Roadmap on signal processing for next generation measurement systems

Dimitris K Iakovidis; Melanie Ooi; Ye Chow Kuang; Serge Demidenko; Alexandr Shestakov; Vladimir Sinitsin; Manus Henry; Andrea Sciacchitano; Stefano Discetti; Silvano Donati; Michele Norgia; Andreas Menychtas; Ilias Maglogiannis; Selina C Wriessnegger; Luis Alberto Barradas Chacon; George Dimas; Dimitris Filos; Anthony H Aletras; Johannes Töger; Feng Dong; Shangjie Ren; Andreas Uhl; Jacek Paziewski; Jianghui Geng; Francesco Fioranelli; Ram M Narayanan; Carlos Fernandez; Christoph Stiller; Konstantina Malamousi; Spyros Kamnis; Konstantinos Delibasis; Dong Wang; Jianjing Zhang; Robert X Gao

doi:10.1088/1361-6501/ac2dbd

Roadmap on signal processing for next generation measurement systems

Dimitris K Iakovidis, Melanie Ooi, Ye Chow Kuang, Serge Demidenko, Alexandr Shestakov, Vladimir Sinitsin, Manus Henry, Andrea Sciacchitano, Stefano Discetti, Silvano Donati, Michele Norgia, Andreas Menychtas, Ilias Maglogiannis, Selina C Wriessnegger, Luis Alberto Barradas Chacon, George Dimas, Dimitris Filos, Anthony H Aletras, Johannes Töger, Feng DongShangjie Ren, Andreas Uhl, Jacek Paziewski, Jianghui Geng, Francesco Fioranelli, Ram M Narayanan, Carlos Fernandez, Christoph Stiller, Konstantina Malamousi, Spyros Kamnis, Konstantinos Delibasis, Dong Wang, Jianjing Zhang, Robert X Gao

Research Centre for Fluid and Complex Systems

Research output: Contribution to journal › Review article › peer-review

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https://www.youtube.com/watch?v=Er0aM-JV1hg

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.

Original language	English
Article number	012002
Number of pages	48
Journal	Measurement Science and Technology
Volume	33
Issue number	1
Early online date	16 Nov 2021
DOIs	https://doi.org/10.1088/1361-6501/ac2dbd
Publication status	Published - 1 Jan 2022

Bibliographical note

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Funder

S D acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 949085).
This research received funding from the Hellenic Foundation for Research & Innovation (HFRI), the Faculty of Medicine at Lund University, Sweden, the Region of Scania, Sweden, the Swedish strategic e-science research program eSSENCE, and Swedish Research Council Grant 2018-03721.
The author acknowledges the support by the National Natural Science Foundation of China (Nos. 61227006, 61571321,61671322, 81827806, and 61971304).
This work has been partially supported by the Austrian Science Fund, Project No. P32201.
This contribution was supported by the National Science Centre, Poland (No. 2016/23/D/ST10/01546) and National Science Foundation of China (No. 42025401).
This work was supported in part by the Royal Society of New Zealand Te Ap¯arangi through a Rutherford Discovery Fellowship conferred to Melanie Ooi.
The authors would like to acknowledge the support from the UK Research & Innovation (UKRI). Project Grant 132885.
The research work was fully supported by the National Natural Science Foundation of China under Grant No. 51975355.

Funding

S D acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 949085). This research received funding from the Hellenic Foundation for Research & Innovation (HFRI), the Faculty of Medicine at Lund University, Sweden, the Region of Scania, Sweden, the Swedish strategic e-science research program eSSENCE, and Swedish Research Council Grant 2018-03721. The author acknowledges the support by the National Natural Science Foundation of China (Nos. 61227006, 61571321,61671322, 81827806, and 61971304). This work has been partially supported by the Austrian Science Fund, Project No. P32201. This contribution was supported by the National Science Centre, Poland (No. 2016/23/D/ST10/01546) and National Science Foundation of China (No. 42025401). This work was supported in part by the Royal Society of New Zealand Te Ap¯arangi through a Rutherford Discovery Fellowship conferred to Melanie Ooi. The authors would like to acknowledge the support from the UK Research & Innovation (UKRI). Project Grant 132885. The research work was fully supported by the National Natural Science Foundation of China under Grant No. 51975355.

Funders	Funder number
Horizon Europe	949085
Hellenic Foundation for Research and Innovation
Lund University
Vetenskapsrådet	2018-03721
National Natural Science Foundation of China	61227006, 61571321, 61671322, 81827806, 61971304, 51975355, 42025401
Austrian Science Fund	P32201
National Science Centre	2016/23/D/ST10/01546
Royal Society of New Zealand
UK Research and Innovation	132885

Keywords

signal processing
measurement systems
optical measurements
machine learning
biomedical applications
environmental applications
industrial applications

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Iakovidis, D. K., Ooi, M., Kuang, Y. C., Demidenko, S., Shestakov, A., Sinitsin, V., Henry, M., Sciacchitano, A., Discetti, S., Donati, S., Norgia, M., Menychtas, A., Maglogiannis, I., Wriessnegger, S. C., Chacon, L. A. B., Dimas, G., Filos, D., Aletras, A. H., Töger, J., ... Gao, R. X. (2022). Roadmap on signal processing for next generation measurement systems. Measurement Science and Technology, 33(1), Article 012002. https://doi.org/10.1088/1361-6501/ac2dbd

Iakovidis, DK, Ooi, M, Kuang, YC, Demidenko, S, Shestakov, A, Sinitsin, V, Henry, M, Sciacchitano, A, Discetti, S, Donati, S, Norgia, M, Menychtas, A, Maglogiannis, I, Wriessnegger, SC, Chacon, LAB, Dimas, G, Filos, D, Aletras, AH, Töger, J, Dong, F, Ren, S, Uhl, A, Paziewski, J, Geng, J, Fioranelli, F, Narayanan, RM, Fernandez, C, Stiller, C, Malamousi, K, Kamnis, S, Delibasis, K, Wang, D, Zhang, J & Gao, RX 2022, 'Roadmap on signal processing for next generation measurement systems', Measurement Science and Technology, vol. 33, no. 1, 012002. https://doi.org/10.1088/1361-6501/ac2dbd

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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.",

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AU - Sinitsin, Vladimir

AU - Henry, Manus

AU - Sciacchitano, Andrea

AU - Discetti, Stefano

AU - Donati, Silvano

AU - Norgia, Michele

AU - Menychtas, Andreas

AU - Maglogiannis, Ilias

AU - Wriessnegger, Selina C

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AU - Dimas, George

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AU - Aletras, Anthony H

AU - Töger, Johannes

AU - Dong, Feng

AU - Ren, Shangjie

AU - Uhl, Andreas

AU - Paziewski, Jacek

AU - Geng, Jianghui

AU - Fioranelli, Francesco

AU - Narayanan, Ram M

AU - Fernandez, Carlos

AU - Stiller, Christoph

AU - Malamousi, Konstantina

AU - Kamnis, Spyros

AU - Delibasis, Konstantinos

AU - Wang, Dong

AU - Zhang, Jianjing

AU - Gao, Robert X

N1 - Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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N2 - 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.

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KW - optical measurements

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KW - environmental applications

KW - industrial applications

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ER -

Roadmap on signal processing for next generation measurement systems

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