Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks.
Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice command...
| Publicado en: | Computer (00189162) Vol. 51; no. 5; pp. 60 - 68 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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IEEE
May2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=129841003&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129841003 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: May2018 vid: 51 iid: 5 pid: 13605 pub: IEEE artinfo: ui: 129841003 10.1109/MC.2018.2381119 ppf: 60 ppct: 8 formats: tig: atl: Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks. aug: au: Chauhan, Jagmohan Seneviratne, Suranga Hu, Yining Misra, Archan Seneviratne, Aruna Lee, Youngki affil: Aalto University University of Sydney Data61 and University of New South Wales Singapore Management University University of New South Wales su: Computer access control Internet of things Artificial neural networks Machine learning Constraint satisfaction sug: subj: Computer access control Internet of things Artificial neural networks Machine learning Constraint satisfaction keyword: AI artificial intelligence authentication breathing breathing based authentication deep learning IoT LSTM mobile and embedded deep learning performance recurrent neural networks RNN security wearables ab: Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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