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  • 1
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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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  • 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 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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  • 4
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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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  • 5
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    Cuba After Castro (2023)
    RAND Corporation
    Details
    Publication Date: 2023-10-05
    Description: When the end of the Castro era arrives, the successor government and the Cuban people will need to answer certain questions: How is Castro's more than four-decade rule likely to affect a post-Castro Cuba? What will be the political, social, and economic challenges Cuba will confront? What are the impediments to Cuba's economic development and democratic transition? The authors examine Castro's political legacies, Cuba's generational and racial divisions, its demographic predicament, the legacy of a centralized economy, and the need for industrial restructuring.
    Keywords: Political Science ; Technology ; bic Book Industry Communication::J Society & social sciences::JP Politics & government::JPS International relations ; bic Book Industry Communication::J Society & social sciences::JW Warfare & defence ; bic Book Industry Communication::T Technology, engineering, agriculture::TT Other technologies & applied sciences::TTM Military engineering
    Language: English
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  • 6
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    IPOC Creep (2017)
    GFZ Data Services
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    Publication Date: 2020-02-12
    Language: English
    Type: info:eu-repo/semantics/workingPaper
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  • 7
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    Hypocentral temperatures, crustal seismogenic thickness and 3D thermal model of the South Caribbean and NW South America (2022)
    GFZ Data Services
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    Publication Date: 2023-01-17
    Description: This data repository contains the 3D steady-state thermal field computed for the South Caribbean and NW South America down to 75 km depth, the modelled hypocentral temperatures, the depths to the upper and lower stability transitions, as well as the seismogenic thickness calculated from selected earthquakes of the ISC Bulletin (International Seismological Centre, 2022). All methodological details can be found in the main publication (see section 2). We used the uppermost 75 km of the gravity-constrained structural and density model of Gómez-García et al. (2020, 2021) to derive the 3D thermal configuration of the study area. A steady-state approach was followed, in which upper and lower boundary conditions were set to run the thermal experiments using the software GOLEM (Cacace amp; Jacquey, 2017; Jacquey amp; Cacace, 2017). We selected earthquakes from the ISC Bulletin from January 1980 to January 2021 (International Seismological Centre, 2022), considering the magnitude of completeness for different periods, removing earthquakes without depth, set as 0 km or fixed, as well as those with reported hypocentral depth errors gt;30 km. Of this set, we selected the crustal earthquakes, located between the topo-bathymetry from the GEBCO relief (Weatherall et al., 2015) and the Moho depth from the GEMMA model (Reguzzoni amp; Sampietro, 2015), interpolated to a resolution of 5 km. From this earthquake subset we computed the upper and lower stability transitions for seismogenesis, as the 10th and 90th percentiles (D10 and D90), respectively, of the hypocentral depths. These percentiles were mapped on a latitude-longitude grid, using for each grid node its 20 closest earthquakes as sample. The hypocentral temperatures and the temperatures at the D10 and D90 crustal depths were calculated from the lithospheric-scale thermal model. Lastly, the crustal seismogenic thickness was computed as the difference between D90 and D10 for each grid node. For more details about the modelling approach and interpretation of the results, we kindly ask the reader to refer to the main publication: Gomez-Garcia et al., (2022).
    Language: English
    Type: info:eu-repo/semantics/workingPaper
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  • 8
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    Anillo (2021)
    GFZ Data Services
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    Publication Date: 2021-06-22
    Description: The Anillo is a dense temporary seismic and geodetic network extending approximately 200 km along the strike of the subduction zone in North Chile in order to investigate how earthquakes and aseismic slip scale over a broader spectrum of source sizes, to understand the complex relationships between seismic and aseismic deformation, and to identify possible structural controls. This experiment is embedded into a larger scale experimental effort carried out by institutions in Germany and Chile. Waveform data are available from the GEOFON data centre, under network code Y6.
    Language: English
    Type: info:eu-repo/semantics/workingPaper
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  • 9
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    IPOC Creep (2017)
    GFZ Data Services
    Details
    Publication Date: 2023-06-12
    Description: Abstract
    Description: IPOC Creep is an array of 11 creepmeters installed along 4 active segments oft eh Atacama Fault Zone in Northern Chile. Installation of instruments started in 2008 within the framework of the Integrated Plate-boundary Observatory Chile (IPOC) and was completed in 2011. All installations are designed by the authors and follow a general concept, but are adapted to each site specifically. All the installed instruments use solid 12 mm thick invar rods as length standards, which are firmly attached to a concrete foundation in the hanging wall of the fault and pass through a PVC pipe to the footwall side of the fault where it is fixed to another concrete foundation. The creepmeters are buried at a depth of 30 - 70 cm, in order to increase the signal-to-noise ratio. We use a LVDT (linear variable differential transformer) with a range of 50 mm to monitor the relative displacement of the free end of the rod relative to the fixation point. Displacement is measured as voltage change and stored on a data logger with a sampling rate of 1/min (2008-2011 and 2/min (since 2011). Temperature at the rod is continuously measured with the same sampling rate to correct for thermal expansion and contraction of the length standard. The length of the instrument is dependent on the geometry at each site and ranges between 2 and 9 m. More specific information on each site can be found on http://www.ipoc-network.org/index.php/observatory/creepmeter.html . The Data is stored as time series since the initial start of operation of each creepmeter until July 2016. Data format is asci and contains 4 columns: 1st column Date[D.M.Y] 2nd column Time [HH:MM:SS] 3rd column ReferenceSensor[V]The reference signal is a steady signal of 1V and fluctuations indicate general voltage fluctuations in the setup. By normalizing to the reference signal it is possible to correct for these voltage changes. 4th column CreepSensor[V]The measured voltage of the CreepSensor is linearly proportional to the actual displacement. It can be converted to micrometers as follows: Displacement(µm) = (CreepSensor(t2)[V] - CreepSensor(t1)[V]) * 10000.
    Keywords: Tectonic Creep ; Active Faults ; Fault Displacement Rate ; Convergent Margin ; Trench Parallel Fault System ; IPOC
    Language: English
    Type: Dataset
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    DATA GFZ
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    Evaluation of the Military Spouse Employment Partnership (2023)
    RAND Corporation
    Details
    Publication Date: 2023-10-05
    Description: In this first of two reports, RAND researchers assess whether and how The Military Spouse Employment Partnership addresses its objectives in supporting the employment of military spouses and closing the wage gap between military and civilian spouses. This report documents the first phase of research to help meet the 2015 National Defense Authorization Act requirement for a “report evaluating the progress of military spouse employment programs."
    Keywords: Management & Organizational Behavior ; Law ; Finance ; bic Book Industry Communication::K Economics, finance, business & management::KF Finance & accounting::KFC Accounting ; bic Book Industry Communication::K Economics, finance, business & management::KJ Business & management::KJM Management & management techniques::KJMV Management of specific areas::KJMV2 Personnel & human resources management ; bic Book Industry Communication::L Law::LN Laws of Specific jurisdictions::LNH Employment & labour law
    Language: English
    Format: image/jpeg
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