Integrated Memristor Network for Physiological Signal Processing
Abstract: Humans are complex organisms made by millions of physiological systems. Therefore, physiological activities can represent physical or mental states of the human body. Physiological signal processing is essential in monitoring human physiological features. For example, non‐invasive electroencephalography (EEG) signals can be used to reconstruct brain consciousness and detect eye movements for identity verification. However, physiological signal processing requires high resolution, high sensitivity, fast responses, and low power consumption, hindering practical hardware design for physiological signal processing. The bionic capability of memristor devices is very promising in the context of building physiological signal processing hardware and they have demonstrated a handful of advantages over the traditional Von Neumann architecture system in accelerating neural networks. Memristor networks can be integrated as a hardware system for physiological signal processing that can deliver higher energy efficiency and lower latency compared to traditional implementations. This review paper first introduces memristor characteristics, followed by a comprehensive literature study of memristor‐based networks. Physiology signal processing applications enabled by these integrated memristor networks are also presented in this review. In summary, this paper aims to provide a new perspective on physiological signal processing using integrated memristor networks.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Integrated Memristor Network for Physiological Signal Processing ; day:17 ; month:04 ; year:2023 ; extent:35
Advanced electronic materials ; (17.04.2023) (gesamt 35)
- Urheber
-
Cai, Lei
Yu, Lianfeng
Yue, Wenshuo
Zhu, Yihang
Yang, Zhiyu
Li, Yuqi
Tao, Yaoyu
Yang, Yuchao
- DOI
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10.1002/aelm.202300021
- URN
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urn:nbn:de:101:1-2023041815275480063406
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 14.08.2025, 10:55 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Cai, Lei
- Yu, Lianfeng
- Yue, Wenshuo
- Zhu, Yihang
- Yang, Zhiyu
- Li, Yuqi
- Tao, Yaoyu
- Yang, Yuchao
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