In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-the-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $\alpha $ - $\beta $ filter, features extraction, and finally classification of targets by means of a $k$ -nearest neighbor ( $k$ -NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks).

The authors propose a simple model for the operation of MOSFETs in both weak and strong inversion. The proposed model shows better agreement to experimental results than previous models in the subthreshold and threshold regions, and is well suited for use in circuit simulation programs; the authors have implemented it in MSINC and SPICE programs, and simulation results are compared to experimental data for a micropower amplifier

This article compares different rainfall observing techniques available in the City of Genoa (Italy): the Ligurian regional tipping-bucket rain gauge (TBRG) network and the Monte Settepani long-range weather radar (WR) operated by the Ligurian Regional Environmental Protection Agency (ARPAL) and Smart Rainfall System (SRS), a network of microwave sensors for satellite down-links developed by the University of Genoa and Artys Srl. SRS and WR are the two indirect monitoring systems considered in this analysis. The respective performance of the rainfall measurements was assessed by comparison with the reference observations made in situ by seven TBRG stations selected within the boundary of the City of Genoa. The analysis was performed by considering the main precipitation events that occurred between January 2017 and December 2018. The results show an agreement between the precision of the 10-min rainfall intensity (RI) measurements, quantified by the root mean square errors (RMSEs), made by the WR and the SRS system for the seven sites. Similar performance has been also observed when comparing the peak of RI and the total rain accumulation with the reference TBRG values for the selected precipitation events. An assessment of the WR and SRS accuracy in providing the 2-D RI fields was also performed by computing the map of the RMSE differences over the City of Genoa territory. It is shown that the SRS RI maps are generally associated with a lower level of RMSE with respect to the WR in the areas that are characterized by a higher density of sensors.

A prototype of a radar system for classification of multiple targets passing through a road gate is presented in this paper. It allows to identify different types of targets, i.e., pedestrians, motorcycles, cars, and trucks. The developed system is based on a low-cost 24 GHz off-the-shelf FMCW radar, combined with an embedded Raspberry Pi PC for data acquisition and transmission to a remote processing PC, which take care of detection and classification. The processing chain relies upon a tracking algorithm to follow the targets during traversal, combined with a classification scheme based on support vector machines. The approach has been validated with experimental data acquired in different scenarios, showing good identification capabilities.

This paper considers the formulation of the variational model (VM) of autonomous circuits (oscillators) working in periodic steady-state conditions. The shooting method, which is largely used to compute the solution in the time domain when the VM is forced by a small-signal perturbation, is studied. The proposed analytical approach can be exploited to improve accuracy in the simulation of the effects of noise sources. In particular, we justify from an analytical standpoint the adoption of a suitable periodicity constraint in the shooting method. We exploit the properties of block circulant matrices that naturally arise in the description of the problem. We prove that the frequency of the small-signal perturbation must be different from that of the unperturbed oscillator to avoid inaccuracy of the shooting method due to the existence of singularities in the VM formulation, and derive a method that allows us to get closer to the singularity.