Denominazione: Environmental Sensing with Artificial Intelligence
Acronimo: SENS-AI
Tipologia: progetto PRIN 2022 finanziato da MUR- Next Generation EU; Anno inizio 2023
Responsabile scientifico unità Sapienza: Marco Laracca
Ente finanziatore: Missione 4 “Istruzione e Ricerca” - Componente C2 Investimento 1.1
“Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN)” Decreto Direttoriale n. 104 del 02 febbraio 2022 - Avviso pubblico per la presentazione di Progetti di ricerca di Rilevante Interesse Nazionale (PRIN) da finanziare nell’ambito del PNRR
PRIN 2022 2022R5HWJN - CUP MASTER H53D23000520006 - CUP B53D23002850006
Importo finanziato: 66371 €
Partners
- Dipartimento di Ingegneria Astronautica, Elettrica, Energetica, SAPIENZA Università di Roma
- Dipartimento di Ingegneria Elettrica e dell'Informazione "Maurizio Scarano", Università degli Studi di Cassino e del lazio Meridionale
- Dipartimento di Scienze e Tecnologie Chimiche, Università degli Studi di Roma Tor Vergata
Finalità
- The Project has the aim to develop hand-held and compact sensing platforms, modified by CNMs (carbon nanotubes, nanodiamonds and graphene nanoplatelets), in view of exploiting their superior and outstanding sensitivity. Detection and classification will be carried out by means of techniques suitable for in-situ characterization, such as Voltammetry (VA) and Electrical Impedance Spectroscopy (EIS). Novel data processing approaches based on Artificial Intelligence (AI) will be implemented.
- This Project aims at taking a further step towards the development of a smart distributed monitoring system, whose nodes are the sensing platforms developed here, equipped with an AI-based “learning” capability, able to compensate measurements uncertainties, due to fabrication (sensor reproducibility), operating conditions (setup, environment, noise) and ageing.
Risultati attesi
The main objectives of the project are the following:
Obj.1
Assessed techniques for material preparation and platform integration
Assessment of reliable techniques to treat CNMs and integrate them into the sensing platforms. Characterization of materials and modified platforms.
Obj.2
Assessed measurement techniques and sensing principle demonstration.
Assessment of measurement techniques (VA and EIS): design and implementation of setups, definition of the figures of merit. Demonstration of the sensing performance of the platforms.
Obj.3
Assessed AI-based signal processing techniques.
Assessment of reliable signal processing techniques based on AI-algorithms, for robust real-time detection and classification in presence of noise.
Obj.4
Conceptual design of a distributed monitoring system.
Conceptual design of a smart distributed system whose nodes are the proposed sensing platforms: definition of system architectures, identification of miniaturized devices for local measurements and data processing (e.g., AI-edge), protocols, energy management, system-level processing.
Risultati raggiunti