a nmr signal use resnet to denoise in matlab

To denoise an NMR signal using ResNet in Matlab, you can follow these steps:

1. Load the NMR signal data in Matlab using the load function.

main.m
data = load('nmr_signal.mat');
nmr_signal = data.nmr_signal;
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2. Preprocess the signal data by normalizing the values.

main.m
nmr_signal = nmr_signal/max(nmr_signal);
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3. Split the signal into training and testing sets.

main.m
training_signal = nmr_signal(1:5000);
testing_signal = nmr_signal(5001:end);
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4. Create the ResNet model using the resnet50 function in Matlab's Deep Learning Toolbox.

main.m
resnet = resnet50;
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5. Define the options for the training process, including the learning rate, mini-batch size, and number of epochs.

main.m
options = trainingOptions('adam', ...
    'LearnRateSchedule','piecewise',...
    'LearnRateDropFactor',0.2,...
    'LearnRateDropPeriod',5,...
    'MiniBatchSize',32,...
    'MaxEpochs',10,...
    'InitialLearnRate',1e-4,...
    'Plots','training-progress');
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6. Create a data set for training the ResNet model using the prepareData function.

main.m
training_data = prepareData(training_signal);
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7. Train the ResNet model using the trainNetwork function with the training data and options.

main.m
resnet = trainNetwork(training_data, resnet, options);
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8. Use the trained ResNet model to denoise the testing signal by passing it through the network.

main.m
denoised_signal = predict(resnet, testing_signal);
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9. Compare the original signal and the denoised signal using a plot.

Here is an example of the prepareData function:

main.m
function data = prepareData(signal)
data = [];
for i = 1:length(signal)-9
    data = cat(4, data, signal(i:i+9)); 
end
end
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This function takes a signal vector as input and outputs a data set with the last dimension equal to 10, which represents the 10-sample window used to denoise the signal.