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Machine Olfaction, Signal Processing and Pattern Recognition

[New Challenges and Solutions in Electronic Nose]

Paper:

Lei Zhang* and David Zhang, Efficient Solutions for Discreteness, Drift, and Disturbance (3D) in Electronic Olfaction, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, doi: 10.1109/TSMC.2016.2597800. [paper]

Abstract:

In this paper, we aim at presenting the new challenges of electronic noses (E-noses) and proposing effective methods for handling the new challenging scientific issues to be solved, such as signal discreteness (reproducibility), systematical drift and nontarget disturbances. We first review the progress of E-noses in applications, systems, and algorithms during the past two decades. Recall a number of significant achievements and motivated by the current issues that hinder large-scale application pace of E-nose technology, we propose to address three key issues: 1) discreteness; 2) drift; and 3) disturbance (simplified as 3D issues), which are sensor induced and sensor specific. For each issue, a highly effective and efficient method is proposed. Specifically, for discreteness issue, a global affine transformation method is introduced for E-nose instruments batch calibration; for drift issue, an unsupervised feature adaptation model is proposed to achieve effective drift adaptation; additionally, for disturbance issue, we proposed a simple targets-to-targets self-representation classifier method for fast nontargets detection, without knowing any prior knowledge of thousands of nontarget disturbances in real world. For each method, a closed form solution can be analytically determined and the simplicity is guaranteed. Experiments demonstrate the effectiveness and efficiency of the proposed methods for addressing the proposed 3D issues in real applications of E-noses.

Unsupervised Feature Adaptation (UFA) for Drift issue.

Experimental Results

Targets-to-Targets Self-Representation Classifier for Disturbance issue:

Experimental Results

Representation errors of three continuous observation sets, in which the red dashed line denotes the error boundary for separating between the targets and nontargets (disturbances). (a) Nontarget: smoke smell. (b) Nontarget: perfume. (c) Mixture of target and nontarget.

References:

[1] L. Zhang et al., “On-line sensor calibration transfer among electronic nose instruments for monitoring volatile organic chemicals in indoor air quality,” Sensors Actuators B Chem., vol. 160, no. 1, pp. 899–909, 2011.

[2] L. Zhang, F.-C. Tian, X.-W. Peng, and X. Yin, “A rapid discreteness correction scheme for reproducibility enhancement among a batch of MOS gas sensors,” Sens. Actuators A Phys., vol. 205, pp. 170–176, Jan. 2014.

[3] L. Zhang et al., “A novel background interferences elimination method in electronic nose using pattern recognition,” Sens. Actuators A Phys., vol. 201, pp. 254–263, Oct. 2013.

[4] K. Brudzewski, S. Osowski, and A. Dwulit, “Recognition of coffee using differential electronic nose,” IEEE Trans. Instrum. Meas., vol. 61, no. 6, pp. 1803–1810, Jun. 2012.