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  • 1
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    UAV or Drones for Remote Sensing Applications : Volume 2 (2018)
    Basel, Beijing, Wuhan, Barcelona, Belgrade : MDPI
    Details
    Keywords: Precision agriculture ; construction, mining, pest detection, forestry, mammal species tracking search and rescue ; target tracking, the monitoring of the atmosphere ; chemical, biological, and natural disaster phenomena ; fire prevention, flood prevention ; reef, volcanic monitoring, Earth science research pollution monitoring, micro-climates , land use precision agriculture, ecology, atmospheric research, bio-security, forestry, fire monitoring, quick response measurements for emergency disaster, pollution monitoring, volcanic gas sampling, monitoring of gas pipelines, biological/chemosensing tasks, and humanitarian observations
    Description / Table of Contents: Poblete, T.; Ortega-Farías, S.; Ryu, D. Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard. Sensors 2018, 18(2), 397; https://doi.org/10.3390/s18020397 --- Qu, Y.; Huang, J.; Zhang, X. Rapid 3D Reconstruction for Image Sequence Acquired from UAV Camera. Sensors 2018, 18(1), 225; https://doi.org/10.3390/s18010225 --- Ribeiro-Gomes, K.; Hernández-López, D.; Ortega, J.; Ballesteros, R.; Poblete, T.; Moreno, M. Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors 2017, 17(10), 2173; https://doi.org/10.3390/s17102173 --- Ridolfi, E.; Buffi, G.; Venturi, S.; Manciola, P. Accuracy Analysis of a Dam Model from Drone Surveys. Sensors 2017, 17(8), 1777; https://doi.org/10.3390/s17081777 --- Rivas Casado, M.; González, R.; Ortega, J.; Leinster, P.; Wright, R. Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization. Sensors 2017, 17(10), 2210; https://doi.org/10.3390/s17102210 --- Sandino, J.; Wooler, A.; Gonzalez, F. Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery. Sensors 2017, 17(10), 2196; https://doi.org/10.3390/s17102196 --- Sandino, J.; Gonzalez, F.; Mengersen, K.; Gaston, K. UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands. Sensors 2018, 18(2), 605; https://doi.org/10.3390/s18020605 --- Sandino, J.; Pegg, G.; Gonzalez, F.; Smith, G. Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence. Sensors 2018, 18(4), 944; https://doi.org/10.3390/s18040944 --- Seo, S.; Choi, J.; Song, J. Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard. Sensors 2017, 17(10), 2200; https://doi.org/10.3390/s17102200 --- Shen, J.; Su, Y.; Liang, Q.; Zhu, X. Calculation and Identification of the Aerodynamic Parameters for Small-Scaled Fixed-Wing UAVs. Sensors 2018, 18(1), 206; https://doi.org/10.3390/s18010206 --- Sola-Guirado, R.; Castillo-Ruiz, F.; Jiménez-Jiménez, F.; Blanco-Roldan, G.; Castro-Garcia, S.; Gil-Ribes, J. Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery. Sensors 2017, 17(8), 1743; https://doi.org/10.3390/s17081743 --- Torres-Rua, A. Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature. Sensors 2017, 17(7), 1499; https://doi.org/10.3390/s17071499 --- Uddin, M.; Mansour, A.; Jeune, D.; Ayaz, M.; Aggoune, e. UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring. Sensors 2018, 18(2), 555; https://doi.org/10.3390/s18020555 --- Vanegas, F.; Bratanov, D.; Powell, K.; Weiss, J.; Gonzalez, F. A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data. Sensors 2018, 18(1), 260; https://doi.org/10.3390/s18010260 --- Wang, X.; Jiang, P.; Li, D.; Sun, T. Curvature Continuous and Bounded Path Planning for Fixed-Wing UAVs. Sensors 2017, 17(9), 2155; https://doi.org/10.3390/s17092155 --- Zhao, Y.; Ma, J.; Li, X.; Zhang, J. Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. Sensors 2018, 18(3), 712; https://doi.org/10.3390/s18030712 --- Zheng, H.; Bai, T.; Wang, Q.; Cao, F.; Shao, L.; Sun, Z. Experimental Study of Multispectral Characteristics of an Unmanned Aerial Vehicle at Different Observation Angles. Sensors 2018, 18(2), 428; https://doi.org/10.3390/s18020428 --- Zhong, J.; Lei, T.; Yao, G. Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks. Sensors 2017, 17(12), 2720; https://doi.org/10.3390/s17122720
    Pages: Online-Ressource (X, 332 Seiten) , Illustrationen, Diagramme, Karten
    Edition: Printed Edition of the Special Issue Published in Sensors
    ISBN: 9783038971122
    URL: https://www.mdpi.com/books/pdfview/book/868
    Language: English
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  • 2
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    UAV or Drones for Remote Sensing Applications : Volume 1 (2018)
    Basel, Beijing, Wuhan, Barcelona, Belgrade : MDPI
    Details
    Keywords: Precision agriculture ; construction, mining, pest detection, forestry, mammal species tracking search and rescue ; target tracking, the monitoring of the atmosphere ; chemical, biological, and natural disaster phenomena ; fire prevention, flood prevention ; reef, volcanic monitoring, Earth science research pollution monitoring, micro-climates , land use precision agriculture, ecology, atmospheric research, bio-security, forestry, fire monitoring, quick response measurements for emergency disaster, pollution monitoring, volcanic gas sampling, monitoring of gas pipelines, biological/chemosensing tasks, and humanitarian observations
    Description / Table of Contents: Aleotti, J.; Micconi, G.; Caselli, S.; Benassi, G.; Zambelli, N.; Bettelli, M.; Zappettini, A. Detection of Nuclear Sources by UAV Teleoperation Using a Visuo-Haptic Augmented Reality Interface. Sensors 2017, 17(10), 2234; https://doi.org/10.3390/s17102234 --- Audi, A.; Pierrot-Deseilligny, M.; Meynard, C.; Thom, C. Implementation of an IMU Aided Image Stacking Algorithm in a Digital Camera for Unmanned Aerial Vehicles. Sensors 2017, 17(7), 1646; https://doi.org/10.3390/s17071646 --- Bae, M.; Yoo, S.; Jung, J.; Park, S.; Kim, K.; Lee, J.; Kim, H. Devising Mobile Sensing and Actuation Infrastructure with Drones. Sensors 2018, 18(2), 624; https://doi.org/10.3390/s18020624 --- Brede, B.; Lau, A.; Bartholomeus, H.; Kooistra, L. Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sensors 2017, 17(10), 2371; https://doi.org/10.3390/s17102371 --- Christiansen, M.; Laursen, M.; Jørgensen, R.; Skovsen, S.; Gislum, R. Designing and Testing a UAV Mapping System for Agricultural Field Surveying. Sensors 2017, 17(12), 2703; https://doi.org/10.3390/s17122703 --- Ćwiąkała, P.; Kocierz, R.; Puniach, E.; Nędzka, M.; Mamczarz, K.; Niewiem, W.; Wiącek, P. Assessment of the Possibility of Using Unmanned Aerial Vehicles (UAVs) for the Documentation of Hiking Trails in Alpine Areas. Sensors 2018, 18(1), 81; https://doi.org/10.3390/s18010081 --- Dong, Q.; Liu, J. Seamline Determination Based on PKGC Segmentation for Remote Sensing Image Mosaicking. Sensors 2017, 17(8), 1721; https://doi.org/10.3390/s17081721 --- Fernández-Guisuraga, J.; Sanz-Ablanedo, E.; Suárez-Seoane, S.; Calvo, L. Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges. Sensors 2018, 18(2), 586; https://doi.org/10.3390/s18020586 --- Hassan-Esfahani, L.; Ebtehaj, A.; Torres-Rua, A.; McKee, M. Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture. Sensors 2017, 17(9), 2106; https://doi.org/10.3390/s17092106 --- Hinas, A.; Roberts, J.; Gonzalez, F. Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System. Sensors 2017, 17(12), 2929; https://doi.org/10.3390/s17122929 --- Hoshiba, K.; Washizaki, K.; Wakabayashi, M.; Ishiki, T.; Kumon, M.; Bando, Y.; Gabriel, D.; Nakadai, K.; Okuno, H. Design of UAV-Embedded Microphone Array System for Sound Source Localization in Outdoor Environments. Sensors 2017, 17(11), 2535; https://doi.org/10.3390/s17112535 --- Kikutis, R.; Stankūnas, J.; Rudinskas, D.; Masiulionis, T. Adaptation of Dubins Paths for UAV Ground Obstacle Avoidance When Using a Low Cost On-Board GNSS Sensor. Sensors 2017, 17(10), 2223; https://doi.org/10.3390/s17102223 --- Klosterman, S.; Richardson, A. Observing Spring and Fall Phenology in a Deciduous Forest with Aerial Drone Imagery. Sensors 2017, 17(12), 2852; https://doi.org/10.3390/s17122852 --- Kong, W.; Hu, T.; Zhang, D.; Shen, L.; Zhang, J. Localization Framework for Real-Time UAV Autonomous Landing: An On-Ground Deployed Visual Approach. Sensors 2017, 17(6), 1437; https://doi.org/10.3390/s17061437 --- Li, M.; Chen, R.; Zhang, W.; Li, D.; Liao, X.; Wang, L.; Pan, Y.; Zhang, P. A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching. Sensors 2017, 17(9), 2060; https://doi.org/10.3390/s17092060 --- Mesas-Carrascosa, F.; Verdú Santano, D.; Pérez Porras, F.; Meroño-Larriva, J.; García-Ferrer, A. The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants. Sensors 2017, 17(6), 1329; https://doi.org/10.3390/s17061329 --- Ortega-Terol, D.; Hernandez-Lopez, D.; Ballesteros, R.; Gonzalez-Aguilera, D. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images. Sensors 2017, 17(10), 2352; https://doi.org/10.3390/s17102352 --- Parsons, M.; Bratanov, D.; Gaston, K.; Gonzalez, F. UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring. Sensors 2018, 18(7), 2026; https://doi.org/10.3390/s18072026 --- Peterson, J.; Chaudhry, H.; Abdelatty, K.; Bird, J.; Kochersberger, K. Online Aerial Terrain Mapping for Ground Robot Navigation. Sensors 2018, 18(2), 630; https://doi.org/10.3390/s18020630 --- Poblete, T.; Ortega-Farías, S.; Moreno, M.; Bardeen, M. Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV). Sensors 2017, 17(11), 2488; https://doi.org/10.3390/s17112488
    Pages: Online-Ressource (X, 368 Seiten) , Illustrationen, Diagramme, Karten
    Edition: Printed Edition of the Special Issue Published in Sensors
    ISBN: 9783038970927
    URL: https://www.mdpi.com/books/pdfview/book/867
    Language: English
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  • 3
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    UAV‐Based Remote Sensing : Volume 1 (2018)
    Basel, Beijing, Wuhan, Barcelona, Belgrade : MDPI
    Details
    Keywords: UAV-Based Remote Sensing ; drones ; aerial robotics ; environmental science ; remote sensing ; UAV images ; augmented reality tools ; segmentation in digital surface models for 3D reconstruction ; detection ; location and grasping objects ; multi-target localization ; vision-based tracking in cooperative multi-UAV systems ; noise suppression techniques ; rectification for oblique images ; two-UAV communication system ; fuzzy-based hybrid control algorithms ; pedestrian detection and tracking as well as a range of atmospheric ; geological ; agricultural ; ecological ; reef ; wildlife ; building and construction ; coastal area coverage ; search and rescue (SAR) ; water plume temperature measurements ; aeromagnetic and archaeological surveys
    Description / Table of Contents: Ali, Z.; Wang, D.; Aamir, M. Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV. Sensors 2016, 16(5), 652; https://doi.org/10.3390/s16050652 --- Al-Kaff, A.; García, F.; Martín, D.; De La Escalera, A.; Armingol, J. Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs. Sensors 2017, 17(5), 1061; https://doi.org/10.3390/s17051061 --- Alvarado, M.; Gonzalez, F.; Erskine, P.; Cliff, D.; Heuff, D. A Methodology to Monitor Airborne PM10 Dust Particles Using a Small Unmanned Aerial Vehicle. Sensors 2017, 17(2), 343; https://doi.org/10.3390/s17020343 --- Bai, G.; Liu, J.; Song, Y.; Zuo, Y. Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform. Sensors 2017, 17(1), 98; https://doi.org/10.3390/s17010098 --- Balampanis, F.; Maza, I.; Ollero, A. Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing. Sensors 2017, 17(4), 808; https://doi.org/10.3390/s17040808 --- DeMario, A.; Lopez, P.; Plewka, E.; Wix, R.; Xia, H.; Zamora, E.; Gessler, D.; Yalin, A. Water Plume Temperature Measurements by an Unmanned Aerial System (UAS). Sensors 2017, 17(2), 306; https://doi.org/10.3390/s17020306 --- Evans, L.; Jones, T.; Pang, K.; Saimin, S.; Goossens, B. Spatial Ecology of Estuarine Crocodile (Crocodylus porosus) Nesting in a Fragmented Landscape. Sensors 2016, 16(9), 1527; https://doi.org/10.3390/s16091527 --- Fu, C.; Duan, R.; Kircali, D.; Kayacan, E. Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model. Sensors 2016, 16(9), 1406; https://doi.org/10.3390/s16091406 --- Gašparović, M.; Jurjević, L. Gimbal Influence on the Stability of Exterior Orientation Parameters of UAV Acquired Images. Sensors 2017, 17(2), 401; https://doi.org/10.3390/s17020401 --- Greatwood, C.; Richardson, T.; Freer, J.; Thomas, R.; MacKenzie, A.; Brownlow, R.; Lowry, D.; Fisher, R.; Nisbet, E. Atmospheric Sampling on Ascension Island Using Multirotor UAVs. Sensors 2017, 17(6), 1189; https://doi.org/10.3390/s17061189 --- Itkin, M.; Kim, M.; Park, Y. Development of Cloud-Based UAV Monitoring and Management System. Sensors 2016, 16(11), 1913; https://doi.org/10.3390/s16111913 --- Kedzierski, M.; Delis, P. Fast Orientation of Video Images of Buildings Acquired from a UAV without Stabilization. Sensors 2016, 16(7), 951; https://doi.org/10.3390/s16070951 --- Kyristsis, S.; Antonopoulos, A.; Chanialakis, T.; Stefanakis, E.; Linardos, C.; Tripolitsiotis, A.; Partsinevelos, P. Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm. Sensors 2016, 16(11), 1844; https://doi.org/10.3390/s16111844 --- Li, B.; Jiang, Y.; Sun, J.; Cai, L.; Wen, C. Development and Testing of a Two-UAV Communication Relay System. Sensors 2016, 16(10), 1696; https://doi.org/10.3390/s16101696 --- Li, H.; Wu, L.; Li, Y.; Li, C.; Li, H. A Novel Method for Vertical Acceleration Noise Suppression of a Thrust-Vectored VTOL UAV. Sensors 2016, 16(12), 2054; https://doi.org/10.3390/s16122054 --- Liu, Z.; Wang, Z.; Xu, M. Cubature Information SMC-PHD for Multi-Target Tracking. Sensors 2016, 16(5), 653; https://doi.org/10.3390/s16050653 --- Liu, J.; Guo, B.; Jiang, W.; Gong, W.; Xiao, X. Epipolar Rectification with Minimum Perspective Distortion for Oblique Images. Sensors 2016, 16(11), 1870; https://doi.org/10.3390/s16111870 --- Liu, C.; Liu, J.; Song, Y.; Liang, H. A Novel System for Correction of Relative Angular Displacement between Airborne Platform and UAV in Target Localization. Sensors 2017, 17(3), 510; https://doi.org/10.3390/s17030510 --- Ma, Y.; Wu, X.; Yu, G.; Xu, Y.; Wang, Y. Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery. Sensors 2016, 16(4), 446; https://doi.org/10.3390/s16040446 --- Macharet, D.; Perez-Imaz, H.; Rezeck, P.; Potje, G.; Benyosef, L.; Wiermann, A.; Freitas, G.; Garcia, L.; Campos, M. Autonomous Aeromagnetic Surveys Using a Fluxgate Magnetometer. Sensors 2016, 16(12), 2169; https://doi.org/10.3390/s16122169
    Pages: Online-Ressource (VII, 385 Seiten) , Illustrationen, Diagramme, Karten
    Edition: Printed Edition of the Special Issue Published in Sensors
    ISBN: 9783038427780
    URL: http://www.mdpi.com/books/pdfview/book/604
    Language: English
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  • 4
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    UAV‐Based Remote Sensing : Volume 2 (2018)
    Basel, Beijing, Wuhan, Barcelona, Belgrade : MDPI
    Details
    Keywords: UAV-Based Remote Sensing ; drones ; aerial robotics ; environmental science ; remote sensing ; UAV images ; augmented reality tools ; segmentation in digital surface models for 3D reconstruction ; detection ; location and grasping objects ; multi-target localization ; vision-based tracking in cooperative multi-UAV systems ; noise suppression techniques ; rectification for oblique images ; two-UAV communication system ; fuzzy-based hybrid control algorithms ; pedestrian detection and tracking as well as a range of atmospheric ; geological ; agricultural ; ecological ; reef ; wildlife ; building and construction ; coastal area coverage ; search and rescue (SAR) ; water plume temperature measurements ; aeromagnetic and archaeological surveys
    Description / Table of Contents: Mesas-Carrascosa, F.; Notario García, M.; Meroño de Larriva, J.; García-Ferrer, A. An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas. Sensors 2016, 16(11), 1838; https://doi.org/10.3390/s16111838 --- Ni, J.; Yao, L.; Zhang, J.; Cao, W.; Zhu, Y.; Tai, X. Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System. Sensors 2017, 17(3), 502; https://doi.org/10.3390/s17030502 --- Popescu, D.; Ichim, L.; Stoican, F. Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing. Sensors 2017, 17(3), 446; https://doi.org/10.3390/s17030446 --- Ramon Soria, P.; Arrue, B.; Ollero, A. Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments. Sensors 2017, 17(1), 103; https://doi.org/10.3390/s17010103 --- Rodriguez Salazar, L.; Cobano, J.; Ollero, A. Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation. Sensors 2017, 17(1), 8; https://doi.org/10.3390/s17010008 --- Ruano, S.; Cuevas, C.; Gallego, G.; García, N. Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators. Sensors 2017, 17(2), 297; https://doi.org/10.3390/s17020297 --- Shi, C.; Salous, S.; Wang, F.; Zhou, J. Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment. Sensors 2016, 16(12), 2072; https://doi.org/10.3390/s16122072 --- Sørensen, L.; Jacobsen, L.; Hansen, J. Low Cost and Flexible UAV Deployment of Sensors. Sensors 2017, 17(1), 154; https://doi.org/10.3390/s17010154 --- Sun, J.; Li, B.; Jiang, Y.; Wen, C. A Camera-Based Target Detection and Positioning UAV System for Search and Rescue (SAR) Purposes. Sensors 2016, 16(11), 1778; https://doi.org/10.3390/s16111778 --- Tian, J.; Li, X.; Duan, F.; Wang, J.; Ou, Y. An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement. Sensors 2016, 16(5), 662; https://doi.org/10.3390/s16050662 --- Tožička, J.; Komenda, A. Diverse Planning for UAV Control and Remote Sensing. Sensors 2016, 16(12), 2199; https://doi.org/10.3390/s16122199 --- Vanhoutte, E.; Mafrica, S.; Ruffier, F.; Bootsma, R.; Serres, J. Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot. Sensors 2017, 17(3), 571; https://doi.org/10.3390/s17030571 --- Vetrella, A.; Fasano, G.; Accardo, D.; Moccia, A. Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems. Sensors 2016, 16(12), 2164; https://doi.org/10.3390/s16122164 --- Villa, T.; Gonzalez, F.; Miljievic, B.; Ristovski, Z.; Morawska, L. An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives. Sensors 2016, 16(7), 1072; https://doi.org/10.3390/s16071072 --- Villa, T.; Salimi, F.; Morton, K.; Morawska, L.; Gonzalez, F. Development and Validation of a UAV Based System for Air Pollution Measurements. Sensors 2016, 16(12), 2202; https://doi.org/10.3390/s16122202 --- Wang, X.; Liu, J.; Zhou, Q. Real-Time Multi-Target Localization from Unmanned Aerial Vehicles. Sensors 2017, 17(1), 33; https://doi.org/10.3390/s17010033 --- Wang, Y.; Cao, Y. Coordinated Target Tracking via a Hybrid Optimization Approach. Sensors 2017, 17(3), 472; https://doi.org/10.3390/s17030472 --- Xu, Y.; Yu, G.; Wang, Y.; Wu, X.; Ma, Y. A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images. Sensors 2016, 16(8), 1325; https://doi.org/10.3390/s16081325 --- Xu, L.; Luo, H.; Hui, B.; Chang, Z. Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters. Sensors 2016, 16(9), 1443; https://doi.org/10.3390/s16091443 --- Yan, Y.; Gao, F.; Deng, S.; Su, N. A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction. Sensors 2017, 17(2), 222; https://doi.org/10.3390/s17020222
    Pages: Online-Ressource (VII, 393 Seiten) , Illustrationen, Diagramme, Karten
    Edition: Printed Edition of the Special Issue Published in Sensors
    ISBN: 9783038428565
    URL: http://www.mdpi.com/books/pdfview/book/605
    Language: English
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  • 5
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    Trajectory planning for hypersonic reentry vehicle satisfying deterministic and probabilistic constraints (2020)
    Elsevier
    Details
    Publication Date: 2020-12-01
    Print ISSN: 0094-5765
    Electronic ISSN: 1879-2030
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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  • 6
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    Comparison of station keeping strategies for long endurance autonomous surface vehicle (2019)
    Springer
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    Publication Date: 2019-02-13
    Print ISSN: 0948-4280
    Electronic ISSN: 1437-8213
    Topics: Geosciences , Technology
    Published by Springer
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  • 7
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    An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost (2018)
    Elsevier
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    Publication Date: 2018-04-01
    Print ISSN: 0968-090X
    Electronic ISSN: 1879-2359
    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 8
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    Aerospace, Vol. 5, Pages 66: A Multi-Fidelity Approach for Aerodynamic Performance Computations of Formation Flight (2018)
    MDPI
    In: Aerospace
    Details
    Publication Date: 2018-06-16
    Description: Aerospace, Vol. 5, Pages 66: A Multi-Fidelity Approach for Aerodynamic Performance Computations of Formation Flight Aerospace doi: 10.3390/aerospace5020066 Authors: Diwakar Singh Antonios Antoniadis Panagiotis Tsoutsanis Hyo-Sang Shin Antonios Tsourdos Samuel Mathekga Karl Jenkins This paper introduces a multi-fidelity computational framework for the analysis of aerodynamic performance of flight formation. The Vortex Lattice and Reynolds Averaged Navier–Stokes methods form the basis of the framework, as low- and high-fidelity, respectively. Initially, the computational framework is validated for an isolated wing, and then two rectangular NACA23012 wings are considered for assessing the aerodynamic performance of this formation; the optimal relative position is through the multi-fidelity framework based on the total drag reduction. The performance estimates are in good agreement with experimental measurements of the same configuration. Total aerodynamic performance of formation flight is also assessed with respect to attitude variations of the lifting bodies involved. The framework is also employed to determine the optimal position of blended-wing-body unmanned aerial vehicles in tandem formation flight.
    Electronic ISSN: 2226-4310
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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  • 9
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    Aerospace, Vol. 5, Pages 66: A Multi-Fidelity Approach for Aerodynamic Performance Computations of Formation Flight (2018)
    MDPI
    In: Aerospace
    Details
    Publication Date: 2018
    Description: This paper introduces a multi-fidelity computational framework for the analysis of aerodynamic performance of flight formation. The Vortex Lattice and Reynolds Averaged Navier–Stokes methods form the basis of the framework, as low- and high-fidelity, respectively. Initially, the computational framework is validated for an isolated wing, and then two rectangular NACA23012 wings are considered for assessing the aerodynamic performance of this formation; the optimal relative position is through the multi-fidelity framework based on the total drag reduction. The performance estimates are in good agreement with experimental measurements of the same configuration. Total aerodynamic performance of formation flight is also assessed with respect to attitude variations of the lifting bodies involved. The framework is also employed to determine the optimal position of blended-wing-body unmanned aerial vehicles in tandem formation flight.
    Electronic ISSN: 2226-4310
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
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  • 10
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    Sensors, Vol. 19, Pages 1824: Inspection of Aircraft Wing Panels Using Unmanned Aerial Vehicles (2019)
    MDPI
    In: Sensors
    Details
    Publication Date: 2019
    Description: In large civil aircraft manufacturing, a time-consuming post-production process is the non-destructive inspection of wing panels. This work aims to address this challenge and improve the defects’ detection by performing automated aerial inspection using a small off-the-shelf multirotor. The UAV is equipped with a wide field-of-view camera and an ultraviolet torch for implementing non-invasive imaging inspection. In particular, the UAV is programmed to perform the complete mission and stream video, in real-time, to the ground control station where the defects’ detection algorithm is executed. The proposed platform was mathematically modelled in MATLAB/SIMULINK in order to assess the behaviour of the system using a path following method during the aircraft wing inspection. In addition, two defect detection algorithms were implemented and tested on a dataset containing images obtained during inspection at Airbus facilities. The results show that for the current dataset the proposed methods can identify all the images containing defects.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by MDPI
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