MLP Neural Network Based Gas Classification System on Zynq SoC

Zhai, Xiaojun, Ait Si ali, Amine, Amira, Abbes and Bensaali, Faycal (2016) MLP Neural Network Based Gas Classification System on Zynq SoC. IEEE Access, 4. pp. 8138-8146. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2016.2619181

Abstract

Systems 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.

Item Type: Article
Subjects: G400 Computer Science
G700 Artificial Intelligence
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Amine Ait Si Ali
Date Deposited: 22 Aug 2017 09:01
Last Modified: 19 Dec 2017 19:59
URI: http://nrl.northumbria.ac.uk/id/eprint/31473

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