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AuthorZhai, Xiaojun
AuthorAli, Amine Ait Si
AuthorAmira, Abbes
AuthorBensaali, Faycal
Available date2021-09-05T05:40:13Z
Publication Date2016
Publication NameIEEE Access
ResourceScopus
ISSN21693536
URIhttp://dx.doi.org/10.1109/ACCESS.2016.2619181
URIhttp://hdl.handle.net/10576/22685
AbstractSystems based on wireless gas sensor networks offer a powerful tool to observe and analyze data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in the case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a multi-layer perceptron (MLP) artificial neural network to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex micro hotplates. The overall system acquires the gas sensor data through radio-frequency identification (RFID), and processes the sensor data with the proposed MLP classifier implemented on a system on chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and the achieved results have shown that an accuracy of 97.4% has been obtained. 2013 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectArtificial neural network
FPGA
gas identification
system on chip (SoC)
ZYNQ
TitleMLP Neural Network Based Gas Classification System on Zynq SoC
TypeArticle
Pagination8138-8146
Volume Number4
dc.accessType Open Access


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