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Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks

Country : Singapore
Department : Singapore Management University
Project Title : Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks
Researcher : CHAUHAN, Jagmohan , SENEVIRATNE, Suranga , HU, Yining , MISRA, Archan , SENEVIRATNE, Aruna , LEE, Youngki
Keyword : Digital Communications and Networking , OS and Networks
Publisher : Institutional Knowledge at Singapore Management University
Year End : 2018
Identifier : https://ink.library.smu.edu.sg/sis_research/4054 , https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5057&context=sis_research
Source : Research Collection School Of Information Systems
Abstract / Description :

Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.

References

CHAUHAN, Jagmohan and others / et al. (2018). Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks.  Singapore: Singapore Management University.
CHAUHAN, Jagmohan and others / et al. 2018. "Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks".  Singapore: Singapore Management University.
CHAUHAN, Jagmohan and others / et al. "Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks."  Singapore: Singapore Management University, 2018. Print.
CHAUHAN, Jagmohan and others / et al. Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks. Singapore: Singapore Management University; 2018.

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