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

• Published in: IEEE sensors journal Vol. 9; no. 8; pp. 929 - 935
• Main Authors: Gardner, J.W., Taylor, J.E.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-08-2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2009.2024856

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.