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Imaging and Sensing for Unmanned Aircraft Systems: Deployment and Applications, Volume 2 [Kõva köide]

Edited by (Luleå University of Technology (LTU), Department of), Edited by (Universidade Federal Fluminense (UFF), Telecommunications Department, Brazil), Edited by (Instituto Tecnológico de Aeronáutica (ITA), Brazil), Edited by , Edited by (Karunya University (KU), ECE Department, India)
  • Formaat: Hardback, 277 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 18-Jun-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785616447
  • ISBN-13: 9781785616440
Teised raamatud teemal:
Imaging and Sensing for Unmanned Aircraft Systems: Deployment and Applications, Volume 2Suurem pilt
  • Formaat: Hardback, 277 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 18-Jun-2020
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785616447
  • ISBN-13: 9781785616440
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781785616440.html
Märksõnad:
This two-volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS).



Volume 1 concentrates on UAS control and performance methodologies including Computer Vision and Data Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision in Indoor and Outdoor Drones, Sensors and Computer Vision, and Small UAV for Persistent Surveillance.



Volume 2 focuses on UAS deployment and applications including UAV-CPSs as a Testbed for New Technologies and a Primer to Industry 5.0, Human-Machine Interface Design, Open Source Software (OSS) and Hardware (OSH), Image Transmission in MIMO-OSTBC System, Image Database, Communications Requirements, Video Streaming, and Communications Links, Multispectral vs Hyperspectral Imaging, Aerial Imaging and Reconstruction of Infrastructures, Deep Learning as an Alternative to Super Resolution Imaging, and Quality of Experience (QoE) and Quality of Service (QoS).
About the editors xi
Preface xiii
1 UAV-CPSs as a test bed for new technologies and a primer to Industry 5.0
1(22)
Ana Carolina B. Monteiro
Reinaldo P. Franca
Vania V. Estrela
Sandro R. Fernandes
Abdeldjalil Khelassi
R. Jenice Aroma
Kumudha Raimond
Yuzo Iano
Ali Arshaghi
1.1 Introduction
2(3)
1.2 Cloud computing
5(2)
1.3 Collective UAV learning
7(1)
1.4 Human computation, crowdsourcing and call centres
8(1)
1.5 Open-source and open-access resources
8(2)
1.6 Challenges and future directions
10(3)
1.7 Conclusions
13(1)
References
13(10)
2 UAS human factors and human-machine interface design
23(26)
Yixiang Lim
Alessandro Gardi
Roberto Sabatini
2.1 Introduction
23(3)
2.2 UAS HMI functionalities
26(3)
2.2.2 Reconfigurable displays
28(1)
2.2.2 Sense and avoid
28(1)
2.2.2 Mission planning and management
28(1)
2.2.2 Multi-platform coordination
28(1)
2.3 GCS HMI elements
29(4)
2.4 Human factors program
33(11)
2.4.4 Requirements definition, capture and refinement
36(2)
2.4.4 Task analysis
38(1)
2.4.4 Hierarchal task analysis
38(1)
2.4.4 Cognitive task analysis
39(1)
2.4.4 Critical task analysis
39(1)
2.4.4 Operational sequence diagram
40(1)
2.4.4 Systems design and development
41(1)
2.4.4 Design evaluation
42(1)
2.4.4 Verification and validation
43(1)
2.5 Future work
44(2)
2.6 Conclusions
46(1)
References
46(3)
3 Open-source software (OSS) and hardware (OSH) in UAVs
49(18)
Pawel Burdziakoxvski
Navid Razmjooy
Vania V. Estrela
Jude Hemanth
3.1 Introduction
49(1)
3.2 Open source software
50(1)
3.3 Open source UAS
51(4)
3.4 Universal messaging protocol
55(2)
3.5 GCS software
57(1)
3.6 Processing software
57(2)
3.7 Operator information and communication
59(2)
3.8 Open source platform
61(1)
3.9 Future work
61(3)
3.9.9 OSH challenges
61(1)
3.9.9 Open data
62(1)
3.9.9 Cloud data centre
62(1)
3.9.9 Crowd-sourced data in UAV-CPSs
63(1)
3.9.9 Control of UAV swarms
63(1)
3.10 Conclusions
64(1)
References
64(3)
4 Image transmission in UAV MIMO IWB-OSTBC system over Rayleigh channel using multiple description coding (MDC)
67(24)
Ali Arshaghi
Navid Razmjooy
Vania V. Estrela
Pawel Burdziakowski
Douglas A. Nascimento
Anand Deshpande
Prashant P. Patavardhan
4.1 Introduction
67(5)
4.1.1 The efficiency of the flat Rayleigh fading channel
72(1)
4.2 Multiple description coding
72(2)
4.3 Multiple input-multiple output
74(2)
4.4 Diversity
76(1)
4.5 Simulations results
77(5)
4.6 Discussion and future trends
82(2)
4.7 Conclusion
84(1)
References
85(6)
5 Image database of low-altitude UAV flights with flight condition-logged for photogrammetry, remote sensing, and computer vision
91(22)
Helosman Valente de Figueiredo
Osamu Saotome
Elcio H. Shiguemori
Paulo Silva Filho
Vania V. Estrela
5.1 Introduction
92(2)
5.1.1 Image processing system for UAVs
92(2)
5.2 The aerial image database framework
94(1)
5.2.2 Database requirements
94(1)
5.2.2 Database design
94(1)
5.3 Image capture process
95(3)
5.4 Results
98(2)
5.4.4 Images collected
98(2)
5.5 Use of the image database
100(6)
5.5.5 Mosaics
100(3)
5.5.5 Development of CV algorithms
103(3)
5.6 Conclusion and future works
106(1)
Acknowledgements
107(1)
References
107(6)
6 Communications requirements, video streaming, communications links and networked UAVs
113(20)
Hermes J. Loschi
Vania V. Estrela
D. Jude Hemanth
Sandro R. Fernandes
Yuzo Iano
Asif Ali Laghari
Asiya Khan
Hui He
Robert Sroufe
6.1 Introduction
114(1)
6.2 Flying Ad-hoc Networks
114(1)
6.3 The FANET protocol
115(4)
6.4 FANET: streaming and surveillance
119(2)
6.5 Discussion and future trends
121(7)
6.5.5 FNs' placement search algorithms
121(1)
6.5.5 Event detection and video quality selection algorithms
122(1)
6.5.5 Onboard video management (UAV)
123(1)
6.5.5 Video-rate adaptation for the fleet platform
123(1)
6.5.5 FNs coordination
123(1)
6.5.5 Data collection and presentation
124(1)
6.5.5 Software-Defined Networking
124(1)
6.5.5 Network Function Virtualisation
125(1)
6.5.5 Data Gathering versus Energy Harvesting
126(2)
6.6 Conclusion
128(1)
References
128(5)
7 Multispectral vs hyperspectral imaging for unmanned aerial vehicles: current and prospective state of affairs
133(24)
R. Jenice Aroma
Kumudha Raimond
Navid Razmjooy
Vania V. Estrela
Jude Hemanth
7.1 Introduction
133(3)
7.2 UAV imaging architecture and components
136(2)
7.2.2 Future scope for UAV
138(1)
7.3 Multispectral vs hyperspectral imaging instruments
138(3)
7.3.3 Multispectral imaging
138(2)
7.3.3 Hyperspectral imaging
140(1)
7.3.3 Satellite imaging vs UAV imaging
140(1)
7.4 UAV image processing workflow
141(3)
7.4.4 Atmospheric correction
142(1)
7.4.4 Spectral influence mapping
142(1)
7.4.4 Dimensionality reduction
143(1)
7.4.4 Computational tasks
143(1)
7.5 Data processing toolkits for spatial data
144(1)
7.6 UAV open data sets for research - multispectral and hyperspectral
144(3)
7.7 Applications of MSI and HSI UAV imaging
147(1)
7.7.7 Agriculture monitoring
147(1)
7.7.7 Coastal monitoring
147(1)
7.7.7 Forestry
147(1)
7.7.7 Urban planning
148(1)
7.7.7 Defence applications
148(1)
7.7.7 Environmental monitoring
148(1)
7.7.7 Other commercial uses
148(1)
7.8 Conclusion and future scope
148(2)
References
150(7)
8 Aerial imaging and reconstruction of infrastructures by UAVs
157(20)
Christoforos Kanellakis
Sina Sharif Mansouri
Emil Fresk
Dariusz Kominiak
George Nikolakopoulos
8.1 Introduction
157(1)
8.2 Related Studies
158(2)
8.3 Visual sensors and mission planner
160(2)
8.3.3 Image projection
160(1)
8.3.3 Path planner
161(1)
8.4 3D reconstruction
162(2)
8.4.4 Stereo mapping
162(1)
8.4.4 Monocular mapping
163(1)
8.5 Data-set collection
164(4)
8.5.5 Experimental setup
164(2)
8.5.5 Data set 1
166(1)
8.5.5 Data set 2
167(1)
8.6 Experimental results
168(3)
8.6.6 Indoor scenario
168(1)
8.6.6 Outdoor Scenario 1
168(2)
8.6.6 Outdoor Scenario 2
170(1)
8.6.6 Underground scenario
171(1)
8.7 Future trends
171(2)
8.8 Conclusions
173(1)
References
173(4)
9 Deep learning as an alternative to super-resolution imaging in UAV systems
177(38)
Anand Deshpande
Prashant P. Patavardhan
Vania V. Estrela
Navid Razmjooy
Jude Hemanth
9.1 Introduction
177(1)
9.2 The super-resolution model
178(7)
9.2.2 Motion estimation
181(1)
9.2.2 Dehazing
182(1)
9.2.2 Patch selection
183(1)
9.2.2 Super-resolution
183(2)
9.3 Experiments and results
185(1)
9.3.3 Peak signal-to-noise ratio
186(1)
9.4 Critical issues in SR deployment in UAV-CPSs
186(13)
9.4.4 Big data
186(2)
9.4.4 Cloud computing services
188(1)
9.4.4 Image acquisition hardware limitations
188(1)
9.4.4 Video SR
189(1)
9.4.4 Efficient metrics and other evaluation strategies
190(1)
9.4.4 Multiple priors
191(1)
9.4.4 Regularisation
192(1)
9.4.4 Novel architectures
193(2)
9.4.4 3D SR
195(2)
9.4.4 Deep learning and computational intelligence
197(1)
9.4.4 Network design
198(1)
9.5 Conclusion
199(1)
References
199(16)
10 Quality of experience (QoE) and quality of service (QoS) in UAV systems
215(32)
Asif Ali Laghari
Asiya Khan
Hui He
Vania V. Estrela
Navid Razmjooy
Jude Hemanth
Hermes J. Loschi
10.1 Introduction
216(2)
10.1.1 Airborne network from a CPS perspective
217(1)
10.2 Definitions
218(8)
10.2.2 Parameters that impact QoS/QoE
219(1)
10.2.2 Impact of cloud distance on QoS/QoE
220(1)
10.2.2 QoS/QoE monitoring framework in UAV-CPSs
220(2)
10.2.2 Application-level management
222(1)
10.2.2 Network-level management
223(1)
10.2.2 Cloud distance management
223(1)
10.2.2 QoS/QoE service-level management
223(1)
10.2.2 QoS/QoE metrics in UAV-CPSs
223(1)
10.2.2 Mapping of QoS to QoE
224(1)
10.2.2 Subjective vs objective measurement
224(1)
10.2.2 Tools to measure QoS/QoE
225(1)
10.3 Applications
226(2)
10.3.3 Social networks, gaming and human-machine interfaces
226(1)
10.3.3 Data centres
227(1)
10.3.3 Electric power grid and energy systems
227(1)
10.3.3 Networking systems
227(1)
10.3.3 Surveillance
227(1)
10.4 Case studies
228(4)
10.4.4 Application scenario 1: UAV-CPSs in traffic congestion management
228(3)
10.4.4 Application scenario 2: congestion and accident avoidance using intelligent vehicle systems
231(1)
10.5 Future and open challenges
232(5)
10.5.5 Modelling and design
232(1)
10.5.5 Collaborative services
233(1)
10.5.5 Streaming
234(1)
10.5.5 Security
234(1)
10.5.5 Flying ad hoc networks
235(2)
10.5.5 User emotions
237(1)
10.6 Conclusion
237(1)
References
238(9)
11 Conclusions
247(2)
Vania V. Estrela
Jude Hemanth
Osamu Saotome
George Nikolakopoulos
Roberto Sabatini
Index 249
Vania Estrela is a faculty/researcher at Telecommunications Department, Universidade Federal Fluminense (UFF) and a visiting scholar at UNICAMP. Her research interests include biomedical engineering, electronic instrumentation, modelling/simulation, sustainable design, multimedia, artificial intelligence, remote sensing, STEM education, environment, and digital inclusion. She has served as a reviewer for IEEE, Elsevier, ACM, IET, Springer-Verlag, and MDPI. She has extensive experience as a project manager, post-graduate advisor (M.Sc. and D.Sc.), as well as an editor of books and special issues.



Jude Hemanth is an associate professor in the ECE Department of Karunya University (KU), India. He is a member of the IEEE task force on deep learning and serves as associate editor and editorial board member for several international refereed journals.



Osamu Saotome is a professor at the Instituto Tecnológico de Aeronáutica (ITA), Brazil. He has been involved in several international research and cooperation projects with the Brazilian Air Force, INPE, IEAv (France, Sweden, USA, and Japan).



George Nikolakopoulos is a professor in robotics and automation at the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology (LTU), Sweden. He is also a member of the ARTEMIS Scientific Council of the European Commission. He has significant experience in Managing European and National R&D&I projects funded by the EU, ESA, Swedish and the Greek National Ministry of Research.



Roberto Sabatini is a professor of aerospace engineering and aviation in the School of Engineering at RMIT University (Australia) specialising in Avionics and Intelligent/Autonomous Systems for Aerospace and Defence applications. Currently, he serves as Deputy Director (Aerospace) of the Sir Lawrence Wackett Centre and Chair of the Cyber-Physical Systems Group at RMIT University. Professor Sabatini is a Fellow and Executive Member of the Institution of Engineers Australia, Fellow of the Royal Aeronautical Society, and Fellow the Royal Institute of Navigation. Throughout his career, he led numerous industry and government-funded research projects and he has authored or co-authored over 250 peer-reviewed international publications. In addition to his primary faculty duties, Professor Sabatini serves as Vice-Chair of the IEEE-AESS Avionics Systems Panel and editor for several high-impact international journals.