Abstract
The high sensitivity, stability, selectivity and adaptivity of mailman olfactory system is a result of a large number of olfactory receptors feeding into an extensive layers of neural processing units. Olfactory receptor cells (ORC) contribute significantly in the sense of smells. Bloodhounds have four billion ORC making them ideal for tracking while human have about 30 million ORC. E-nose stability, sensitivity and selectivity have been a challenging task. We hypothesize that appropriate signal processing with an increase number of sensory receptors can significantly improve odour recognition in e-nose. Adding physical receptors to e-nose is costly and can increase system complexity. Therefore, we propose an Artificial Olfactory Receptor Cells Model (AORCM) inspired by neural circuits of the vertebrate olfactory system to improve e-nose performance. Secondly, we introduce and adaptation layer to cope with drift and unknown changes. The major layers in our model are the sensory transduction layer, sensory adaptation layer, artificial olfactory receptors layer (AORL) and artificial olfactory cortex layer (AOCL). Each layer in the proposed system is biologically inspired by the mammalian olfactory system. The experiments are executed using chemo-sensory arrays data generated over three-year period. The propose model resulted in a better performance and stability compared to other models. To our knowledge, e-nose stability, selectivity and sensitivity are still unsolved problem. Our paper provides a new approach in improving e-nose pattern recognition over long period of time.
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Acknowledgment
This work was partially supported by the following research grants: (1) No. LY14F020036 from the Natural Science Foundation of Zhejiang Province, China; (2) No. BK20141420 from the Natural Science Foundation of Jiangsu Province, China; (3) No. 61272261 from the Natural Science Foundation of China.
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Al-Maskari, S., Guo, W., Zhao, X. (2016). Biologically Inspired Pattern Recognition for E-nose Sensors. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_10
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