Using Spike-Based Bio-Inspired Olfactory Model for Data Processing in Electronic Noses

被引：12

作者：

Liu, Ying-Jie ^{[1
]}

Meng, Qing-Hao ^{[1
]}

Qi, Pei-Feng ^{[1
]}

Sun, Biao ^{[1
]}

Zhu, Xin-Shan ^{[1
]}

机构：

[1] Tianjin Univ, Inst Robot & Autonomous Syst, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2018年 / 18卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Bio-inspired olfactory model; spike encoding; olfactory bulb; electronic nose; data processing; SENSOR; INFORMATION; FRAMEWORK; PATTERNS; NETWORK;

D O I：

10.1109/JSEN.2017.2774438

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Conventional electronic noses need complicated data preprocessing and tedious feature reduction steps for different sensors and applications. To overcome the drawbacks, a bio-inspired data processing method using a spike-based olfactory model is proposed in this paper, which consists of spike encoding by virtual olfactory receptor neurons (VORNs) and subsequent processing in a bionic olfactory bulb (BOB) model. Each VORN transduces the continuous sensor responses into spike time points, which are relayed to the BOB to enhance the operation efficiency. It is easy to extract useful features from BOB's outputs due to their specific oscillation patterns, which simplifies the subsequent steps of feature generation. Three classification methods are used to identify seven Chinese liquors. The experimental results show that the proposed method achieves a better classification performance than the conventional methods.

引用

页码：692 / 702

页数：11

共 42 条

[41] An olfactory neuronal network for vapor recognition in an artificial nose [J].