The context here is I am using a MATLAB function which takes a parameter y (x_est) which is shown to be a [4×1] signal above. When ‘make model’ is clicked it gives the error below:

On inspection of the diagnostic report:

This shows that the [4×1] signal (which is initialised as [4×1] by an integrator block) is being interpreted as [3×1]. I am clueless as to why.The context here is I am using a MATLAB function which takes a parameter y (x_est) which is shown to be a [4×1] signal above. When ‘make model’ is clicked it gives the error below:

On inspection of the diagnostic report:

This shows that the [4×1] signal (which is initialised as [4×1] by an integrator block) is being interpreted as [3×1]. I am clueless as to why. The context here is I am using a MATLAB function which takes a parameter y (x_est) which is shown to be a [4×1] signal above. When ‘make model’ is clicked it gives the error below:

On inspection of the diagnostic report:

This shows that the [4×1] signal (which is initialised as [4×1] by an integrator block) is being interpreted as [3×1]. I am clueless as to why. simulink, matlab function MATLAB Answers — New Questions

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On a RHEL7 platform, we are running a shared (synchronous) MATLAB session as a systemd service. We are also running 3 python systemd services that connect to the shared matlab engine using matlab_eng = matlab.engine.connect_matlab("OUR_SESSION_NAME_HERE"). After successfully connecting to the shared matlab-engine, the python service will do some MATLAB processing, then do a matlab_eng.quit() to disconnect from the shared matlab-engine. I have 4 questions that I hope somebody could answer. See below.
Regards Debbie
Is matlab_eng.quit() the correct way to disconnect from the shared matlab-engine?
Can you confirm that each of the 3 python systemd services will have it’s own MATLAB workspace?
After our python services have run awhile, we start seeing trouble connecting to the shared matlab-engine. The stack trace looks similar to as shown below. Why would these connection errors only occationally occur and how can we fix this? We have assumed that each of our python processes can use the shared matlab-engine concurrently, is this not correct?
As a follow-up to question 3, why is _future.result showing up in the trace? We started a synchronous matlab-engine, the documentation mentions future results only with respect to asynchronous matlab-engines.
<class ‘matlab.engine.EngineError’>
Unable to connect to MATLAB session ‘OUR_SESSION_NAME_HERE’.
matlab_eng = matlab.engine.connect_matlab(‘OUR_SESSION_NAME_HERE’)
!! File "/usr/lib/python2.7/site-packages/matlab/engine/__init__.py", line 182, in connect_matlab
eng = future.result()
!! File "/usr/lib/python2.7/site-packages/matlab/engine/futureresult.py", line 67, in result
return self.__future.result(timeout)
!! File "/usr/lib/python2.7/site-packages/matlab/engine/matlabfuture.py", line 87, in result
handle = pythonengine.getMATLAB(self._future)On a RHEL7 platform, we are running a shared (synchronous) MATLAB session as a systemd service. We are also running 3 python systemd services that connect to the shared matlab engine using matlab_eng = matlab.engine.connect_matlab("OUR_SESSION_NAME_HERE"). After successfully connecting to the shared matlab-engine, the python service will do some MATLAB processing, then do a matlab_eng.quit() to disconnect from the shared matlab-engine. I have 4 questions that I hope somebody could answer. See below.
Regards Debbie
Is matlab_eng.quit() the correct way to disconnect from the shared matlab-engine?
Can you confirm that each of the 3 python systemd services will have it’s own MATLAB workspace?
After our python services have run awhile, we start seeing trouble connecting to the shared matlab-engine. The stack trace looks similar to as shown below. Why would these connection errors only occationally occur and how can we fix this? We have assumed that each of our python processes can use the shared matlab-engine concurrently, is this not correct?
As a follow-up to question 3, why is _future.result showing up in the trace? We started a synchronous matlab-engine, the documentation mentions future results only with respect to asynchronous matlab-engines.
<class ‘matlab.engine.EngineError’>
Unable to connect to MATLAB session ‘OUR_SESSION_NAME_HERE’.
matlab_eng = matlab.engine.connect_matlab(‘OUR_SESSION_NAME_HERE’)
!! File "/usr/lib/python2.7/site-packages/matlab/engine/__init__.py", line 182, in connect_matlab
eng = future.result()
!! File "/usr/lib/python2.7/site-packages/matlab/engine/futureresult.py", line 67, in result
return self.__future.result(timeout)
!! File "/usr/lib/python2.7/site-packages/matlab/engine/matlabfuture.py", line 87, in result
handle = pythonengine.getMATLAB(self._future) On a RHEL7 platform, we are running a shared (synchronous) MATLAB session as a systemd service. We are also running 3 python systemd services that connect to the shared matlab engine using matlab_eng = matlab.engine.connect_matlab("OUR_SESSION_NAME_HERE"). After successfully connecting to the shared matlab-engine, the python service will do some MATLAB processing, then do a matlab_eng.quit() to disconnect from the shared matlab-engine. I have 4 questions that I hope somebody could answer. See below.
Regards Debbie
Is matlab_eng.quit() the correct way to disconnect from the shared matlab-engine?
Can you confirm that each of the 3 python systemd services will have it’s own MATLAB workspace?
After our python services have run awhile, we start seeing trouble connecting to the shared matlab-engine. The stack trace looks similar to as shown below. Why would these connection errors only occationally occur and how can we fix this? We have assumed that each of our python processes can use the shared matlab-engine concurrently, is this not correct?
As a follow-up to question 3, why is _future.result showing up in the trace? We started a synchronous matlab-engine, the documentation mentions future results only with respect to asynchronous matlab-engines.
<class ‘matlab.engine.EngineError’>
Unable to connect to MATLAB session ‘OUR_SESSION_NAME_HERE’.
matlab_eng = matlab.engine.connect_matlab(‘OUR_SESSION_NAME_HERE’)
!! File "/usr/lib/python2.7/site-packages/matlab/engine/__init__.py", line 182, in connect_matlab
eng = future.result()
!! File "/usr/lib/python2.7/site-packages/matlab/engine/futureresult.py", line 67, in result
return self.__future.result(timeout)
!! File "/usr/lib/python2.7/site-packages/matlab/engine/matlabfuture.py", line 87, in result
handle = pythonengine.getMATLAB(self._future) matlab-engine python MATLAB Answers — New Questions

​

Hello Everyone,
I have a problem that I need to use opencv_contrib for aruco marker detection in simulink, but opencv importer doesn’t seem to be able to make a simulink block for opencv_contrib.
I would like to know if there are any successful imports using opencv importer for opencv_contrib, and if not are there any other ways to use opencv_contrib in simulink. Although I can use opencv functions in matlab using the mex interface.Hello Everyone,
I have a problem that I need to use opencv_contrib for aruco marker detection in simulink, but opencv importer doesn’t seem to be able to make a simulink block for opencv_contrib.
I would like to know if there are any successful imports using opencv importer for opencv_contrib, and if not are there any other ways to use opencv_contrib in simulink. Although I can use opencv functions in matlab using the mex interface. Hello Everyone,
I have a problem that I need to use opencv_contrib for aruco marker detection in simulink, but opencv importer doesn’t seem to be able to make a simulink block for opencv_contrib.
I would like to know if there are any successful imports using opencv importer for opencv_contrib, and if not are there any other ways to use opencv_contrib in simulink. Although I can use opencv functions in matlab using the mex interface. opencv, aruco, simulink MATLAB Answers — New Questions

​

classdef ChannelAttentionLayer < nnet.layer.Layer
properties
% Reduction ratio used in the channel attention mechanism
ReductionRatio
end

properties (Learnable)
% Layer learnable parameters
Weights1
Bias1
Weights2
Bias2
end

methods
function layer = ChannelAttentionLayer(reduction_ratio, input_channels, name)
% Constructor for ChannelAttentionLayer
layer.Name = name;
layer.ReductionRatio = reduction_ratio;

% Calculate reduced channels based on reduction ratio
reduced_channels = max(1, round(input_channels / reduction_ratio));

% Initialize weights and biases
layer.Weights1 = randn([1, 1, input_channels, reduced_channels], ‘single’);
layer.Bias1 = zeros([1, 1, reduced_channels], ‘single’);
layer.Weights2 = randn([1, 1, reduced_channels, input_channels], ‘single’);
layer.Bias2 = zeros([1, 1, input_channels], ‘single’);
end

function Z = forward(layer, X)
% Forward pass for training mode
% Ensure X is a dlarray
X = dlarray(X);

% Get input size
[H, W, C] = size(X);

% Global Average Pooling (GAP)
avg_pool = mean(X, [1, 2]); % Mean over height and width
avg_pool = reshape(avg_pool, [1, 1, C]); % Reshape to [1, 1, Channels]

% Global Max Pooling (GMP)
max_pool = max(X, [], [1, 2]); % Max over height and width
max_pool = reshape(max_pool, [1, 1, C]); % Reshape to [1, 1, Channels]

% First fully connected layer applied to both avg and max pooled outputs
avg_out = fullyconnect(avg_pool, layer.Weights1, layer.Bias1, C, layer.ReductionRatio);
max_out = fullyconnect(max_pool, layer.Weights1, layer.Bias1, C, layer.ReductionRatio);

% Apply ReLU
avg_out = relu(avg_out);
max_out = relu(max_out);

% Second fully connected layer
avg_out = fullyconnect(avg_out, layer.Weights2, layer.Bias2, layer.ReductionRatio, C);
max_out = fullyconnect(max_out, layer.Weights2, layer.Bias2, layer.ReductionRatio, C);

% Combine average and max pooled outputs
Z = avg_out + max_out;

% Apply sigmoid to get attention weights
Z = sigmoid(Z);

% Reshape attention map and multiply with input
Z = reshape(Z, [1, 1, C]);
Z = X .* Z;

% Ensure Z is unformatted
Z = dlarray(Z);
end

function Z = predict(layer, X)
% Predict pass for inference mode
Z = forward(layer, X);
end
end
end

% Fully connected operation for 1×1 conv
function out = fullyconnect(input, weights, bias, input_channels, output_channels)
% Ensure the number of input channels matches the weights’ channels
[H, W, C_in] = size(input);
[~, ~, C, ~] = size(weights);

if C_in ~= C
error(‘Number of channels in input and weights do not match.’);
end

% Flatten input dimensions
input_reshaped = reshape(input, [], C_in); % Flatten spatial dimensions

% Perform matrix multiplication and add bias
weights_reshaped = reshape(weights, [C_in, output_channels]);
out = input_reshaped * weights_reshaped + reshape(bias, [1, output_channels]);

% Reshape back to original dimensions
out = reshape(out, [1, 1, output_channels]);
endclassdef ChannelAttentionLayer < nnet.layer.Layer
properties
% Reduction ratio used in the channel attention mechanism
ReductionRatio
end

properties (Learnable)
% Layer learnable parameters
Weights1
Bias1
Weights2
Bias2
end

methods
function layer = ChannelAttentionLayer(reduction_ratio, input_channels, name)
% Constructor for ChannelAttentionLayer
layer.Name = name;
layer.ReductionRatio = reduction_ratio;

% Calculate reduced channels based on reduction ratio
reduced_channels = max(1, round(input_channels / reduction_ratio));

% Initialize weights and biases
layer.Weights1 = randn([1, 1, input_channels, reduced_channels], ‘single’);
layer.Bias1 = zeros([1, 1, reduced_channels], ‘single’);
layer.Weights2 = randn([1, 1, reduced_channels, input_channels], ‘single’);
layer.Bias2 = zeros([1, 1, input_channels], ‘single’);
end

function Z = forward(layer, X)
% Forward pass for training mode
% Ensure X is a dlarray
X = dlarray(X);

% Get input size
[H, W, C] = size(X);

% Global Average Pooling (GAP)
avg_pool = mean(X, [1, 2]); % Mean over height and width
avg_pool = reshape(avg_pool, [1, 1, C]); % Reshape to [1, 1, Channels]

% Global Max Pooling (GMP)
max_pool = max(X, [], [1, 2]); % Max over height and width
max_pool = reshape(max_pool, [1, 1, C]); % Reshape to [1, 1, Channels]

% First fully connected layer applied to both avg and max pooled outputs
avg_out = fullyconnect(avg_pool, layer.Weights1, layer.Bias1, C, layer.ReductionRatio);
max_out = fullyconnect(max_pool, layer.Weights1, layer.Bias1, C, layer.ReductionRatio);

% Apply ReLU
avg_out = relu(avg_out);
max_out = relu(max_out);

% Second fully connected layer
avg_out = fullyconnect(avg_out, layer.Weights2, layer.Bias2, layer.ReductionRatio, C);
max_out = fullyconnect(max_out, layer.Weights2, layer.Bias2, layer.ReductionRatio, C);

% Combine average and max pooled outputs
Z = avg_out + max_out;

% Apply sigmoid to get attention weights
Z = sigmoid(Z);

% Reshape attention map and multiply with input
Z = reshape(Z, [1, 1, C]);
Z = X .* Z;

% Ensure Z is unformatted
Z = dlarray(Z);
end

function Z = predict(layer, X)
% Predict pass for inference mode
Z = forward(layer, X);
end
end
end

% Fully connected operation for 1×1 conv
function out = fullyconnect(input, weights, bias, input_channels, output_channels)
% Ensure the number of input channels matches the weights’ channels
[H, W, C_in] = size(input);
[~, ~, C, ~] = size(weights);

if C_in ~= C
error(‘Number of channels in input and weights do not match.’);
end

% Flatten input dimensions
input_reshaped = reshape(input, [], C_in); % Flatten spatial dimensions

% Perform matrix multiplication and add bias
weights_reshaped = reshape(weights, [C_in, output_channels]);
out = input_reshaped * weights_reshaped + reshape(bias, [1, output_channels]);

% Reshape back to original dimensions
out = reshape(out, [1, 1, output_channels]);
end classdef ChannelAttentionLayer < nnet.layer.Layer
properties
% Reduction ratio used in the channel attention mechanism
ReductionRatio
end

properties (Learnable)
% Layer learnable parameters
Weights1
Bias1
Weights2
Bias2
end

methods
function layer = ChannelAttentionLayer(reduction_ratio, input_channels, name)
% Constructor for ChannelAttentionLayer
layer.Name = name;
layer.ReductionRatio = reduction_ratio;

% Calculate reduced channels based on reduction ratio
reduced_channels = max(1, round(input_channels / reduction_ratio));

% Initialize weights and biases
layer.Weights1 = randn([1, 1, input_channels, reduced_channels], ‘single’);
layer.Bias1 = zeros([1, 1, reduced_channels], ‘single’);
layer.Weights2 = randn([1, 1, reduced_channels, input_channels], ‘single’);
layer.Bias2 = zeros([1, 1, input_channels], ‘single’);
end

function Z = forward(layer, X)
% Forward pass for training mode
% Ensure X is a dlarray
X = dlarray(X);

% Get input size
[H, W, C] = size(X);

% Global Average Pooling (GAP)
avg_pool = mean(X, [1, 2]); % Mean over height and width
avg_pool = reshape(avg_pool, [1, 1, C]); % Reshape to [1, 1, Channels]

% Global Max Pooling (GMP)
max_pool = max(X, [], [1, 2]); % Max over height and width
max_pool = reshape(max_pool, [1, 1, C]); % Reshape to [1, 1, Channels]

% First fully connected layer applied to both avg and max pooled outputs
avg_out = fullyconnect(avg_pool, layer.Weights1, layer.Bias1, C, layer.ReductionRatio);
max_out = fullyconnect(max_pool, layer.Weights1, layer.Bias1, C, layer.ReductionRatio);

% Apply ReLU
avg_out = relu(avg_out);
max_out = relu(max_out);

% Second fully connected layer
avg_out = fullyconnect(avg_out, layer.Weights2, layer.Bias2, layer.ReductionRatio, C);
max_out = fullyconnect(max_out, layer.Weights2, layer.Bias2, layer.ReductionRatio, C);

% Combine average and max pooled outputs
Z = avg_out + max_out;

% Apply sigmoid to get attention weights
Z = sigmoid(Z);

% Reshape attention map and multiply with input
Z = reshape(Z, [1, 1, C]);
Z = X .* Z;

% Ensure Z is unformatted
Z = dlarray(Z);
end

function Z = predict(layer, X)
% Predict pass for inference mode
Z = forward(layer, X);
end
end
end

% Fully connected operation for 1×1 conv
function out = fullyconnect(input, weights, bias, input_channels, output_channels)
% Ensure the number of input channels matches the weights’ channels
[H, W, C_in] = size(input);
[~, ~, C, ~] = size(weights);

if C_in ~= C
error(‘Number of channels in input and weights do not match.’);
end

% Flatten input dimensions
input_reshaped = reshape(input, [], C_in); % Flatten spatial dimensions

% Perform matrix multiplication and add bias
weights_reshaped = reshape(weights, [C_in, output_channels]);
out = input_reshaped * weights_reshaped + reshape(bias, [1, output_channels]);

% Reshape back to original dimensions
out = reshape(out, [1, 1, output_channels]);
end matlab MATLAB Answers — New Questions

​