Non Invasive Temperature Estimation for RF safety

Input data for these steps will be available soon on Google Drive in a CST_Data Folder. If any of the links are broken in the future, please open an issue.

1. [MATLAB] Data shaping after CST EM/Thermal simulation

• File: nite/MatFolder/TestSlicesSequence.m
• Input: Google Drive links: CST_Data folder
• Output: Mat files for each slices (training and test). Move output files to nite/MatFolder

2. [MATLAB] Data preparation for deep learning training

• File: nite/MatFolder/Data_mass_main.m
• Input: x_Data mat file
• Output: xtrain-4.mat, ytrain-4.mat, xtest-4.mat, ytest-4.mat
• Move output files to nite/PyFolder

3. [Python] Building and running the neural network for training

• File : nite/PyFolder/Train_Test.ipynb
• Input: xtrain-4.mat, ytrain-4.mat, xtest-4.mat, ytest-4.mat
• Output: trained model (trained_model.h5)

4. [MATLAB] Data preparation for deep learning testing

• File: nite/MatFolder/Data_mass_main_prep4test.m
• Input: t_Data mat files [changes with the considered slice]
• Output: NITE_x_test_sl_HUGO4.mat with the variable x_test, NITE_x_test_sl_NELLY4.mat with the variable x_test [changes with the considered slice]
• Move output files to nite/PyFolder

5. [Python] Testing slices with trained model

• File: nite/PyFolder/Training_Testing.ipynb
• Input: NITE_x_test_sl_HUGO4.mat, NITE_x_test_sl_NELLY4.mat [changes with the considered slice]
• Output: Reconstructed temperature maps (NITE_recon_HUGO_4-new.mat, NITE_recon_NELLY_4-new.mat)
• Move output files to nite/MatFolder/

6. [MATLAB] Temperature maps plot and comparison between NITE map and CST map

• Run nite/MatFolder/Data_mass_main.m again.
• File: nite/MatFolder/Data_mass_main_testresvis.m
• Input: NITE_x_test_sl_HUGO4.mat, NITE_x_test_sl_NELLY4.mat [changes with the considered slice]
• Output: Plot and comparison of temperature maps