Browsing by Author "Ooi M"

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    How to measure light pollution - a systematic review of methods and applications
    (Elsevier, 17/02/2023) Mander S; Alam F; Lovreglio R; Ooi M
    Artificial light at night (ALAN) is ever-present in modern society and has revolutionised our lives. Along with its many benefits, ALAN can have adverse effects that are studied across many fields, including astronomy, epidemiology, and ecology. This paper collates multi-disciplinary knowledge on measuring light pollution through a systematic review of 140 articles. The measurement tools used in each context are comprehensively discussed, gaps are identified in current techniques, and benchmarking information is provided to aid future research. It is found that no single device meets all measurement objectives, and opportunities exist for further development, such as refining low-cost imaging tools. The paper highlights that each tool's limitations must be acknowledged to avoid incorrect conclusions. Thorough experimental reporting, including details on instrument location and aiming, is also essential for the replication of research. Collaboration is needed to develop unified measurement techniques, units and terminologies rather than maintaining disciplinary silos. This will enable light measurement to keep pace with lighting technologies and the growing effects of light pollution.
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    Roadmap on signal processing for next generation measurement systems
    (IOP Publishing, 2022-01-01) 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
    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.