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Publikasjoner (10 av 15) Visa alla publikasjoner
Johansson, S. L., Said, H. O., Forsberg, H., Tsog, N. & Flordal, O. (2023). Comparing Ext4 and ZFS for Onboard Data Processing: A Systematic Mapping and Experimental Evaluation. In: Proc. European Data Handl. Data Process. Conf. Space, EDHPC: . Paper presented at Proceedings of the 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023. Institute of Electrical and Electronics Engineers Inc.
Åpne denne publikasjonen i ny fane eller vindu >>Comparing Ext4 and ZFS for Onboard Data Processing: A Systematic Mapping and Experimental Evaluation
Vise andre…
2023 (engelsk)Inngår i: Proc. European Data Handl. Data Process. Conf. Space, EDHPC, Institute of Electrical and Electronics Engineers Inc. , 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Selecting the correct file system is critical for space applications where risks are present. This study systematically maps and tests Ext4 versus ZFS for onboard data processing on the iX10-100 and iX5-100 payload processors. The test sets are presented along with results on several performance metrics. The conclusion is that both ZFS and Ext4 are useful, but based on certain considerations of onboard data processing, Ext4 is better than the other.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc., 2023
Emneord
Benchmarking, Ext4, File System Performance, Onboard Data Processing, Systematic Mapping Study, ZFS, Data handling, File organization, Mapping, Space applications, Experimental evaluation, Filesystem, On-board data processing, Systematic mapping, Systematic mapping studies, Systems performance, Test sets
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-66090 (URN)10.23919/EDHPC59100.2023.10396086 (DOI)2-s2.0-85184850283 (Scopus ID)9789090379241 (ISBN)
Konferanse
Proceedings of the 2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023
Tilgjengelig fra: 2024-02-20 Laget: 2024-02-20 Sist oppdatert: 2024-02-20bibliografisk kontrollert
Tsog, N., Mubeen, S., Sjödin, M. & Bruhn, F. (2021). A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated In-Orbit Space Systems. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 19(5), 700-708, Article ID 19.700.
Åpne denne publikasjonen i ny fane eller vindu >>A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated In-Orbit Space Systems
2021 (engelsk)Inngår i: Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, E-ISSN 1884-0485, Vol. 19, nr 5, s. 700-708, artikkel-id 19.700Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

On-board data processing is one of the prior on-orbit activities that improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters higher energy consumption compared to traditional on-board space systems. This is because the traditional space systems employ simple processing units such as single-core microprocessors as the systems do not require heavy data processing. Moreover, solving the radiation hardness problem is crucial in space, and adopting a new processing unit is challenging.

In this paper, we consider a Graphics Processing Unit (GPU) accelerated in-orbit space system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of energy consumption and computing potential using this heterogeneous computing method in our GPU accelerated in-orbit space systems.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.

Emneord
On-board Data Processing, Heterogeneous Computing, Energy Efficiency, GPU Accelerated On-board Computer
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-56078 (URN)10.2322/tastj.19.700 (DOI)
Tilgjengelig fra: 2021-10-01 Laget: 2021-10-01 Sist oppdatert: 2021-10-22bibliografisk kontrollert
Tsog, N., Mubeen, S., Bruhn, F., Behnam, M. & Sjödin, M. (2021). Offloading Accelerator-intensive Workloads in CPU-GPU Heterogeneous Processors. In: 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021: . Paper presented at 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021; Virtual, Vasteras 7 September 2021 through 10 September 2021.
Åpne denne publikasjonen i ny fane eller vindu >>Offloading Accelerator-intensive Workloads in CPU-GPU Heterogeneous Processors
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2021 (engelsk)Inngår i: 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021, 2021Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Autonomous vehicular systems require computer vision and intelligent on-board decision making functionalities that include a mix of sequential and parallel workloads. The execution times of the workloads and power consumption in these functionalities can be lowered by utilizing the accelerators (e.g., GPU) instead of running the workloads entirely on the host processing units (CPU). However, allocating all the parallelizable workload to accelerators can create a computation bottleneck in the accelerators that, in turn, can have an adverse effect on schedulability of the systems. This paper presents a novel framework that can allocate the accelerate-intensive workloads to the accelerators as well as to the non-accelerated host processing units. Within the context of this framework, the paper introduces five offloading techniques to mitigate the accelerator-intensive workloads by utilizing excess capacity of non-accelerated processing units under dynamic scheduling in CPU-GPU heterogeneous processors. The proposed techniques are evaluated using simulation experiments. The evaluation results indicate that one of the proposed techniques can achieve up to 16% improvement in schedulability of the task sets compared to the traditional non-offloading technique.

HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-56081 (URN)10.1109/ETFA45728.2021.9613666 (DOI)000766992600230 ()2-s2.0-85122955086 (Scopus ID)9781728129891 (ISBN)
Konferanse
26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021; Virtual, Vasteras 7 September 2021 through 10 September 2021
Tilgjengelig fra: 2021-10-01 Laget: 2021-10-01 Sist oppdatert: 2022-11-08bibliografisk kontrollert
Tsog, N., Mubeen, S., Behnam, M., Sjödin, M. & Bruhn, F. (2021). Simulation and Analysis of In-Orbit Applications under Radiation Effects on COTS Platforms. In: 42nd IEEE Aerospace Conference 2021 IEEEAC2021: . Paper presented at 42nd IEEE Aerospace Conference 2021 IEEEAC2021, 06 Mar 2021, Big Sky, Montana, United States.
Åpne denne publikasjonen i ny fane eller vindu >>Simulation and Analysis of In-Orbit Applications under Radiation Effects on COTS Platforms
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2021 (engelsk)Inngår i: 42nd IEEE Aerospace Conference 2021 IEEEAC2021, 2021Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Radiation effects research is crucial as it defines risk to both human bodies and spacecraft. Employing radiation-hardened products is one way to mitigate radiation effects on in-orbit systems. However, radiation effects prohibit most of the state-of-the-art commercial off-the-shelf (COTS) technologies from use in space. Furthermore, radiation effects on software components are less studied compared to hardware components. In this work, we introduce a simulation tool that analyzes the impact of radiation effects on schedulability of task sets executing on COTS system-on-chip (SoC) platforms in the in-orbit systems. In order to provide a meaningful verification environment, single-event effects (SEEs) are introduced as aleatory disturbances characterized by probability distribution of occurrence using their predefined models. The tool supports interoperability with several other tools as it uses the extensible markup language (XML) model files for input and output, i.e., for importing input task sets and radiation effects and exporting the simulation results.

Serie
IEEE Aerospace Conference Proceedings, ISSN 1095-323X
Emneord
Radiation toleranceCPU-GPUSimulation toolSchedulabilityCOTS components
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-53949 (URN)10.1109/AERO50100.2021.9438255 (DOI)000681710101024 ()2-s2.0-85111365681 (Scopus ID)
Konferanse
42nd IEEE Aerospace Conference 2021 IEEEAC2021, 06 Mar 2021, Big Sky, Montana, United States
Prosjekter
DPAC - Dependable Platforms for Autonomous systems and Control
Tilgjengelig fra: 2021-05-24 Laget: 2021-05-24 Sist oppdatert: 2021-11-05bibliografisk kontrollert
Tsog, N., Gallardo, M., Chakraborty, S., Martinson, T., Hengl, A., Moberg, M., . . . Mubeen, S. (2021). Supporting Autonomous Vehicle Applications on the Heterogeneous System Architecture. In: ACM International Conference Proceeding Series: . Paper presented at 7th Conference on the Engineering of Computer Based Systems, ECBS 2021, 26 May 2021 through 27 May 2021. Association for Computing Machinery
Åpne denne publikasjonen i ny fane eller vindu >>Supporting Autonomous Vehicle Applications on the Heterogeneous System Architecture
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2021 (engelsk)Inngår i: ACM International Conference Proceeding Series, Association for Computing Machinery , 2021Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The contemporary processors are unable to meet the increasing data-intensive and computation-demanding requirements in autonomous vehicle software applications. Recently, the new Heterogeneous System Architecture (HSA) has emerged as a promising solution to meet these requirements. The HSA reduces the latency of data exchange between the compute units and cache-coherent shared memory, which is not supported by the non-HSA compliant heterogeneous platforms with acceleration support. The main goal of the paper is to investigate the performance gain by the HSA and conduct a comparative evaluation of the HSA and non-HSA compliant heterogeneous platforms. The paper aims at evaluating these platforms by using two computation-intensive software functions in autonomous vehicles, namely the object detection and vehicle movement. In order to achieve this goal, the CUDA-accelerated source code of the functions is ported from a non-HSA compliant heterogeneous platform to the HSA platform. In this regard, the paper presents the architecture of a proof-of-concept prototype and provides evaluation using the prototype.

sted, utgiver, år, opplag, sider
Association for Computing Machinery, 2021
Emneord
CNN, code migration, convolutional neural network, CuDNN., Heterogeneous System Architecture, HIP, HSA, MIOpen, OpenCL, parallel computing architectures, Application programs, Electronic data interchange, Function evaluation, Memory architecture, Object detection, Comparative evaluations, Computation intensives, Heterogeneous platforms, Heterogeneous systems, Software applications, Software functions, Vehicle applications, Vehicle movements, Autonomous vehicles
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-58800 (URN)10.1145/3459960.3459970 (DOI)2-s2.0-85107176588 (Scopus ID)9781450390576 (ISBN)
Konferanse
7th Conference on the Engineering of Computer Based Systems, ECBS 2021, 26 May 2021 through 27 May 2021
Merknad

Conference code: 169185; Export Date: 8 June 2022; Conference Paper; Funding details: VINNOVA; Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KKS; Funding text 1: The work in this paper is supported by the Swedish Knowledge Foundation (KKS) via the DPAC project and by the Swedish Governmental Agency for Innovation Systems (VINNOVA) via the DESTINE and PROVIDENT projects. We thank our industrial partners, especially Volvo Construction Equipment for their valuable input to this work.

Tilgjengelig fra: 2022-07-13 Laget: 2022-07-13 Sist oppdatert: 2022-11-25bibliografisk kontrollert
Bruhn, F., Tsog, N., Kunkel, F., Flordal, O. & Troxel, I. (2020). Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space. CEAS Space Journal, 12(4), 551-564
Åpne denne publikasjonen i ny fane eller vindu >>Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space
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2020 (engelsk)Inngår i: CEAS Space Journal, ISSN 1868-2502, E-ISSN 1868-2510, Vol. 12, nr 4, s. 551-564Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The last decade has seen a dramatic increase in small satellite missions for commercial, public, and government intelligence applications. Given the rapid commercialization of constellation-driven services in Earth Observation, situational domain awareness, communications including machine-to-machine interface, exploration etc., small satellites represent an enabling technology for a large growth market generating truly Big Data. Examples of modern sensors that can generate very large amounts of data are optical sensing, hyperspectral, Synthetic Aperture Radar (SAR), and Infrared imaging. Traditional handling and downloading of Big Data from space requires a large onboard mass storage and high bandwidth downlink with a trend towards optical links. Many missions and applications can benefit significantly from onboard cloud computing similarly to Earth-based cloud services. Hence, enabling space systems to provide near real-time data and enable low latency distribution of critical and time sensitive information to users. In addition, the downlink capability can be more effectively utilized by applying more onboard processing to reduce the data and create high value information products. This paper discusses current implementations and roadmap for leveraging high performance computing tools and methods on small satellites with radiation tolerant hardware. This includes runtime analysis with benchmarks of convolutional neural networks and matrix multiplications using industry standard tools (e.g., TensorFlow and PlaidML). In addition, a 1/2 CubeSat volume unit (0.5U) (10 x 10 x 5 cm(3)) cloud computing solution, called SpaceCloud (TM) iX5100 based on AMD 28 nm APU technology is presented as an example of heterogeneous computer solution. An evaluation of the AMD 14 nm Ryzen APU is presented as a candidate for future advanced onboard processing for space vehicles.

sted, utgiver, år, opplag, sider
SPRINGER WIEN, 2020
Emneord
OBDP, Machine learning, GPU, Small satellites, Heterogeneous computing
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-50616 (URN)10.1007/s12567-020-00321-9 (DOI)000541029200001 ()2-s2.0-85086737276 (Scopus ID)
Tilgjengelig fra: 2020-09-21 Laget: 2020-09-21 Sist oppdatert: 2021-10-01bibliografisk kontrollert
Tsog, N., Sjödin, M. & Bruhn, F. (2019). A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated Real-Time Systems. In: : . Paper presented at The 32nd International Symposium on Space Technology and Science, Fukui, Japan.
Åpne denne publikasjonen i ny fane eller vindu >>A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated Real-Time Systems
2019 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

On-board data processing is one of the prior on-orbit activities that it improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters with higher energy consumption compared to traditional space systems. Because traditional space systems employ simple processing units such as micro-controllers or a single-core processor as the systems require no heavy data processing on orbit. Moreover, solving the radiation hardness problem is crucial in space and adopting a new processing unit is challenging.

In this paper, we consider a GPU accelerated real-time system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of power consumption and computing potential using this heterogeneous computing method in our GPU accelerated real-time system.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.

Emneord
Trade-off, Computing power, Energy consumption, on-board data processing, GPU acceleration, Real-time systems
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-45938 (URN)
Konferanse
The 32nd International Symposium on Space Technology and Science, Fukui, Japan
Prosjekter
DPAC - Dependable Platforms for Autonomous systems and Control
Tilgjengelig fra: 2019-11-11 Laget: 2019-11-11 Sist oppdatert: 2019-11-22bibliografisk kontrollert
Tsog, N. (2019). Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems. (Licentiate dissertation). Västerås: Mälardalen University
Åpne denne publikasjonen i ny fane eller vindu >>Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

This thesis investigates the efficacy of heterogeneous computing architectures in real-time systems.The goals of the thesis are twofold. First, to investigate various characteristics of the Heterogeneous System Architectures (HSA) compliant reference platforms focusing on computing performance and power consumption. The investigation is focused on the new technologies that could boost on-board data processing systems in satellites and spacecraft. Second, to enhance the usage of the heterogeneous processing units by introducing a technique for static allocation of parallel segments of tasks.

The investigation and experimental evaluation show that our method of GPU allocation for the parallel segments of tasks is more energy efficient compared to any other studied allocation. The investigation is conducted under different types of environments, such as process-level isolated environment, different software stacks, including kernels, and various task set scenarios. The evaluation results indicate that a balanced use of heterogeneous processing units (CPU and GPU) could improve schedulability of task sets up to 90% with the proposed allocation technique.

Abstract [sv]

Denna avhandling undersöker effektiviteten hos heterogena datorarkitekturer i realtidssystem. Målet med avhandlingen är tvåfaldigt. Till att börja med, att undersöka olika egenskaper hos plattformar baserade på Heterogeneous System Architecture, med fokus på datorprestanda och strömförbrukning. Undersökningen är inriktad på tekniker som kan öka datorbehandlingssystemen ombord i satelliter och rymdskepp. För det andra förbättra användningen av heterogena arkitekturer genom att införa en teknik för statisk allokering av parallella programsegment.

Undersökningen och den experimentella utvärderingen visar att vår metod för effektiv användning av GPU-allokering för parallella programsegment är den mest energieffektiva jämfört med någon annan studerad allokering. Undersökningarna har genomförts i olika typer av miljöer, såsom processisolerad miljö, olika mjukvarustackar, inklusive kernel, och olika uppsättningsscenarier. Utvärderingsresultaten indikerar dessutom att en balanserad användning av heterogena beräkningsenheter (CPU och GPU) kan förbättra schemaläggningen för vissa program upp till 90% jämfört med de tidigare föreslagna allokeringsteknikerna.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2019
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 286
Emneord
on-board data processing, CPU-GPU, heterogeneous architectures, real-time systems
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-45940 (URN)978-91-7485-450-3 (ISBN)
Presentation
2019-12-18, Kappa, Mälardalens högskola, Västerås, 09:15 (engelsk)
Opponent
Veileder
Prosjekter
DPAC - Dependable Platforms for Autonomous systems and Control
Tilgjengelig fra: 2019-11-11 Laget: 2019-11-11 Sist oppdatert: 2019-11-19bibliografisk kontrollert
Binios, A., Leverone, F., Stavrakakis, H.-A. -., Tsog, N., Haslam, S., Dalbins, J., . . . Laufer, R. (2019). Moon compact satellite for hazard assessment (MOOCHA) - Proposing an international Earth-Moon small satellite constellation. In: Proceedings of the International Astronautical Congress, IAC: . International Astronautical Federation, IAF
Åpne denne publikasjonen i ny fane eller vindu >>Moon compact satellite for hazard assessment (MOOCHA) - Proposing an international Earth-Moon small satellite constellation
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2019 (engelsk)Inngår i: Proceedings of the International Astronautical Congress, IAC, International Astronautical Federation, IAF , 2019Konferansepaper (Fagfellevurdert)
Abstract [en]

The recent developments in space exploration have reinstated the Moon as a primary target for near future space missions. The principal reasons include the Moon being the closest test bed and analogue for planetary space missions and the prospect of scientific lunar bases and orbital stations within the next decade. Previous space missions have vastly improved our understanding on hazards of human spaceflights but not fully regarding the threats affecting a prospective lunar base or orbital station. The micrometeorite hazard has been partially addressed as an issue which can potentially impact both astronauts' health and safety as well as create issues for lunar bases and orbital stations, such as degradation or permanent damage of equipment and facilities. The current understanding is based partly on dust and micrometeoroid flux measurements and impact flash observations. However, observations with improved spatial and temporal resolution are imperative for advancing existing hazard models. In this paper, a mission concept of a constellation of nanosatellites is proposed that can both observe larger parts of cis-lunar and trans-lunar space while providing higher temporal resolution. Nanosatellite missions are a cost-effective solution providing data for significant improvement of our current understanding of lunar micrometeoroid flux models, and thus directly the scale of hazards caused by micrometeoroid impacts to future lunar missions. Additionally, such a distributed constellation mission will offer countless opportunities for academia, students and young scientists worldwide. The mission concept (Moon Compact Satellite for Hazard Assessment - MOOCHA) is a result of the Nordic-European Astrobiology Campus Summer School 2018 themed “Microsatellites in Planetary and Atmospheric Research” and was further developed during the 2019 follow-up summer school “Design of Small Satellite Missions for Planetary Studies”, both taking place in Tartu, Estonia and co-organized by the Stockholm University Astrobiology Centre, the University of Tartu, the European Astrobiology Campus and the Nordic Network of Astrobiology and supported by European Union's European Regional Development Fund and Estonia.

sted, utgiver, år, opplag, sider
International Astronautical Federation, IAF, 2019
Emneord
Cis-Lunar, International Moon Fleet, Lunar Micrometeoroid Flux, Lunar Micrometeoroid Hazard Assessment for Crewed Missions and Equipment, Moon Compact Satellite, Moon CubeSat, Trans-Lunar Satellite Platform
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-51291 (URN)2-s2.0-85079114089 (Scopus ID)
Tilgjengelig fra: 2020-10-07 Laget: 2020-10-07 Sist oppdatert: 2020-10-07bibliografisk kontrollert
Tsog, N., Becker, M., Bruhn, F., Behnam, M. & Nolin, M. (2019). Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems. In: : . Paper presented at IEEE 45th Annual Conference of the Industrial Electronics Society, IECON2019.
Åpne denne publikasjonen i ny fane eller vindu >>Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems
Vise andre…
2019 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Autonomous driving is one of the main challenges of modern cars. Computer visions and intelligent on-board decision making are crucial in autonomous driving and require heterogeneous processors with high computing capability under low power consumption constraints. The progress of parallel computing using heterogeneous processing units is further supported by software frameworks like OpenCL, OpenMP, CUDA, and C++AMP. These frameworks allow the allocation of parallel computation on different compute resources. This, however, creates a difficulty in allocating the right computation segments to the right processing units in such a way that the complete system meets all its timing requirements. In this paper, we consider pre-runtime static allocations of parallel tasks to perform their execution either sequentially on CPU or in parallel using a GPU. This allows for improving any unbalanced use of GPU accelerators in a heterogeneous environment. By performing several heuristic algorithms, we show that the overuse of accelerators results in a bottle-neck of the entire system execution. The experimental results show that our allocation schemes that target a balanced use of GPU improve the system schedulability up to 90%.

Emneord
Parallel task, Parallel segment, Alternative execution, CPU-GPU, Heterogeneous processors, Real-time systems
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-45934 (URN)10.1109/IECON.2019.8926767 (DOI)000522050604083 ()2-s2.0-85084110257 (Scopus ID)9781728148786 (ISBN)
Konferanse
IEEE 45th Annual Conference of the Industrial Electronics Society, IECON2019
Prosjekter
DPAC - Dependable Platforms for Autonomous systems and Control
Tilgjengelig fra: 2019-11-11 Laget: 2019-11-11 Sist oppdatert: 2021-10-01bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-8096-3891