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Electronics
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UAV-Femtocell Systems and Applications
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UAV-Femtocell Systems and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 8908

Special Issue Editor

Dr. Francesco Beritelli

E-Mail Website
Guest Editor
Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, 95125 Catania, Italy
Interests: audio signal processing; biometrics; IoT; drone/UAV communications; rainfall estimation and monitoring; post-earthquake geolocation; image processing, computer vision, machine learning-based applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) have spread into numerous applications involving information technologies, including smart remote sensing, extension of mobile radio coverage, disaster management, searching for people, localization, pollution monitoring, crowd detection, and smart sensors.

UAVs have recently attracted a great deal of attention in the area of wireless communication networks due to their capacity to complement conventional fixed networks, thanks to their mobility and flexibility. UAV-mounted infrastructures can temporarily support service recovery initiatives and local interim communication facilities for potentially damaged infrastructures, for example, in case of a catastrophic event. In addition, UAV-assisted relaying can help to extend base station connectivity, and UAV-mounted fronthaul and backhaul frameworks have been considered as a promising approach to handle unexpected or temporarily large amounts of information. Finally, the robust wireless communication capabilities of UAVs (air-to-air) and to/from ground stations or sensors (air-to-ground) have received much attention in recent years.

This Special Issue is devoted to reporting novel scientific ideas, approaches, results, and (prototype) solutions/applications on UAV-femtocell systems and applications. Contributions are solicited in the wide spectrum of topics related to UAV communication, as exemplarily listed as “keywords” below. We particularly welcome submissions addressing the joint use of onboard UAVs and femtocells and new paradigms and studies which explore the advantages of mobile radio network scenarios that integrate the concept of “mobile base stations”

Prof. Dr. Francesco Beritelli
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • UAV-Femtocell systems
  • Drones communications
  • UAV Remote sensing
  • UAV-assisted communications
  • UAV Trajectory optimization
  • UAV-based free space optical communication (FSOC)
  • UAV-based crowd detection
  • Artificial intelligence
  • UAV applications

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Published Papers (3 papers)

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Research

19 pages, 17646 KiB  
Article
UAV Object Tracking Application Based on Patch Color Group Feature on Embedded System
by Ming-Hwa Sheu, Yu-Syuan Jhang, S M Salahuddin Morsalin, Yao-Fong Huang, Chi-Chia Sun and Shin-Chi Lai
Electronics 2021, 10(15), 1864; https://doi.org/10.3390/electronics10151864 - 3 Aug 2021
Cited by 4 | Viewed by 2197
Abstract
The discriminative object tracking system for unmanned aerial vehicles (UAVs) is widely used in numerous applications. While an ample amount of research has been carried out in this domain, implementing a low computational cost algorithm on a UAV onboard embedded system is still [...] Read more.
The discriminative object tracking system for unmanned aerial vehicles (UAVs) is widely used in numerous applications. While an ample amount of research has been carried out in this domain, implementing a low computational cost algorithm on a UAV onboard embedded system is still challenging. To address this issue, we propose a low computational complexity discriminative object tracking system for UAVs approach using the patch color group feature (PCGF) framework in this work. The tracking object is separated into several non-overlapping local image patches then the features are extracted into the PCGFs, which consist of the Gaussian mixture model (GMM). The object location is calculated by the similar PCGFs comparison from the previous frame and current frame. The background PCGFs of the object are removed by four directions feature scanning and dynamic threshold comparison, which improve the performance accuracy. In the terms of speed execution, the proposed algorithm accomplished 32.5 frames per second (FPS) on the x64 CPU platform without a GPU accelerator and 17 FPS in Raspberry Pi 4. Therefore, this work could be considered as a good solution for achieving a low computational complexity PCGF algorithm on a UAV onboard embedded system to improve flight times. Full article
(This article belongs to the Special Issue UAV-Femtocell Systems and Applications)
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17 pages, 3523 KiB  
Article
A Robust Quadruplet and Faster Region-Based CNN for UAV Video-Based Multiple Object Tracking in Crowded Environment
by Happiness Ugochi Dike and Yimin Zhou
Electronics 2021, 10(7), 795; https://doi.org/10.3390/electronics10070795 - 27 Mar 2021
Cited by 15 | Viewed by 3005
Abstract
Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) videos has faced several challenges such as motion capture and appearance, clustering, object variation, high altitudes, and abrupt motion. Consequently, the volume of objects captured by the UAV is usually quite small, and the [...] Read more.
Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) videos has faced several challenges such as motion capture and appearance, clustering, object variation, high altitudes, and abrupt motion. Consequently, the volume of objects captured by the UAV is usually quite small, and the target object appearance information is not always reliable. To solve these issues, a new technique is presented to track objects based on a deep learning technique that attains state-of-the-art performance on standard datasets, such as Stanford Drone and Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (UAVDT) datasets. The proposed faster RCNN (region-based convolutional neural network) framework was enhanced by integrating a series of activities, including the proper calibration of key parameters, multi-scale training, hard negative mining, and feature collection to improve the region-based CNN baseline. Furthermore, a deep quadruplet network (DQN) was applied to track the movement of the captured objects from the crowded environment, and it was modelled to utilize new quadruplet loss function in order to study the feature space. A deep 6 Rectified linear units (ReLU) convolution was used in the faster RCNN to mine spatial–spectral features. The experimental results on the standard datasets demonstrated a high performance accuracy. Thus, the proposed method can be used to detect multiple objects and track their trajectories with a high accuracy. Full article
(This article belongs to the Special Issue UAV-Femtocell Systems and Applications)
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18 pages, 972 KiB  
Article
Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network
by Yishi Xue, Bo Xu, Wenchao Xia, Jun Zhang and Hongbo Zhu
Electronics 2020, 9(9), 1397; https://doi.org/10.3390/electronics9091397 - 28 Aug 2020
Cited by 7 | Viewed by 2715
Abstract
Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) [...] Read more.
Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue UAV-Femtocell Systems and Applications)
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