基于新卷积的简化人工嗅粘膜信号处理技术

J. Gardner, J.E. Taylor
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引用次数: 2

摘要

随着我们对人类嗅觉系统了解的增加,我们设计新颖建筑以模仿生物系统的能力也在增强。人工嗅觉粘膜的概念代表了仿生学领域的一个新发展。在这里,我们分析了由这种仿生系统产生的信号,该系统包含了以前在机器嗅觉或所谓的电子鼻领域中未遇到的时空元素。本文探讨了基于卷积的信号处理方法的使用,以利用这一更丰富的数据集,并改善众所周知的传感器噪声和漂移问题。我们表明,在某些条件下,人工粘膜结合基于卷积的分类器比传统的电子鼻表现更好。
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Novelconvolution Based Signal Processing Techniques for a Simplified Artificial Olfactory Mucosa
As our understanding of the human olfactory system increases, so does our ability to design novel architectures in order to mimic the biological system. The concept of an artificial olfactory mucosa represents a new development in the field of biomimetics. Here we analyse the signals produced by such a biomimetic system that contain a spatio-temporal element not previously encountered within the field of machine olfaction or so-called electronic noses. This paper explores the use of convolution-based signal processing methodologies to exploit this richer data-set and ameliorate the well-known problems of sensor noise and drift. We show that, under certain conditions, an artificial mucosa combined with a convolution based classifier performs better than a conventional electronic nose.
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