High Speed Visualization in the JetOS Aviation Operating System Using Hardware Acceleration

Barladian, Boris; Deryabin, N. B.; Voloboy, Alexey; Галактионов, В. А.; Shapiro, L. Z.

doi:10.51130/graphicon-2020-2-4-3

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…We tested the rendering speed of the AMM application developed by ARINC 661 server [3]. We rendered it in the pilot display visualization system [4] elaborated for the perspective low power consuming processor i.MX6 with hardware support for Vivante GPU, running under the JetOS aircraft real time operating system [5]. The speed demonstrated by tests is 2-3 frames per second.…”

Section: Ua Cdsmentioning

confidence: 99%

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Optimizing ARINC 661 Rendering for OpenGL with Hardware Support in the JetOS Aviation Operating System

Barladian¹,

Shapiro²,

Deryabin³

et al. 2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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This paper denotes to the problem of the pilot display visualization speed. The software used in avionics has to follow strict rules prescribed by many standards. The studies used OpenGL Safety Critical (SC) with hardware support for Vivante GPU running in the aircraft real time operating system JetOS. One of the avionics standards – ARINC 661 – defines the application rendered in a cockpit display system. It raises the issue of efficient OpenGL SC using to ensure the acceptable visualization speed. Due to the specific of application prepared by the ARINC 661 server the visualization speed for the prospective aircraft platform (i.MX6 processor with Vivante GPU) is too slow to meet aviation requirements. An efficient visualization speed acceleration algorithm has been proposed and implemented. Firstly the OpenGL calls were optimized. But this optimization cannot be directly integrated into the ARINC 661 server. So a special intermediate module was designed and elaborated. The proposed approach makes it possible to achieve a visualization speed acceptable for an aircraft pilot display.

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Section: Ua Cdsmentioning

confidence: 99%

“…Interaction scheme between UA, CDS and OpenGL server is shown in Figure 6. We have implemented a solution close to the approach used in [4] for multi-window display visualization. The interaction between CDS and the OpenGL server is as follows:…”

Section: Opengl Servermentioning

confidence: 99%

Optimizing ARINC 661 Rendering for OpenGL with Hardware Support in the JetOS Aviation Operating System

Barladian¹,

Shapiro²,

Deryabin³

et al. 2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

Self Cite

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“…Задача оценки обстановки в значительной мере аналогична задаче распознавания образов [10][11][12]. Опираясь на эту аналогию, отработка основных подходов к решению сформулированной проблемы была проведена на примере робототехнических средств с использованием реальных данных, полученных путем видео и фотосьемки роботизированной платформой «PATROLBOT», базовая версия которой представлена на рисунке 2.…”

Section: описание технологииunclassified

Application of the boundary detection method to the problem of recognizing the situation

Karandeev

Osipov²,

Baluta³

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 1

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This paper presents the results of one of the solutions to the problem of increasing the speed of decision-making by an intelligent agent when modeling the behavior of complex systems on a virtual electronic polygon. Such a training ground is currently considered as an instrumental platform for testing technologies for training intelligent agents in conditions of varying complexity in order to subsequently transfer the developed methods to real objects for solving practical problems. As an example, the control of a robotic device operating in an enclosed space is considered. The article describes the technology of reducing the volume and dimension of the processed data in order to increase the responsiveness to changes in the situation and the development of solutions for moving a robotic device. The technology is based on the preprocessing of video images for the formation of a training sample, as well as the procedure and results of deep learning of a convolutional neural network. The paper uses an open source library of OpenCV computer vision algorithms implemented in C / C++. It is shown that focusing on the selection of object boundaries can significantly reduce the amount of data for analyzing the situation and increase the speed of decision-making by the robot to move.

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“…Ef􀅫iciency is related to compositor [21]. With EGLFS [22] graphical output is not needed to send to a compositor like Xserver, which brings ef􀅫iciency. The simple stack can be seen in Figure 12.…”

Section: Graphical Structure and Qtmentioning

confidence: 99%

Industrial user interface software design for visual python AI applications using embedded linux based systems

Dikme¹

2021

J. appl. phys. sci.

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Python software is one of the most popular languages for arti􀅫icial intelligence applications, especially in the academic area, because of its easy syntax and ready-to-use libraries. But for industrial usage, due to the nature of the declarative languages, python is not the preferred language when it is needed to add more functionality to the application, like user interactions or other software abstractions, which needs more system resources and stability, especially in embedded systems. If there is not enough resource to build a new model for AI application for desired software language, it will be perfect to have the advantage of the ability of python in the AI 􀅫ield. Instead of creating one python application or more than one python layer in the system, it is ef􀅫icient to abstract the AI application, which is written in python language, and handle all other activities with more ef􀅫icient languages or frameworks. In this paper, we will see how we can use a visual python AI application, which communicates with another software layer written by C++ using the Qt framework for a user interface in an ef􀅫icient way, running in the backend to handle only AI-related processes. In the example, the python application detects faces in the backend and sends related visual data to the frontend application using interprocess communication. The frontend application will be ef􀅫icient from a memory usage perspective and 􀅫lexible for customer usage in an industrial way. The whole working demo, consisting of a python face detection application and a C++ program, is available in the given GitHub link [1] and is explained in a detailed way for software design and the user interface, which will be written in QML language.

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High Speed Visualization in the JetOS Aviation Operating System Using Hardware Acceleration

Cited by 7 publications

References 8 publications

Optimizing ARINC 661 Rendering for OpenGL with Hardware Support in the JetOS Aviation Operating System

Optimizing ARINC 661 Rendering for OpenGL with Hardware Support in the JetOS Aviation Operating System

Application of the boundary detection method to the problem of recognizing the situation

Industrial user interface software design for visual python AI applications using embedded linux based systems

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