| 1. |
- Casimiro, A.C., et al.
(författare)
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The KARYON project: Predictable and safe coordination in cooperative vehicular systems
- 2013
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Ingår i: Proceedings of the International Conference on Dependable Systems and Networks. - 9781479901814
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Konferensbidrag (refereegranskat)abstract
- KARYON, a kernel-based architecture for safety-critical control, is a European project that proposes a new perspective to improve performance of smart vehicle coordination. The key objective of KARYON is to provide system solutions for predictable and safe coordination of smart vehicles that autonomously cooperate and interact in an open and inherently uncertain environment. One of the main challenges is to ensure high performance levels of vehicular functionality in the presence of uncertainties and failures. This paper describes some of the steps being taken in KARYON to address this challenge, from the definition of a suitable architectural pattern to the development of proof-of-concept prototypes intended to show the applicability of the KARYON solutions. The project proposes a safety architecture that exploits the concept of architectural hybridization to define systems in which a small local safety kernel can be built for guaranteeing functional safety along a set of safety rules. KARYON is also developing a fault model and fault semantics for distributed, continuous-valued sensor systems, which allows abstracting specific sensor faults and facilitates the definition of safety rules in terms of quality of perception. Solutions for improved communication predictability are proposed, ranging from network inaccessibility control at lower communication levels to protocols for assessment of cooperation state at the process level. KARYON contributions include improved simulation and fault-injection tools for evaluating safety assurance according to the ISO 26262 safety standard. The results will be assessed using selected use cases in the automotive and avionic domains. © 2013 IEEE.
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| 2. |
- Casimiro, Antonio, et al.
(författare)
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KARYON: Towards Safety Kernels for Cooperative Vehicular Systems
- 2012
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642335358 ; 7596, s. 232-235
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Konferensbidrag (refereegranskat)abstract
- KARYON, a kernel-based architecture for safety-critical control, is a European project that proposes a new perspective to improve performance of smart vehicle coordination focusing on Advanced Driver Assistance Systems (ADASs) and Unmanned Aerial Systems (UAS). The key objective is to provide system solutions for predictable and safe coordination of smart vehicles that autonomously cooperate and interact in an open and inherently uncertain environment. Currently, these systems are not allowed to operate on the public roads or in the air space, as the risk of causing severe damage cannot be excluded with sufficient certainty. The impact of the project is two-fold; it will provide improved vehicle density without driver involvement and increased traffic throughput to maintain mobility without a need to build new traffic infrastructures. The results will improve interaction in cooperation scenarios while preserving safety and assessing it according to standards. The prospective project results include self-stabilizing algorithms for vehicle coordination, communication and synchronization. In addition, we aim at showing that the safety kernel can be designed to be a self-stabilizing one.
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| 3. |
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| 4. |
- Kaiser, M., et al.
(författare)
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VEDLIoT: Very Efficient Deep Learning in IoT
- 2022
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Ingår i: Proceedings of the 2022 Design, Automation and Test in Europe Conference and Exhibition, DATE 2022. - : IEEE. - 9783981926361
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Konferensbidrag (refereegranskat)abstract
- The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.
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| 5. |
- Marchant, Alan, et al.
(författare)
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AUX1 promotes lateral root formation by facilitating indole-3-acetic acid distribution between sink and source tissues in the Arabidopsis seedling
- 2002
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Ingår i: The Plant Cell. - : Oxford University Press (OUP). - 1040-4651 .- 1532-298X. ; 14:3, s. 589-597
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Tidskriftsartikel (refereegranskat)abstract
- Arabidopsis root architecture is regulated by shoot-derived signals such as nitrate and auxin. We report that mutations in the putative auxin influx carrier AUX1 modify root architecture as a result of the disruption in hormone transport between indole-3-acetic acid (IAA) source and sink tissues. Gas chromatography-selected reaction monitoring-mass spectrometry measurements revealed that the aux1 mutant exhibited altered IAA distribution in young leaf and root tissues, the major IAA source and sink organs, respectively, in the developing seedling. Expression studies using the auxin-inducible reporter IAA2::uidA revealed that AUX1 facilitates IAA loading into the leaf vascular transport system. AUX1 also facilitates IAA unloading in the primary root apex and developing lateral root primordium. Exogenous application of the synthetic auxin 1-naphthylacetic acid is able to rescue the aux1 lateral root phenotype, implying that root auxin levels are suboptimal for lateral root primordium initiation in the mutant.
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| 6. |
- Mika, Kevin, et al.
(författare)
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VEDLIoT: Next generation accelerated AIoT systems and applications
- 2023
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Ingår i: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. - 9798400701405
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Konferensbidrag (refereegranskat)abstract
- The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.
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