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Preethi Krishna Rao Mane
BMS College of Engineering
Computer Science & IT Electrical & Electronics Engineering

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Analytical framework for optimized feature extraction for upgrading occupancy sensing performance Preethi Krishna Rao Mane; K. Narasimha Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.353 KB) | DOI: 10.11591/ijece.v10i4.pp4093-4100

Abstract

The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.
Co-Authors K. Narasimha Rao