Yazar "Bakirci, Murat" seçeneğine göre listele

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    5G Network Supported Unmanned Aerial Vehicle Avionics for Military Zone Surveillance and Security
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bakirci, Murat; Ozer, Muhammed Mirac
    In this study, the avionics design of an unmanned aerial vehicle (UAV) that can autonomously reach areas with high security measures and requiring urgent intervention, such as military zones, in the shortest possible time during an emergency call or during a task assigned by the operator is carried out. The avionics system has been designed with the capabilities to perform fully autonomous landing and take-off under the supervision of ground command and control station operators, autonomously transfer to the mission area, and fulfill the task with its other equipment. Through the onboard computers and hardware, the UAV can transmit and receive the data received from the functional payloads, as well as the commands received from the ground station, in real-time with 5G supported high-bandwidth. The study comprehensively discussed the development of an original autopilot system, inertial measurement unit, communication subsystems, route planning interface, functional payload control, and real-time warning interface of the fully autonomous UAV. © 2023 IEEE.
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    A drone-based approach to enhance spatial insight into surrounding air pollutant distributions for healthier indoor environments
    (Elsevier, 2024) Bakirci, Murat
    Current investigations into the interplay between indoor and outdoor air quality in buildings often rely on limited measurements of a small subset of air pollutants taken outside the structure. Implicit in this methodology is the assumption of a homogeneous distribution of air pollutants around the building, a premise that may not accurately reflect real-world conditions. This study introduces a more accurate and consistent approach to comprehending air pollutant distributions around buildings through systematic measurements conducted using a drone platform equipped with essential air quality sensors. Employing a fully autonomous drone, the study adopts a parametric approach to collect numerous air pollutant measurements around buildings with unconventional shapes. The resulting air pollutant maps offer comprehensive insights into the spatial distribution of pollutants around the building. A detailed analysis, achieved by dividing the building surroundings into different zones, revealed that concentrations of NO2, CO, PM10, and PM2.5 on the inward facade of the 3/4 torus-shaped building were 12.64%, 14.49%, 7.66%, and 15.77% higher, respectively, than in the other measured areas around the building. These findings highlight that air pollutants around the building are notably influenced by the building's geometry, challenging the assumption of homogeneity. Instead, pollutants tend to accumulate at specific points, underscoring the significance of considering these factors in air quality assessments. Furthermore, the noted uneven distribution of pollutants around the building has distinct effects on various sections within the structure, as revealed by indoor measurements. Bureaus on the inward facade, where higher levels of outdoor pollutants are measured, can exhibit up to 44% more indoor air pollutants than those on the other side. This study not only contributes to a nuanced understanding of air pollutant distributions but also showcases the superior potential of drone platforms in advancing such research.
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    A Low-Cost UAV Design for Surveillance Purposes in Swarm Systems
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bakirci, Murat; Ozer, Muhammed Mirac
    This study focuses on a low-cost UAV design for a swarm UAV system to be used in reconnaissance and surveillance applications. The individual UAV, which will form the swarm and serve in this context, is designed in accordance with the swarm behaviors and requirements such as autonomous behavior, formation and coordination. Within the scope of the planned flight missions, the necessary communication architectures were emphasized by considering the communication between the ground control station and other swarm members. The most cost-effective equipment for fundamental tasks such as flight control, swarm intercommunication, object detection has been studied, and individual swarm members are equipped in line with these requirements and limitations. The problem of consistent sharing of data obtained during the flight mission has been examined in detail and appropriate solutions have been produced with proper communication and data transfer protocols. A low-cost solution has been obtained for aerial surveillance applications of the swarm UAV system, which can provide direct point-to-point communication through selected equipment and network message transmission during flight. © 2023 IEEE.
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    A Novel Swarm Unmanned Aerial Vehicle System: Incorporating Autonomous Flight, Real-Time Object Detection, and Coordinated Intelligence for Enhanced Performance
    (Int Information & Engineering Technology Assoc, 2023) Bakirci, Murat
    Presently, swarm Unmanned Aerial Vehicle (UAV) systems confront an array of obstacles and constraints that detrimentally affect their efficiency and mission performance. These include restrictions on communication range, which impede operations across extensive terrains or remote locations; inadequate processing capabilities for intricate tasks such as real-time object detection or advanced data analytics; network congestion due to a large number of UAVs, resulting in delayed data exchange and potential communication failures; and power management inefficiencies reducing flight duration and overall mission endurance. Addressing these issues is paramount for the successful implementation and operation of swarm UAV systems across various real-world applications. This paper proposes a novel system designed to surmount these challenges through salient features such as fortified communication, collaborative hardware integration, task distribution, optimized network topology, and efficient routing protocols. Cost-effectiveness was prioritized in selecting the most accessible equipment satisfying minimum requirements, identified through comprehensive literature and market review. By focusing on energy efficiency and high performance, successful cooperation was facilitated through harmonized equipment and effective task division. The proposed system utilizes Raspberry Pi and Jetson Nano for task division, endowing the UAVs with superior intelligence for navigating intricate environments, real-time object detection, and the execution of coordinated actions. The incorporation of the Ad Hoc UAV Network's decentralized approach enables system adaptability and expansion in response to evolving environments and mission demands. An efficient routing protocol was selected for the system, minimizing unnecessary broadcasting and reducing network congestion, thereby ensuring extended flight durations and enhanced mission capabilities for UAVs with limited battery capacity. Through the careful selection and testing of hardware and software components, the proposed swarm UAV system improves communication range, processing power, autonomy, scalability, and energy efficiency. This makes it highly adaptable and effective for a broad spectrum of real-world applications. The proposed system sets a new standard in the field, demonstrating how the integration of intelligent hardware, optimized task division, and efficient networking can overcome the limitations of current swarm UAV systems.
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    Adapting Swarm Intelligence to a Fixed Wing Unmanned Combat Aerial Vehicle Platform
    (Springer Science and Business Media Deutschland GmbH, 2023) Bakirci, Murat; Ozer, Muhammed Mirac
    The majority of the swarm UAV studies focus on a single aspect, only investigating the stages such as formation development, path planning, or target tracking for a swarm currently in mission flight. Besides, the dynamic coordination and operation of the system based on the new commands that can be transmitted to the swarm during the mission are not taken into account; that is, the input of the ground resources is often ignored. In this study, all stages of a swarm of unmanned combat aerial vehicles (UCAV), from take-off to the end of the mission, are detailed in a single holistic framework, including communication with the ground station and intercommunication between swarm members. The designed solution is a platform that will enable the swarm structure to prevail by developing alternative strategies and tactics against existing manned or unmanned air, land, and sea platforms. In this context, operational algorithms have been developed for fixed-wing, fully autonomous controlled UCAVs, which can successfully detect in-sight and beyond-sight targets for a desired period of time, and can communicate seamlessly with ground stations. Furthermore, dynamic swarm-type algorithms have been developed in order to fulfill the desired task in the event of the loss of any UCAV during the mission, to replace the lost vehicle with a new vehicle, and to communicate directly with the UCAVs in the swarm. As a result of adapting swarm intelligence to the UCAV platform, all individuals in the swarm perform tasks such as taking off in formation, adding or removing new individuals to the swarm, and formation protection. Moreover, they have the ability to change direction in a swarm, change formation, split or merge, navigate, ascend and descend, and simultaneous/sequential auto-landing as a swarm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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    Advanced aerial monitoring and vehicle classification for intelligent transportation systems with YOLOv8 variants
    (Academic Press Ltd- Elsevier Science Ltd, 2025) Bakirci, Murat
    Aerial monitoring assumes a pivotal role within the domain of Intelligent Transportation Systems (ITS), imparting invaluable data and discernments that ameliorate the efficacy, security, and holistic operability of transportation networks. Image processing, encompassing the derivation of valuable insights through the manipulation of visual data captured by imaging apparatus, resides at the core and is poised to establish a firm footing in forthcoming ITS applications. In this context, numerous machine learning methodologies have been devised to enhance image processing, with novel approaches continually emerging. YOLOv8 emerged earlier this year and is still in the process of assimilating its potential application within the domain of ITS. In this study, a comprehensive assessment was conducted on all constituent variants of YOLOv8, specifically within the context of its application in the domain of aerial traffic monitoring. Using a custom-modified commercial drone, extensive datasets were acquired encompassing a diverse range of flight scenarios and traffic dynamics. To optimize model performance, meticulous consideration was given to ensuring dataset inclusivity, encompassing the full spectrum of vehicular typologies, while maintaining a homogeneous structure that accommodates an array of environmental nuances, including illumination and shading variations. The outcomes evince that both YOLOv8l and YOLOv8x exhibit notable superiority over other variants, manifesting exceptional detection efficacy even amid high-density traffic scenarios and the presence of obstructive elements. Contrastingly, in comparison to earlier iterations of YOLO, the current models demonstrate heightened precision in vehicle classification, yielding a reduction in misclassification instances. Although YOLOv8n exhibits a relatively subdued performance relative to other models, its potential is discernible in real-time applications, particularly within the purview of ITS, owing to its commendable proficiency in detection rates.
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    Advanced ship detection and ocean monitoring with satellite imagery and deep learning for marine science applications
    (Elsevier, 2025) Bakirci, Murat
    Ship detection from satellite imagery is a powerful tool in marine science, offering crucial understanding of vessel traffic patterns, fishing activities, and environmental impacts on marine ecosystems. The ability to monitor ship movements on a large scale aids in assessing anthropogenic pressures on sensitive habitats, enforcing regulatory compliance, and supporting conservation efforts in protected areas. This study offers a fresh perspective on ship detection in oceanic environments by pioneering the evaluation of YOLOv9, a cutting-edge detection algorithm, within this domain for the first time. Applying a diverse set of data augmentation techniques significantly improved the algorithm's ability to detect small ships. Additionally, atmospheric scattering effects commonly present in satellite images were mitigated through filtering, further enhancing detection performance. With a remarkable increase in speed and significantly superior performance, particularly in detecting small ships and minimizing detection time, YOLOv9 emerges as the premier candidate for real-time applications and timesensitive critical operations.
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    An Avionics System for Light-Weight Multi-Rotor Unmanned Aerial Vehicles
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bakirci, Murat; Ozer, Muhammed Mirac
    Small and light unmanned aerial vehicles (UAV) are increasingly used because of a wide variety of advantages they provide. These vehicles, which are especially preferred in applications such as 3D mapping, atmospheric measurements, and intelligent transportation systems, are at the heart of many researches due to their easy accessibility and ease of use. Moreover, the importance of these vehicles becomes even more evident in situations and locations where human intervention is not possible. However, their ability to perform these tasks in an ideal way reveals the necessity of a good avionics system. In particular, when it comes to autonomous flight, this becomes even more important. A properly designed avionics system is the first step for a UAV in flight to successfully accomplish its mission. In other words, the performance of the UAV is directly related to the functionality of the avionics. In this study, an avionics system is designed for a small and light-weight UAV, through careful consideration of the systems and components needed for autonomous flight, navigation, and communication. Within the scope of application areas, the system requirements for the UAV have been determined, and a design has been made in accordance with its capacity. © 2023 IEEE.
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    An IoT-Based Modular Avionics and Electrical System for Nanosatellite Systems
    (Springer Science and Business Media Deutschland GmbH, 2023) Bakirci, Murat; Özer, Muhammed Mirac
    This study presents an avionics and electrical system design using reliable, high-performance hardware and sensors for advanced scientific experimentation missions compatible with air-land-sea vehicle platforms, particularly nanosatellite platforms. The nanosatellite avionics, which has a real-time operating system that supports frequently used interfaces, processes and manages sensory and physical data based on a central processor. It executes all operations by defining IoT requirements and computing connection parameters for IoT applications. The modular design brought into the system provides both ease of access and integration into the target platform, and also provides reliable storage for telemetry and flight data. Through the IoT station, it reliably receives information from the satellite and transmits it to smart devices while maintaining the desired signal quality. Moreover, through processing the data obtained from the sensors, critical information such as instant detection and tracking of systems errors are transmitted to the cloud, and as a result, proper control can be provided regardless of location. This critical data obtained from the cloud is straightforwardly tracked by the software platform. This design will provide the space technologies inventory as the basis for a new satellite platform and a system design for researchers to further develop. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Assessment of YOLO11 for Ship Detection in SAR Imagery under Open Ocean and Coastal Challenges
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bakirci, Murat; Bayraktar, Irem
    Ship detection from Synthetic Aperture Radar (SAR) images plays a crucial role in maritime surveillance and safety. This study focuses on evaluating the performance of the latest state-of-The-Art YOLO algorithm, YOLO11, for ship detection, particularly because it has not been tested on SAR images prior to this research. YOLO11 was selected for its recent release and potential improvements over previous iterations. To assess its effectiveness, the algorithm is compared with earlier YOLO versions using SAR imagery. The dataset is categorized into two subsets: open ocean images and coastal images, where distinguishing ships from coastal structures presents a significant challenge. The advantages and limitations of YOLO11 are thoroughly examined through a comparative analysis with its predecessors. Results indicate that YOLO11 outperforms earlier versions in most scenarios, particularly excelling in open ocean environments. Although ship detection from SAR images is inherently difficult, YOLO11 achieves promising Precision, Recall, and mAP values of 0.865, 0.813, and 0.792, respectively. Its performance in open ocean images exceeds these average values, highlighting YOLO11's efficacy in maritime surveillance. However, performance in coastal images is lower, with YOLOv10 performing closely to YOLO11 in these cases. The findings underscore YOLO11's effectiveness for ship detection from SAR images, showcasing its enhanced ability to detect ships in challenging environments and emphasizing its relevance for future maritime applications. © 2024 IEEE.
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    Avionics Design for a Subsonic Rocket Carrying a Scientific Experiment Unit
    (Ieee, 2022) Bakirci, Murat; Ozer, Muhammed Mirac
    In this study, the avionics system of a solid propellant rocket flying at subsonic speed, which can carry, distribute and safely recover a payload carrying out a scientific experiment unit at an altitude of 10000 feet, is designed within the scope of the specified functionality and requirements of the system. The main avionics system is designed to contain components used to document flight data, transmit them instantaneously to the ground station, and perform recovery. In addition, a redundant avionics system has been designed to ensure safe operation and recovery in case of potential problems. Based on the analysis of the system requirements, an avionics system scheme has been proposed that offers efficient, low-cost, reliable flight and mission capability. Methods of minimizing the effects of in-flight vibrational effects on the payload and avionics system were also discussed. All analyzes and tests of the designed systems are provided to ensure that engineers extract as much information as possible to verify the design, ensure a safe flight and a successful payload mission.
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    Boosting Aircraft Monitoring and Security through Ground Surveillance Optimization with YOLOv9
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bakirci, Murat; Bayraktar, Irem
    The integration of object detection algorithms into aircraft tracking and ground surveillance systems presents a myriad of security advantages, bolstering the protection of critical infrastructure. These algorithms are instrumental in enforcing access control measures by continuously monitoring and discerning between authorized and unauthorized access to parked aircraft. With ongoing refinements, they serve as crucial components of intrusion detection systems, promptly alerting security personnel to any suspicious or unauthorized activities in the vicinity of grounded aircraft. Effective training of detection algorithms enhances their analytical capabilities, enabling them to discern between routine operations and security-threatening situations with greater precision. Notably, one of the pivotal applications lies in supporting digital forensic investigations, as these algorithms provide detailed activity logs, facilitating comprehensive post-incident analyses and bolstering forensic efforts to understand security incidents or breaches. In this investigation, we assessed the efficacy of the YOLOv9 detection algorithm in identifying aircraft situated on the ground surface. Furthermore, we highlight the significance of satellite imagery in dataset acquisition for object detection algorithms, particularly emphasizing the role of Low-Earth-Orbit (LEO) satellites in real-time image acquisition. Through this comprehensive analysis, we underscore the pivotal role of YOLOv9 in enhancing security measures and compliance with aviation security standards and regulations, ultimately fortifying the security posture within aviation environments. © 2024 IEEE.
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    Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bakirci, Murat; Dmytrovych, Petro; Bayraktar, Irem; Anatoliyovych, Oleh
    Aerial imaging and object detection with unmanned aerial vehicle (UAV) systems present unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of two cutting-edge object detection algorithms, YOLOv9 and YOLOv10, specifically tailored for vehicle detection in UAVcaptured traffic images. Leveraging a custom dataset derived from UAV aerial imaging, both algorithms were trained and evaluated to assess their performance in terms of speed and accuracy. The experimental results reveal that while YOLOv9 demonstrates a marginally superior inference speed, YOLOv10 excels slightly in detection accuracy. © 2024 IEEE.
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    Comparative Performance of YOLOv9 and YOLOv10 for Vehicle Detection Towards Real-Time Traffic Surveillance with UAVs
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bakirci, Murat; Bayraktar, Irem
    Intelligent transportation systems (ITS) have gained significant traction since the 1980s and 1990s, driven by technological advancements and increasing urbanization, which have caused intense transportation challenges. Drone systems, with their superior imaging capabilities, offer critical solutions for swift traffic surveillance, surpassing traditional monitoring systems. In this context, object recognition is crucial, and the YOLO algorithm stands out for its speed and efficiency. This study conducts a detailed performance evaluation of the YOLOv9 and YOLOv10 networks for motor vehicle classification through aerial images captured by drone platforms. Datasets were created from these UAV-based traffic images, and the performances of both algorithms were measured and compared. The results were analyzed to highlight YOLOv9 and YOLOv10's strengths and drawbacks. Additionally, the study discusses qualitative aspects, including advantages, disadvantages, and potential improvements for both algorithms in aerial traffic monitoring. © 2024 IEEE.
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    Data-Driven System Identification of a Modified Differential Drive Mobile Robot Through On-Plane Motion Tests
    (Aves, 2023) Bakirci, Murat
    A set of system identification experiments are conducted for a modified differential drive robot. A linear model was developed by performing identification experiments to verify the model and determine unknown parameters by utilizing the motion profiles of the robot. A discrete model based on travelled distance increment was used. Nonlinear model estimates have also been generated using automated identification functions from the MATLAB's system identification toolbox. The linear model was tested through obtained data and the results were compared with the nonlinear model. It was observed that the assumption of the linearly time-invariant model allows for the state-space formulas to be implemented so that these reproductions are developed in a relatively simple structure that does not unnecessarily complicate the control system.
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    Design and Aerodynamic Analysis of a Rocket Nose Cone with Specific Fineness Ratio
    (Institute of Electrical and Electronics Engineers Inc., 2021) Bakirci, Murat
    The design of nose cones of rockets has attracting more attention with the developing rocket technology. It is a necessity to design the nose cone that best suits both the physical characteristics of the rocket and the flight parameters to increase flight performance. In this study, nose cone design and aerodynamic analysis for a subsonic rocket are discussed. The geometric design was handled and the tangent ogive, which is the most suitable nose cone geometry for the rocket planned to be produced, was obtained. Depending on the flight parameters, the optimal cone length was calculated and then the entire cone geometry was created. As a result, a tangent ogive nose cone with a fineness ratio of 3.33 was obtained. Through Newton flow theory, aerodynamic coefficients were computed numerically and their variations according to specific parameters were examined. For the nose cone, which will be exposed to high drag during flight, aerodynamic heating has been examined by considering the flight parameters. Thermal analysis was made according to flight data suitable for the rocket under construction, and stagnation/recovery temperatures were computed. By examining the variation of the temperature according to flight parameters, materials that can be used in nose cone production were discussed. © 2021 IEEE.
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    Drone-Assisted Path Planning Optimization for Mobile Robots in Dynamic Scenarios
    (Institute of Electrical and Electronics Engineers Inc., 2023) Bakirci, Murat; Ozer, Muhammed Mirac
    In this study, the seamless integration of drones into military distribution operations is facilitated by coordinating the movements of mobile robots in both time and space. The approach consists of two key stages: first, the development of an iterative exact solution methodology to establish the optimal route for the mobile robot, and second, the optimization of the drone's route through a mixed integer linear programming model. The performance of this approach is assessed by initiating the process with the shortest route for the mobile robot and iteratively refining assignment and routing decisions. The primary objective of this study is to enhance the simultaneous deployment of drones and mobile robots within military zones. By doing so, the aim is to harness the full potential of unmanned aerial vehicles (UAVs) more efficiently and lay a solid foundation for future technological advancements. © 2023 IEEE.
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    Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical perspective
    (Elsevier, 2024) Bakirci, Murat
    It is noteworthy that comprehensive exploration of atmospheric measurements in the horizontal plane using aerial platforms, necessitating high autonomy, has not been extensively covered in the existing literature. This research presents a systematic numerical approach to effective air pollution mapping achieved through the integration of horizontal and vertical air pollution measurements conducted using a fully autonomous unmanned aerial vehicle (UAV) platform. The developed robust navigation model enables the UAV to efficiently scan the extensive measurement area, which is subdivided into smaller sub-areas using the polygonal decomposition technique, resulting in a comprehensive map of the entire region. Furthermore, technical analysis determines the optimal flight speed, leading to air pollution measurements in up to 30 % more areas and ensuring more consistent results. The simulation results illustrate the effective mapping of the entire area by aggregating air pollution measurements from sub-areas, with seamless transitions emphasizing the accuracy and consistency of the employed air pollution mapping technique. This systematic method offers numerous advantages, including rapid air pollution source identification and swift response capabilities. Moreover, this approach holds potential for various applications, such as forest fire monitoring and natural resources assessment, by equipping UAVs with additional equipment like cameras alongside atmospheric sensors.
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    Electrical Architecture of the Recovery System of a High Powered Rocket with Payload
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bakirci, Murat; Ozer, Muhammed Mirac
    In this study, a recovery system design is presented for a high-powered rocket, which can carry and place a payload that performs scientific value experiments up to any altitude, as well as recovering flight data, and providing safe recovery of both the rocket and the payload. Rescue systems were designed, individually tested and integrated as a dual deployment with the drogue parachute opening at the apogee and the main parachute opening at lower altitude. However, the presented system is a reusable recovery mechanism that reduces consumed materials and speeds up the preparation process. This design engineering builds an infrastructure to improve the system by making a positive contribution to future studies and eliminating the shortcomings of past studies with an overview of the advantages and disadvantages of various systems. © 2022 IEEE.
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    Enhancing air pollution mapping with autonomous UAV networks for extended coverage and consistency
    (Elsevier Science Inc, 2024) Bakirci, Murat
    In the context of today's pressing air pollution challenges, accurate and consistent air pollution mapping plays a pivotal role in understanding pollutant distribution and identifying pollution sources. While current technologies, including sensors and unmanned aerial vehicles (UAVs), have begun to address this issue, the full potential of UAV-based solutions remains largely untapped. This pioneering numerical study demonstrates the effective feasibility of precise and consistent air pollution mapping in local areas that exceed the coverage capacity of a single UAV. The approach involves employing multiple UAVs, which requires rigorous mission planning encompassing various complex stages. These stages include subdividing the mapping area into manageable subareas, evaluating the technical capabilities of each UAV, assigning specific tasks to UAVs, and conducting individual mapping operations. By endowing UAVs with full autonomy, horizontal air pollution maps are generated across different layers within the designated area. This method's distinct advantage is its simultaneous acquisition of vertical profiles at all points within the study region, eliminating the need for additional efforts. Through strategic technical analysis, it was revealed that each UAV's mission coverage area could be expanded by over 30%, leading to more consistent air pollution mapping. Furthermore, this finding suggests a reduction of up to 25% in the total number of UAVs required for studies covering significantly larger areas.