Relevant bibliographies by topics / Intelligent UAV

Academic literature on the topic 'Intelligent UAV'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Intelligent UAV"

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Zhou, Bao Yu, and Li Tian. "Designing Highly Intelligent Unmanned Aerial Vehicle-Borne Software." Applied Mechanics and Materials 241-244 (December 2012): 2722–27. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2722.

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The existing software borne by an unmanned aerial vehicle (UAV) is mostly made up of functional modules with a simple design for meeting its functional requirements, thus being unable for the UAV to meet the requirements for its software to be intelligent, integrated and credible. To meet the requirements for the new-type UAV to be functionally varied, highly autonomous and intelligent, we construct the intelligent database and the network-adaptive and real-time middleware so as to greatly enhance the intelligence of the UAV-borne software and the autonomous flight capability of the UAV, paving a firm foundation for enhancing the functional diversity, autonomy and intelligence of the future advanced UAV.
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Wang, Changyu, Weili Yu, Jinrong Lu, Fusheng Zhu, Lihua Fan, and Shengping Li. "UAV-Based Physical-Layer Intelligent Technologies for 5G-Enabled Internet of Things: A Survey." Wireless Communications and Mobile Computing 2022 (January 28, 2022): 1–5. http://dx.doi.org/10.1155/2022/4351518.

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In recent years, the utilization and application of unmanned aerial vehicle (UAV) have attracted much attention, both from academy and industry. UAVs have been widely used in many practical communication scenarios, due to its high flexibility, high mobility, and low cost. Therefore, this paper addresses the key technologies of UAV communication and reviews the current research status, from various aspects including UAV communication transmission, UAV formation control and networking, UAV resource allocation, and intelligent communication from artificial intelligence algorithms. Then, artificial intelligence is introduced into multiple aspects of UAV communication, including channel transmission, control and networking, and resource scheduling, to organically integrate artificial intelligence into UAV communication, which can help reduce the complexity of communication algorithms and improve system spectrum efficiency. This paper will help improve the efficiency of UAV communication and promote the development of UAV-related industries.
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Yin, Wenqiang, Ran An, and Qun Zhao. "Intelligent Recognition of UAV Pilot Training Actions Based on Dynamic Bayesian Network." Journal of Physics: Conference Series 2281, no. 1 (June 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2281/1/012014.

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Abstract The recognition of UAV pilot training actions using artificial intelligence methods can effectively improve the intelligence level of UAV pilot training ability assessment. Based on the flight characteristics of UAV systems, this paper constructs an intelligent recognition model of flight training actions based on dynamic Bayesian network. Combined with the training tasks, the model recognition effect was verified. The results show that the model can accurately recognize different flight phases and typical training actions of UAV, have the advantages of high recognition accuracy and good real-time performance, and effectively improve the training effect of pilots.
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Voloshyn, Denys, and Serhii Bulba. "Intelligent UAV Spoofing Detection Method." Advanced Information Systems 6, no. 1 (April 6, 2022): 88–96. http://dx.doi.org/10.20998/2522-9052.2022.1.15.

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The paper presents an intelligent method for detecting UAV spoofing. A distinctive feature of the method is the use of subtrajectory calculation technology based on visual odometry subtrajectories and GPS positions in a sliding window, taking into account the intelligent estimation of the optical flow and the formation of UAV “Ego-movement” descriptors. In the course of the study, an analysis and comparative studies of a wide range of UAV spoofing methods were carried out, the most frequently recommended and practically used methods were identified. The conclusion is made about the relevance of the problems of GPS spoofing. The analysis of methods of protection against UAV GPS spoofing has been carried out. Promising directions for intelligent detection of UAV spoofing using methods and means of visual odometry are identified. In the course of studying methods for fixing input data, an approach was proposed for estimating the optical flow using a sliding window. At the same time, the need for intelligent processing of input data is argued. The estimation of the optical flow and the formation of descriptors was carried out using recurrent convolutional neural networks. As a result, a block diagram of the UAV spoofing detection method was developed. This allowed us to study the developed method. The results of the experiment for two spoofing scenarios showed the efficiency of estimating the positions of at least two of the three indicators under the conditions of using sliding windows of size 15 or more, with a time delay of half the window size.
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Qu, Haoming, Hongkui Yan, Bo Sun, and Weijia Niu. "The study of Simulation Intelligent Analysis System of UAV Control On the basis of Computer Big Data Technology." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 181–86. http://dx.doi.org/10.54097/hset.v12i.1452.

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With the advent of the era of computer big data, the application of big data is increasing. Computer big data technology is suitable for the analysis of UAV simulation intelligent system, which can clarify its practicability. After the intelligent analysis system is put into use, the accuracy of UAV control is improved. The UAV flight control system is the core part of UAV, and the performance of UAV depends on the design of its flight control system to a great degree. In this paper, the intelligent analysis system of UAV control simulation is studied. The accuracy of massive data set algorithm with adaptive selection and adjustment strategy and the complex causal relationship between data in the intelligent analysis system of UAV control simulation under computer big data technology are studied. The research results show that the global optimization of the whole process can be ensured by combining the structural characteristics and algorithms of data-to-data association algorithm, and the data is more than 58.96%. Therefore, the intelligent analysis system of airborne software simulation, which provides guarantee for the research and development of UAV control through testing, has a certain market application space and prospect.
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Ya-Ping Li, Ya-Ping Li, and Shang-Cai Chi Ya-Ping Li. "Research on Community Intelligent Logistics UAV Scheduling Based on Intelligent Optimization Algorithm." 電腦學刊 33, no. 6 (December 2022): 061–71. http://dx.doi.org/10.53106/199115992022123306005.

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<p>&quot;The last mile&quot; is a key problem that needs to be solved urgently by major e-commerce companies, and also an important means to improve their competitiveness in e-commerce activities. In this paper, aiming at the problems existing in the current community intelligent logistics, from the perspective of the entire logistics system, combined with the actual distribution of logistics sites and distribution methods, we studied the scheduling problem of UAVs using different strategies when returning to the site after distribution. First, the UAV distribution time model is established, and then the model is solved with the minimum completion time of goods distribution as the goal. Finally, the feasibility of the algorithm is verified through simulation experiments, and the optimal scheduling strategy of UAV is given in combination with the actual operation, thus guiding e-commerce to build a reasonable intelligent logistics system.</p> <p>&nbsp;</p>
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Baktayan, Asrar Ahmed, and Ibrahim Ahmed Al-Baltah. "A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions." Sustainable Engineering and Innovation 4, no. 2 (December 16, 2022): 156–90. http://dx.doi.org/10.37868/sei.v4i2.id179.

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.
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Huang, Zishan. "UAV Intelligent Control Based on Machine Vision and Multiagent Decision-Making." Advances in Multimedia 2022 (May 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/8908122.

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In order to improve the effect of UAV intelligent control, this paper will improve machine vision technology. Moreover, this paper adds scale information on the basis of the LSD algorithm, uses the multiline segment standard to merge these candidate line segments for intelligent recognition, and uses the LSD detection algorithm to improve the operating efficiency of the UAV control system and reduce the computational complexity. In addition, this paper combines machine vision technology and multiagent decision-making technology for UAV intelligent control and builds an intelligent control system, which uses intelligent machine vision technology for recognition and multiagent decision-making technology for motion control. The research results show that the UAV intelligent control system based on machine vision and multiagent decision-making proposed in this paper can achieve reliable control of UAVs and improve the work efficiency of UAVs.
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Park, Ki-Won, Hyeon Min Kim, and Oh-Soon Shin. "A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications." Energies 15, no. 14 (July 15, 2022): 5143. http://dx.doi.org/10.3390/en15145143.

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Both the unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) are attracting growing attention as enabling technologies for future wireless networks. In particular, IRS-assisted UAV communication, which incorporates IRSs into UAV communications, is emerging to overcome the limitations and problems of UAV communications and improve the system performance. This article aims to provide a comprehensive survey on IRS-assisted UAV communications. We first present six representative scenarios that integrate IRSs and UAVs according to the installation point of IRSs and the role of UAVs. Then, we introduce and discuss the technical features of the state-of-the-art relevant works on IRS-assisted UAV communications systems from the perspective of the main performance criteria, i.e., spectral efficiency, energy efficiency, security, etc. We also introduce machine learning algorithms adopted in the previous works. Finally, we highlight technical issues and research challenges that need to be addressed to realize IRS-assisted UAV communications systems.
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Zhang, Mingze, Mohammed EI-Hajjar, and Soon Xin Ng. "Intelligent Caching in UAV-Aided Networks." IEEE Transactions on Vehicular Technology 71, no. 1 (January 2022): 739–52. http://dx.doi.org/10.1109/tvt.2021.3125396.

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Dissertations / Theses on the topic "Intelligent UAV"

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Seyyedhasani, Hasan. "INTELLIGENT UAV SCOUTING FOR FIELD CONDITION MONITORING." UKnowledge, 2018. https://uknowledge.uky.edu/ece_etds/113.

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Precision agriculture requires detailed and timely information about field condition. In less than the short flight time a UAV (Unmanned Aerial Vehicle) can provide, an entire field can be scanned at the highest allowed altitude. The resulting NDVI (Normalized Difference Vegetation Index) imagery can then be used to classify each point in the field using a FIS (Fuzzy Inference System). This identifies areas that are expected to be similar, but only closer inspection can quantify and diagnose crop properties. In the remaining flight time, the goal is to scout a set of representative points maximizing the quality of actionable information about the field condition. This quality is defined by two new metrics: the average sampling probability (ASP) and the total scouting luminance (TSL). In simulations, the scouting flight plan created using a GA (Genetic Algorithm) significantly outperformed plans created by grid sampling or human experts, obtaining over 99% ASP while improving TSL by an average of 285%.
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Lin, Rongbin Lanny. "UAV intelligent path planning for wilderness search and rescue /." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2906.pdf.

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Lin, Rongbin. "UAV Intelligent Path Planning for Wilderness Search and Rescue." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1759.

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In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on prior experience and subjective judgment. We propose a Bayesian model that utilizes publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also make it possible to incorporate human-behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayes Chi-squared test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation of a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person's behaviors. Once a probability distribution map is in place, areas with higher probabilities are searched first in order to find the missing person in the shortest expected time. When using a Unmanned Aerial Vehicle (UAV) to support search, the onboard video camera should cover as much of the important areas as possible within a set time. We explore several algorithms (with and without set destination) and describe some novel techniques in solving this path-planning problem and compare their performances against typical WiSAR scenarios. This problem is NP-hard, but our algorithms yield high quality solutions that approximate the optimal solution, making efficient use of the limited UAV flying time. The capability of planning a path with a set destination also enables the UAV operator to plan a path strategically while letting the UAV plan the path locally.
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Charvat, Robert C. "Surveillance for Intelligent Emergency Response Robotic Aircraft (SIERRA Project)." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337888115.

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Persson, Tommy. "Evaluating the use of DyKnow in multi-UAV traffic monitoring applications." Thesis, Linköping University, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17672.

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This Master’s thesis describes an evaluation of the stream-based knowledge pro-cessing middleware framework DyKnow in multi-UAV traffic monitoring applica-tions performed at Saab Aerosystems. The purpose of DyKnow is “to providegeneric and well-structured software support for the processes involved in gen-erating state, object, and event abstractions about the environments of complexsystems." It does this by providing the concepts of streams, sources, computa-tional units (CUs), entity frames and chronicles.

This evaluation is divided into three parts: A general quality evaluation ofDyKnow using the ISO 9126-1 quality model, a discussion of a series of questionsregarding the specific use and functionality of DyKnow and last, a performanceevaluation. To perform parts of this evaluation, a test application implementinga traffic monitoring scenario was developed using DyKnow and the Java AgentDEvelopment Framework (JADE).

The quality evaluation shows that while DyKnow suffers on the usability side,the suitability, accuracy and interoperability were all given high marks.

The results of the performance evaluation high-lights the factors that affect thememory and CPU requirements of DyKnow. It is shown that the most significantfactor in the demand placed on the CPU is the number of CUs and streams. Italso shows that DyKnow may suffer dataloss and severe slowdown if the CPU istoo heavily utilized. However, a reasonably sized DyKnow application, such as thescenario implemented in this report, should run without problems on systems atleast half as fast as the one used in the tests.

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Du, Ruixiang. "An Intelligent Portable Aerial Surveillance System: Modeling and Image Stitching." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/859.

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"Unmanned Aerial Vehicles (UAVs) have been widely used in modern warfare for surveillance, reconnaissance and even attack missions. They can provide valuable battlefield information and accomplish dangerous tasks with minimal risk of loss of lives and personal injuries. However, existing UAV systems are far from perfect to meet all possible situations. One of the most notable situations is the support for individual troops. Besides the incapability to always provide images in desired resolution, currently available systems are either too expensive for large-scale deployment or too heavy and complex for a single solder. Intelligent Portable Aerial Surveillance System (IPASS), sponsored by the Air Force Research Laboratory (AFRL), is aimed at developing a low-cost, light-weight unmanned aerial vehicle that can provide sufficient battlefield intelligence for individual troops. The main contributions of this thesis are two-fold (1) the development and verification of a model-based flight simulation for the aircraft, (2) comparison of image stitching techniques to provide a comprehensive aerial surveillance information from multiple vision. To assist with the design and control of the aircraft, dynamical models are established at different complexity levels. Simulations with these models are implemented in Matlab to study the dynamical characteristics of the aircraft. Aerial images acquired from the three onboard cameras are processed after getting the flying platform built. How a particular image is formed from a camera and the general pipeline of the feature-based image stitching method are first introduced in the thesis. To better satisfy the needs of this application, a homography-based stitching method is studied. This method can greatly reduce computation time with very little compromise in the quality of the panorama, which makes real-time video display of the surroundings on the ground station possible. By implementing both of the methods for image stitching using OpenCV, a quantitative comparison in the performance is accomplished."
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Holsinger, Seth D. "Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1552380066855365.

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Puri, Anuj. "Statistical profile generation of real-time UAV-based traffic data." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002674.

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Hayes, Edwin Laurie. "Machine Learning for Intelligent Control: Application of Reinforcement Learning Techniques to the Development of Flight Control Systems for Miniature UAV Rotorcraft." Thesis, University of Canterbury. Department of Mechanical Engineering, 2013. http://hdl.handle.net/10092/7810.

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This thesis investigates the possibility of using reinforcement learning (RL) techniques to create a flight controller for a quadrotor Micro Aerial Vehicle (MAV). A capable flight control system is a core requirement of any unmanned aerial vehicle. The challenging and diverse applications in which MAVs are destined to be used, mean that considerable time and effort need to be put into designing and commissioning suitable flight controllers. It is proposed that reinforcement learning, a subset of machine learning, could be used to address some of the practical difficulties. While much research has delved into RL in unmanned aerial vehicle applications, this work has tended to ignore low level motion control, or been concerned only in off-line learning regimes. This thesis addresses an area in which accessible information is scarce: the performance of RL when used for on-policy motion control. Trying out a candidate algorithm on a real MAV is a simple but expensive proposition. In place of such an approach, this research details the development of a suitable simulator environment, in which a prototype controller might be evaluated. Then inquiry then proposes a possible RL-based control system, utilising the Q-learning algorithm, with an adaptive RBF-network providing function approximation. The operation of this prototypical control system is then tested in detail, to determine both the absolute level of performance which can be expected, and the effect which tuning critical parameters of the algorithm has on the functioning of the controller. Performance is compared against a conventional PID controller to maximise the usability of the results by a wide audience. Testing considers behaviour in the presence of disturbances, and run-time changes in plant dynamics. Results show that given sufficient learning opportunity, a RL-based control system performs as well as a simple PID controller. However, unstable behaviour during learning is an issue for future analysis. Additionally, preliminary testing is performed to evaluate the feasibility of implementing RL algorithms in an embedded computing environment, as a general requirement for a MAV flight controller. Whilst the algorithm runs successfully in an embedded context, observation reveals further development would be necessary to reduce computation time to a level where a controller was able to update sufficiently quickly for a real-time motion control application. In summary, the study provides a critical assessment of the feasibility of using RL algorithms for motion control tasks, such as MAV flight control. Advantages which merit interest are exposed, though practical considerations suggest at this stage, that such a control system is not a realistic proposition. There is a discussion of avenues which may uncover possibilities to surmount these challenges. This investigation will prove useful for engineers interested in the opportunities which reinforcement learning techniques represent.
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Ernest, Nicholas D. "Genetic Fuzzy Trees for Intelligent Control of Unmanned Combat Aerial Vehicles." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427813213.

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Books on the topic "Intelligent UAV"

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Nawrat, Aleksander. Vision Based Systemsfor UAV Applications. Heidelberg: Springer International Publishing, 2013.

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service), SpringerLink (Online, ed. Networked Digital Technologies: 4th International Conference, NDT 2012, Dubai, UAE, April 24-26, 2012. Proceedings, Part I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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service), SpringerLink (Online, ed. Networked Digital Technologies: 4th International Conference, NDT 2012, Dubai, UAE, April 24-26, 2012, Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Fontgalland, Glauco. Smart Systems: Theory and Advances. Amplla Editora, 2022. http://dx.doi.org/10.51859/amplla.sst631.1122-0.

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This book aims to highlight the strength and state-of-art of some techniques and methods applied to intelligent systems. Rather to cover the variety of techniques and methods available in the literature, which is out of scope of this book, it focuses on those consolidated and applied and on those with high potential of implementation to smart systems. This book has fourteen chapters covering abroad range of topics in communications. The first three chapters are devoted to state-of-art and review papers on planar filters, unmanned aerial vehicles (UAV), negative group delay, nanoclusters, and tunable lights, while the remain chapters cover specific topics such as smart monitoring, V2I, high-speed links, RF and Optical sensors, composite material, metamaterial, energy harvesting, radar, SWIPT, and electromagnetic sources.
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Ku, Zygmunt, and Aleksander Nawrat. Vision Based Systemsfor UAV Applications. Springer International Publishing AG, 2015.

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Sebbane, Yasmina Bestaoui. Intelligent Autonomy of Uavs. Taylor & Francis Group, 2020.

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Sebbane, Yasmina Bestaoui. Intelligent Autonomy of UAVs: Advanced Missions and Future Use. Taylor & Francis Group, 2018.

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Sebbane, Yasmina Bestaoui. Intelligent Autonomy of UAVs: Advanced Missions and Future Use. Taylor & Francis Group, 2018.

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Sebbane, Yasmina Bestaoui. Intelligent Autonomy of UAVs: Advanced Missions and Future Use. Taylor & Francis Group, 2018.

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Sebbane, Yasmina Bestaoui. Intelligent Autonomy of UAVs: Advanced Missions and Future Use. Taylor & Francis Group, 2018.

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Book chapters on the topic "Intelligent UAV"

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Al-Kaff, Abdulla, Francisco Miguel Moreno, Luis Javier San José, Fernando García, David Martín, Arturo de la Escalera, Alberto Nieva, and José Luis Meana Garcéa. "VBII-UAV: Vision-Based Infrastructure Inspection-UAV." In Advances in Intelligent Systems and Computing, 221–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56538-5_24.

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Verma, Manish, Sayed Imran Ali, and Gaurav Agrawal. "UAV—A Boon Towards Agriculture." In Algorithms for Intelligent Systems, 299–303. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_27.

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Jeong, Yongseop, and In So Kweon. "Relative Pose Estimation for an Integrated UGV-UAV Robot System." In Intelligent Robotics and Applications, 625–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40852-6_63.

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Patil, Vaishnavi, Vaishnavi Potphode, Utkarsha Potdukhe, Vishal Badgujar, and Kaushiki Upadhyaya. "Smart UAV Framework for Multi-Assistance." In ICT with Intelligent Applications, 241–49. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4177-0_26.

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Chen, Mengqing, Yang Chen, Zhihuan Chen, and Yanhua Yang. "Path Planning of UAV-UGV Heterogeneous Robot System in Road Network." In Intelligent Robotics and Applications, 497–507. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27529-7_42.

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Yang, Moude, Jiezhuo Zhong, and Longjuan Wang. "Research on UAV Control Improvement." In Advances in Intelligent Systems and Computing, 769–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62746-1_118.

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Kuchár, Daniel, and Peter Schreiber. "Comparison of UAV Landing Site Classifications with Deep Neural Networks." In Artificial Intelligence in Intelligent Systems, 55–63. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77445-5_6.

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Jiang, Jun, Houde Liu, Bo Yuan, Xueqian Wang, and Bin Liang. "A New Concept of UAV Recovering System." In Intelligent Robotics and Applications, 327–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27535-8_30.

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Hong, Ye, Jiancheng Fang, and Ye Tao. "Ground Control Station Development for Autonomous UAV." In Intelligent Robotics and Applications, 36–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88518-4_5.

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Wen, Zhang, and Qin Guo-Shun. "Design of UAV Ground Control Station." In Advances in Intelligent and Soft Computing, 249–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29455-6_36.

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Conference papers on the topic "Intelligent UAV"

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Schrage, Daniel, and George Vachtsevanos. "Software Enabled Control (SEC) for Intelligent UAVs." In 1st UAV Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-3450.

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Boskovic, Jovan, Ravi Prasanth, and Raman Mehra. "A Multi-Layer Architecture for Intelligent Control of Unmanned Aerial Vehicles." In 1st UAV Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-3473.

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Murphy, Robin R. "Proposals for New UGV, UMV, UAV, and HRI standards for rescue robots." In the 10th Performance Metrics for Intelligent Systems Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/2377576.2377579.

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Shi, Zitan, Zhengjia Wang, Lizhi Li, Junjie Yu, and Wang Xie. "Design of Quadrotor Intelligent Rescue UAV Based on UAV Vision." In 2021 IEEE 4th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2021. http://dx.doi.org/10.1109/auteee52864.2021.9668780.

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Zaza, Theopisti, and Arthur Richards. "Intelligent foresight for UAV routing problems." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737644.

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Schoenung, Susan, Steve Wegener, Jeremy Frank, Chad Frost, Michael Freed, and Joseph Totah. "Intelligent UAV Airborne Science Missions [invited]." In Infotech@Aerospace. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-6937.

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Han, Pengfei, Hongwei Zhao, and Changzheng Chen. "UAV intelligent system for patrol missions." In 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icmse-18.2018.17.

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D.S., Permyakov, and Noskov A.G. "PROSPECTS OF USING UAVS IN AGRICULTURE." In OF THE ANNIVERSARY Х INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE «INNOVATIVE TECHNOLOGIES IN SCIENCE AND EDUCATION» («ITSE 2022» CONFERENCE). DSTU-Print, 2022. http://dx.doi.org/10.23947/itse.2022.240-244.

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The purpose of the study: Regularized solutions for intelligent agriculture, including the use of unmanned aerial vehicles (UAVs), are considered for agricultural applications. UAVs combine information and communication technologies, robots, artificial intelligence, big data and the Internet of Things. Agricultural UAVs have great capabilities, and their use has expanded in all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, as well as growth assessment and mapping. Accordingly, it is expected that the agricultural UAV market will continue to grow with the corresponding technologies. The article discusses the latest trends and areas of application of advanced technologies related to agricultural UAVs, control technologies, equipment and developments. Variants of the use of UAVs in real agricultural conditions on the example of the Kaliningrad region are given.
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Erel-Ozcevik, Muge. "UAV-Coin: Blockchain assisted UAV as a Service." In 2022 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2022. http://dx.doi.org/10.1109/asyu56188.2022.9925279.

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Gu, Da-Wei, Waseem Kamal, and Ian Postlethwaite. "A UAV Waypoint Generator." In AIAA 1st Intelligent Systems Technical Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-6227.

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Reports on the topic "Intelligent UAV"

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Zhang, Yangjun. Unsettled Topics Concerning Flying Cars for Urban Air Mobility. SAE International, May 2021. http://dx.doi.org/10.4271/epr2021011.

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Flying cars—as a new type of vehicle for urban air mobility (UAM)—have become an important development trend for the transborder integration of automotive and aeronautical technologies and industries. This article introduces the 100-year history of flying cars, examines the current research status for UAM air buses and air taxis, and discusses the future development trend of intelligent transportation and air-to-land amphibious vehicles. Unsettled Topics Concerning Flying Cars for Urban Air Mobility identifies the major bottlenecks and impediments confronting the development of flying cars, such as high power density electric propulsion, high lift-to-drag ratio and lightweight body structures, and low-altitude intelligent flight. Furthermore, it proposes three phased goals and visions for the development of flying cars in China, suggesting the development of a flying vehicle technology innovation system that integrates automotive and aeronautic industries.
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