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Sensors 2018, 18(4), 1179; https://doi.org/10.3390/s18041179

Study on Interference Suppression Algorithms for Electronic Noses: A Review

1
College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongwen Road 2nd, Nan’an District, Chongqing 400065, China
2
College of Communication Engineering, Chongqing University, 174 ShaZheng Street, ShaPingBa District, Chongqing 400044, China
3
Advanced Robotics and Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
*
Authors to whom correspondence should be addressed.
Received: 24 March 2018 / Revised: 6 April 2018 / Accepted: 6 April 2018 / Published: 12 April 2018
(This article belongs to the Section Chemical Sensors)
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Abstract

Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc. View Full-Text
Keywords: electronic nose; interference; suppression electronic nose; interference; suppression
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Liang, Z.; Tian, F.; Yang, S.X.; Zhang, C.; Sun, H.; Liu, T. Study on Interference Suppression Algorithms for Electronic Noses: A Review. Sensors 2018, 18, 1179.

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