A committee machine gas identification system based on dynamically reconfigurable FPGA

Minghua Shi, Amine Bermak, Shrutisagar Chandrasekaran, Abbes Amira, Sofiane Brahim-Belhouari

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors.
Original languageEnglish
Pages (from-to)403-414
JournalIEEE Sensors Journal
Issue number4
Publication statusPublished - 2008
Externally publishedYes


  • committee machine (CM)
  • dynamically reconfigurable field programmable gate array (FPGA)
  • gas identification
  • pattern recognition

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