Nowadays, people and environment health and safety are increasingly threatened by human activities. Industrial processes, or more in general, air and water contamination are worsening the planet life conditions. Increasing people awareness and improving the regulation system play a key role to face and tackle this global phenomenon. To this aim, reliable and low-cost technologies for a pervasive and ubiquitous environmental monitoring are really needed, especially for developing and poorer countries. This Ph.D. thesis has been focused on the development of the SENSIPLUS Embedded System for health and safety applications. It has been designed considering low-cost, miniaturization and low--power as main requirements for the whole developing and experimental phases. More in detail, it is designed according to Internet of Things and Edge Computing paradigms, integrating sensing, elaboration and communication capabilities. Sensing is mainly based on the SENSIPLUS chip, which is a micro analytical tool integrating heterogeneous sensors typologies. As for the elaboration and communication, embedded software based on statistical and artificial intelligence solutions is adopted for data analysis and technologies as Wi--Fi, USB and Bluetooth Low Energy have been integrated to transmit processing results. The embedded software has been tested on low--resources Micro Controller Units as the ESP32, STM32 and CC2541 manufactured by Espressif, STMicroelectronics and Texas Instrument, respectively. Three different applications have been addressed in this thesis: state of health monitoring of activated carbon filters and biofilters; contaminants detection and recognition in air; contaminants detection and recognition in water. Both the hardware and software components have been developed and customized for these three applications and real scenarios experimental activities have been conducted to test and validate the proposed solutions. Positive results have been obtained providing the validation of the developed technology for the addressed applications. The activities carried out for this thesis have different European research projects (Horizon 2020 and European Defence Agency) as background and reference. Furthermore, multiple collaborations with public and private research centers have characterized the design, developing and experimental activities.

A Learning Sensors Platform for Health and Safety Applications / Ferdinandi, Marco. - (2020 Mar 18).

A Learning Sensors Platform for Health and Safety Applications

FERDINANDI, Marco
2020-03-18

Abstract

Nowadays, people and environment health and safety are increasingly threatened by human activities. Industrial processes, or more in general, air and water contamination are worsening the planet life conditions. Increasing people awareness and improving the regulation system play a key role to face and tackle this global phenomenon. To this aim, reliable and low-cost technologies for a pervasive and ubiquitous environmental monitoring are really needed, especially for developing and poorer countries. This Ph.D. thesis has been focused on the development of the SENSIPLUS Embedded System for health and safety applications. It has been designed considering low-cost, miniaturization and low--power as main requirements for the whole developing and experimental phases. More in detail, it is designed according to Internet of Things and Edge Computing paradigms, integrating sensing, elaboration and communication capabilities. Sensing is mainly based on the SENSIPLUS chip, which is a micro analytical tool integrating heterogeneous sensors typologies. As for the elaboration and communication, embedded software based on statistical and artificial intelligence solutions is adopted for data analysis and technologies as Wi--Fi, USB and Bluetooth Low Energy have been integrated to transmit processing results. The embedded software has been tested on low--resources Micro Controller Units as the ESP32, STM32 and CC2541 manufactured by Espressif, STMicroelectronics and Texas Instrument, respectively. Three different applications have been addressed in this thesis: state of health monitoring of activated carbon filters and biofilters; contaminants detection and recognition in air; contaminants detection and recognition in water. Both the hardware and software components have been developed and customized for these three applications and real scenarios experimental activities have been conducted to test and validate the proposed solutions. Positive results have been obtained providing the validation of the developed technology for the addressed applications. The activities carried out for this thesis have different European research projects (Horizon 2020 and European Defence Agency) as background and reference. Furthermore, multiple collaborations with public and private research centers have characterized the design, developing and experimental activities.
18-mar-2020
sensors; IoT; smart sensors; electrical measurements; metrological characterization; artificial intelligence; machine learning; deep learning; air monitoring; water monitoring; edge computing;
A Learning Sensors Platform for Health and Safety Applications / Ferdinandi, Marco. - (2020 Mar 18).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/74754
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