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dc.contributor.authorIakovidis, Dimitris K.en_NZ
dc.contributor.authorOoi, Melanieen_NZ
dc.contributor.authorKuang, Ye Chowen_NZ
dc.contributor.authorDemidenko, Sergeen_NZ
dc.contributor.authorShestakov, Alexandren_NZ
dc.contributor.authorSinitsin, Vladimiren_NZ
dc.contributor.authorHenry, Manusen_NZ
dc.contributor.authorSciacchitano, Aen_NZ
dc.contributor.authorDiscetti, Sen_NZ
dc.contributor.authorDonati, Sen_NZ
dc.contributor.authorNorgia, Men_NZ
dc.contributor.authorMenychtas, Aen_NZ
dc.contributor.authorMaglogiannis, Ien_NZ
dc.contributor.authorWriessnegger, SCen_NZ
dc.contributor.authorChacon, LABen_NZ
dc.contributor.authorDimas, Gen_NZ
dc.contributor.authorFilos, Den_NZ
dc.contributor.authorAletras, AHen_NZ
dc.contributor.authorToger, Jen_NZ
dc.contributor.authorDong, Fen_NZ
dc.contributor.authorRen, Sen_NZ
dc.contributor.authorUhl, Aen_NZ
dc.contributor.authorPaziewski, Jen_NZ
dc.contributor.authorGeng, Jen_NZ
dc.contributor.authorFioranelli, Fen_NZ
dc.contributor.authorNarayanan, RMen_NZ
dc.contributor.authorFernandez, Cen_NZ
dc.contributor.authorStiller, Cen_NZ
dc.contributor.authorMalamousi, Ken_NZ
dc.contributor.authorKamnis, Sen_NZ
dc.contributor.authorDelibasis, Ken_NZ
dc.contributor.authorWang, Dongen_NZ
dc.contributor.authorZhang, Jianjingen_NZ
dc.contributor.authorGao, Robert X.en_NZ
dc.date.accessioned2023-07-10T23:27:29Z
dc.date.available2023-07-10T23:27:29Z
dc.date.issued2022-01-01en_NZ
dc.identifier.issn0957-0233en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/15876
dc.description.abstractSignal 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.
dc.format.mimetypeapplication/pdf
dc.language.isoEnglishen_NZ
dc.publisherIOP Publishing Ltden_NZ
dc.rights©2021 The Author(s). This work is licensed under a CC BY 4.0 licence.
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectEngineering, Multidisciplinaryen_NZ
dc.subjectInstruments & Instrumentationen_NZ
dc.subjectEngineeringen_NZ
dc.subjectsignal processingen_NZ
dc.subjectmeasurement systemsen_NZ
dc.subjectoptical measurementsen_NZ
dc.subjectmachine learningen_NZ
dc.subjectbiomedical applicationsen_NZ
dc.subjectenvironmental applicationsen_NZ
dc.subjectindustrial applicationsen_NZ
dc.subjectCONVOLUTIONAL NEURAL-NETWORKen_NZ
dc.subjectUNCERTAINTY EVALUATIONen_NZ
dc.subjectSPECTRAL KURTOSISen_NZ
dc.subjectCAPSULE ENDOSCOPYen_NZ
dc.subjectACOUSTIC-EMISSIONen_NZ
dc.subjectSMOOTHNESS INDEXen_NZ
dc.subjectBIG DATAen_NZ
dc.subjectFUTUREen_NZ
dc.subjectCHALLENGESen_NZ
dc.subjectRADARen_NZ
dc.titleRoadmap on signal processing for next generation measurement systemsen_NZ
dc.typeJournal Article
dc.identifier.doi10.1088/1361-6501/ac2dbden_NZ
dc.relation.isPartOfMEASUREMENT SCIENCE AND TECHNOLOGYen_NZ
pubs.elements-id266145
pubs.issue1en_NZ
pubs.publication-statusPublisheden_NZ
pubs.volume33en_NZ
dc.identifier.eissn1361-6501en_NZ
uow.identifier.article-noARTN 012002


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