A Platform Programming Paradigm for Heterogeneous Systems Integration

Gemlau, Kai-Björn; Köhler, Leonie; Ernst, Rolf

doi:10.1109/jproc.2020.3035874

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…Given the heterogeneous network paradigm [6] and different applications such as terrestrial communication network [7], mobile marine network [8], aerostat platform [9] and satellite communication networks [10], the form of cyberspace is often complex and heterogeneous, so the re-search of this paper is based on complex-heterogeneous cyberspace [11].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Complex-Heterogeneous Cyberspace Cartographic Visualization

Zhang¹,

Liu²,

Wang³

et al. 2023

J Multimed Inf Syst

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It seems complicated to imagine our life without the Internet, but it is only a few decades old, and the Internet forms the most extensive cyberspace environment. Cyberspace is the non-physical environment created by joint computers inter-operating on a network. The complex heterogeneous cyberspace is a virtual computer world that encompasses a variety of computers, network devices, and systems that have been manufactured by different entities. As an essential spatial cognitive tool, the map has significantly contributed to human civilization for thousands of years. However, the cartographic elements of traditional maps are mostly geospatial entities or phenomena, and only some people apply them to draw abstract and virtual cyberspace resources, resulting in the development of cyberspace cartography lagging. Additionally, the process of data visualization involves the conversion of data into visual forms such as charts and graphs, with the aim of effectively conveying the data's importance. The cartographic visualization is to realize the visual expression of cyberspace, which is an essential basis for understanding cyberspace. Therefore, this paper introduces a deep neural network (DNN) to study complex-heterogeneous cyberspace cartographic visualization. At first, locally linear embedding is adopted to reduce the data dimensionality. Then, DNN is used to train the cartographic coordinates data obtained after data dimensionality reduction. Finally, several different data modeling methods are studied concerning temporal and spatial attributes to achieve complex-heterogeneous cyberspace cartographic visualization effectively. The simulation is powered by datasets sourced from cyberspace, which are accessible via data.world. The results of the simulation demonstrate that the suggested approach is superior in efficiency when compared to the baseline methods.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Complex-Heterogeneous Cyberspace Cartographic Visualization

Zhang¹,

Liu²,

Wang³

et al. 2023

J Multimed Inf Syst

View full text Add to dashboard Cite

show abstract

Wartbarkeit und Updatebarkeit von ADAS Software

Möstl,

Schlatow,

Gemlau

et al. 2024

Handbuch Assistiertes Und Automatisiertes Fahren

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Robust Cause-Effect Chains with Bounded Execution Time and System-Level Logical Execution Time

Köhler

Hertha²,

Beckert³

et al. 2023

ACM Trans. Embed. Comput. Syst.

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In automotive and industrial real-time software systems, the primary timing constraints relate to cause-effect chains. A cause-effect chain is a sequence of linked tasks and it typically implements the process of reading sensor data, computing algorithms, and driving actuators. The classic timing analysis computes the maximum end-to-end latency of a given cause-effect chain to verify that its end-to-end deadline can be satisfied in all cases. This information is useful but not sufficient in practice: Software is usually evolving and updates may always alter the maximum end-to-end latency. It would be desirable to judge the quality of a software design a priori by quantifying how robust the timing of a given cause-effect chain will be in the presence of software updates. In this paper, we derive robustness margins which guarantee that if software extensions stay within certain bounds, then the end-to-end deadline of a cause-effect chain can still be satisfied. Robustness margins are also useful to know if the system model has uncertain parameters. A robust system design can tolerate bounded deviations from the nominal system model without violating timing constraints. The results are applicable to both the bounded execution time programming model and the (system-level) logical execution time programming model. In this paper, we study both an industrial use case from the automotive industry and analyze synthetically generated experiments with our open-source tool TORO.

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A Platform Programming Paradigm for Heterogeneous Systems Integration

Cited by 5 publications

References 46 publications

Deep Neural Network Based Complex-Heterogeneous Cyberspace Cartographic Visualization

Deep Neural Network Based Complex-Heterogeneous Cyberspace Cartographic Visualization

Wartbarkeit und Updatebarkeit von ADAS Software

Robust Cause-Effect Chains with Bounded Execution Time and System-Level Logical Execution Time

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