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Noise level estimation using haar wavelet packet trees for sensor robust outlier detection

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Authors

The paper is related to the on-line noise variance estimation. In practical use, it is important to estimate the noise level from the data rather than to assume that the noise level is known. The paper presented a free thresholding method related to the on-line peak noise variance estimation even for signal with small S/N ratio. The basic idea is to characterize the noise like an incoherent part of the measured signal. This is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum. The paper provides to show nice general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB’s industry division.
Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2006 : international conference, Glasgow, UK, May 8 - 11, 2006; proceedings
EditorsMarina Gavrilova, Osvaldo Gervasi, Vipin Kumar, C.J. Kenneth Tan, David Taniar, Antonio Laganà, Youngsong Mun, Hyunseung Choo
Number of pages10
Volume1
Place of PublicationBerlin
PublisherSpringer
Publication date01.01.2006
Pages847-856
ISBN (print)3-540-34070-X, 978-3-540-34070-6
ISBN (electronic)978-3-540-34071-3
DOIs
Publication statusPublished - 01.01.2006
Externally publishedYes
EventInternational Conference on Computational Science and Its Applications - ICCSA 2006 - Glasgow, United Kingdom
Duration: 08.05.2006 → 11.05.2006
Conference number: 6

    Research areas

  • Engineering - Noise Variance, Wavelet Packet, Minimum Description Length, Thresholding Method, Wavelet Shrinkage

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Research output: Journal contributions › Journal articles › Research › peer-review

DOI

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