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A survey on signal processing methods in fiber optic sensor for oxidized carbon steel

Jaafar, N.S.M. and Aziz, I.A. and Jaafar, J. and Mahmood, A.K. and Gilal, A.R. (2019) A survey on signal processing methods in fiber optic sensor for oxidized carbon steel. Advances in Intelligent Systems and Computing, 765 . pp. 13-24.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

This paper provides a broad overview of the adaptive methods for noise reduction used in the analysis of data in the different sensors such as acoustic emissions sensors, power quality signal analysis. The two algorithms are the Empirical Mode Decomposition and the Ensemble Empirical Mode Decomposition. We selected these two algorithms because our focus is on these methods. Firstly, this paper exhibits the inner workings of each algorithm both in the original authors� intuition and the mathematical model utilized. Next, we discuss the advantages of each of the algorithms based on recent and credible research papers and articles. We also critically dissect the limitations of each algorithm. This paper aims to give a general understanding on these algorithms which we hope will spur more research in improving the field of signal processing in the fiber optic sensor for the oxidised carbon steel. © 2019, Springer International Publishing AG, part of Springer Nature.

Item Type:Article
Impact Factor:cited By 0; Conference of 7th Computer Science On-line Conference, CSOC 2018 ; Conference Date: 25 April 2018 Through 28 April 2018; Conference Code:213719
Uncontrolled Keywords:Carbon steel; Fiber optic sensors; Fiber optics; Intelligent systems; Processing; Quality control; Software engineering; Steel fibers, Adaptive methods; Analysis of data; Empirical Mode Decomposition; Ensemble empirical mode decomposition; In-fiber; Oxidized carbons; Research papers, Signal processing
Departments / MOR / COE:Research Institutes > Institute for Autonomous Systems
ID Code:22238
Deposited By: Ahmad Suhairi
Deposited On:28 Feb 2019 02:47
Last Modified:26 Mar 2019 00:50

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