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1. DESIGN RESEARCH BASED DEVELOPMENT OF CAMOUFLAGE LANDSCAPE FEATURES TO PREVENT CRIMINAL UAV ACTIVITY

   Hewitt, Robert and (January 2019, Landscape research record)

   The popularity of small consumer drones (UAVs) has prompted increased use of these vehicles in and around public outdoor spaces, with expected commercial drone numbers to reach nearly 7 million by 2030 (FAA). Research in landscape architecture related to UAV (drone) use in public space to date, has just begun to address conceptual approaches to landscape assessment, representation, and park user behavior (Kullmann, Park). While research related to the development of countermeasures for security purposes is more extensive, no research to date addresses the development of landscape countermeasures for the use of UAVs in criminal activities. The paper presents design-based research (DBR) methodology and findings funded by a multiyear, multidisciplinary NSF grant to develop landscape architectural interventions that discourage the use of UAVs for criminal purposes at correctional facilities. Consistent with design based research (DBR) models (Brown), this project is complex, incorporating the development of a) landscape assessments for potential UAV launch and landing sites around prisons; b) the creation of UAV tracking and monitoring systems, and c) the development of model countermeasures. The paper describes design and placement of embedded landscape features utilizing landscape camouflage principles for UAV detection systems in forested upstate North Carolina. Modelled camouflage mimicked landscape features and were fabricated in two stages: 1) landscape superstructure, and 2) landscape camouflage. The embedded landscape features incorporated a launch warning system capable of alerting prison officials of drone launch locations, identifying future drone operators, and predicting drone flight paths. 

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2. “Assessment of Public Space Visitors of Unmanned Aerial Vehicles (UAVs) in Outdoor Public Space and the development of Countermeasures to Control Aerial Visual Access”

   Nassar, Hala; Hewitt, Robert; Cummings, Mary (January 2020, Landscape research record)

   Use of unmanned aerial vehicle (UAV) technology is predicted to increase dramatically from more than 600,000 drones registered just with the US Federal Aviation Administration (FAA) to nearly 7,000,000 over the next 12 years ( FAA 1,2 ) This popularity is evident in their increasing use in and around public outdoor spaces, including parks, stadiums, outdoor amphitheaters, festival grounds, or outdoor markets. While there is considerable research on unmanned aerial vehicle (UAV) applications and navigation (Koh 2012, Nemeth 2010) and an emerging body of work in landscape architecture (Kullmann 2017, Park 2016), there is no research addressing increasing conflicts between public space visitors, drone navigation in public space, and its effect on the planning and design of public space. The paper presents initial findings from funded research to develop landscape architectural design and planning responses supporting low cost detection technology to deter the illegal use of drones in public spaces. Methods of data collection employed surveys of botanical garden visitors concerning their preferences for site landscape features and experiences, their awareness, attitudes, and preferences about the presence of drones in public space, and potential aerial visual access to a range of forested and open landscapes frequented by visitors in the garden. Findings suggest that given public concern about the presence of drones, landscape planning and design of such public spaces should provide continuous landscape features with restricted aerial visual access surrounding and connecting public areas with open aerial visual access. 

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3. Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

   https://doi.org/10.1002/ppj2.20100  

   Gano, Boubacar; Bhadra, Sourav; Vilbig, Justin M; Ahmed, Nurzaman; Sagan, Vasit; Shakoor, Nadia (December 2024, The Plant Phenome Journal)

   Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies. 

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4. UAVs Control Using 3D Hand Keypoint Gestures

   Van Khoi Nguyen and R. Alba-Flores (February 2022, SoutheastCon 2022)

   IEEE (Ed.)

   Over past few years, unmanned aircraft vehicles (UAVs) have been becoming more and more popular for various purposes such as surveillance, automated industry, robotics, vehicle guidance, traffic monitoring and control system. It is very important to have multiple methods of UAVs controlling to fit in UAVs usages. The goal of this work was to develop a new technique to control an UAV by using different hand gestures. To achieve this, a hand keypoint detection algorithm was used to detect 21 keypoints in the hand. Then this keypoints were used as the input to an intelligent system based on Convolutional Neural Networks (CNN) that was able to classify the hand gestures. To capture the hand gestures, the video camera of the UAV was used. A database containing 2400 hand images was created and used to train the CNN. The database contained 8 different hand gestures that were selected to send specific motion commands to the UAV. The accuracy of the CNN to classify the hand gestures was 93%. To test the capabilities of our intelligent control system, a small UAV, the DJI Ryze Tello drone, was used. The experimental results demonstrated that the DJI Tello drone was able to be successfully controlled by hand gestures in real time. 

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5. Interference Analysis and Mitigation for UAV Communications in Drone Corridors

   https://doi.org/10.1109/WPMC59531.2023.10338970  

   Ellis, Cameron; Zhang, Minglong; Marojevic, Vuk (November 2023, IEEE)

   Unmanned aerial vehicles (UAVs) have witnessed widespread adoption in the modern world, with their development set to continue into the future. As UAV technology and applications advance, it becomes imperative to understand their communication capabilities. UAVs experience distinct radio propagation conditions compared to ground-based radio nodes, necessitating a critical investigation into aerial radio node performance. This paper analyzes interference in UAV-to-UAV (U2U) communications within drone corridors and proposes an interference mitigation strategy utilizing millimeter wave (mmWave) beamforming. Employing a semi-persistent scheduling approach from the Third Generation Partnership Project (3GPP) sidelink communications for low altitude aerial nodes in drone corridors, the study primarily examines interference from drone clusters within designated air corridors. To assess U2U communication performance, a 3GPP standard-compliant cross-layer simulator is developed. Simulation results demonstrate that employing mmWave beamforming instead of isotropic transmission substantially reduces interference, leading to higher communications reliability and enabling more UAVs to occupy and communicate in the airspace. 

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