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Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation

Research output: Contributions to collected editions/works › Chapter › peer-review

Standard

Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. / Mercorelli, Paolo.
New Developments in Mathematics Research. ed. / Natalie L Clarke; Alex P Ronson. Nova Science Publishers, Inc., 2011. p. 85-104.

Research output: Contributions to collected editions/works › Chapter › peer-review

Harvard

Mercorelli, P 2011, Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. in NL Clarke & AP Ronson (eds), New Developments in Mathematics Research. Nova Science Publishers, Inc., pp. 85-104.

APA

Mercorelli, P. (2011). Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. In N. L. Clarke, & A. P. Ronson (Eds.), New Developments in Mathematics Research (pp. 85-104). Nova Science Publishers, Inc..

Vancouver

Mercorelli P. Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation. In Clarke NL, Ronson AP, editors, New Developments in Mathematics Research. Nova Science Publishers, Inc. 2011. p. 85-104

Bibtex

@inbook{8b524f19b1944e37b6f09474d286ca03,
title = "Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation",
abstract = "The noise detection and the data cleaning find application in data com-pressions for images and voice as well as in their analysis and recognition,datatransmission,datareconciliation,fault detection and in general in all application area of the signal processing and measurements.The content of this paper can offer the possibility to improve the state of the art of all those procedures with denoising methods which use a thresholding tech-nique implying a free thresholding one,running in wavelet packets.The author presents a technique which deals with a free thresholding method related to the on-line peak noise variance estimation even for signals with a small S/N ratio.The se cond innovative aspect consists of use of wavelet packets which give more elasticity to the technique.The basic idea is to characterize the noise like an in coherent part of the measure dsignal.It is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum.In this sense the proposed median based algorithm can be seen a s an entropy function and this analogyis shown.The paper provides to show general properties of the wavelet packet son which the proposed procedure is based.The developed algorithmis to tally general even though it is applied by using Haar wavelet packets and it is present in some industrial software plat-forms to detect sensor out liers because of their easy structure. More,it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit with in ABB's(Asea Brown Boveri)industry division.",
keywords = "Data reconciliation, Fault detection, Haar functions, Noise detection, Signal processing, Variance, Wavelets, Wavelets'packets, Engineering",
author = "Paolo Mercorelli",
year = "2011",
language = "English",
isbn = "9781613242520",
pages = "85--104",
editor = "Clarke, {Natalie L} and Ronson, {Alex P}",
booktitle = "New Developments in Mathematics Research",
publisher = "Nova Science Publishers, Inc.",
address = "United States",

}

RIS

TY - CHAP

T1 - Median based algorithm as an entropy function for noise detectionin wavelet trees for data reconciliation

AU - Mercorelli, Paolo

PY - 2011

Y1 - 2011

N2 - The noise detection and the data cleaning find application in data com-pressions for images and voice as well as in their analysis and recognition,datatransmission,datareconciliation,fault detection and in general in all application area of the signal processing and measurements.The content of this paper can offer the possibility to improve the state of the art of all those procedures with denoising methods which use a thresholding tech-nique implying a free thresholding one,running in wavelet packets.The author presents a technique which deals with a free thresholding method related to the on-line peak noise variance estimation even for signals with a small S/N ratio.The se cond innovative aspect consists of use of wavelet packets which give more elasticity to the technique.The basic idea is to characterize the noise like an in coherent part of the measure dsignal.It is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum.In this sense the proposed median based algorithm can be seen a s an entropy function and this analogyis shown.The paper provides to show general properties of the wavelet packet son which the proposed procedure is based.The developed algorithmis to tally general even though it is applied by using Haar wavelet packets and it is present in some industrial software plat-forms to detect sensor out liers because of their easy structure. More,it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit with in ABB's(Asea Brown Boveri)industry division.

AB - The noise detection and the data cleaning find application in data com-pressions for images and voice as well as in their analysis and recognition,datatransmission,datareconciliation,fault detection and in general in all application area of the signal processing and measurements.The content of this paper can offer the possibility to improve the state of the art of all those procedures with denoising methods which use a thresholding tech-nique implying a free thresholding one,running in wavelet packets.The author presents a technique which deals with a free thresholding method related to the on-line peak noise variance estimation even for signals with a small S/N ratio.The se cond innovative aspect consists of use of wavelet packets which give more elasticity to the technique.The basic idea is to characterize the noise like an in coherent part of the measure dsignal.It is performed through the wavelet tree by choosing the subspaces where the median value of the wavelet components has minimum.In this sense the proposed median based algorithm can be seen a s an entropy function and this analogyis shown.The paper provides to show general properties of the wavelet packet son which the proposed procedure is based.The developed algorithmis to tally general even though it is applied by using Haar wavelet packets and it is present in some industrial software plat-forms to detect sensor out liers because of their easy structure. More,it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit with in ABB's(Asea Brown Boveri)industry division.

KW - Data reconciliation

KW - Fault detection

KW - Haar functions

KW - Noise detection

KW - Signal processing

KW - Variance

KW - Wavelets

KW - Wavelets'packets

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84895347837&partnerID=8YFLogxK

M3 - Chapter

AN - SCOPUS:84895347837

SN - 9781613242520

SP - 85

EP - 104

BT - New Developments in Mathematics Research

A2 - Clarke, Natalie L

A2 - Ronson, Alex P

PB - Nova Science Publishers, Inc.

ER -

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Fernandes, F. M., Dos Santos, M. F., De Morais, M. H. F., Lima, J., Pereira, A. I. & Mercorelli, P., 2025, Proceedings of the 2025 26th International Carpathian Control Conference (ICCC): Grandhotel Starý Smokovec, High Tatras, Starý Smokovec, Slovak Republic, 19-21 May 2025. Kacur, J., Skovranek, T., Laciak, M. & Mojzisova, A. (eds.). Piscataway: Institute of Electrical and Electronics Engineers Inc., 6 p. (International Carpathian Control Conference, ICCC; vol. 26).

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

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

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Pimentel, G. O., Santos, M. F. D., Lima, J., Mercorelli, P. & Fernandes, F. M., 15.01.2025, In: Sensors. 25, 2, 27 p., 479.

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

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Rigon, S., Haus, B., Mercorelli, P. & Zigliotto, M., 27.02.2025, In: IEEE Open Journal of Power Electronics. 6, p. 734-746 13 p.

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

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