Federated Learning: example dataset (FMCW 122GHz radars)

Federated Learning: example dataset (FMCW 122GHz radars)

Citation Author(s):
Stefano
Savazzi
CNR-IEIIT
Submitted by:
Stefano Savazzi
Last updated:
Wed, 08/07/2019 - 07:53
DOI:
10.21227/8yqc-1j15
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Abstract: 

Database for FMCW THz radars (HR workspace) and sample code for federated learning 

Instructions: 

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 (512-point 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 (512-point 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)

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[1] Stefano Savazzi, "Federated Learning: example dataset (FMCW 122GHz radars)", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/8yqc-1j15. Accessed: Aug. 23, 2019.
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doi = {10.21227/8yqc-1j15},
url = {http://dx.doi.org/10.21227/8yqc-1j15},
author = {Stefano Savazzi },
publisher = {IEEE Dataport},
title = {Federated Learning: example dataset (FMCW 122GHz radars)},
year = {2019} }
TY - DATA
T1 - Federated Learning: example dataset (FMCW 122GHz radars)
AU - Stefano Savazzi
PY - 2019
PB - IEEE Dataport
UR - 10.21227/8yqc-1j15
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Stefano Savazzi. (2019). Federated Learning: example dataset (FMCW 122GHz radars). IEEE Dataport. http://dx.doi.org/10.21227/8yqc-1j15
Stefano Savazzi, 2019. Federated Learning: example dataset (FMCW 122GHz radars). Available at: http://dx.doi.org/10.21227/8yqc-1j15.
Stefano Savazzi. (2019). "Federated Learning: example dataset (FMCW 122GHz radars)." Web.
1. Stefano Savazzi. Federated Learning: example dataset (FMCW 122GHz radars) [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/8yqc-1j15
Stefano Savazzi. "Federated Learning: example dataset (FMCW 122GHz radars)." doi: 10.21227/8yqc-1j15