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study aims to automate a substantial part of monitor-
ing system development. The approach suggests im-
provements in design efficiency of complex monitor-
ing systems and processing algorithms. Furthermore,
it is expandable in terms of additional degrees of free-
dom.
Future work will focus on detailed, specific mon-
itoring tasks in industrial environment with long tail
detection and classification problems. We assume that
a more systematic decomposition of system design
tasks will lead to more compact designs. We encour-
age the research community to continue the ground-
work presented in this study by extending the models
and rule set to allow for more detailed design deci-
sions.
ACKNOWLEDGEMENT
The authors would like to thank the German Federal
Ministry for Economic Affairs and Climate Action
(BMWK) for public funding of the research project
AirCarbon III within Lufo-IV and the German Fed-
eral Ministry of Education and Research (BMBF) for
supporting the project SaMoA within VIP+.
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