Instrumentation & Measurement Magazine 26-1 - 22
instrumentationand
measurementsystems continued
detection of ultrasonic xylem cavitation emissions related to plant
water-stress, " in Proc. 2020 Symp. Design, Test, Integration and
Packaging of MEMS and MOEMS (DTIP), pp. 1-4, 2020.
[10] G. E. Karniadakis, I. G. Kevredekis, L. Lu et al, " Physics-informed
machine learning, " Nat. Rev. Phys. 3, pp. 422-440, 2021.
[11] " NT Adaptive.VAS-an intelligent vibro-acoustic sensor
for monitoring rotation equipment, " Neurotechnologies
UAB, accessed Jun. 2022. [Online], Available: https://
neurotechnologijos.com/nt-adaptive-vas.
[12] D. Oletic, S. Rosner and V. Bilas, " Verifying sensitivity of a sensor
system for logging xylem's acoustic emissions related to drought
stress, " in Proc. 2021 IEEE Int. Workshop Metrology for Agriculture
and Forestry (MetroAgriFor), pp. 125-129, 2021
[13] K. M. Donnell, " The importance of the fundamentals [Future
Trends in I&M], " IEEE Instrum. Meas. Mag., vol. 18, no. 4, pp.
51-52, Aug. 2015.
Davorin Ambruš (Davorin.Ambrus@fer.hr) is a Senior Researcher
at the University of Zagreb, Faculty of Electrical
Engineering and Computing in Zagreb, Croatia and a Member
of the Laboratory for Intelligent Sensor Systems. He
received his Dipl.Ing., M.Sc. and Ph.D. degrees in electrical
engineering from the University of Zagreb in 1999, 2005,
and 2019, respectively. His research interests include computational
electromagnetic sensing and sensor electronic
systems.
Vedran Bilas (Vedran.Bilas@fer.hr) is a Professor with the
University of Zagreb, Faculty of Electrical Engineering and
Computing in Zagreb, Croatia and the Head of the Laboratory
for Intelligent Sensor Systems. He received his Dipl.Ing.,
M.Sc. and Ph.D. degrees in electrical engineering from the University
of Zagreb in 1991, 1995 and 1999, respectively. He has
over 30 years of research, development, and technology transfer
experience in the area of sensors and electronic systems. His
research interests include inductive methods, intelligent sensors,
sensor modeling, low power sensing and wireless sensor
networks.
22
IEEE Instrumentation & Measurement Magazine
February 2023
http://www.neurotechnologijos.com/nt-adaptive-vas
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Instrumentation & Measurement Magazine 26-1
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