Ricerc@Sapienza

The new procedure of the architectural survey sets the "numerical model" or "model made by points" as central structure. This structure is able to describe precisely the architectural object in its own morphology. From the numerical model we can process the "mesh model" or "surface model", which produces a careful description of the skin of the architecture; this last model can be subsequently textured by color images.

Has now become more important than ever to guarantee an always present connectivity to users, especially in emergency scenarios. However, in case of a disaster, network infrastructures are often damaged, with consequent connectivity disruption, isolating users when are more in need for information and help. Drones may supply with a recovery network, thanks to their capabilities to provide network connectivity on the fly. However, users typically need special devices or applications to reach these networks, reducing their applicability and adoption.

We consider the problem of assigning tasks and related trajectories to a fleet of drones, in critical scenarios
requiring early anomaly discovery and intervention. Drones visit target points in consecutive trips, with recharging and data

Abstract—Fleets of cooperative drones are a powerful tool in monitoring critical scenarios requiring early anomaly discovery and
intervention. Due to limited energy availability and application requirements, drones may visit target points in consecutive trips, with
recharging and data offloading in between. To capture timeliness of intervention and prioritize early coverage, we propose the new
notion of Weighted Progressive Coverage, which is based on the definition of time dependent weights. Weighted progressive coverage

Nowadays the proliferation of small unmanned aerial systems or vehicles (UAS/
Vs), formerly known as drones, coupled with an increasing interest in tools for environmental
monitoring, have led to an exponential use of these unmanned aerial
platforms for many applications in the most diverse fields of science. In particular,
ecologists require data collected at appropriate spatial and temporal resolutions to
describe ecological processes. For these reasons, we are witnessing the proliferation of

This paper presents results of an experimental trial aimed at detecting a car and a flying drone, passively, by exploiting transmissions of opportunity from a staring Holographic radar. Results show that targets could be successfully detected using a low-cost passive receiver prototype. This may be considered a first step towards a radar network that could provide more comprehensive coverage, over a wider area, against a broader set of target types and without requiring additional spectral resources.

The focus of this paper is the evaluation of the mono-static Radar Cross Section (RCS) of a commercial Drone (IRIS) through both simulation and measurements in the 1-4 GHz frequency range. In particular, for the simulations an approximate model of the drone geometry and its material complex permittivity was carried out by means of an electromagnetic CAD. Most importantly, measurements have been performed in an anechoic chamber equipped with a network analyzer and a horn antenna. The obtained results show RCS values between-18 dB and-1 dB in the 1-4 GHz frequency range.

This article addresses the problem of delay mitigation for video streaming applications in congested cellular macro-cells by using a mobile micro-cell mounted on an Unmanned Aerial Vehicle (UAV). Small-scale UAVs are at a mature stage of development and can carry lightweight commercial micro-cells with small form factor. The mobile micro-cell is used to offload users from a congested macro-cell to optimize the bandwidth usage of video streaming applications.

Timely weed mapping in crop post-emergence situations is a challenging task required for developing precision weed management solutions. It is necessary to discriminate the crop from the weeds and, if possible, to distinguish different weed species. The ability to map weeds using hyperspectral images acquired from an unmanned airborne vehicle (UAV) over a maize field was evaluated by comparing different classification strategies. The results were mainly affected by the variability in crop and weed spectral signatures.