| 1. |
- Batista, Gracieth Cavalcanti, et al.
(författare)
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Machine learning algorithm partially reconfigured on FPGA for an image edge detection system
- 2024
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Ingår i: Journal of Electronic Science and Technology. - : Elsevier BV. - 1674-862X. ; 22:2
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Tidskriftsartikel (refereegranskat)abstract
- Unmanned aerial vehicles (UAVs) have been widely used in military, medical, wireless communications, aerial surveillance, etc. One key topic involving UAVs is pose estimation in autonomous navigation. A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system (GNSS) signal. However, some factors can interfere with the GNSS signal, such as ionospheric scintillation, jamming, or spoofing. One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images. But a high effort is required for image edge extraction. In this paper, a support vector regression (SVR) model is proposed to reduce this computational load and processing time. The dynamic partial reconfiguration (DPR) of part of the SVR datapath is implementated to accelerate the process, reduce the area, and analyze its granularity by increasing the grain size of the reconfigurable region. Results show that the implementation in hardware is 68 times faster than that in software. This architecure with DPR also facilitates the low power consumption of 4 mW, leading to a reduction of 57% than that without DPR. This is also the lowest power consumption in current machine learning hardware implementations. Besides, the circuitry area is 41 times smaller. SVR with Gaussian kernel shows a success rate of 99.18% and minimum square error of 0.0146 for testing with the planning trajectory. This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application, thus contributing to lower power consumption, smaller hardware area, and shorter execution time.
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| 2. |
- Estrela, Vania V., et al.
(författare)
-
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
-
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
-
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
-
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
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 7. |
-
Imaging and sensing for unmanned aircraft systems Volume 1 : Control and performance
- 2020
-
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. |
-
Imaging and sensing for unmanned aircraft systems Volume 2: Deployment and applications
- 2020
-
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. |
- Trujilho, Leandro, et al.
(författare)
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Dependable I2C Communication with FPGA
- 2022
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Ingår i: PROCEEDINGS OF THE 7TH BRAZILIAN TECHNOLOGY SYMPOSIUM (BTSYM'21). - Cham : Springer Nature. ; , s. 383-395
-
Konferensbidrag (refereegranskat)abstract
- This work introduces the I2C bus availability problem for Cube Satellites and robotics applications. It suggests the use of FPGAs as an alternative to microcontroller systems as they are capable of recovering from bus faults via partial reconfiguration without mission interruption, and triple wire redundancy is feasible because more pins are available. The proposed topology makes use of 3 AXI IIC IP core instances, a TMR voter, and a comparator along with a watchdog timer to detect faults and recover from them. The bus successfully recovered from fault injections instantly with IIC resets and after 17 ms via partial reconfiguration, occupying 5% of the LUTs, 1% of the LUTRAMs, 3% of Flip Flops and IO, consuming 1.684W with a total of 2.43% essential bits.
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