Biologically inspired feature rank codes for hardware friendly gas identification with the array of gas sensors

Muhammad Hassan, Amine Bermak

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

In this paper, we propose a biologically inspired rank-order-based classifier to facilitate the development of a smart electronic olfaction system, because the state-of-the-art pattern recognition algorithms are not suitable for the said objective due to their computationally intensive nature. In order to mimic biological olfactory rank codes, the features of gas sensors in an electronic olfaction system are ranked to form rank codes in our classifier instead of treating them as multidimensional data points. This classifier relies on probability rank tables, which are built for all target gases, because one-to-one mapping between the rank codes and the target gases is not practically found with the existing gas sensor technology. The table for each target gas records the probability of each sensor ID at each rank, and hence also serves as a visual interpretation tool. Due to the limited diversity in electronic olfaction system rank codes compared with biological olfactory codes, gas pairs are considered in this classifier by transforming the original multi-gas identification problem into pairwise classification problems in order to evaluate the discriminatory power between the rank codes for each pair of target gases. This discriminatory power at each rank not only helps to determine the applicability of the classifier but also serves as a weight for that rank in order to improve the classification performance. In addition, insightful quantitative feedback is integrated into the classifier in order to avoid misclassifications, and as a result, we achieve a 100% classification performance at the cost of a 3.79% rejection of uncertain predictions with a data set of an array of FIS inc. and Figaro inc. gas sensors exposed to five target gases.

Original languageEnglish
Article number7475485
Pages (from-to)5776-5784
Number of pages9
JournalIEEE Sensors Journal
Volume16
Issue number14
DOIs
Publication statusPublished - 15 Jul 2016

Fingerprint

Chemical sensors
hardware
Classifiers
Hardware
classifiers
sensors
Gases
gases
electronics
Pattern recognition
pattern recognition
rejection
Feedback
Sensors
predictions

Keywords

  • gas identification
  • Gas sensor array
  • probability rank table
  • rank codes

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Biologically inspired feature rank codes for hardware friendly gas identification with the array of gas sensors. / Hassan, Muhammad; Bermak, Amine.

In: IEEE Sensors Journal, Vol. 16, No. 14, 7475485, 15.07.2016, p. 5776-5784.

Research output: Contribution to journalArticle

@article{ccebdbfe924a48aab53be4b71b870d43,
title = "Biologically inspired feature rank codes for hardware friendly gas identification with the array of gas sensors",
abstract = "In this paper, we propose a biologically inspired rank-order-based classifier to facilitate the development of a smart electronic olfaction system, because the state-of-the-art pattern recognition algorithms are not suitable for the said objective due to their computationally intensive nature. In order to mimic biological olfactory rank codes, the features of gas sensors in an electronic olfaction system are ranked to form rank codes in our classifier instead of treating them as multidimensional data points. This classifier relies on probability rank tables, which are built for all target gases, because one-to-one mapping between the rank codes and the target gases is not practically found with the existing gas sensor technology. The table for each target gas records the probability of each sensor ID at each rank, and hence also serves as a visual interpretation tool. Due to the limited diversity in electronic olfaction system rank codes compared with biological olfactory codes, gas pairs are considered in this classifier by transforming the original multi-gas identification problem into pairwise classification problems in order to evaluate the discriminatory power between the rank codes for each pair of target gases. This discriminatory power at each rank not only helps to determine the applicability of the classifier but also serves as a weight for that rank in order to improve the classification performance. In addition, insightful quantitative feedback is integrated into the classifier in order to avoid misclassifications, and as a result, we achieve a 100{\%} classification performance at the cost of a 3.79{\%} rejection of uncertain predictions with a data set of an array of FIS inc. and Figaro inc. gas sensors exposed to five target gases.",
keywords = "gas identification, Gas sensor array, probability rank table, rank codes",
author = "Muhammad Hassan and Amine Bermak",
year = "2016",
month = "7",
day = "15",
doi = "10.1109/JSEN.2016.2571342",
language = "English",
volume = "16",
pages = "5776--5784",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "14",

}

TY - JOUR

T1 - Biologically inspired feature rank codes for hardware friendly gas identification with the array of gas sensors

AU - Hassan, Muhammad

AU - Bermak, Amine

PY - 2016/7/15

Y1 - 2016/7/15

N2 - In this paper, we propose a biologically inspired rank-order-based classifier to facilitate the development of a smart electronic olfaction system, because the state-of-the-art pattern recognition algorithms are not suitable for the said objective due to their computationally intensive nature. In order to mimic biological olfactory rank codes, the features of gas sensors in an electronic olfaction system are ranked to form rank codes in our classifier instead of treating them as multidimensional data points. This classifier relies on probability rank tables, which are built for all target gases, because one-to-one mapping between the rank codes and the target gases is not practically found with the existing gas sensor technology. The table for each target gas records the probability of each sensor ID at each rank, and hence also serves as a visual interpretation tool. Due to the limited diversity in electronic olfaction system rank codes compared with biological olfactory codes, gas pairs are considered in this classifier by transforming the original multi-gas identification problem into pairwise classification problems in order to evaluate the discriminatory power between the rank codes for each pair of target gases. This discriminatory power at each rank not only helps to determine the applicability of the classifier but also serves as a weight for that rank in order to improve the classification performance. In addition, insightful quantitative feedback is integrated into the classifier in order to avoid misclassifications, and as a result, we achieve a 100% classification performance at the cost of a 3.79% rejection of uncertain predictions with a data set of an array of FIS inc. and Figaro inc. gas sensors exposed to five target gases.

AB - In this paper, we propose a biologically inspired rank-order-based classifier to facilitate the development of a smart electronic olfaction system, because the state-of-the-art pattern recognition algorithms are not suitable for the said objective due to their computationally intensive nature. In order to mimic biological olfactory rank codes, the features of gas sensors in an electronic olfaction system are ranked to form rank codes in our classifier instead of treating them as multidimensional data points. This classifier relies on probability rank tables, which are built for all target gases, because one-to-one mapping between the rank codes and the target gases is not practically found with the existing gas sensor technology. The table for each target gas records the probability of each sensor ID at each rank, and hence also serves as a visual interpretation tool. Due to the limited diversity in electronic olfaction system rank codes compared with biological olfactory codes, gas pairs are considered in this classifier by transforming the original multi-gas identification problem into pairwise classification problems in order to evaluate the discriminatory power between the rank codes for each pair of target gases. This discriminatory power at each rank not only helps to determine the applicability of the classifier but also serves as a weight for that rank in order to improve the classification performance. In addition, insightful quantitative feedback is integrated into the classifier in order to avoid misclassifications, and as a result, we achieve a 100% classification performance at the cost of a 3.79% rejection of uncertain predictions with a data set of an array of FIS inc. and Figaro inc. gas sensors exposed to five target gases.

KW - gas identification

KW - Gas sensor array

KW - probability rank table

KW - rank codes

UR - http://www.scopus.com/inward/record.url?scp=84976477553&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84976477553&partnerID=8YFLogxK

U2 - 10.1109/JSEN.2016.2571342

DO - 10.1109/JSEN.2016.2571342

M3 - Article

AN - SCOPUS:84976477553

VL - 16

SP - 5776

EP - 5784

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 14

M1 - 7475485

ER -