Flying mobile edge computing towards 5G and beyond: an overview on current use cases and challenges

The increasing computational capacity of multiple devices, the advent of complex applications, and data generation create new challenges of scalability, ubiquity, and seamless services to meet the most diverse network demands and requirements, such as reliability, latency, battery lifetime. For this reason, the 5th Generation (5G) network comes to mitigate the most diverse challenges inherent to the current dynamic mobile networks and their increasing data rates. Unmanned Aerial Vehicles (UAVs) have also been considered as communication relays or mobile base stations to assist mobile users with limited or no available wireless infrastructure. They can provide connections for mobile users in hard-to-reach areas, replacing damaged or overloaded ground infrastructure and working as mobile clouds, providing low but increasing computational power. However, the feasibility of a Flying Edge Computing requires special attention in terms of resource allocation techniques, cooperation with existing ground units and among multiple UAVs, coordination with user mobility, computation efficiency, collision avoidance, and recharging approaches. Thus, the cooperation among UAVs and the current terrestrial Mobile Edge Computing can be relevant in some cases once the computation power of a single UAV might be insufficient. It is important to understand the feasibility of current proposals and establish new approaches that consider the usage of multiple UAVs and recharging approaches. In this paper we discuss the challenges of a 5G extended network through the help of UAVs. The proposed multi-tier architecture employs UAVs with different mobility models, providing support to ground nodes. Moreover, the support of the UAVs as edge nodes will also be evaluated.publishe

TRADE: Object Tracking with 3D Trajectory and Ground Depth Estimates for UAVs

We propose TRADE for robust tracking and 3D localization of a moving target in cluttered environments, from UAVs equipped with a single camera. Ultimately TRADE enables 3d-aware target following. Tracking-by-detection approaches are vulnerable to target switching, especially between similar objects. Thus, TRADE predicts and incorporates the target 3D trajectory to select the right target from the tracker's response map. Unlike static environments, depth estimation of a moving target from a single camera is a ill-posed problem. Therefore we propose a novel 3D localization method for ground targets on complex terrain. It reasons about scene geometry by combining ground plane segmentation, depth-from-motion and single-image depth estimation. The benefits of using TRADE are demonstrated as tracking robustness and depth accuracy on several dynamic scenes simulated in this work. Additionally, we demonstrate autonomous target following using a thermal camera by running TRADE on a quadcopter's board computer

Modello di deep learning tramite YOLO (You Only Look Once) per il riconoscimento della flavescenza dorata su Glera

Diverse aziende viticole negli ultimi periodi stanno avendo problemi di gravi patologie come la FD (Flavescenza Dorata) che causa gravi danni alla produzione avendo come unica soluzione, l'estirpo. L'obbiettivo di questo studio è la creazione di un modello di intelligenza artificiale ad alte prestazioni tramite YOLO (You Only Look Once), un algoritmo per la identificazione in tempo reale degli oggetti. Per l’apprendimento del modello sono stati utilizzati molti campioni provenienti da appezzamenti diversi nella zona di Valdobbiadene dove la situazione di FD è più drastica, data la vicinanza di molti vigneti simili tra loro. Anche lo studio del diverso stadio fenologico è stato monitorato per migliorare le prestazioni dell'algoritmo