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AuthorAkbar, Muhammad Ali
AuthorAli, Amine Ait Si
AuthorAmira, Abbes
AuthorBensaali, Faycal
AuthorBenammar, Mohieddine
AuthorHassan, Muhammad
AuthorBermak, Amine
Available date2021-09-01T10:02:43Z
Publication Date2016
Publication NameIEEE Sensors Journal
ResourceScopus
URIhttp://dx.doi.org/10.1109/JSEN.2016.2565721
URIhttp://hdl.handle.net/10576/22372
AbstractIncreasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated $4 \times 4$ tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the $4 \times 4$ array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz. 2001-2012 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAlgorithms
Application specific integrated circuits
Decision trees
Discriminant analysis
Electronic nose
Gases
Hardware
MATLAB
Programmable logic controllers
Reconfigurable hardware
System-on-chip
Tin oxides
Discriminant functions
Feature reduction
Gas identification
Hardware implementations
Identification algorithms
Linear discriminant analysis
Software implementation
System-on-chip platforms
Principal component analysis
TitleAn Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification
TypeArticle
Pagination5734-5746
Issue Number14
Volume Number16
dc.accessType Abstract Only


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