COMPLETE ENSEMBLE EMPIRICAL MODE DECOMPOSITION (CEEMD) FOR REAL-TIME SIGNAL DETRENDING IN IOT APPLICATIONS | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 1, Volume 16, Issue 1, January 2016, Page 1-17 PDF (902.96 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2016.9150 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| M Abduridha; A Tolba; M Rashad | ||||
| Computer Science Department,Faculty of Computers and Information, Mansoura University, Egypt | ||||
| Abstract | ||||
| The Internet of Things (IOT) is a promising area which will boost the world economy. The constituent components of the IOT are smart objects which generate actuation signals or receive sensory signals which are usually noisy, have trend or has small signal-to-noise ratio. Processing these signals for filtering, detrending and enhancing the signal-to-noise ratio is crucial for embedding intelligence in these smart objects. This research discovers the potential of CEEMD in preparing signals for further intelligent applications such as event detection or pattern recognition in smart objects. Algorithms are presented for signal filtering, detrending and event detection based on a combination of both CEEMD, the autocorrelation function and the learning vector quantization classifier.The performance of the proposed algorithms is compared for both CEEMD and the least squares fit approach. The CEEMD has shown promising results. | ||||
| Keywords | ||||
| Internet of Things; Real-time Signal Detrending; Empirical mode decomposition; Complete Ensemble Empirical Mode Decomposition; Signal Denoising; Thresholding; Event Detection; Learning Vector Quantization | ||||
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