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The ash fall-out phenomenonfollowing the explosive activity of volcanoes represents a considerablefactor of risk for people and a serious hazard for air traffic. Researchers at Department of Electronic and Information Engineering of the University of Catania are facing the venture of developinga low-cost smart multisensor system providing a real time and continuous information on the ongoing ash fall-out phenomenonwith particular regards toash granulometryclassification and flow-rate estimation. Moreover, the monitoring system must be selective in respect to volcanic ash against others sediments. Ash granulometry detection exploits a piezoelectric transducer to convert ash impacts into electrical signals. Receiver Operating Characteristic (ROC) analysis has been adopted as a theoretical support to properly implement the classification approach. The proposed idea for ash flow-rate estimation is to measure the time interval required to collect a fixed amount of ash inside an instrumented beaker. Finally, to implement the selectivity task the intrinsic paramagnetic property of ash particles is exploited. The low-cost adopted sensing methodologies as respect to traditional instrumentation, the real time and continuous monitoring, the selectivity as respect to volcanic ash and the multi-parameter estimationare considered main claims and novelties of the methodology proposed in the following. Moreover, the low-cost architecture allows to implement a distributed monitoring network which can guarantee high spatial resolution information on ash fall-out phenomenon. The latter represents a very attractive goal as it is a mandatory information to be usedby models to predict the time-space evolution of ash transportation.

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