Federated Learning: example dataset (FMCW 122GHz radars)
 Citation Author(s):
 Submitted by:
 Stefano Savazzi
 Last updated:
 Wed, 08/07/2019  07:53
 DOI:
 10.21227/8yqc1j15
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Database for FMCW THz radars (HR workspace) and sample code for federated learning
Open with (python code):
import scipy.io as sio database = sio.loadmat('data_base_all_sequences_random.mat')
The database contains 5 files:

Data_test_2.mat: dimension 16000 x 512  x_test = database['Data_test_2'] Contains 16000 FFT range measurements (512point FFT of beat signal after DC removal) used for test database with corresponding labels in label_test_2.mat

Data_train_2.mat: dimension 16000 x 512  x_train = database['Data_train_2']Contains 16000 FFT range measurements (512point FFT of beat signal after DC removal) used for training database with corresponding labels in lable_train_2.mat

label_test_2.mat: dimension 16000 x 1  y_test = database['label_test_2'] Contains the true labels for test data (Data_test_2.mat), namely classes (true labels) correspond to integers from 0 to 7: Class 0: human worker at safe distance >3.5m from the radar (safe distance) Class 1: human worker at distance (critical) <0.5m from the corresponding radar Class 2: human worker at distance (critical) 0.5m  1m from the corresponding radar Class 3: human worker at distance (critical) 1m  1.5m from the corresponding radar Class 4: human worker at distance (safe) 1.5m  2m from the corresponding radar Class 5: human worker at distance (safe) 2m  2.5m from the corresponding radar Class 6: human worker at distance (safe) 2.5m  3m from the corresponding radar Class 7: human worker at distance (safe) 3m  3.5m from the corresponding radar

label_train_2.mat: dimension 16000 x 1  y_train = database['label_train_2'] Contains the true labels for train data (Data_train_2.mat), namely classes (true labels) correspond to integers from 0 to 7: Class 0: human worker at safe distance >3.5m from the radar (safe distance) Class 1: human worker at distance (critical) <0.5m from the corresponding radar Class 2: human worker at distance (critical) 0.5m  1m from the corresponding radar Class 3: human worker at distance (critical) 1m  1.5m from the corresponding radar Class 4: human worker at distance (safe) 1.5m  2m from the corresponding radar Class 5: human worker at distance (safe) 2m  2.5m from the corresponding radar Class 6: human worker at distance (safe) 2.5m  3m from the corresponding radar Class 7: human worker at distance (safe) 3m  3.5m from the corresponding radar

permut.mat (1 x 16000) contains the chosen random permutation for data partition among nodes/device and federated learnig simulation (see python code)
Dataset Files
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doi = {10.21227/8yqc1j15},
url = {http://dx.doi.org/10.21227/8yqc1j15},
author = {Stefano Savazzi },
publisher = {IEEE Dataport},
title = {Federated Learning: example dataset (FMCW 122GHz radars)},
year = {2019} }
T1  Federated Learning: example dataset (FMCW 122GHz radars)
AU  Stefano Savazzi
PY  2019
PB  IEEE Dataport
UR  10.21227/8yqc1j15
ER 