Feature Extraction for Smart Sensing Using Multi-perspectives Transformation

  • Sanad Al-Maskari
  • Ibrahim A. Ibrahim
  • Xue Li
  • Eimad Abusham
  • Abdulqader Almars
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10837)

Abstract

Air quality sensing systems, such as e-nose, are one of the complex dynamic systems; due to their sensitivity to electromagnetic interference, humidity, temperature, pressure and airflow. This yield to a Multi-Dependency effect over the output signal. To address the Multi-Dependency effect, we propose a multi-dimensional signal transformation for feature extraction. Our idea is analogous to viewing one huge object from different angles and arriving at different perspectives. Every perspective is partially true, but the final picture can be inferred by combining all perspectives. We evaluated our method extensively on two data sets including a publicly available e-nose dataset generated over a three-year period. Our results show higher performance in term of accuracies, F-measure, and stability when compared to standard methods.

Keywords

E-nose Air quality Feature extraction Time series Pattern recognition Classification Sensors 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sanad Al-Maskari
    • 1
    • 2
  • Ibrahim A. Ibrahim
    • 1
  • Xue Li
    • 1
  • Eimad Abusham
    • 2
  • Abdulqader Almars
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  2. 2.Faculty of Computing and Information TechnologySohar UniversitySoharOman

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