Improvement of selectivity and sensitivity of odor sensor using stochastic resonance and subspace method

本研究探讨了如何提高气味传感系统的选择性和灵敏度。随机共振可以期望提高气体传感器的灵敏度，然后通过数值模拟考虑系统参数出现随机共振，特别是就系统中的非线性分量而言。提出了基于子空间的proony方法，并证明了该方法可以提取传感器对多组分气体响应的特征，这些特征可以用来提高选择性，也可以给出输入气味中包含的气体组分的数量信息。

In this study, an improvement of selectivity and sensitivity of odor sensor system are investigated. The stochastic resonance can be expected to improve a sensitivity of a gas sensor, then we have considered the system parameter to appear the stochastic resonance by numerical simulations, especially, in terms of the nonlinear component in the system. The subspace-based Prony's method is proposed and we have shown that the method can extract features of a sensor response to multicomponent gasses which can be used to improve a selectivity and also give an information of the number of the gas components contained in an input odor.