Roadmap on signal processing for next generation measurement systems

Author:

Iakovidis Dimitris KORCID,Ooi MelanieORCID,Kuang Ye ChowORCID,Demidenko SergeORCID,Shestakov Alexandr,Sinitsin VladimirORCID,Henry ManusORCID,Sciacchitano AndreaORCID,Discetti StefanoORCID,Donati SilvanoORCID,Norgia MicheleORCID,Menychtas Andreas,Maglogiannis IliasORCID,Wriessnegger Selina CORCID,Chacon Luis Alberto Barradas,Dimas George,Filos Dimitris,Aletras Anthony HORCID,Töger Johannes,Dong FengORCID,Ren ShangjieORCID,Uhl Andreas,Paziewski JacekORCID,Geng Jianghui,Fioranelli FrancescoORCID,Narayanan Ram MORCID,Fernandez Carlos,Stiller Christoph,Malamousi Konstantina,Kamnis Spyros,Delibasis Konstantinos,Wang DongORCID,Zhang Jianjing,Gao Robert X

Abstract

Abstract Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.

Funder

Royal Society of New Zealand Te Apārangi

UK Research and Innovation

Hellenic Foundation for Research and Innovation

Institute of Electrical and Electronics Engineers

National Natural Science Foundation of China

H2020 European Research Council

Vetenskapsrådet

Lund University Medical Faculty Foundation

Narodowe Centrum Nauki

Austrian Science Fund

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Reference202 articles.

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2. Big data, dataism and measurement;Petri;IEEE Instrum. Meas. Mag.,2020

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