Novel Convolution-based Signal Processing Technique for an Artificial Olfactory Mucosa

Julian W. Gardner and James E. Taylor


Artifical organ, Biomedical signal processing, Artificial olfactory mucosa, Convolution method


Recent advances in our understanding of the biological olfactory system have led to the development of new types of artificial olfactory devices. Inspired by the concept of nasal chromatography (odour components being separated by the olfactory epithelium), the artificial olfactory mucosa or e-mucosa has been conceived. However, the data generated by this device are of a class not encountered in traditional field of artificial olfaction before, due to its spatio-temporal characteristics. A novel processing approach is required to exploit fully the information rich nature of this data. Here a novel convolution based signal processing method is proposed. It is shown how the convolution integral can be applied successfully to different odour data-sets and thus further the development of the artificial or e-mucosa for medical application

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