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Zhang, Yin; Gravina, Raffaele; Lu, Huimin; Villari, Massimo; Fortino, Giancarlo
Journal of network and computer applications, 09/2018, Letnik: 117Journal Article
Currently, ECG-based authentication is considered highly promising in terms of user identification for smart healthcare systems because of its inimitability, suitability, accessibility and comfortability. However, it is a great challenge to improve the authentication accuracy, especially for scenarios that include a large number of users. Thus, this paper proposes a parallel ECG-based authentication called PEA. Specifically, this paper proposes a hybrid ECG feature extraction method that integrated fiducial- and non-fiducial-based features to extract more comprehensive ECG features and thereby improve the authentication stability. Furthermore, this paper proposes a parallel ECG pattern recognition framework to improve the recognition efficiency in multiple ECG feature spaces. Through the experiments, the performance of the proposed authentication is verified.
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Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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