Compressive sensing based electronic nose platform

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• 作者：Hamza Djelouat ; ^{djelouat.hmz@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Amine Ait Si AliAuthor Vitae ; Abbes AmiraAuthor Vitae ; Faycal BensaaliAuthor Vitae
• 关键词：Compressive sensing ; Distributed compressive sensing ; Reconstruction algorithms ; Classification ; Gas sensors
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：350-359
• 全文大小：1420 K

文摘

Electronic nose (EN) systems play a significant role for gas monitoring and identification in gas plants. Using an EN system which consists of an array of sensors provides a high performance. Nevertheless, this performance is bottlenecked by the high system complexity incorporated with the high number of sensors. In this paper a new EN system is proposed using data sets collected from an in-house fabricated 31721a19b98e27c46f25a343" title="Click to view the MathML source">4×4$4\times 4$ tin-oxide gas array sensor. The system exploits the theory of compressive sensing (CS) and distributed compressive sensing (DCS) to reduce the storage capacity and power consumption. The obtained results have shown that compressing the transmitted data to 20% of its original size will preserve the information by achieving a high reconstruction quality. Moreover, exploiting DCS will maintain the same reconstruction quality for just 15% of the original size. This high quality of reconstruction is explored for classification using several classifiers such as decision tree (DT), K-nearest neighbour (KNN) and extended nearest neighbour (ENN) along with linear discrimination analysis (LDA) as feature reduction technique. CS-based reconstructed data has achieved a 95% classification accuracy. Furthermore, DCS-based reconstructed data achieved a 98.33% classification accuracy which is the same as using original data without compression.