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| 2. |
- Estrela, Vania V., et al.
(författare)
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Conclusions
- 2020
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Ingår i: Imaging and Sensing for Unmanned Aircraft Systems Volume 1. - : Institution of Engineering and Technology. - 9781785616426 - 9781785616433 ; , s. 333-335
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The current awareness in UAVs has prompted not only military applications but also civilian uses. Aerial vehicles’ requirements aspire to guarantee a higher level of safety comparable to see-and-avoid conditions for piloted aeroplanes. The process of probing obstacles in the path of a vehicle, and to determine if they pose a threat, alongside measures to avoid problems, is known as see-and-avoid or sense and-avoid involves a great deal of decision-making. Other types of decisionmaking tasks can be accomplished using computer vision and sensor integration since they have great potential to improve the performance of UAVs. Macroscopically, Unmanned Aerial Systems (UASs) are cyber-physical systems (CPSs) that can benefit from all types of sensing frameworks, despite severe design constraints such as precision, reliable communication, distributed processing capabilities, and data management.
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| 3. |
- Estrela, Vania V., et al.
(författare)
-
Conclusions
- 2020
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Ingår i: Imaging and Sensing for Unmanned Aircraft Systems Volume 2. - : Institution of Engineering and Technology. - 9781785616440 - 9781785616457 ; , s. 247-248
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The current awareness in unmanned aerial vehicles (UAVs) has prompted not only military applications but also civilian uses. Aerial vehicles’ requirements aspire to guarantee a higher level of safety comparable to see-and-avoid conditions for piloted aeroplanes. The process of probing obstacles in the path of a vehicle and determining whether they pose a threat, alongside measures to avoid these issues, is known as see and avoid or sense and avoid. Other types of decision-making tasks can be accomplished using computer vision and sensor integration since they have a great potential to improve the performance of the UAVs. Macroscopically, UAVs are cyber-physical systems (CPSs) that can benefit from all types of sensing frameworks, despite severe design constraints, such as precision, reliable communication, distributed processing capabilities and data management. This book is paying attention to several issues that are still under discussions in the field of UAV-CPSs. Thus, several trends and needs are discussed to foster criticism from the readers and to provide further food for thought.
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| 4. |
- Estrela, Vania V., et al.
(författare)
-
Introduction to advances in UAV avionics for imaging and sensing
- 2020
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Ingår i: Imaging and Sensing for Unmanned Aircraft Systems Volume 1: Control and Performance. - : Institution of Engineering and Technology. - 9781785616426 - 9781785616433 ; , s. 1-21
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- An unmanned aerial vehicle (UAV) - aka drone, unmanned aircraft system or remotely piloted aircraft system - is an aircraft without a human pilot on board. Its flight can be controlled autonomously by computers in the vehicle or by remote control. UAVs can uniquely penetrate areas, which may be too dangerous or too difficult to reach for piloted craft. The UAV cyber-physical system comprises all the subsystems and interfaces for processing and communication functions performed by the embedded electronic system (avionics) and the ground control station. To accomplish the desired real-time autonomy, the avionics is highly tied with aerodynamics sensing and actuation. An entirely autonomous UAV can (i) obtain evidence about the environment, (ii) work for an extended period of time without human interference, (iii) move either all or part of itself all over its operating location devoid of human help and (iv) stay away from risky situations for people and their assets. This chapter intends to introduce the material addressed in further chapters of this book. The next sections go through some concepts that are recurrent in the book.
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| 5. |
- Estrela, Vania V., et al.
(författare)
-
Preface
- 2020
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Ingår i: Imaging and sensing for unmanned aircraft systems. - : Institution of Engineering and Technology. ; , s. xiii-xiv
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Estrela, Vania V., et al.
(författare)
-
Preface
- 2020
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Ingår i: Imaging and Sensing for Unmanned Aircraft Systems. - : Institution of Engineering and Technology. ; , s. xiii-xviii
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 7. |
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Imaging and sensing for unmanned aircraft systems Volume 1 : Control and performance
- 2020
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- 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 UAVP 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).
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| 8. |
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Imaging and sensing for unmanned aircraft systems Volume 2: Deployment and applications
- 2020
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Samlingsverk (redaktörskap) (övrigt vetenskapligt/konstnärligt)abstract
- 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 UAVP 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).
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| 9. |
- Lundholm, Ida V., et al.
(författare)
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Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging
- 2018
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Ingår i: IUCrJ. - : International Union of Crystallography. - 2052-2525. ; 5, s. 531-541
-
Tidskriftsartikel (refereegranskat)abstract
- Diffraction before destruction using X-ray free-electron lasers (XFELs) has the potential to determine radiation-damage-free structures without the need for crystallization. This article presents the three-dimensional reconstruction of the Melbournevirus from single-particle X-ray diffraction patterns collected at the LINAC Coherent Light Source (LCLS) as well as reconstructions from simulated data exploring the consequences of different kinds of experimental sources of noise. The reconstruction from experimental data suffers from a strong artifact in the center of the particle. This could be reproduced with simulated data by adding experimental background to the diffraction patterns. In those simulations, the relative density of the artifact increases linearly with background strength. This suggests that the artifact originates from the Fourier transform of the relatively flat background, concentrating all power in a central feature of limited extent. We support these findings by significantly reducing the artifact through background removal before the phase-retrieval step. Large amounts of blurring in the diffraction patterns were also found to introduce diffuse artifacts, which could easily be mistaken as biologically relevant features. Other sources of noise such as sample heterogeneity and variation of pulse energy did not significantly degrade the quality of the reconstructions. Larger data volumes, made possible by the recent inauguration of high repetition-rate XFELs, allow for increased signal-to-background ratio and provide a way to minimize these artifacts. The anticipated development of three-dimensional Fourier-volume-assembly algorithms which are background aware is an alternative and complementary solution, which maximizes the use of data.
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| 10. |
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