120 citations · 120 across the 2 of their papers we have counts for
2 papers
eess.SP2020
Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices
Saverio De Vito, Girolamo Di Francia, Elena Esposito +3
Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutan…
cs.AI2017★ 120 cited
Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative Machine Learning approaches
S. De Vito, E. Esposito, M. Salvato +4
Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges…