I’m designing movmean function on simulink using Matlab function block
And when i try to test the block by inputing timeseries data, the input and output of ‘movmean’ function is same.

I attach pics below

1) Moving Average function block

2) code of Moving Average block

3) input and output of Moving Average block (output is not computed well)I’m designing movmean function on simulink using Matlab function block
And when i try to test the block by inputing timeseries data, the input and output of ‘movmean’ function is same.

I attach pics below

1) Moving Average function block

2) code of Moving Average block

3) input and output of Moving Average block (output is not computed well) I’m designing movmean function on simulink using Matlab function block
And when i try to test the block by inputing timeseries data, the input and output of ‘movmean’ function is same.

I attach pics below

1) Moving Average function block

2) code of Moving Average block

3) input and output of Moving Average block (output is not computed well) matlab, simulink, movmean MATLAB Answers — New Questions

​

clearvars -except Radius_list Six_plots

Circle_num = 4

nlist = 1;
%Six_plots = F_six_plots(Circle_num, Radius_list);
Six_plots;

sz = size(Six_plots,2);

Plot_num(1:sz) = compassplot(ones)
jplot = 1;

while jplot < sz
Plot_num(nlist) = compassplot(Six_plots(1:nlist));
nlist = nlist+1;
jplot = jplot+1
end
X = Plot_num
Stepping thru the while works. Plot_num(nlist) is fine
X is all "handle to deleted PolarCompassPlot"clearvars -except Radius_list Six_plots

Circle_num = 4

nlist = 1;
%Six_plots = F_six_plots(Circle_num, Radius_list);
Six_plots;

sz = size(Six_plots,2);

Plot_num(1:sz) = compassplot(ones)
jplot = 1;

while jplot < sz
Plot_num(nlist) = compassplot(Six_plots(1:nlist));
nlist = nlist+1;
jplot = jplot+1
end
X = Plot_num
Stepping thru the while works. Plot_num(nlist) is fine
X is all "handle to deleted PolarCompassPlot" clearvars -except Radius_list Six_plots

Circle_num = 4

nlist = 1;
%Six_plots = F_six_plots(Circle_num, Radius_list);
Six_plots;

sz = size(Six_plots,2);

Plot_num(1:sz) = compassplot(ones)
jplot = 1;

while jplot < sz
Plot_num(nlist) = compassplot(Six_plots(1:nlist));
nlist = nlist+1;
jplot = jplot+1
end
X = Plot_num
Stepping thru the while works. Plot_num(nlist) is fine
X is all "handle to deleted PolarCompassPlot" plot_num MATLAB Answers — New Questions

​

Hello,
I need a code for optimal location and sizing of DG in IEEE 14 bus bar system using fuzzy logic algorithm Or ANFIS technique. My objective function is power loss reduction and voltage profile improvement with and without DG. If this code is available in MATLAB with anyone. Please share it with me on my email: naveedkhanuet@gmail.com

Thank you.Hello,
I need a code for optimal location and sizing of DG in IEEE 14 bus bar system using fuzzy logic algorithm Or ANFIS technique. My objective function is power loss reduction and voltage profile improvement with and without DG. If this code is available in MATLAB with anyone. Please share it with me on my email: naveedkhanuet@gmail.com

Thank you. Hello,
I need a code for optimal location and sizing of DG in IEEE 14 bus bar system using fuzzy logic algorithm Or ANFIS technique. My objective function is power loss reduction and voltage profile improvement with and without DG. If this code is available in MATLAB with anyone. Please share it with me on my email: naveedkhanuet@gmail.com

Thank you. dg, optimization MATLAB Answers — New Questions

​

When using PIVlab it seems that the calibration options are rather limited, e.g. as far as I can see one cannot use camera extrinsics to compensate for camera orientation (one can only link pixel size to real world distance). Or should that somehow be taken into account when preprocessing the inout images ? The compensation for camera distortion (intrinsic) has to be done prior to importing the images into PIVlab, it would be great if an integration with the camera calibration app would be added to PIVlab, I’m sure this will save peoples time while a lot of mistakes can be avoided.When using PIVlab it seems that the calibration options are rather limited, e.g. as far as I can see one cannot use camera extrinsics to compensate for camera orientation (one can only link pixel size to real world distance). Or should that somehow be taken into account when preprocessing the inout images ? The compensation for camera distortion (intrinsic) has to be done prior to importing the images into PIVlab, it would be great if an integration with the camera calibration app would be added to PIVlab, I’m sure this will save peoples time while a lot of mistakes can be avoided. When using PIVlab it seems that the calibration options are rather limited, e.g. as far as I can see one cannot use camera extrinsics to compensate for camera orientation (one can only link pixel size to real world distance). Or should that somehow be taken into account when preprocessing the inout images ? The compensation for camera distortion (intrinsic) has to be done prior to importing the images into PIVlab, it would be great if an integration with the camera calibration app would be added to PIVlab, I’m sure this will save peoples time while a lot of mistakes can be avoided. pivlab, calibration camera extrinsics MATLAB Answers — New Questions

​

Hello,
I m trying to use viewer3d as a openGL viewer and it works well for 3D surface and 3D points using images.ui.graphics.Surface and images.ui.graphics.Points but i wonder if it is possible to draw line or connected line inside the viewer ? I dont mean how to show wireframe when using a surface or annotation line.
I just try for exemple to attached 3 vectors in X,Y and Z direction to each 3D object.
I saw 2 functions WideLine and Thinline but i did not find any documentation about them.
Thanks for your helpHello,
I m trying to use viewer3d as a openGL viewer and it works well for 3D surface and 3D points using images.ui.graphics.Surface and images.ui.graphics.Points but i wonder if it is possible to draw line or connected line inside the viewer ? I dont mean how to show wireframe when using a surface or annotation line.
I just try for exemple to attached 3 vectors in X,Y and Z direction to each 3D object.
I saw 2 functions WideLine and Thinline but i did not find any documentation about them.
Thanks for your help Hello,
I m trying to use viewer3d as a openGL viewer and it works well for 3D surface and 3D points using images.ui.graphics.Surface and images.ui.graphics.Points but i wonder if it is possible to draw line or connected line inside the viewer ? I dont mean how to show wireframe when using a surface or annotation line.
I just try for exemple to attached 3 vectors in X,Y and Z direction to each 3D object.
I saw 2 functions WideLine and Thinline but i did not find any documentation about them.
Thanks for your help viewer3d, thinline, wideline, images.ui.graphics.viewer MATLAB Answers — New Questions

​

Hi, I have a graph where Im plotting on the x-axis time by using tic then in a loop making measurements ‘Y’ and performing tend=toc
I dor ecord the actual date / time at the start too.

dt = datestr(now,’dd-mmm-yyyy HH-MM-SS’); %get current dateTime

Once my loop has finished an I have a plot of all the Y’s against their time of measurements (starting at 0), how can I add the "duration" onto the actual datestr, so I can have on the x-axis the actual date time.

thanks
JasonHi, I have a graph where Im plotting on the x-axis time by using tic then in a loop making measurements ‘Y’ and performing tend=toc
I dor ecord the actual date / time at the start too.

dt = datestr(now,’dd-mmm-yyyy HH-MM-SS’); %get current dateTime

Once my loop has finished an I have a plot of all the Y’s against their time of measurements (starting at 0), how can I add the "duration" onto the actual datestr, so I can have on the x-axis the actual date time.

thanks
Jason Hi, I have a graph where Im plotting on the x-axis time by using tic then in a loop making measurements ‘Y’ and performing tend=toc
I dor ecord the actual date / time at the start too.

dt = datestr(now,’dd-mmm-yyyy HH-MM-SS’); %get current dateTime

Once my loop has finished an I have a plot of all the Y’s against their time of measurements (starting at 0), how can I add the "duration" onto the actual datestr, so I can have on the x-axis the actual date time.

thanks
Jason datestr, tic, toc MATLAB Answers — New Questions

​

I tried to import an YOLO ONNX model to MATLAB (I have the DeepLearning Toolbox installed), but got an error.
I created my ONNX model using the following python code:
from ultralytics import YOLO

YOLO("yolo_model.pt").export(
format="onnx", opset=12, simplify=True, dynamic=False, nms=False)
(I tried also with dynamic=True and without specifying opset arg but got same results)

On MATLAB I used this code to import the model:
modelPath = ‘yolo_model.onnx’;
net = importNetworkFromONNX(modelPath);
dummyInput = dlarray(rand(1, 3, 256, 256), ‘BCSS’);
net = initialize(net, dummyInput);
out = predict(net, dummyInput);
and got the following:
Warning:
Unable to import some ONNX operators, because they are not supported.
They have been replaced by placholder layers or by placeholder functions inside generated custom layers. To find the
placeholder layers, call the function findPlaceholderLayers on the returned object.

2 operator(s) : Attribute ‘atialias’ is not supported.

To import the ONNX network as a function, use importONNXFunction.
> In nnet.internal.cnn.onnx.importONNXNetwork>iWarnIfContainsPlaceholders (line 163)
In nnet.internal.cnn.onnx.importONNXNetwork>iHandleTranslationIssues (line 104)
In nnet.internal.cnn.onnx.importONNXNetwork (line 57)
In importNetworkFromONNX (line 77)
In LoadONNX (line 6)
Warning: Returning an uninitialized dlnetwork. Initialize the network by passing example input data to the initialized object function.
> In nnet.internal.cnn.onnx.importONNXNetwork>constructDlnetwork (line 93)
In nnet.internal.cnn.onnx.importONNXNetwork (line 62)
In nnet.internal.cnn.onnx.importNetworkFromONNX (line 13)
In importNetworkFromONNX (line 77)
In LoadONNX (line 6)
Error using dlnetwork/initialize (line 600)
Invalid network.

Error in LoadONNX (line 10)
net = initialize(net, dummyInput);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Caused by:
Layer ‘Reshape_To_ReshapeLayer1072’: Error using the predict function in layer yolo_model.Reshape_To_ReshapeLayer1072. The function threw an error and could not be executed.
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that dimension.

Error in dlarray/reshape (line 40)
objdata = reshape(objdata, varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Error in yolo_model.Reshape_To_ReshapeLayer1072>Reshape_To_ReshapeGraph1066 (line 204)
Vars.x_model_10_m_m_0__9 = reshape(Vars.x_model_10_m_m_0__19, shape{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Error in yolo_model.Reshape_To_ReshapeLayer1072>Reshape_To_ReshapeFcn (line 190)
[x_model_10_m_m_0__11, x_model_10_m_m_0__12, x_model_10_m_m_0__8, x_model_10_m_m_0__13, x_model_10_m_m_0__11NumDims, x_model_10_m_m_0__12NumDims, x_model_10_m_m_0__8NumDims,
x_model_10_m_m_0__13NumDims, state] = Reshape_To_ReshapeGraph1066(x_model_10_m_m_0__18, NumDims.x_model_10_m_m_0__18, Vars, NumDims, Training, params.State);

Error in yolo_model.Reshape_To_ReshapeLayer1072/predict (line 34)
[x_model_10_m_m_0__11, x_model_10_m_m_0__12, x_model_10_m_m_0__8, x_model_10_m_m_0__13, x_model_10_m_m_0__11NumDims, x_model_10_m_m_0__12NumDims,
x_model_10_m_m_0__8NumDims, x_model_10_m_m_0__13NumDims] = Reshape_To_ReshapeFcn(x_model_10_m_m_0__18, x_model_10_m_m_0__18NumDims, onnxParams, ‘Training’, false, …

Please helpI tried to import an YOLO ONNX model to MATLAB (I have the DeepLearning Toolbox installed), but got an error.
I created my ONNX model using the following python code:
from ultralytics import YOLO

YOLO("yolo_model.pt").export(
format="onnx", opset=12, simplify=True, dynamic=False, nms=False)
(I tried also with dynamic=True and without specifying opset arg but got same results)

On MATLAB I used this code to import the model:
modelPath = ‘yolo_model.onnx’;
net = importNetworkFromONNX(modelPath);
dummyInput = dlarray(rand(1, 3, 256, 256), ‘BCSS’);
net = initialize(net, dummyInput);
out = predict(net, dummyInput);
and got the following:
Warning:
Unable to import some ONNX operators, because they are not supported.
They have been replaced by placholder layers or by placeholder functions inside generated custom layers. To find the
placeholder layers, call the function findPlaceholderLayers on the returned object.

2 operator(s) : Attribute ‘atialias’ is not supported.

To import the ONNX network as a function, use importONNXFunction.
> In nnet.internal.cnn.onnx.importONNXNetwork>iWarnIfContainsPlaceholders (line 163)
In nnet.internal.cnn.onnx.importONNXNetwork>iHandleTranslationIssues (line 104)
In nnet.internal.cnn.onnx.importONNXNetwork (line 57)
In importNetworkFromONNX (line 77)
In LoadONNX (line 6)
Warning: Returning an uninitialized dlnetwork. Initialize the network by passing example input data to the initialized object function.
> In nnet.internal.cnn.onnx.importONNXNetwork>constructDlnetwork (line 93)
In nnet.internal.cnn.onnx.importONNXNetwork (line 62)
In nnet.internal.cnn.onnx.importNetworkFromONNX (line 13)
In importNetworkFromONNX (line 77)
In LoadONNX (line 6)
Error using dlnetwork/initialize (line 600)
Invalid network.

Error in LoadONNX (line 10)
net = initialize(net, dummyInput);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Caused by:
Layer ‘Reshape_To_ReshapeLayer1072’: Error using the predict function in layer yolo_model.Reshape_To_ReshapeLayer1072. The function threw an error and could not be executed.
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that dimension.

Error in dlarray/reshape (line 40)
objdata = reshape(objdata, varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Error in yolo_model.Reshape_To_ReshapeLayer1072>Reshape_To_ReshapeGraph1066 (line 204)
Vars.x_model_10_m_m_0__9 = reshape(Vars.x_model_10_m_m_0__19, shape{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Error in yolo_model.Reshape_To_ReshapeLayer1072>Reshape_To_ReshapeFcn (line 190)
[x_model_10_m_m_0__11, x_model_10_m_m_0__12, x_model_10_m_m_0__8, x_model_10_m_m_0__13, x_model_10_m_m_0__11NumDims, x_model_10_m_m_0__12NumDims, x_model_10_m_m_0__8NumDims,
x_model_10_m_m_0__13NumDims, state] = Reshape_To_ReshapeGraph1066(x_model_10_m_m_0__18, NumDims.x_model_10_m_m_0__18, Vars, NumDims, Training, params.State);

Error in yolo_model.Reshape_To_ReshapeLayer1072/predict (line 34)
[x_model_10_m_m_0__11, x_model_10_m_m_0__12, x_model_10_m_m_0__8, x_model_10_m_m_0__13, x_model_10_m_m_0__11NumDims, x_model_10_m_m_0__12NumDims,
x_model_10_m_m_0__8NumDims, x_model_10_m_m_0__13NumDims] = Reshape_To_ReshapeFcn(x_model_10_m_m_0__18, x_model_10_m_m_0__18NumDims, onnxParams, ‘Training’, false, …

Please help I tried to import an YOLO ONNX model to MATLAB (I have the DeepLearning Toolbox installed), but got an error.
I created my ONNX model using the following python code:
from ultralytics import YOLO

YOLO("yolo_model.pt").export(
format="onnx", opset=12, simplify=True, dynamic=False, nms=False)
(I tried also with dynamic=True and without specifying opset arg but got same results)

On MATLAB I used this code to import the model:
modelPath = ‘yolo_model.onnx’;
net = importNetworkFromONNX(modelPath);
dummyInput = dlarray(rand(1, 3, 256, 256), ‘BCSS’);
net = initialize(net, dummyInput);
out = predict(net, dummyInput);
and got the following:
Warning:
Unable to import some ONNX operators, because they are not supported.
They have been replaced by placholder layers or by placeholder functions inside generated custom layers. To find the
placeholder layers, call the function findPlaceholderLayers on the returned object.

2 operator(s) : Attribute ‘atialias’ is not supported.

To import the ONNX network as a function, use importONNXFunction.
> In nnet.internal.cnn.onnx.importONNXNetwork>iWarnIfContainsPlaceholders (line 163)
In nnet.internal.cnn.onnx.importONNXNetwork>iHandleTranslationIssues (line 104)
In nnet.internal.cnn.onnx.importONNXNetwork (line 57)
In importNetworkFromONNX (line 77)
In LoadONNX (line 6)
Warning: Returning an uninitialized dlnetwork. Initialize the network by passing example input data to the initialized object function.
> In nnet.internal.cnn.onnx.importONNXNetwork>constructDlnetwork (line 93)
In nnet.internal.cnn.onnx.importONNXNetwork (line 62)
In nnet.internal.cnn.onnx.importNetworkFromONNX (line 13)
In importNetworkFromONNX (line 77)
In LoadONNX (line 6)
Error using dlnetwork/initialize (line 600)
Invalid network.

Error in LoadONNX (line 10)
net = initialize(net, dummyInput);
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Caused by:
Layer ‘Reshape_To_ReshapeLayer1072’: Error using the predict function in layer yolo_model.Reshape_To_ReshapeLayer1072. The function threw an error and could not be executed.
Error using reshape
Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size for that dimension.

Error in dlarray/reshape (line 40)
objdata = reshape(objdata, varargin{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Error in yolo_model.Reshape_To_ReshapeLayer1072>Reshape_To_ReshapeGraph1066 (line 204)
Vars.x_model_10_m_m_0__9 = reshape(Vars.x_model_10_m_m_0__19, shape{:});
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Error in yolo_model.Reshape_To_ReshapeLayer1072>Reshape_To_ReshapeFcn (line 190)
[x_model_10_m_m_0__11, x_model_10_m_m_0__12, x_model_10_m_m_0__8, x_model_10_m_m_0__13, x_model_10_m_m_0__11NumDims, x_model_10_m_m_0__12NumDims, x_model_10_m_m_0__8NumDims,
x_model_10_m_m_0__13NumDims, state] = Reshape_To_ReshapeGraph1066(x_model_10_m_m_0__18, NumDims.x_model_10_m_m_0__18, Vars, NumDims, Training, params.State);

Error in yolo_model.Reshape_To_ReshapeLayer1072/predict (line 34)
[x_model_10_m_m_0__11, x_model_10_m_m_0__12, x_model_10_m_m_0__8, x_model_10_m_m_0__13, x_model_10_m_m_0__11NumDims, x_model_10_m_m_0__12NumDims,
x_model_10_m_m_0__8NumDims, x_model_10_m_m_0__13NumDims] = Reshape_To_ReshapeFcn(x_model_10_m_m_0__18, x_model_10_m_m_0__18NumDims, onnxParams, ‘Training’, false, …

Please help onnx, yolo MATLAB Answers — New Questions

​

Hello everyone,
I am simulating a 2D–3D image registration pipeline for 6D motion estimation (3D translation + rotation) from two 2D X-ray projections, based on the following paper:
[1] Fu & Kuduvalli, “A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery,” Med. Phys., 2008.
The pipeline consists of:
Generating a 3D CT volume with known fiducials
Applying a known 6D rigid transformation to the CT
Generating two 2D projections (A and B)
Performing accurate 2D–2D registration between each X-ray and its corresponding DRR
Estimating the final 6D transformation using geometric back-projection / analytical decomposition (as described in the paper)
Problem Description
The 2D image registrations are accurate and stable (x, y, in-plane rotation, and roll are recovered correctly for both projections).
However, the final 6D estimation shows significant error, especially in:
Out-of-plane translation
Out-of-plane rotations
Because the 2D registrations behave as expected, I suspect the issue is in the geometric back-projection / 2D-to-3D decomposition step, rather than in the similarity metric or optimization.
Specifically, I am unsure whether:
My implementation of the analytical back-projection equations is correct
The projection geometry (scaling, sign conventions, coordinate frames) is consistent between the two views
Additional geometric constraints or assumptions are required for observability
What I Am Looking For
I would greatly appreciate any insights or suggestions on:
Correct implementation of 2D-to-3D geometric back-projection for dual-view registration
Common pitfalls in coordinate systems, scaling factors, or sign conventions
Whether a cone-beam vs. parallel-beam assumption materially affects the 6D solution
Any recommended validation or sanity checks for this type of pipeline
I have attached the MATLAB code below for reference.
Thank you very much for your time and support.
Reference
[1] Fu, D., & Kuduvalli, G. (2008). A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery. Medical Physics, 35(5), 2180–2194.Hello everyone,
I am simulating a 2D–3D image registration pipeline for 6D motion estimation (3D translation + rotation) from two 2D X-ray projections, based on the following paper:
[1] Fu & Kuduvalli, “A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery,” Med. Phys., 2008.
The pipeline consists of:
Generating a 3D CT volume with known fiducials
Applying a known 6D rigid transformation to the CT
Generating two 2D projections (A and B)
Performing accurate 2D–2D registration between each X-ray and its corresponding DRR
Estimating the final 6D transformation using geometric back-projection / analytical decomposition (as described in the paper)
Problem Description
The 2D image registrations are accurate and stable (x, y, in-plane rotation, and roll are recovered correctly for both projections).
However, the final 6D estimation shows significant error, especially in:
Out-of-plane translation
Out-of-plane rotations
Because the 2D registrations behave as expected, I suspect the issue is in the geometric back-projection / 2D-to-3D decomposition step, rather than in the similarity metric or optimization.
Specifically, I am unsure whether:
My implementation of the analytical back-projection equations is correct
The projection geometry (scaling, sign conventions, coordinate frames) is consistent between the two views
Additional geometric constraints or assumptions are required for observability
What I Am Looking For
I would greatly appreciate any insights or suggestions on:
Correct implementation of 2D-to-3D geometric back-projection for dual-view registration
Common pitfalls in coordinate systems, scaling factors, or sign conventions
Whether a cone-beam vs. parallel-beam assumption materially affects the 6D solution
Any recommended validation or sanity checks for this type of pipeline
I have attached the MATLAB code below for reference.
Thank you very much for your time and support.
Reference
[1] Fu, D., & Kuduvalli, G. (2008). A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery. Medical Physics, 35(5), 2180–2194. Hello everyone,
I am simulating a 2D–3D image registration pipeline for 6D motion estimation (3D translation + rotation) from two 2D X-ray projections, based on the following paper:
[1] Fu & Kuduvalli, “A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery,” Med. Phys., 2008.
The pipeline consists of:
Generating a 3D CT volume with known fiducials
Applying a known 6D rigid transformation to the CT
Generating two 2D projections (A and B)
Performing accurate 2D–2D registration between each X-ray and its corresponding DRR
Estimating the final 6D transformation using geometric back-projection / analytical decomposition (as described in the paper)
Problem Description
The 2D image registrations are accurate and stable (x, y, in-plane rotation, and roll are recovered correctly for both projections).
However, the final 6D estimation shows significant error, especially in:
Out-of-plane translation
Out-of-plane rotations
Because the 2D registrations behave as expected, I suspect the issue is in the geometric back-projection / 2D-to-3D decomposition step, rather than in the similarity metric or optimization.
Specifically, I am unsure whether:
My implementation of the analytical back-projection equations is correct
The projection geometry (scaling, sign conventions, coordinate frames) is consistent between the two views
Additional geometric constraints or assumptions are required for observability
What I Am Looking For
I would greatly appreciate any insights or suggestions on:
Correct implementation of 2D-to-3D geometric back-projection for dual-view registration
Common pitfalls in coordinate systems, scaling factors, or sign conventions
Whether a cone-beam vs. parallel-beam assumption materially affects the 6D solution
Any recommended validation or sanity checks for this type of pipeline
I have attached the MATLAB code below for reference.
Thank you very much for your time and support.
Reference
[1] Fu, D., & Kuduvalli, G. (2008). A fast, accurate, and automatic 2D–3D image registration for image-guided cranial radiosurgery. Medical Physics, 35(5), 2180–2194. registration, 2d-3d image registration, optimization, iterative joint optimization MATLAB Answers — New Questions

​

For multiple experiments, I’d like to fit a simple process model where 2 poles (tp1, tp2) are the same for all experiments, but gain (Kp), zero (tz) and time delay (td) can be different for each experiment. Is there an elegant way to to this with the System Identification Toolbox? Each experiment comprises 2 time series: one forcing input & one response ouput.For multiple experiments, I’d like to fit a simple process model where 2 poles (tp1, tp2) are the same for all experiments, but gain (Kp), zero (tz) and time delay (td) can be different for each experiment. Is there an elegant way to to this with the System Identification Toolbox? Each experiment comprises 2 time series: one forcing input & one response ouput. For multiple experiments, I’d like to fit a simple process model where 2 poles (tp1, tp2) are the same for all experiments, but gain (Kp), zero (tz) and time delay (td) can be different for each experiment. Is there an elegant way to to this with the System Identification Toolbox? Each experiment comprises 2 time series: one forcing input & one response ouput. system identification, procest MATLAB Answers — New Questions

​

I´ve tried to install Matlab R2025b on Debian 13. I could install it via this commands:
sudo unzip -XK matlab.zip -d matlab_install
cd matlab_install
sudo su
xhost +SI:localuser:root
sudo ./install
This mehod I´ve used for installing Matlab R2024b on Debian 12.
Matlab R2025b should support Debian 13 see here: https://de.mathworks.com/support/requirements/matlab-linux.html
Since no other older Matlab Version supports Debian 13 I had only the choice for Matlab R2025b.

The installation was fine, but when I lunch it from terminal via this command:
matlab
it opens the matlab porgramm looks like a normal start (left at the buttom of the matlab programm it says initializing…), but in a sudden it stops and printing this message:
Unable to communicate with required MathWorks services (error 5201)

For help with this issue, contact support:
https://www.mathworks.com/support/contact_us.html

Unable to launch MVM server: License Error: Licensing shutdown
When I search for an activation file like in the installed folder there isn´t anyone in /urs/local/MATLAB/R2025b/bin/glnxa64.
Has somebody managed to run matlab on Debian 13?
Thank you very much for you help in forward.I´ve tried to install Matlab R2025b on Debian 13. I could install it via this commands:
sudo unzip -XK matlab.zip -d matlab_install
cd matlab_install
sudo su
xhost +SI:localuser:root
sudo ./install
This mehod I´ve used for installing Matlab R2024b on Debian 12.
Matlab R2025b should support Debian 13 see here: https://de.mathworks.com/support/requirements/matlab-linux.html
Since no other older Matlab Version supports Debian 13 I had only the choice for Matlab R2025b.

The installation was fine, but when I lunch it from terminal via this command:
matlab
it opens the matlab porgramm looks like a normal start (left at the buttom of the matlab programm it says initializing…), but in a sudden it stops and printing this message:
Unable to communicate with required MathWorks services (error 5201)

For help with this issue, contact support:
https://www.mathworks.com/support/contact_us.html

Unable to launch MVM server: License Error: Licensing shutdown
When I search for an activation file like in the installed folder there isn´t anyone in /urs/local/MATLAB/R2025b/bin/glnxa64.
Has somebody managed to run matlab on Debian 13?
Thank you very much for you help in forward. I´ve tried to install Matlab R2025b on Debian 13. I could install it via this commands:
sudo unzip -XK matlab.zip -d matlab_install
cd matlab_install
sudo su
xhost +SI:localuser:root
sudo ./install
This mehod I´ve used for installing Matlab R2024b on Debian 12.
Matlab R2025b should support Debian 13 see here: https://de.mathworks.com/support/requirements/matlab-linux.html
Since no other older Matlab Version supports Debian 13 I had only the choice for Matlab R2025b.

The installation was fine, but when I lunch it from terminal via this command:
matlab
it opens the matlab porgramm looks like a normal start (left at the buttom of the matlab programm it says initializing…), but in a sudden it stops and printing this message:
Unable to communicate with required MathWorks services (error 5201)

For help with this issue, contact support:
https://www.mathworks.com/support/contact_us.html

Unable to launch MVM server: License Error: Licensing shutdown
When I search for an activation file like in the installed folder there isn´t anyone in /urs/local/MATLAB/R2025b/bin/glnxa64.
Has somebody managed to run matlab on Debian 13?
Thank you very much for you help in forward. debian13, linux, installation MATLAB Answers — New Questions

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