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Calibrating chemical multisensory devices for real world applications:
  An in-depth comparison of quantitative Machine Learning approaches

Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative Machine Learning approaches

30 August 2017
S. D. Vito
E. Esposito
M. Salvato
O. Popoola
F. Formisano
Roderic L. Jones
G. Francia
ArXiv (abs)PDFHTML

Papers citing "Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative Machine Learning approaches"

3 / 3 papers shown
Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost
  Sensors
Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors
Pau Ferrer-Cid
Julio Garcia-Calvete
Aina Main-Nadal
Zhencheng Ye
Jose M. Barcelo-Ordinas
J. García-Vidal
59
14
0
14 Dec 2021
MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost
  Air Monitoring Sensors
MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors
Haomin Yu
Yangli-ao Geng
Yingjun Zhang
Qingyong Li
Jiayu Zhou
76
2
0
10 May 2021
Adaptive machine learning strategies for network calibration of IoT
  smart air quality monitoring devices
Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devicesPattern Recognition Letters (Pattern Recognit. Lett.), 2020
S. D. Vito
G. Francia
E. Esposito
S. Ferlito
F. Formisano
E. Massera
33
52
0
24 Mar 2020
1
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