Novel Convolution-Based Signal Processing Techniques for an Artificial Olfactory Mucosa

As our understanding of the human olfactory system has grown, so has our ability to design artificial devices that mimic its functionality, so called electronic noses (e-noses). This has led to the development of a more sophisticated biomimetic system known as an artificial olfactory mucosa (e-mucos...

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Veröffentlicht in:	IEEE sensors journal 2009-08, Vol.9 (8), p.929-935
Hauptverfasser:	Gardner, J.W., Taylor, J.E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Biomedical signal processing Biomimetics Biosensors Classification Convolution Design engineering electronic nose Electronic noses Human Humans Olfactory Robustness Sensor arrays Sensor systems Sensors Signal processing
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creator	Gardner, J.W. Taylor, J.E.
description	As our understanding of the human olfactory system has grown, so has our ability to design artificial devices that mimic its functionality, so called electronic noses (e-noses). This has led to the development of a more sophisticated biomimetic system known as an artificial olfactory mucosa (e-mucosa) that comprises a large distributed sensor array and artificial mucous layer. In order to exploit fully this new architecture, new approaches are required to analyzing the rich data sets that it generates. In this paper, we propose a novel convolution based approach to processing signals from the e-mucosa. Computer simulations are performed to investigate the robustness of this approach when subjected to different real-world problems, such as sensor drift and noise. Our results demonstrate a promising ability to classify odors from poor sensor signals.
doi_str_mv	10.1109/JSEN.2009.2024856
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subjects	Biomedical signal processing Biomimetics Biosensors Classification Convolution Design engineering electronic nose Electronic noses Human Humans Olfactory Robustness Sensor arrays Sensor systems Sensors Signal processing
title	Novel Convolution-Based Signal Processing Techniques for an Artificial Olfactory Mucosa
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