We present a set-up for proton Computed Tomography (pCT), composed of a microstrip silicon tracker and a YAG:Ce calorimeter, able to directly measure the Relative Stopping Power (RSP) maps to be used in hadron therapy. The system, tested with an electron density calibration phantom at the Trento proton Therapy Center, is able to correlate measured and expected RSP with discrepancies less than 1%. Further, proton-CT tomographies of an anthropomorphous head phantom taken with our device, when compared with x-CT images of the same object, evidence a significant reduction of artifacts induced by titanium spinal bone prosthesis and tungsten dental filling.

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<p>The aim of this paper is to discuss the characterisation of a solar energy harvesting system to be integrated in a wireless sensor node, to be deployed on means of transport to pervasively collect measurements of Particulate Matter (PM) concentration in urban areas. The sensor node is based on the use of low-cost PM sensors and exploits LoRaWAN connectivity to remotely transfer the collected data. The node also integrates GPS localisation features, that allow to associate the measured values with the geographical coordinates of the sampling site. In particular, the system is provided with an innovative, small-scale, solar-based powering solution that allows its energy self-sufficiency and then its functioning without the need for a connection to the power grid. Tests concerning the energy production of the solar cell were performed in order to optimise the functioning of the sensor node: satisfactory results were achieved in terms of number of samplings per hour. Finally, field tests were carried out with the integrated environmental monitoring device proving its effectiveness.</p>

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Here, we propose a novel application of a low-cost robust gravimetric system for public place access monitoring purposes. The proposed solution is intended to be exploited in a multi-sensor scenario, where heterogeneous information, coming from different sources (e.g., metal detectors and surveillance cameras), are collected in a central data fusion unit to obtain a more detailed and accurate evaluation of notable events. Specifically, the word “notable” refers essentially to two event categories: the first category is represented by irregular events, corresponding typically to multiple people passing together through a security gate; the second category includes some event subsets, whose notification can be interesting for assistance provision (in the case of people with disabilities), or for statistical analysis. The employed gravimetric sensor, compared to other devices existing in the literature, exhibits a simple scalable robust structure, made up of an array of rigid steel plates, each laid on four load cells. We developed a tailored hardware and software to individually acquire the load cell signals, and to post-process the data to formulate a classification of the notable events. The results are encouraging, showing a remarkable detectability of irregularities (95.3% of all the test cases) and a satisfactory identification of the other event types.