Design and implementation of a gas identification system on Zynq SoC platform

Amine Ait Si Ali*, Abbes Amira, Faycal Bensaali, Mohieddine Benammar, Muhammad Ali Akbar, Muhammad Hassan, Amine Bermak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The Zynq-7000 based platforms are increasingly being used in different applications including image and signal processing. The Zynq system on chip (SoC) architecture combines a processing system based on a dual core ARM Cortex processor with a programmable logic (PL) based on a Xilinx 7 series field programmable gate arrays (FPGAs). Using the Zynq platform, real-time hardware acceleration can be performed on the programmable logic and controlled by a software running on the ARM-based processing system (PS). In this paper, a design and implementation of a gas identification system on the Zynq platform which shows promising results is presented. The gas identification system is based on a 16- Array SnO2 gas sensor, principal component analysis (PCA) for dimensionality reduction and decision tree (DT) for classification. The main part of the system that will be executed on the PL for hardware acceleration takes the form of an IP developed in C and synthesized using Vivado High Level Synthesis for the conversion from C to register transfer level, a hardware design for the entire system that allows the execution of the IP on the PL and the remaining parts of the identification system on the PS is developed in Vivado using IP Integrator. The communication between the processing system and programmable logic is performed using advanced extensible interface protocol (AXI). A software application is developed and executed on the ARM processor to control the hardware acceleration on the programmable logic of the previously designed IP core and the board is programmed using Software Development Kit. The maximum accuracy achieved by the system to classify three types of gases CO, C2H6O and H2 is 96.66%.

Original languageEnglish
Pages (from-to)9758-9764
Number of pages7
JournalARPN Journal of Engineering and Applied Sciences
Issue number20
Publication statusPublished - 11 Oct 2015


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