the nmr sequence wavelet coefficients are input as cnn-resnet, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet output for denoising in javascript

To use wavelet coefficients as inputs to a CNN-ResNet for NMR sequence denoising in JavaScript, follow these steps:

1. Load the NMR sequence data and convert it into wavelet coefficients using a JavaScript library like "Wavelet Analysis and Synthesis Library" (WASL).

2. Preprocess the input wavelet coefficients to normalize the data and make it more suitable for consumption by the CNN-ResNet.

3. Build a CNN-ResNet model in JavaScript using a tool such as TensorFlow.js. This model should take the preprocessed wavelet coefficients as input and output the corresponding denoised wavelet coefficients.

4. Train the CNN-ResNet model with NMR sequence data sets containing both noisy and clean wavelet coefficients.

5. Use the trained model to denoise new NMR sequence data sets by feeding the noisy wavelet coefficients into the CNN-ResNet and taking the denoised coefficients as output.

Here is a code snippet to demonstrate how Python code for loading and preprocessing the NMR sequence data can be used in JavaScript with the TensorFlow.js library:

index.tsx
// Load the NMR sequence data into a variable
const nmrData = loadNMRData();

// Use a JavaScript library like WASL to convert the data into wavelet coefficients and preprocess them
const waveletData = preprocessWavelets(nmrData);

// Build the CNN-ResNet model using TensorFlow.js
const model = buildCNNResNetModel();

// Train the model with noisy and clean wavelet coefficient data sets
model.train(trainWavelets, trainClean, ...);

// Use the model to denoise new NMR sequence data sets
const denoisedWavelets = model.predict(noisyWavelets);
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Note that this code is just an example and may need to be adapted to your specific use case.