Design and management of image processing pipelines within CPS: Acquired experience towards the end of the FitOptiVis ECSEL Project

  • Carlo Sau (Corresponding author)
  • , Claudia Rinaldi
  • , Luigi Pomante
  • , Francesca Palumbo
  • , Giacomo Valente
  • , Tiziana Fanni
  • , Marcos Martinez
  • , Frank van der Linden
  • , Twan Basten
  • , Marc Geilen
  • , Geran Peeren
  • , Jiří Kadlec
  • , Pekka Jääskeläinen
  • , Lubomír Bulej
  • , Francisco Barranco
  • , Jukka Saarinen
  • , Tero Säntti
  • , Maria Katiuscia Zedda
  • , Victor Sanchez
  • , Shayan Tabatabaei Nikkhah
  • Dip Goswami, Guillermo Amat, Lukáš Maršík, Mark van Helvoort, Luis Medina, Zaid Al-Ars, Ad de Beer

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

Abstract

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.

Original languageEnglish
Article number104350
Number of pages23
JournalMicroprocessors and Microsystems
Volume87
DOIs
Publication statusPublished - Nov 2021

Bibliographical note

Funding Information:
This work is part of the FitOptiVis project [1] funded by the ECSEL Joint Undertaking under grant number H2020-ECSEL-2017?2?783,162. Several national funding agencies also contributed to the project funding. A special thanks to all the FitOptiVis consortium partners that contributed to the FitOptiVis project on which this paper is based. It is worth noting some ECSEL projects that have provided background and/or reusable results taken into account in FitOptiVis: MegaM@rt2 [138], AQUAS [138], CASPER [139], AFarCloud [141].

Funding

This work is part of the FitOptiVis project [1] funded by the ECSEL Joint Undertaking under grant number H2020-ECSEL-2017?2?783,162. Several national funding agencies also contributed to the project funding. A special thanks to all the FitOptiVis consortium partners that contributed to the FitOptiVis project on which this paper is based. It is worth noting some ECSEL projects that have provided background and/or reusable results taken into account in FitOptiVis: MegaM@rt2 [138], AQUAS [138], CASPER [139], AFarCloud [141].

Keywords

  • Cyber-physical systems
  • Distributed system
  • Heterogeneous system
  • Image processing
  • Multi-objective optimization
  • Video processing

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