Indoor air monitoring represents one of the most challenging global aims for the protection of people health and safety. Lots of efforts, either in the academic or industrial field, are addressed to the development and integration of sensing technologies and Artificial Intelligence techniques for the realization of a smart system capable to detect and recognize gases. In this work, we propose a first prototype of an integrated system involving both sensing and Artificial Intelligence technologies, developed as a two layer architecture. The Hardware Layer is the SENSIPLUS(R) microchip, a smart sensor IoT ready node endowed with on board sensors and implementing novel measuring technique based on current/voltage correlations. The Software Layer is the SENSIPLUS® Deep Machine, a Deep Learning module based on a Long Short-Term Memory neural network, particularly suitable for times series analysis. The paper presents preliminary experiments for the recognition of three distinct gases with respect to air that demonstrates the proposed system effectiveness.
A Novel Integrated Smart System for Indoor Air Monitoring and Gas Recognition
Cerro, G.;Ferrigno, L.;Molinara, M.;Tortorella, F.;
2018-01-01
Abstract
Indoor air monitoring represents one of the most challenging global aims for the protection of people health and safety. Lots of efforts, either in the academic or industrial field, are addressed to the development and integration of sensing technologies and Artificial Intelligence techniques for the realization of a smart system capable to detect and recognize gases. In this work, we propose a first prototype of an integrated system involving both sensing and Artificial Intelligence technologies, developed as a two layer architecture. The Hardware Layer is the SENSIPLUS(R) microchip, a smart sensor IoT ready node endowed with on board sensors and implementing novel measuring technique based on current/voltage correlations. The Software Layer is the SENSIPLUS® Deep Machine, a Deep Learning module based on a Long Short-Term Memory neural network, particularly suitable for times series analysis. The paper presents preliminary experiments for the recognition of three distinct gases with respect to air that demonstrates the proposed system effectiveness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
