scholarly journals VIRTUALIZING SUPER-COMPUTATION ON-BOARD UAS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1291-1298 ◽  
Author(s):  
E. Salami ◽  
J. A. Soler ◽  
R. Cuadrado ◽  
C. Barrado ◽  
E. Pastor
Keyword(s):  
Real Time ◽  
High Performance ◽  
Unmanned Aerial Systems ◽  
Sensing Applications ◽  
Recent Developments ◽  
Level Information ◽  
Information Products ◽  
High Level ◽  
The Cost ◽  
Aerial Systems

Unmanned aerial systems (UAS, also known as UAV, RPAS or drones) have a great potential to support a wide variety of aerial remote sensing applications. Most UAS work by acquiring data using on-board sensors for later post-processing. Some require the data gathered to be downlinked to the ground in real-time. However, depending on the volume of data and the cost of the communications, this later option is not sustainable in the long term. This paper develops the concept of virtualizing super-computation on-board UAS, as a method to ease the operation by facilitating the downlink of high-level information products instead of raw data. Exploiting recent developments in miniaturized multi-core devices is the way to speed-up on-board computation. This hardware shall satisfy size, power and weight constraints. Several technologies are appearing with promising results for high performance computing on unmanned platforms, such as the 36 cores of the TILE-Gx36 by Tilera (now EZchip) or the 64 cores of the Epiphany-IV by Adapteva. The strategy for virtualizing super-computation on-board includes the benchmarking for hardware selection, the software architecture and the communications aware design. A parallelization strategy is given for the 36-core TILE-Gx36 for a UAS in a fire mission or in similar target-detection applications. The results are obtained for payload image processing algorithms and determine in real-time the data snapshot to gather and transfer to ground according to the needs of the mission, the processing time, and consumed watts.

scholarly journals A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

2019 ◽  
Vol 11 (18) ◽  
pp. 2144 ◽  
Author(s):  
Paula Fraga-Lamas ◽  
Lucía Ramos ◽  
Víctor Mondéjar-Guerra ◽  
Tiago M. Fernández-Caramés
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Collision Avoidance ◽  
Large Scale ◽  
Obstacle Detection ◽  
Unmanned Aerial Systems ◽  
Promising Alternative ◽  
Communication Architectures ◽  
Recent Developments ◽  
Aerial Systems

Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

scholarly journals A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 627
Author(s):  
David Marquez-Viloria ◽  
Luis Castano-Londono ◽  
Neil Guerrero-Gonzalez
Keyword(s):  
Real Time ◽  
High Performance ◽  
Interference Mitigation ◽  
Parallel Implementation ◽  
Computational Time ◽  
Successful Implementation ◽  
Interchannel Interference ◽  
The Difference ◽  
High Level ◽  
Performance Computing

A methodology for scalable and concurrent real-time implementation of highly recurrent algorithms is presented and experimentally validated using the AWS-FPGA. This paper presents a parallel implementation of a KNN algorithm focused on the m-QAM demodulators using high-level synthesis for fast prototyping, parameterization, and scalability of the design. The proposed design shows the successful implementation of the KNN algorithm for interchannel interference mitigation in a 3 × 16 Gbaud 16-QAM Nyquist WDM system. Additionally, we present a modified version of the KNN algorithm in which comparisons among data symbols are reduced by identifying the closest neighbor using the rule of the 8-connected clusters used for image processing. Real-time implementation of the modified KNN on a Xilinx Virtex UltraScale+ VU9P AWS-FPGA board was compared with the results obtained in previous work using the same data from the same experimental setup but offline DSP using Matlab. The results show that the difference is negligible below FEC limit. Additionally, the modified KNN shows a reduction of operations from 43 percent to 75 percent, depending on the symbol’s position in the constellation, achieving a reduction 47.25% reduction in total computational time for 100 K input symbols processed on 20 parallel cores compared to the KNN algorithm.

scholarly journals Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
Margaret Kalacska ◽  
Oliver Lucanus ◽  
J. Pablo Arroyo-Mora ◽  
Étienne Laliberté ◽  
Kathryn Elmer ◽  
...  
Keyword(s):  
Low Cost ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Model Errors ◽  
Positional Accuracy ◽  
Ground Control Points ◽  
Digital Elevation ◽  
High Level ◽  
Aerial Systems

The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.

scholarly journals “Open” Category Unmanned Aerial Systems Harmonized Standards and Requirements Complex

2019 ◽  
Vol 25 (3) ◽  
pp. 74-80
Author(s):  
Andon Andonov
Keyword(s):  
Aviation Safety ◽  
Global Scale ◽  
Civil Aviation ◽  
Critical Conditions ◽  
Unmanned Aerial Systems ◽  
Unmanned Systems ◽  
The Common ◽  
High Level ◽  
The Military ◽  
Aerial Systems

Abstract The steadily increasing use of unmanned aerial systems (UAS) is an important factor for the military and civil aviation safety on a global scale. One of the critical conditions for the efficient functioning of the European aviation safety system is the establishment and implementation in practice of a comprehensive regulatory framework for the use of unmanned systems in the Common European Airspace. The aviation authorities and industry strive to introduce a set of rules and requirements that adequately and flexibly guarantee a high level of safety without limiting the development of the UAS market. This article proposes a set of standards that should be met by “Open” category UAS with the intention to execute operations in the European airspace.

Making E-Training Cost Effective through Quality Assurance

2009 ◽  
pp. 623-631
Author(s):  
Lichia Yiu ◽  
Raymond Saner
Keyword(s):  
High Performance ◽  
Cost Effective ◽  
Web Based ◽  
Corporate Learning ◽  
Use Of Technology ◽  
Training Cost ◽  
E Learning ◽  
American Society ◽  
High Level ◽  
The Cost

Since the 1990s, more and more corporate learning has been moved online to allow for flexibility, just-in-time learning, and cost saving in delivering training. This trend has been evolved along with the introduction of Web-based applications for HRM purposes, known as electronic Human Resource Management (e-HRM). By 2005, 39.67% of the corporate learning, among the ASTD (American Society for Training and Development) benchmarking forum companies, was delivered online in comparison to 10.5% in 2001. E-learning has now reached “a high level of (technical) sophistication, both in terms of instructional development and the effective management of resources” in companies with high performance learning function (ASTD, 2006, p.4). The cost per unit, reported by ASTD in its 2006 State of Industry Report, has been declining since 2000 despite the higher training hours received per employee thanks to the use of technology based training delivery and its scalability. However, the overall quality of e-learning either public available in the market or implemented at the workplace remains unstable.

scholarly journals Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery

2018 ◽  
Vol 7 (11) ◽  
pp. 445 ◽  
Author(s):  
Niti Mishra ◽  
Kumar Mainali ◽  
Bharat Shrestha ◽  
Jackson Radenz ◽  
Debendra Karki
Keyword(s):  
Extreme Environments ◽  
Species Level ◽  
Future Research ◽  
Unmanned Aerial Systems ◽  
Object Based ◽  
Ecological Patterns ◽  
Geographic Object ◽  
The Cost ◽  
Aerial Systems ◽  
The Himalaya

Understanding ecological patterns and response to climate change requires unbiased data on species distribution. This can be challenging, especially in biodiverse but extreme environments like the Himalaya. This study presents the results of the first ever application of Unmanned Aerial Systems (UAS) imagery for species-level mapping of vegetation in the Himalaya following a hierarchical Geographic Object Based Image Analysis (GEOBIA) method. The first level of classification separated green vegetated objects from the rest with overall accuracy of 95%. At the second level, seven cover types were identified (including four woody vegetation species). For this, the suitability of various spectral, shape and textural features were tested for classifying them using an ensemble decision tree algorithm. Spectral features alone yielded ~70% accuracy (kappa 0.66) whereas adding textural and shape features marginally improved the accuracy (73%) but at the cost of a substantial increase in processing time. Contrast in plant morphological traits was the key to distinguishing nearby stands as different species. Hence, broad-leaved versus fine needle leaved vegetation were mapped more accurately than structurally similar classes such as Rhododendron anthopogon versus non-photosynthetic vegetation. Results highlight the potential and limitations of the suggested UAS-GEOBIA approach for detailed mapping of plant communities and suggests future research directions.

scholarly journals A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 272 ◽  
Author(s):  
Ajmal Hinas ◽  
Roshan Ragel ◽  
Jonathan Roberts ◽  
Felipe Gonzalez
Keyword(s):  
Target Detection ◽  
Low Cost ◽  
Target Position ◽  
Position Estimation ◽  
Quality Data ◽  
Unmanned Aerial Systems ◽  
Modular Software ◽  
Ground Target ◽  
High Level ◽  
Aerial Systems

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

Detection of Conflicts Between ADS-B-Equipped Aircraft and Unmanned Aerial Systems

2020 ◽  
Vol 2674 (1) ◽  
pp. 197-204
Author(s):  
John H. Mott ◽  
Zachary A. Marshall ◽  
Mark A. Vandehey ◽  
Mike May ◽  
Darcy M. Bullock
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Federal Aviation Administration ◽  
Unmanned Aircraft ◽  
Sensor Technology ◽  
Unmanned Aerial Systems ◽  
Aircraft Flight ◽  
Flight Operations ◽  
Aerial Systems ◽  
Operational Simplicity

Versatile unmanned aerial system (UAS) platforms have grown significantly in popularity by virtue of their low cost relative to manned aircraft, high performance, and operational simplicity. While the Federal Aviation Administration (FAA) currently regulates the operating altitudes, speeds, weights, pilot qualifications, and locations of drones, a lack of capacity and technology prohibits sufficient enforcement of these restrictions. To assess the frequency and severity of manned and unmanned aircraft separation incidents, and to examine the emerging sensor technology available to facilitate such assessment, flight operations in controlled airspace around Orlando Melbourne International Airport (KMLB) were monitored. One sensor system deployed at KMLB reported UAS locations, altitudes, and flight durations, while a second system reported manned aircraft positions, altitudes, and timestamps using ADS-B signals. Evaluation of flight operations data in the vicinity of KMLB revealed eight potential drone incursions over a 2-week period. Aircraft flight paths were retroactively tracked to map these unmanned and manned aerial conflicts; aircraft identification information was also researched to contextualize the incidents. The frequency and magnitude of identified events suggest the need for additional research to further explore the problem scope and potential solutions.

scholarly journals Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 103 ◽  
Author(s):  
Trevor Kistan ◽  
Alessandro Gardi ◽  
Roberto Sabatini
Keyword(s):  
Machine Learning ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Unmanned Aerial Systems ◽  
Research Attention ◽  
Cognitive Ergonomics ◽  
Traffic Flow Management ◽  
Recent Developments ◽  
Aerial Systems

Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human–machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.

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