TinyML for UWB-radar based presence detection

Tiny Machine Learning (TinyML) is a novel research area aiming at designing machine and deep learning models and algorithms able to be executed on tiny devices such as Internet-of-Things units, edge devices or embedded systems. In this paper we introduce, for the first time in the literature, a TinyML solution for presence-detection based on UltrawideBand (UWB) radar, which is a particularly promising radar technology for pervasive systems. To achieve this goal we introduce a novel family of tiny convolutional neural networks for the processing of UWB-radar data characterized by a reduced memory footprint and computational demand so as to satisfy the severe technological constraints of tiny devices. From this technological perspective, UWB-radars are particularly relevant in the presence-detection scenario since they do not acquire sensitive information of users (e.g., images, videos or audio), hence preserving their privacy.The proposed solution has been successfully tested on a public-available benchmark for the indoor presence detection and on a real-world application of in-car presence detection

Multiparametric study of the February-April 2013 paroxysmal phase of Mt. Etna New South-East crater

Between January 2011 and April 2013, Mt. Etna's eruptive activity consisted of episodic intracrater strombolian explosions and paroxysms from Bocca Nuova, Voragine, and the New South-East (NSEC) summit craters, respectively. Eruptions from NSEC consisted of initial increasing strombolian activity and lava flow output, passing to short-lasting lava fountaining. In this study we present seismic, infrasound, radiometric, plume SO2 and HCl fluxes and geodetic data collected by the INGV monitoring system between May 2012 and April 2013. The multiparametric approach enabled characterization of NSEC eruptive activity at both daily and monthly time scales and tracking of magma movement within Mt. Etna's plumbing system. While seismic, infrasound and radiometric signals give insight on the energy and features of the 13 paroxysms fed by NSEC, SO2 and halogen fluxes shed light on the likely mechanisms triggering the eruptive phenomena. GPS data provided clear evidence of pressurization of Mt. Etna's plumbing system from May 2012 to middle February 2013 and depressurization during the February-April 2013 eruptive activity. Taking into account geochemical data, we propose that the paroxysms' sequence represented the climax of a waxing-waning phase of degassing that had started as early as December 2012, and eventually ended in April 2013. Integration of the multidisciplinary observations suggests that the February-April 2013 eruptive activity reflects a phase of release of a volatile-rich batch of magma that had been stored in the shallow volcano plumbing system at least 4 months before, and with the majority of gas released between February and March 2013

Optimization of RPCs read-out panel with electromagnetic simulation

With the upgrade of the RPCs [1]-[2] and the increase of its performances, the study and the optimization of the read-out panel is necessary in order to maintain the signal integrity and to reduce the intrinsic crosstalk. Through Electromagnetic Simulation, performed with CST Studio Suite, new panels design are tested and their crosstalk property are studied. The behavior of different type of panel is shown, in particular a panel with the decoupling strip connected through their characteristic impedance to the ground plane is simulated