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Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID.

Authors :
Ait Si Ali, Amine
Farhat, Ali
Mohamad, Saqib
Amira, Abbes
Bensaali, Faycal
Benammar, Mohieddine
Bermak, Amine
Source :
IEEE Sensors Journal; 6/1/2018, Vol. 18 Issue 11, p4633-4642, 10p
Publication Year :
2018

Abstract

This paper presents the development of a wireless low power reconfigurable self-calibrated multi-sensing platform for gas sensing applications. The proposed electronic nose (EN) system monitors gas temperatures, concentrations, and mixtures wirelessly using the radio-frequency identification (RFID) technology. The EN takes the form of a set of gas and temperature sensors and multiple pattern recognition algorithms implemented on the Zynq system on chip (SoC) platform. The gas and temperature sensors are integrated on a semi-passive RFID tag to reduce the consumed power. Various gas sensors are tested, including an in-house fabricated $4\times 4$ SnO2based sensor and seven commercial Figaro sensors. The data is transmitted to the Zynq based processing unit using a RFID reader, where it is processed using multiple pattern recognition algorithms for dimensionality reduction and classification. Multiple algorithms are explored for optimum performance, including principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction while decision tree (DT) and k-nearest neighbors (KNN) are assessed for classification purpose. Different gases are targeted at diverse concentration, including carbon monoxide (CO), ethanol (C2H6O), carbon dioxide (CO2), propane (C3H8), ammonia (NH3), and hydrogen (H2). An accuracy of 100% is achieved in many cases with an overall accuracy above 90% in most scenarios. Finally, the hardware/software heterogeneous solution to implementation PCA, LDA, DT, and KNN on the Zynq SoC shows promising results in terms of resources usage, power consumption, and processing time. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
1530437X
Volume :
18
Issue :
11
Database :
Complementary Index
Journal :
IEEE Sensors Journal
Publication Type :
Academic Journal
Accession number :
129614599
Full Text :
https://doi.org/10.1109/JSEN.2018.2822711