Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Demidenko SSubject: search.filters.subject.machine learning

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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.
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    Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired from Thermopiles and Machine Learning
    (IEEE, 4/06/2021) Faulkner N; Alam F; Legg M; Demidenko S
    The development of an accurate passive localization system utilizing thermopile sensing and artificial intelligence is discussed in this paper. Several machine learning techniques are explored to create robust angular and radius coordinate models for a localization target with respect to thermopile sensors. These models are leveraged to develop a reconfigurable passive localization system that can use a varying number of thermopiles without the need for retraining. The proposed robust system achieves high localization accuracy (with the median error between 0.13 m and 0.2 m) while being trained using a single human subject and tested against multiple other subjects. It is shown that the proposed system does not experience any significant performance deterioration when localizing a subject at different ambient temperatures or with different configurations of the thermopile sensors placement.