a time signal use resnet denoise in matlab

To denoise a time series signal using ResNet in Matlab, follow the steps below:

Load the signal into Matlab using the load function.


main.m
load('signal.mat');
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Define the ResNet architecture. A ResNet can be defined using the resnet function in Matlab's deep learning toolbox.


main.m
layers = [
    imageInputLayer([numFeatures 1 1])
    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    additionLayer(2)
    reluLayer
    convolution2dLayer(3,numFilters,'Padding','same')
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
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Create an augmentedImageDatastore to prepare the training data.


main.m
imds = augmentedImageDatastore(inputSize,trainDatastore);
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Train the ResNet using the trainNetwork function. Here, we use a Mean Square Error (MSE) loss function and the stochastic gradient descent (SGD) optimizer.


main.m
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.1, ...
    'MaxEpochs',10, ...
    'MiniBatchSize',64, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false);

resnet = trainNetwork(imds,layers,options);
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Apply the trained ResNet to the time series signal to denoise it.


main.m
yDenoised = predict(resnet, yNoisy);
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Here, yNoisy is the noisy time series signal and yDenoised is the denoised time series signal.

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