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Self-Sustaining Acoustic Sensor With Programmable Pattern Recognition for Underwater Monitoring

Authors :
Philipp Mayer
Luca Benini
Michele Magno
Mayer P.
Magno M.
Benini L.
Source :
IEEE Transactions on Instrumentation and Measurement. 68:2346-2355
Publication Year :
2019
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2019.

Abstract

Minimizing the power consumption of always-on sensors is crucial for extending the lifetime of battery-operated devices that are required to monitor events continuously and for long periods. This paper proposes a novel programmable $\mu \text{W}$ event-driven acoustic detector featuring “always-on” audio pattern recognition. The event-driven detector detects up to eight programmable spectral–temporal features extracted with a low-power single-channel analog circuit and classifies the features by an onboard microcontroller. The event-driven detector is combined with novel microbial fuel cells (MFCs) to achieve self-sustainability in an underwater scenario. Experimental results demonstrate that the power consumption of the detector is only $26.89~\mu \text{W}$ during always-on mode, achieving up to 59-dB sound pressure level of sensitivity. High detection accuracy of up to 95.89% in recognizing acoustic patterns has been experimentally verified. Accurate measurements with commercial MFCs demonstrate the capability to achieve self-sustainability in always-on monitoring.

Details

ISSN :
15579662 and 00189456
Volume :
68
Database :
OpenAIRE
Journal :
IEEE Transactions on Instrumentation and Measurement
Accession number :
edsair.doi.dedup.....36bcbf259d6923afab5174f90c852f88
Full Text :
https://doi.org/10.1109/tim.2018.2890187