,y))’);
title(‘Method 3: Expansion in Spherical Harmonics’);
colorbar;

Cant figure out why… Hi, I get the following error when trying to plot
Unrecognized function or variable ‘spharm’.

Error in FTyD (line 35)
Ylm = spharm(l, m, Y, X); % Evaluate spherical harmonic Y_lm(x,y)

% Define the function f(t)
f = @(t) 139.85 + (15.8404 + 4.76022i) * exp(-1i * t) + (15.8404 – 4.76022i) * exp(1i * t) + …
(4.64917 – 3.3024i) * exp(-2i * t) + (4.64917 + 3.3024i) * exp(2i * t) + …
(7.42191 – 0.300123i) * exp(-3i * t) + (7.42191 + 0.300123i) * exp(3i * t) + …
(0.340877 – 2.54665i) * exp(-4i * t) + (0.340877 + 2.54665i) * exp(4i * t) + …
(7.72422 + 6.71332i) * exp(-5i * t) + (7.72422 – 6.71332i) * exp(5i * t) + …
(3.16511 – 9.14479i) * exp(-6i * t) + (3.16511 + 9.14479i) * exp(6i * t) + …
(3.31502 + 2.18874i) * exp(-7i * t) + (3.31502 – 2.18874i) * exp(7i * t) + …
(9.31827 + 6.94538i) * exp(-8i * t) + (9.31827 – 6.94538i) * exp(8i * t) + …
(5.87173 + 18.8341i) * exp(-9i * t) + (5.87173 – 18.8341i) * exp(9i * t) + …
(8.75949 + 14.7107i) * exp(-10i * t) + (8.75949 – 14.7107i) * exp(10i * t) + …
(19.2903 + 7.78329i) * exp(-11i * t) + (19.2903 – 7.78329i) * exp(11i * t) + …
(-3.39063 – 18.5502i) * exp(-12i * t) + (-3.39063 + 18.5502i) * exp(12i * t) + …
(3.58427 + 18.7438i) * exp(-13i * t) + (3.58427 – 18.7438i) * exp(13i * t) + …
(2.66129 + 20.0781i) * exp(-14i * t) + (2.66129 – 20.0781i) * exp(14i * t) + …
(-8.46335 – 9.96867i) * exp(-15i * t) + (-8.46335 + 9.96867i) * exp(15i * t) + …
(-8.50037 – 1.20377i) * exp(-16i * t) + (-8.50037 + 1.20377i) * exp(16i * t) + …
(-1.36102 – 16.9315i) * exp(-17i * t) + (-1.36102 + 16.9315i) * exp(17i * t) + …
(-5.78964 – 2.96094i) * exp(-18i * t) + (-5.78964 + 2.96094i) * exp(18i * t) + …
(2.14681 + 8.29635i) * exp(-19i * t) + (2.14681 – 8.29635i) * exp(19i * t) + …
(-3.91145 – 10.7712i) * exp(-20i * t) + (-3.91145 + 10.7712i) * exp(20i * t);

% Parameters
lmax = 10; % Maximum degree for spherical harmonics expansion

% Create grid in x and y
x = linspace(-10, 10, 100);
y = linspace(-10, 10, 100);
[X, Y] = meshgrid(x, y);

% Compute f(x,y)
Fxy = zeros(size(X));
for l = 0:lmax
for m = -l:l
Ylm = spharm(l, m, Y, X); % Evaluate spherical harmonic Y_lm(x,y)
integral_value = integral(@(t) f(t) .* conj(Ylm), -pi, pi); % Compute integral
Fxy = Fxy + integral_value * Ylm; % Accumulate to f(x,y)
end
end

% Plot
figure;
surf(X, Y, real(Fxy), ‘EdgeColor’, ‘none’);
xlabel(‘x’);
ylabel(‘y’);
zlabel(‘Re(f(x,y))’);
title(‘Method 3: Expansion in Spherical Harmonics’);
colorbar;

Cant figure out why… 3d plots, spherical, harmonics MATLAB Answers — New Questions

​

I’m trying to add code coverage to my unit test suite. There is one .mlapp file that gives this warning:
Warning: Coverage result is empty for /MyPath/myApp.mlapp because the file is invalid.
How can I find out what is wrong with the file? The app works, so I don’t know why it would be "invalid." Note: a blank .mlapp file does not give the same warning.I’m trying to add code coverage to my unit test suite. There is one .mlapp file that gives this warning:
Warning: Coverage result is empty for /MyPath/myApp.mlapp because the file is invalid.
How can I find out what is wrong with the file? The app works, so I don’t know why it would be "invalid." Note: a blank .mlapp file does not give the same warning. I’m trying to add code coverage to my unit test suite. There is one .mlapp file that gives this warning:
Warning: Coverage result is empty for /MyPath/myApp.mlapp because the file is invalid.
How can I find out what is wrong with the file? The app works, so I don’t know why it would be "invalid." Note: a blank .mlapp file does not give the same warning. code coverage MATLAB Answers — New Questions

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I’m the admin of a server and I need to obtain a MATLAB license. Some details:
Single server
We are using SLURM to balance the workload
There are about 20 users in total
There are about ~5 users that need MATLAB for now. This might be more in the future
Required toolboxes (at least for now): Image Processing, Signal Processing, Optimization
I’ve read online about different options (e.g. network license, parallel server, individual license vs designated computer license), but I’m still confused as to what should I get for this case.I’m the admin of a server and I need to obtain a MATLAB license. Some details:
Single server
We are using SLURM to balance the workload
There are about 20 users in total
There are about ~5 users that need MATLAB for now. This might be more in the future
Required toolboxes (at least for now): Image Processing, Signal Processing, Optimization
I’ve read online about different options (e.g. network license, parallel server, individual license vs designated computer license), but I’m still confused as to what should I get for this case. I’m the admin of a server and I need to obtain a MATLAB license. Some details:
Single server
We are using SLURM to balance the workload
There are about 20 users in total
There are about ~5 users that need MATLAB for now. This might be more in the future
Required toolboxes (at least for now): Image Processing, Signal Processing, Optimization
I’ve read online about different options (e.g. network license, parallel server, individual license vs designated computer license), but I’m still confused as to what should I get for this case. licenses, matlab, server MATLAB Answers — New Questions

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