Abstract
SAW sensor is a well-known chemical sensor used for the identification of different chemical vapors. When a vapor is subjected to the SAW sensor, it results a unique dynamic trace as a transient signal which encodes its identity information. By analyzing this transient signal, the identity of the vapors can be obtained. This work presents an improved wavelet-assisted phase space-based vapor identification methodology for SAW sensor analysis. The phase space is created by computing the transient signal and its first time derivative. The performance of the proposed approach is compared with commonly used wavelet-assisted transient signal analysis in the measurement space. The discrimination analysis is performed qualitatively by principal component analysis and quantitatively by class separability measure. In this work, seven organic vapors are subjected to a PIB-coated SAW sensor in virtual environment and their transient responses are simulated. For making the transient responses near to the actual experimental conditions, random noise is added into the responses. It is observed that the wavelet-assisted phase space analysis shows better discrimination in principal component space and produces more than four times separability in comparison to the wavelet-assisted transient signal analysis.
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Authors gratefully acknowledges DST-SERB, Government of India (N-PDF reference no. PDF/2016/000512) and National Centre for Flexible Electronics, IIT Kanpur, India.
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Singh, P., Verma, P., Panda, S. (2020). Wavelet-Assisted Phase Space Analysis for Improved VOCs Discrimination Using SAW Sensor Transients. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_38
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DOI: https://doi.org/10.1007/978-981-15-2475-2_38
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