Nowadays water monitoring represents one of the most challenging global aims for the protection of people and environment health. In this paper we propose the application of an integrated system for the detection and recognition of contaminants in water. It is based on a two layer architecture: a sensing layer based on SENSIPLUS chip, and a data collection and classification layer, hereafter referred as SENSIPLUS Deep Machine (SDM). The SDM includes: a Micro Controller Unit (MCU), an optional host controller (e.g. laptop, smartphone, etc.) and different software components for data communication, analysis, and classification/regression based on machine learning techniques. Although the SDM classification/regression module can be potentially developed with any machine learning solution, in this paper we adopted an Artificial Neural Network with only one hidden layer to have a lightweight solution suitable to run (for inference) on ultra low power MCU. Aiming at further minimizing the network complexity, two alternative training sessions have been pursued: the first one using raw sensors' data and the second one applying a feature space dimensionality reduction through the Principal Component Analysis technique. Comparable and positive results (higher than 82% as average accuracy) have been obtained, confirming the validity and potentiality of the proposed system.
A novel smart system for contaminants detection and recognition in water
Ferdinandi M.;Molinara M.
;Cerro G.;Ferrigno L.;Claudio Marrocco;Bria A.;Bourelly C.;
2019-01-01
Abstract
Nowadays water monitoring represents one of the most challenging global aims for the protection of people and environment health. In this paper we propose the application of an integrated system for the detection and recognition of contaminants in water. It is based on a two layer architecture: a sensing layer based on SENSIPLUS chip, and a data collection and classification layer, hereafter referred as SENSIPLUS Deep Machine (SDM). The SDM includes: a Micro Controller Unit (MCU), an optional host controller (e.g. laptop, smartphone, etc.) and different software components for data communication, analysis, and classification/regression based on machine learning techniques. Although the SDM classification/regression module can be potentially developed with any machine learning solution, in this paper we adopted an Artificial Neural Network with only one hidden layer to have a lightweight solution suitable to run (for inference) on ultra low power MCU. Aiming at further minimizing the network complexity, two alternative training sessions have been pursued: the first one using raw sensors' data and the second one applying a feature space dimensionality reduction through the Principal Component Analysis technique. Comparable and positive results (higher than 82% as average accuracy) have been obtained, confirming the validity and potentiality of the proposed system.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
