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Cellular Neural Network based artificial antennal lobe

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3 Author(s)
Tuba Ayhan ; Electronics and Communication Engineering Department, Faculty of Electrical and Electronic Engineering, Istanbul Technical University, Maslak, TR-34469 Istanbul, Turkey ; Mehmet K. Miiezzinoglu ; Mü¿tak E. Yalçin

Two fundamental problems in olfactory signal processing is the large time constant and the large variance in the odor receptor code. Depending on the sensing technology and the analyte under investigation, obtaining a steady-state pattern from a sensor array may take minutes, yet still be unreliable. Therefore, odors are encoded in a spatio-temporal fashion in the nature, a task that fits very well in Cellular Neural Network (CNN) paradigm. Inspired by the generic insect olfactory system, we propose a CNN-based signal conditioning system that can be directly applicable on raw sensor data in real time. We interface the system with a Support Vector Machine (SVM) classifier, which maps the dynamically-encoded odor to an identity, and demonstrate the recognition system on a dataset recorded from a metal-oxide odor sensor array.

Published in:

2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010)

Date of Conference:

3-5 Feb. 2010