I am trying to invert an 8×8 symbolic matrix with 32 symbolic variables in total. However, when running the standard inversion methods with inv() or mldivide(), the program takes a lot of time and ended up crashing all the times I tried to invert this matrix. I tried some workarounds, such as a recursive inversion algorithm using Schur decomposition and other ways of using the fact that I know that the matrix is symmetric, but to no avail. The function has a lot of sines and cosines of these variables, which might also be a problem.

I am looking for ways I can get this matrix inverted, or any further suggestion on how to tackle this problem.

Thanks in advance.

For context: the matrix I am trying to invert is the inertia matrix for a tiltrotor UAV. the 32 parameters consist of 6 state variables and 26 model parameters. My goal with this is to get an analytic expression for the system equations of motion:

With that expression, I intend to obtain an analytic linearization of the system in order to get a linear model , where . With these analytical expressions should be able to easily get A, B matrices for different values of the system parameters and formulate robust linear controllers with polytopic uncertainty.I am trying to invert an 8×8 symbolic matrix with 32 symbolic variables in total. However, when running the standard inversion methods with inv() or mldivide(), the program takes a lot of time and ended up crashing all the times I tried to invert this matrix. I tried some workarounds, such as a recursive inversion algorithm using Schur decomposition and other ways of using the fact that I know that the matrix is symmetric, but to no avail. The function has a lot of sines and cosines of these variables, which might also be a problem.

I am looking for ways I can get this matrix inverted, or any further suggestion on how to tackle this problem.

Thanks in advance.

For context: the matrix I am trying to invert is the inertia matrix for a tiltrotor UAV. the 32 parameters consist of 6 state variables and 26 model parameters. My goal with this is to get an analytic expression for the system equations of motion:

With that expression, I intend to obtain an analytic linearization of the system in order to get a linear model , where . With these analytical expressions should be able to easily get A, B matrices for different values of the system parameters and formulate robust linear controllers with polytopic uncertainty. I am trying to invert an 8×8 symbolic matrix with 32 symbolic variables in total. However, when running the standard inversion methods with inv() or mldivide(), the program takes a lot of time and ended up crashing all the times I tried to invert this matrix. I tried some workarounds, such as a recursive inversion algorithm using Schur decomposition and other ways of using the fact that I know that the matrix is symmetric, but to no avail. The function has a lot of sines and cosines of these variables, which might also be a problem.

I am looking for ways I can get this matrix inverted, or any further suggestion on how to tackle this problem.

Thanks in advance.

For context: the matrix I am trying to invert is the inertia matrix for a tiltrotor UAV. the 32 parameters consist of 6 state variables and 26 model parameters. My goal with this is to get an analytic expression for the system equations of motion:

With that expression, I intend to obtain an analytic linearization of the system in order to get a linear model , where . With these analytical expressions should be able to easily get A, B matrices for different values of the system parameters and formulate robust linear controllers with polytopic uncertainty. symbolic, matrix, inverse MATLAB Answers — New Questions

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I have a segmented 3D image dataset that I am trying to visualize. Because there are two segmented phases, I am exploring different colors for each phase to make them stand out from each other. In particular, I would like one of the phases to be blue (RGB [0, 0, 255]) and the other phase to be gray (RGB [128, 128, 128]). I have created a 4D uint8 dataset of the segmented images to allow for this, and when I load the image stack in MATLAB’s sliceViewer everything looks correct. However, when I then load the same dataset in MATLAB’s volshow, it colors the gray pixels yellow while the blue pixels remain blue. See pictures below:
SliceViewer Image Showing Gray = Gray with Blue Colored Phase

Volshow Image Showing Gray = Yellow with Blue Colored Phase

I thought that perhaps there was an issue with the lighting option being colored and casting a color onto gray pixels, but I have the lighting option turned off. I then thought that perhaps there was an issue with grayscale pixels in volshow, but when I convert the gray pixels to white (RGB [255, 255, 255]) they appear white in volshow. The same exact effect is achieved if the blue pixels are reduced in intensity to RGB [0, 0, 128] while leaving the gray pixels as RGB [128, 128, 128] (the sliceViewer will look different showing a darker blue but the volshow looks identical to the picture below with a bright blue and white-colored gray pixels). See pictures below:
SliceViewer Image Showing White = White with Blue Colored Phase

Volshow Image Showing White = White with Blue Colored Phase

I then explored other colors to see if there is a specific issue with the blue/gray combination and found interesting results. When I converted the blue pixels to red (RGB [255, 0, 0]), the gray pixels remained gray in sliceViewer but now display as cyan in volshow. See pictures below:
SliceViewer Image Showing Gray = Gray with Red Colored Phase

Volshow Image Showing Gray = Cyan with Red Colored Phase

The blue/gray and red/gray tests demonstrate that volshow will replace gray pixels with the inverse RGB color of any colored pixels in the dataset (yellow is the opposite RGB of blue ([255, 255, 0] vs. [0, 0, 255]) and cyan is the opposite RGB of red ([0, 255, 255] vs. [255, 0, 0])). However, the yellow and cyan colors do not appear to be their full intensity versions ([255, 255, 0] for yellow and [0, 255, 255] for cyan) and they may have some degree of their typically absent color present, but I cannot tell as these pixels do not actually have these intensity values in the image data so I cannot tell to what the true color is that they are creating. Thus, I cannot determine to what degree the opposite colors are added and the main color is substracted from the gray pixels.
I then tried substituting the colored phase for white to see if that maintained the gray pixels, and this worked (note that it may be difficult to see the difference between white and gray in the volShow, but I turned the volume around, zoomed in, and was able to verify that this was the case). See pictures below:
SliceViewer Image Showing Gray = Gray with White Colored Phase

Volshow Image Showing Gray = Gray with White Colored Phase

I was then curious as to what would happen to the gray pixels if I added a third, differently-colored phase. So, I added a red strip down one side of the blue/gray volume. Now, the gray pixels turned green (again, to what degree the blue and red intensities were added vs. substracted and how much of the green intensity was added, I cannot know). See pictures below:
SliceViewer Image Showing Gray = Gray with Blue and Red Colored Phases

Volshow Image Showing Gray = Green with Blue and Red Colored Phases

I don’t know if I am doing something wrong, so I have provided a snippet of code that will re-create all of this as well as a google drive link (the file is >5 MB so I can’t upload it) to the dataset I am using to investigate this. The code will let you select the data and automatically load it for you and then produce all of the images I have included here in there sliceViewer and volshow formats so that you can explore them). Here is the link to the dataset and here is the code:
%% Test Case %%
close all
clear
clc

% Select image file
fprintf(‘Select an image file.nn’)
[file_name, path_intro] = uigetfile(‘*.*’, ‘Select an image file’);
file_path = sprintf(‘%s%s’, path_intro, file_name);

% Import image message
clc
fprintf(‘Importing image stack…nn’)

% Create tiff
stack_tiff = Tiff(file_path);

% Determine number of images in stack
info = imfinfo(file_path);
no_slice = numel(info);

% Determine necessary tags from tiff
bits = getTag(stack_tiff, ‘BitsPerSample’);
long = getTag(stack_tiff, ‘ImageLength’);
wide = getTag(stack_tiff, ‘ImageWidth’);
samples = getTag(stack_tiff, ‘SamplesPerPixel’);

% Pre-allocate volume array
volume = zeros(long, wide, no_slice, samples, ‘uint8’);

% Import first image
volume(:, :, 1, 🙂 = read(stack_tiff);

% Import loop
for n = 2:no_slice

% Update current directory
nextDirectory(stack_tiff);

% Add current slice to volume
volume(:, :, n, 🙂 = read(stack_tiff);

end

% Close tiff
close(stack_tiff)

% Create transformation matrix
T = [1.31 0 0 0; 0 1.31 0 0; 0 0 1.31 0; 0 0 0 1];
tform = affinetform3d(T);

% Create viewer parameters structure
view_struct = struct;

% Populate viewer parameters structure
view_struct.BackgroundColor = [1 1 1];
view_struct.BackgroundGradient = ‘off’;
view_struct.Lighting = ‘off’;

% Load first slice viewer
clc
fprintf(‘Loading first slice viewer…nn’)
f1 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with Blue Colored Phase’);
sliceViewer(volume, parent = f1);

% Load first volume viewer
clc
fprintf(‘Loading first volume viewer…nn’)
uif1 = uifigure(‘Name’, ‘Gray = Yellow in Volume Viewer with Blue Colored Phase’);
viewer1 = viewer3d(view_struct, parent = uif1);
V1 = volshow(volume, ‘Transformation’, tform, parent = viewer1);

% Convert gray to white in second volume
clc
fprintf(‘Converting gray to white…nn’)
g2w_vol = volume;
g2w_vol(volume == 128) = 255;

% Load second slice viewer
clc
fprintf(‘Loading second slice viewer…nn’)
f2 = figure(‘Name’, ‘White = White in Slice Viewer with Blue Colored Phase’);
sliceViewer(g2w_vol, parent = f2);

% Load second volume viewer
clc
fprintf(‘Loading second volume viewer…nn’)
uif2 = uifigure(‘Name’, ‘White = White in Volume Viewer with Blue Colored Phase’);
viewer2 = viewer3d(view_struct, parent = uif2);
V2 = volshow(g2w_vol, ‘Transformation’, tform, parent = viewer2);

% Convert blue to red in third volume
clc
fprintf(‘Converting blue to red…nn’)
b2r_vol = volume;
b2r_vol(:, :, :, 1) = volume(:, :, :, 3);
b2r_vol(:, :, :, 3) = volume(:, :, :, 1);

% Load third slice viewer
clc
fprintf(‘Loading third slice viewer…nn’)
f3 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with Red Colored Phase’);
sliceViewer(b2r_vol, parent = f3);

% Load third volume viewer
clc
fprintf(‘Loading third volume viewer…nn’)
uif3 = uifigure(‘Name’, ‘Gray = Cyan in Volume Viewer with Red Colored Phase’);
viewer3 = viewer3d(view_struct, parent = uif3);
V3 = volshow(b2r_vol, ‘Transformation’, tform, parent = viewer3);

% Convert blue to white in fourth volume
clc
fprintf(‘Converting blue to white…nn’)
b2w_vol = volume;
b2w_vol(:, :, :, 1) = volume(:, :, :, 3);
b2w_vol(:, :, :, 2) = volume(:, :, :, 3);

% Load fourth slice viewer
clc
fprintf(‘Loading fourth slice viewer…nn’)
f4 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with White Colored Phase’);
sliceViewer(b2w_vol, parent = f4);

% Load fourth volume viewer
clc
fprintf(‘Loading fourth volume viewer…nn’)
uif4 = uifigure(‘Name’, ‘Gray = Gray in Volume Viewer with White Colored Phase’);
viewer4 = viewer3d(view_struct, parent = uif4);
V4 = volshow(b2w_vol, ‘Transformation’, tform, parent = viewer4);

% Add red strip in fifth volume
clc
fprintf(‘Adding red strip…nn’)
br_vol = volume;
br_vol(:, 1:25, :, 1) = 255;
br_vol(:, 1:25, :, 2:3) = 0;

% Load fifth slice viewer
clc
fprintf(‘Loading fifth slice viewer…nn’)
f5 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with White Colored Phase’);
sliceViewer(br_vol, parent = f5);

% Load fifth volume viewer
clc
fprintf(‘Loading fifth volume viewer…nn’)
uif5 = uifigure(‘Name’, ‘Gray = Gray in Volume Viewer with White Colored Phase’);
viewer5 = viewer3d(view_struct, parent = uif5);
V5 = volshow(br_vol, ‘Transformation’, tform, parent = viewer5);

% End message
clc
fprintf(‘Finished!nn’)I have a segmented 3D image dataset that I am trying to visualize. Because there are two segmented phases, I am exploring different colors for each phase to make them stand out from each other. In particular, I would like one of the phases to be blue (RGB [0, 0, 255]) and the other phase to be gray (RGB [128, 128, 128]). I have created a 4D uint8 dataset of the segmented images to allow for this, and when I load the image stack in MATLAB’s sliceViewer everything looks correct. However, when I then load the same dataset in MATLAB’s volshow, it colors the gray pixels yellow while the blue pixels remain blue. See pictures below:
SliceViewer Image Showing Gray = Gray with Blue Colored Phase

Volshow Image Showing Gray = Yellow with Blue Colored Phase

I thought that perhaps there was an issue with the lighting option being colored and casting a color onto gray pixels, but I have the lighting option turned off. I then thought that perhaps there was an issue with grayscale pixels in volshow, but when I convert the gray pixels to white (RGB [255, 255, 255]) they appear white in volshow. The same exact effect is achieved if the blue pixels are reduced in intensity to RGB [0, 0, 128] while leaving the gray pixels as RGB [128, 128, 128] (the sliceViewer will look different showing a darker blue but the volshow looks identical to the picture below with a bright blue and white-colored gray pixels). See pictures below:
SliceViewer Image Showing White = White with Blue Colored Phase

Volshow Image Showing White = White with Blue Colored Phase

I then explored other colors to see if there is a specific issue with the blue/gray combination and found interesting results. When I converted the blue pixels to red (RGB [255, 0, 0]), the gray pixels remained gray in sliceViewer but now display as cyan in volshow. See pictures below:
SliceViewer Image Showing Gray = Gray with Red Colored Phase

Volshow Image Showing Gray = Cyan with Red Colored Phase

The blue/gray and red/gray tests demonstrate that volshow will replace gray pixels with the inverse RGB color of any colored pixels in the dataset (yellow is the opposite RGB of blue ([255, 255, 0] vs. [0, 0, 255]) and cyan is the opposite RGB of red ([0, 255, 255] vs. [255, 0, 0])). However, the yellow and cyan colors do not appear to be their full intensity versions ([255, 255, 0] for yellow and [0, 255, 255] for cyan) and they may have some degree of their typically absent color present, but I cannot tell as these pixels do not actually have these intensity values in the image data so I cannot tell to what the true color is that they are creating. Thus, I cannot determine to what degree the opposite colors are added and the main color is substracted from the gray pixels.
I then tried substituting the colored phase for white to see if that maintained the gray pixels, and this worked (note that it may be difficult to see the difference between white and gray in the volShow, but I turned the volume around, zoomed in, and was able to verify that this was the case). See pictures below:
SliceViewer Image Showing Gray = Gray with White Colored Phase

Volshow Image Showing Gray = Gray with White Colored Phase

I was then curious as to what would happen to the gray pixels if I added a third, differently-colored phase. So, I added a red strip down one side of the blue/gray volume. Now, the gray pixels turned green (again, to what degree the blue and red intensities were added vs. substracted and how much of the green intensity was added, I cannot know). See pictures below:
SliceViewer Image Showing Gray = Gray with Blue and Red Colored Phases

Volshow Image Showing Gray = Green with Blue and Red Colored Phases

I don’t know if I am doing something wrong, so I have provided a snippet of code that will re-create all of this as well as a google drive link (the file is >5 MB so I can’t upload it) to the dataset I am using to investigate this. The code will let you select the data and automatically load it for you and then produce all of the images I have included here in there sliceViewer and volshow formats so that you can explore them). Here is the link to the dataset and here is the code:
%% Test Case %%
close all
clear
clc

% Select image file
fprintf(‘Select an image file.nn’)
[file_name, path_intro] = uigetfile(‘*.*’, ‘Select an image file’);
file_path = sprintf(‘%s%s’, path_intro, file_name);

% Import image message
clc
fprintf(‘Importing image stack…nn’)

% Create tiff
stack_tiff = Tiff(file_path);

% Determine number of images in stack
info = imfinfo(file_path);
no_slice = numel(info);

% Determine necessary tags from tiff
bits = getTag(stack_tiff, ‘BitsPerSample’);
long = getTag(stack_tiff, ‘ImageLength’);
wide = getTag(stack_tiff, ‘ImageWidth’);
samples = getTag(stack_tiff, ‘SamplesPerPixel’);

% Pre-allocate volume array
volume = zeros(long, wide, no_slice, samples, ‘uint8’);

% Import first image
volume(:, :, 1, 🙂 = read(stack_tiff);

% Import loop
for n = 2:no_slice

% Update current directory
nextDirectory(stack_tiff);

% Add current slice to volume
volume(:, :, n, 🙂 = read(stack_tiff);

end

% Close tiff
close(stack_tiff)

% Create transformation matrix
T = [1.31 0 0 0; 0 1.31 0 0; 0 0 1.31 0; 0 0 0 1];
tform = affinetform3d(T);

% Create viewer parameters structure
view_struct = struct;

% Populate viewer parameters structure
view_struct.BackgroundColor = [1 1 1];
view_struct.BackgroundGradient = ‘off’;
view_struct.Lighting = ‘off’;

% Load first slice viewer
clc
fprintf(‘Loading first slice viewer…nn’)
f1 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with Blue Colored Phase’);
sliceViewer(volume, parent = f1);

% Load first volume viewer
clc
fprintf(‘Loading first volume viewer…nn’)
uif1 = uifigure(‘Name’, ‘Gray = Yellow in Volume Viewer with Blue Colored Phase’);
viewer1 = viewer3d(view_struct, parent = uif1);
V1 = volshow(volume, ‘Transformation’, tform, parent = viewer1);

% Convert gray to white in second volume
clc
fprintf(‘Converting gray to white…nn’)
g2w_vol = volume;
g2w_vol(volume == 128) = 255;

% Load second slice viewer
clc
fprintf(‘Loading second slice viewer…nn’)
f2 = figure(‘Name’, ‘White = White in Slice Viewer with Blue Colored Phase’);
sliceViewer(g2w_vol, parent = f2);

% Load second volume viewer
clc
fprintf(‘Loading second volume viewer…nn’)
uif2 = uifigure(‘Name’, ‘White = White in Volume Viewer with Blue Colored Phase’);
viewer2 = viewer3d(view_struct, parent = uif2);
V2 = volshow(g2w_vol, ‘Transformation’, tform, parent = viewer2);

% Convert blue to red in third volume
clc
fprintf(‘Converting blue to red…nn’)
b2r_vol = volume;
b2r_vol(:, :, :, 1) = volume(:, :, :, 3);
b2r_vol(:, :, :, 3) = volume(:, :, :, 1);

% Load third slice viewer
clc
fprintf(‘Loading third slice viewer…nn’)
f3 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with Red Colored Phase’);
sliceViewer(b2r_vol, parent = f3);

% Load third volume viewer
clc
fprintf(‘Loading third volume viewer…nn’)
uif3 = uifigure(‘Name’, ‘Gray = Cyan in Volume Viewer with Red Colored Phase’);
viewer3 = viewer3d(view_struct, parent = uif3);
V3 = volshow(b2r_vol, ‘Transformation’, tform, parent = viewer3);

% Convert blue to white in fourth volume
clc
fprintf(‘Converting blue to white…nn’)
b2w_vol = volume;
b2w_vol(:, :, :, 1) = volume(:, :, :, 3);
b2w_vol(:, :, :, 2) = volume(:, :, :, 3);

% Load fourth slice viewer
clc
fprintf(‘Loading fourth slice viewer…nn’)
f4 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with White Colored Phase’);
sliceViewer(b2w_vol, parent = f4);

% Load fourth volume viewer
clc
fprintf(‘Loading fourth volume viewer…nn’)
uif4 = uifigure(‘Name’, ‘Gray = Gray in Volume Viewer with White Colored Phase’);
viewer4 = viewer3d(view_struct, parent = uif4);
V4 = volshow(b2w_vol, ‘Transformation’, tform, parent = viewer4);

% Add red strip in fifth volume
clc
fprintf(‘Adding red strip…nn’)
br_vol = volume;
br_vol(:, 1:25, :, 1) = 255;
br_vol(:, 1:25, :, 2:3) = 0;

% Load fifth slice viewer
clc
fprintf(‘Loading fifth slice viewer…nn’)
f5 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with White Colored Phase’);
sliceViewer(br_vol, parent = f5);

% Load fifth volume viewer
clc
fprintf(‘Loading fifth volume viewer…nn’)
uif5 = uifigure(‘Name’, ‘Gray = Gray in Volume Viewer with White Colored Phase’);
viewer5 = viewer3d(view_struct, parent = uif5);
V5 = volshow(br_vol, ‘Transformation’, tform, parent = viewer5);

% End message
clc
fprintf(‘Finished!nn’) I have a segmented 3D image dataset that I am trying to visualize. Because there are two segmented phases, I am exploring different colors for each phase to make them stand out from each other. In particular, I would like one of the phases to be blue (RGB [0, 0, 255]) and the other phase to be gray (RGB [128, 128, 128]). I have created a 4D uint8 dataset of the segmented images to allow for this, and when I load the image stack in MATLAB’s sliceViewer everything looks correct. However, when I then load the same dataset in MATLAB’s volshow, it colors the gray pixels yellow while the blue pixels remain blue. See pictures below:
SliceViewer Image Showing Gray = Gray with Blue Colored Phase

Volshow Image Showing Gray = Yellow with Blue Colored Phase

I thought that perhaps there was an issue with the lighting option being colored and casting a color onto gray pixels, but I have the lighting option turned off. I then thought that perhaps there was an issue with grayscale pixels in volshow, but when I convert the gray pixels to white (RGB [255, 255, 255]) they appear white in volshow. The same exact effect is achieved if the blue pixels are reduced in intensity to RGB [0, 0, 128] while leaving the gray pixels as RGB [128, 128, 128] (the sliceViewer will look different showing a darker blue but the volshow looks identical to the picture below with a bright blue and white-colored gray pixels). See pictures below:
SliceViewer Image Showing White = White with Blue Colored Phase

Volshow Image Showing White = White with Blue Colored Phase

I then explored other colors to see if there is a specific issue with the blue/gray combination and found interesting results. When I converted the blue pixels to red (RGB [255, 0, 0]), the gray pixels remained gray in sliceViewer but now display as cyan in volshow. See pictures below:
SliceViewer Image Showing Gray = Gray with Red Colored Phase

Volshow Image Showing Gray = Cyan with Red Colored Phase

The blue/gray and red/gray tests demonstrate that volshow will replace gray pixels with the inverse RGB color of any colored pixels in the dataset (yellow is the opposite RGB of blue ([255, 255, 0] vs. [0, 0, 255]) and cyan is the opposite RGB of red ([0, 255, 255] vs. [255, 0, 0])). However, the yellow and cyan colors do not appear to be their full intensity versions ([255, 255, 0] for yellow and [0, 255, 255] for cyan) and they may have some degree of their typically absent color present, but I cannot tell as these pixels do not actually have these intensity values in the image data so I cannot tell to what the true color is that they are creating. Thus, I cannot determine to what degree the opposite colors are added and the main color is substracted from the gray pixels.
I then tried substituting the colored phase for white to see if that maintained the gray pixels, and this worked (note that it may be difficult to see the difference between white and gray in the volShow, but I turned the volume around, zoomed in, and was able to verify that this was the case). See pictures below:
SliceViewer Image Showing Gray = Gray with White Colored Phase

Volshow Image Showing Gray = Gray with White Colored Phase

I was then curious as to what would happen to the gray pixels if I added a third, differently-colored phase. So, I added a red strip down one side of the blue/gray volume. Now, the gray pixels turned green (again, to what degree the blue and red intensities were added vs. substracted and how much of the green intensity was added, I cannot know). See pictures below:
SliceViewer Image Showing Gray = Gray with Blue and Red Colored Phases

Volshow Image Showing Gray = Green with Blue and Red Colored Phases

I don’t know if I am doing something wrong, so I have provided a snippet of code that will re-create all of this as well as a google drive link (the file is >5 MB so I can’t upload it) to the dataset I am using to investigate this. The code will let you select the data and automatically load it for you and then produce all of the images I have included here in there sliceViewer and volshow formats so that you can explore them). Here is the link to the dataset and here is the code:
%% Test Case %%
close all
clear
clc

% Select image file
fprintf(‘Select an image file.nn’)
[file_name, path_intro] = uigetfile(‘*.*’, ‘Select an image file’);
file_path = sprintf(‘%s%s’, path_intro, file_name);

% Import image message
clc
fprintf(‘Importing image stack…nn’)

% Create tiff
stack_tiff = Tiff(file_path);

% Determine number of images in stack
info = imfinfo(file_path);
no_slice = numel(info);

% Determine necessary tags from tiff
bits = getTag(stack_tiff, ‘BitsPerSample’);
long = getTag(stack_tiff, ‘ImageLength’);
wide = getTag(stack_tiff, ‘ImageWidth’);
samples = getTag(stack_tiff, ‘SamplesPerPixel’);

% Pre-allocate volume array
volume = zeros(long, wide, no_slice, samples, ‘uint8’);

% Import first image
volume(:, :, 1, 🙂 = read(stack_tiff);

% Import loop
for n = 2:no_slice

% Update current directory
nextDirectory(stack_tiff);

% Add current slice to volume
volume(:, :, n, 🙂 = read(stack_tiff);

end

% Close tiff
close(stack_tiff)

% Create transformation matrix
T = [1.31 0 0 0; 0 1.31 0 0; 0 0 1.31 0; 0 0 0 1];
tform = affinetform3d(T);

% Create viewer parameters structure
view_struct = struct;

% Populate viewer parameters structure
view_struct.BackgroundColor = [1 1 1];
view_struct.BackgroundGradient = ‘off’;
view_struct.Lighting = ‘off’;

% Load first slice viewer
clc
fprintf(‘Loading first slice viewer…nn’)
f1 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with Blue Colored Phase’);
sliceViewer(volume, parent = f1);

% Load first volume viewer
clc
fprintf(‘Loading first volume viewer…nn’)
uif1 = uifigure(‘Name’, ‘Gray = Yellow in Volume Viewer with Blue Colored Phase’);
viewer1 = viewer3d(view_struct, parent = uif1);
V1 = volshow(volume, ‘Transformation’, tform, parent = viewer1);

% Convert gray to white in second volume
clc
fprintf(‘Converting gray to white…nn’)
g2w_vol = volume;
g2w_vol(volume == 128) = 255;

% Load second slice viewer
clc
fprintf(‘Loading second slice viewer…nn’)
f2 = figure(‘Name’, ‘White = White in Slice Viewer with Blue Colored Phase’);
sliceViewer(g2w_vol, parent = f2);

% Load second volume viewer
clc
fprintf(‘Loading second volume viewer…nn’)
uif2 = uifigure(‘Name’, ‘White = White in Volume Viewer with Blue Colored Phase’);
viewer2 = viewer3d(view_struct, parent = uif2);
V2 = volshow(g2w_vol, ‘Transformation’, tform, parent = viewer2);

% Convert blue to red in third volume
clc
fprintf(‘Converting blue to red…nn’)
b2r_vol = volume;
b2r_vol(:, :, :, 1) = volume(:, :, :, 3);
b2r_vol(:, :, :, 3) = volume(:, :, :, 1);

% Load third slice viewer
clc
fprintf(‘Loading third slice viewer…nn’)
f3 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with Red Colored Phase’);
sliceViewer(b2r_vol, parent = f3);

% Load third volume viewer
clc
fprintf(‘Loading third volume viewer…nn’)
uif3 = uifigure(‘Name’, ‘Gray = Cyan in Volume Viewer with Red Colored Phase’);
viewer3 = viewer3d(view_struct, parent = uif3);
V3 = volshow(b2r_vol, ‘Transformation’, tform, parent = viewer3);

% Convert blue to white in fourth volume
clc
fprintf(‘Converting blue to white…nn’)
b2w_vol = volume;
b2w_vol(:, :, :, 1) = volume(:, :, :, 3);
b2w_vol(:, :, :, 2) = volume(:, :, :, 3);

% Load fourth slice viewer
clc
fprintf(‘Loading fourth slice viewer…nn’)
f4 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with White Colored Phase’);
sliceViewer(b2w_vol, parent = f4);

% Load fourth volume viewer
clc
fprintf(‘Loading fourth volume viewer…nn’)
uif4 = uifigure(‘Name’, ‘Gray = Gray in Volume Viewer with White Colored Phase’);
viewer4 = viewer3d(view_struct, parent = uif4);
V4 = volshow(b2w_vol, ‘Transformation’, tform, parent = viewer4);

% Add red strip in fifth volume
clc
fprintf(‘Adding red strip…nn’)
br_vol = volume;
br_vol(:, 1:25, :, 1) = 255;
br_vol(:, 1:25, :, 2:3) = 0;

% Load fifth slice viewer
clc
fprintf(‘Loading fifth slice viewer…nn’)
f5 = figure(‘Name’, ‘Gray = Gray in Slice Viewer with White Colored Phase’);
sliceViewer(br_vol, parent = f5);

% Load fifth volume viewer
clc
fprintf(‘Loading fifth volume viewer…nn’)
uif5 = uifigure(‘Name’, ‘Gray = Gray in Volume Viewer with White Colored Phase’);
viewer5 = viewer3d(view_struct, parent = uif5);
V5 = volshow(br_vol, ‘Transformation’, tform, parent = viewer5);

% End message
clc
fprintf(‘Finished!nn’) volshow, image processing, image segmentation MATLAB Answers — New Questions

​

I want design a look up table for fuzzy logic which has 2 inputs and 3 outputs. I referred to help on mathworks however its for 2 inputs and 1 output. Please refer to attached image.
https://www.mathworks.com/help//releases/R2021a/fuzzy/implement-fuzzy-pid-controller-in-simulink-using-lookup-table.html#d123e17778

Please suggest a way to create look up table for fuzzy logic using 2 inputs and 3 outputs.I want design a look up table for fuzzy logic which has 2 inputs and 3 outputs. I referred to help on mathworks however its for 2 inputs and 1 output. Please refer to attached image.
https://www.mathworks.com/help//releases/R2021a/fuzzy/implement-fuzzy-pid-controller-in-simulink-using-lookup-table.html#d123e17778

Please suggest a way to create look up table for fuzzy logic using 2 inputs and 3 outputs. I want design a look up table for fuzzy logic which has 2 inputs and 3 outputs. I referred to help on mathworks however its for 2 inputs and 1 output. Please refer to attached image.
https://www.mathworks.com/help//releases/R2021a/fuzzy/implement-fuzzy-pid-controller-in-simulink-using-lookup-table.html#d123e17778

Please suggest a way to create look up table for fuzzy logic using 2 inputs and 3 outputs. simulink, matlab, fuzzy, fuzzy logic toolbox, lookuptables MATLAB Answers — New Questions

​

I am using a MATLAB GUI app I wrote to connect via serial over USB to an Arduino. Unfortunately whenever I open the connection, the Arduino resets. This is expected behaviour, and there are hardware workarounds (including cutting the reset line, or using a 22uF cap like discussed here https://electronics.stackexchange.com/questions/24743/arduino-resetting-while-reconnecting-the-serial-terminal).

However, it IS also possible to prevent this behaviour by controlling the DTR line on the connection. For example, in Putty, if I select DSR/DTR mode, then the Arduino does not reset, so I know it is possible. I have not however been able to work out how to do it with MATLAB, and don’t know whether it is possible. I have explored the SerialPort object help, and am aware of the DTR option. However, I have exhausted all attempts at getting this to work and am now not sure whether it is even possible.

Has anyone ever done this or got it working?

Many thanks,I am using a MATLAB GUI app I wrote to connect via serial over USB to an Arduino. Unfortunately whenever I open the connection, the Arduino resets. This is expected behaviour, and there are hardware workarounds (including cutting the reset line, or using a 22uF cap like discussed here https://electronics.stackexchange.com/questions/24743/arduino-resetting-while-reconnecting-the-serial-terminal).

However, it IS also possible to prevent this behaviour by controlling the DTR line on the connection. For example, in Putty, if I select DSR/DTR mode, then the Arduino does not reset, so I know it is possible. I have not however been able to work out how to do it with MATLAB, and don’t know whether it is possible. I have explored the SerialPort object help, and am aware of the DTR option. However, I have exhausted all attempts at getting this to work and am now not sure whether it is even possible.

Has anyone ever done this or got it working?

Many thanks, I am using a MATLAB GUI app I wrote to connect via serial over USB to an Arduino. Unfortunately whenever I open the connection, the Arduino resets. This is expected behaviour, and there are hardware workarounds (including cutting the reset line, or using a 22uF cap like discussed here https://electronics.stackexchange.com/questions/24743/arduino-resetting-while-reconnecting-the-serial-terminal).

However, it IS also possible to prevent this behaviour by controlling the DTR line on the connection. For example, in Putty, if I select DSR/DTR mode, then the Arduino does not reset, so I know it is possible. I have not however been able to work out how to do it with MATLAB, and don’t know whether it is possible. I have explored the SerialPort object help, and am aware of the DTR option. However, I have exhausted all attempts at getting this to work and am now not sure whether it is even possible.

Has anyone ever done this or got it working?

Many thanks, matlab, serialport, arduino, dtr MATLAB Answers — New Questions

​

Hello,
I set the simulation time to "inf" in order to be able to change the parameters during real-time.
However it seems that Transport Delay block is not working in this mode. When I switch to finite time, it is working, but then I can not change the parameters real-time.
How to solve this?
Thanks
M.Hello,
I set the simulation time to "inf" in order to be able to change the parameters during real-time.
However it seems that Transport Delay block is not working in this mode. When I switch to finite time, it is working, but then I can not change the parameters real-time.
How to solve this?
Thanks
M. Hello,
I set the simulation time to "inf" in order to be able to change the parameters during real-time.
However it seems that Transport Delay block is not working in this mode. When I switch to finite time, it is working, but then I can not change the parameters real-time.
How to solve this?
Thanks
M. delay, simulink, realtime MATLAB Answers — New Questions

​

I have a bunch of subplots that are linked with linkaxes and I currently use the FEX function <http://www.mathworks.com/matlabcentral/fileexchange/27075-intelligent-dynamic-date-ticks dynamicDateTicks> to update the tick labels when I zoom. BUT. The new datetime format looks better (less crowded) on the graphs. Only I can’t get it (datetime) to work with a surf plot.

Here’s a minimal working example:

X = 25:25:100;
T = datenum(2015,01,01,1:4,0,0);
Z = [ 1 2 3 4;
5 6 7 8;
9 10 11 12;
13 14 15 16];
[Xmesh,Tmesh] = meshgrid(X,T);
% Tmesh = datetime(Tmesh,’ConvertFrom’,’datenum’);
figure;
surf(Tmesh,Xmesh,Z);

If you uncomment the one line, this gives me the error:

Error using surf (line 57)
X, Y, Z, and C cannot be complex.

Anyone else have this problem? It seems like a bug, since it works fine with any other plot. For example:

plot(datetime(2014,01,1:2),1:2)

Any help would be appreciated. And just to be clear, I currently can get it all to work with dynamicDateTicks (datetick), I’m just hoping for a cleaner plot.I have a bunch of subplots that are linked with linkaxes and I currently use the FEX function <http://www.mathworks.com/matlabcentral/fileexchange/27075-intelligent-dynamic-date-ticks dynamicDateTicks> to update the tick labels when I zoom. BUT. The new datetime format looks better (less crowded) on the graphs. Only I can’t get it (datetime) to work with a surf plot.

Here’s a minimal working example:

X = 25:25:100;
T = datenum(2015,01,01,1:4,0,0);
Z = [ 1 2 3 4;
5 6 7 8;
9 10 11 12;
13 14 15 16];
[Xmesh,Tmesh] = meshgrid(X,T);
% Tmesh = datetime(Tmesh,’ConvertFrom’,’datenum’);
figure;
surf(Tmesh,Xmesh,Z);

If you uncomment the one line, this gives me the error:

Error using surf (line 57)
X, Y, Z, and C cannot be complex.

Anyone else have this problem? It seems like a bug, since it works fine with any other plot. For example:

plot(datetime(2014,01,1:2),1:2)

Any help would be appreciated. And just to be clear, I currently can get it all to work with dynamicDateTicks (datetick), I’m just hoping for a cleaner plot. I have a bunch of subplots that are linked with linkaxes and I currently use the FEX function <http://www.mathworks.com/matlabcentral/fileexchange/27075-intelligent-dynamic-date-ticks dynamicDateTicks> to update the tick labels when I zoom. BUT. The new datetime format looks better (less crowded) on the graphs. Only I can’t get it (datetime) to work with a surf plot.

Here’s a minimal working example:

X = 25:25:100;
T = datenum(2015,01,01,1:4,0,0);
Z = [ 1 2 3 4;
5 6 7 8;
9 10 11 12;
13 14 15 16];
[Xmesh,Tmesh] = meshgrid(X,T);
% Tmesh = datetime(Tmesh,’ConvertFrom’,’datenum’);
figure;
surf(Tmesh,Xmesh,Z);

If you uncomment the one line, this gives me the error:

Error using surf (line 57)
X, Y, Z, and C cannot be complex.

Anyone else have this problem? It seems like a bug, since it works fine with any other plot. For example:

plot(datetime(2014,01,1:2),1:2)

Any help would be appreciated. And just to be clear, I currently can get it all to work with dynamicDateTicks (datetick), I’m just hoping for a cleaner plot. surf, datetime, 3d plots MATLAB Answers — New Questions

​

Hello, i’m getting an error in my code:
Unable to perform assignment because the indices on the left side are not compatible with
the size of the right side.

Error in anfisloop (line 6)
[fuzout(1,i),trnRMSE(1,i)] = evaltrainset(T(1,i),trainin,trainou);
my code: the function i’m calling:
this function will create 5 models each one will be in a column in T[].
so T(1,1) is the first model, T(1,2) the second …..
the idea of what i want to do is:
create a variable (here fuzout[]) that will stack in each column the output of a model
and another variable that will stack in each column the RMSE of a model.
function [T,fuzout,trnRMSE] = anfisloop(trainin,trainou,testin,testout,fis)
epoch=5;
for i = 1:epoch
opt=anfisopt(testin,testout,fis,i);
T(1,i) = anfis([trainin trainou],opt);
[fuzout(1,i),trnRMSE(1,i)] = evaltrainset(T(1,i),trainin,trainou);
end
end
the function evaltrainset that the precious function call:
evalfis is a matlab’s function
function [fuzout,trnRMSE] = evaltrainset(fis,trainin,trainout)
fuzout = evalfis(fis,trainin);
trnRMSE = norm(fuzout-trainout)/sqrt(length(fuzout));
end
how i am calling the function :
>> [A,fuzout,trnRMSE]=anfisloop(trainin,trainout,testin,testout,fis)
please help.Hello, i’m getting an error in my code:
Unable to perform assignment because the indices on the left side are not compatible with
the size of the right side.

Error in anfisloop (line 6)
[fuzout(1,i),trnRMSE(1,i)] = evaltrainset(T(1,i),trainin,trainou);
my code: the function i’m calling:
this function will create 5 models each one will be in a column in T[].
so T(1,1) is the first model, T(1,2) the second …..
the idea of what i want to do is:
create a variable (here fuzout[]) that will stack in each column the output of a model
and another variable that will stack in each column the RMSE of a model.
function [T,fuzout,trnRMSE] = anfisloop(trainin,trainou,testin,testout,fis)
epoch=5;
for i = 1:epoch
opt=anfisopt(testin,testout,fis,i);
T(1,i) = anfis([trainin trainou],opt);
[fuzout(1,i),trnRMSE(1,i)] = evaltrainset(T(1,i),trainin,trainou);
end
end
the function evaltrainset that the precious function call:
evalfis is a matlab’s function
function [fuzout,trnRMSE] = evaltrainset(fis,trainin,trainout)
fuzout = evalfis(fis,trainin);
trnRMSE = norm(fuzout-trainout)/sqrt(length(fuzout));
end
how i am calling the function :
>> [A,fuzout,trnRMSE]=anfisloop(trainin,trainout,testin,testout,fis)
please help. Hello, i’m getting an error in my code:
Unable to perform assignment because the indices on the left side are not compatible with
the size of the right side.

Error in anfisloop (line 6)
[fuzout(1,i),trnRMSE(1,i)] = evaltrainset(T(1,i),trainin,trainou);
my code: the function i’m calling:
this function will create 5 models each one will be in a column in T[].
so T(1,1) is the first model, T(1,2) the second …..
the idea of what i want to do is:
create a variable (here fuzout[]) that will stack in each column the output of a model
and another variable that will stack in each column the RMSE of a model.
function [T,fuzout,trnRMSE] = anfisloop(trainin,trainou,testin,testout,fis)
epoch=5;
for i = 1:epoch
opt=anfisopt(testin,testout,fis,i);
T(1,i) = anfis([trainin trainou],opt);
[fuzout(1,i),trnRMSE(1,i)] = evaltrainset(T(1,i),trainin,trainou);
end
end
the function evaltrainset that the precious function call:
evalfis is a matlab’s function
function [fuzout,trnRMSE] = evaltrainset(fis,trainin,trainout)
fuzout = evalfis(fis,trainin);
trnRMSE = norm(fuzout-trainout)/sqrt(length(fuzout));
end
how i am calling the function :
>> [A,fuzout,trnRMSE]=anfisloop(trainin,trainout,testin,testout,fis)
please help. function, functions, matlab, matlab function, code, programming, fuzzy MATLAB Answers — New Questions

​

I am training a simple DL model for binary class classification, with 2 blocks of these layers, followed by a softmax layer:
featureInputLayer -> fullyConnectedLayer -> layerNormalizationLayer -> reluLayer
For a fixed train, validation and test set, I am getting diverse results in accuracy, ranging from 45-85%. Kindly suggest how to stabilise the results.
Here, training instances are 100 (50 per class), validation 24 (10 per class) and using
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 100, … % Adjust based on dataset size
‘MiniBatchSize’, 16, … % Choose batch size based on memory
‘ValidationData’, valDS, … % Validation data
‘ValidationFrequency’, 5, … % Adjust based on dataset size
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘OutputNetwork’, ‘best-validation-loss’, … % Save best model
‘Shuffle’, ‘every-epoch’,…
‘LearnRateSchedule’, ‘cosine’);I am training a simple DL model for binary class classification, with 2 blocks of these layers, followed by a softmax layer:
featureInputLayer -> fullyConnectedLayer -> layerNormalizationLayer -> reluLayer
For a fixed train, validation and test set, I am getting diverse results in accuracy, ranging from 45-85%. Kindly suggest how to stabilise the results.
Here, training instances are 100 (50 per class), validation 24 (10 per class) and using
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 100, … % Adjust based on dataset size
‘MiniBatchSize’, 16, … % Choose batch size based on memory
‘ValidationData’, valDS, … % Validation data
‘ValidationFrequency’, 5, … % Adjust based on dataset size
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘OutputNetwork’, ‘best-validation-loss’, … % Save best model
‘Shuffle’, ‘every-epoch’,…
‘LearnRateSchedule’, ‘cosine’); I am training a simple DL model for binary class classification, with 2 blocks of these layers, followed by a softmax layer:
featureInputLayer -> fullyConnectedLayer -> layerNormalizationLayer -> reluLayer
For a fixed train, validation and test set, I am getting diverse results in accuracy, ranging from 45-85%. Kindly suggest how to stabilise the results.
Here, training instances are 100 (50 per class), validation 24 (10 per class) and using
options = trainingOptions(‘adam’, …
‘MaxEpochs’, 100, … % Adjust based on dataset size
‘MiniBatchSize’, 16, … % Choose batch size based on memory
‘ValidationData’, valDS, … % Validation data
‘ValidationFrequency’, 5, … % Adjust based on dataset size
‘Verbose’, true, …
‘Plots’, ‘training-progress’, …
‘OutputNetwork’, ‘best-validation-loss’, … % Save best model
‘Shuffle’, ‘every-epoch’,…
‘LearnRateSchedule’, ‘cosine’); deep learning, softmax layer, stabilization MATLAB Answers — New Questions

​

I’m using fmincon for optimizing a system for trajectory optimization. Recently, my optimization problem seems to go well below 1e-06 feasibility for the ‘interior point’ solver. For this project, I will have to perform multiple trajectory optimizations and hence want my sovler to stop < 1e-06 feasibility. When I add the option of constraint tolerance, the solver continues to further iterate. How do I tackle this problem. I found the same problem wiith StepTolerance option as wellI’m using fmincon for optimizing a system for trajectory optimization. Recently, my optimization problem seems to go well below 1e-06 feasibility for the ‘interior point’ solver. For this project, I will have to perform multiple trajectory optimizations and hence want my sovler to stop < 1e-06 feasibility. When I add the option of constraint tolerance, the solver continues to further iterate. How do I tackle this problem. I found the same problem wiith StepTolerance option as well I’m using fmincon for optimizing a system for trajectory optimization. Recently, my optimization problem seems to go well below 1e-06 feasibility for the ‘interior point’ solver. For this project, I will have to perform multiple trajectory optimizations and hence want my sovler to stop < 1e-06 feasibility. When I add the option of constraint tolerance, the solver continues to further iterate. How do I tackle this problem. I found the same problem wiith StepTolerance option as well fmincon MATLAB Answers — New Questions

​