TY - JOUR

T1 - Noise level estimation and detection

AU - Mercorelli, P.

N1 - Cited By (since 1996): 1 Export Date: 22 May 2012 Source: Scopus doi: 10.2174/1874476111003010066 Language of Original Document: English Correspondence Address: Mercorelli, P.; University of Applied Sciences Wolfsburg, Faculty of Automotive Engineering, Robert Koch Platz 12, D-38440 Wolfsburg, Germany; email: p.mercorelli@ostfalia.de References: Dayal, A., (2007), US20077295695Stephen, B.A., Wei, A., (2009), US20097610074Rouge, B., (2003), FR2840087Berkenr, K., Schwartz, E.L., Gormisch, M.J., (2009), US20097564585Berkenr, K., Schwartz, E.L., Gormisch, M.J., (2009), US20097599095Stuard, S.A., (2009), US20090257674Stuard, S.A., (2009), US20090257675Frick, A., Mercorelli, P., (2003), DE10225344Donoho, D.L., Johnstone, I.M., Ideal spatial adaptation by wavelet shrinkage (1994) Biometrika, 81 (3), pp. 425-455; Donoho, D.L., (2005), www-stat.stanford.edu/wavelab/Chang, S.G., Yu, B., Vetterli, M., Adaptive wavelet thresholding for image denoising and compression (2000) IEEE Trans Image Proc, 9 (9), pp. 1532-1546; Ruggeri, F., Vidakovic, B., A Bayesian decision theoretic approach to the choice of thresholding parameter (1999) Stat Sin, 9, pp. 1532-1546; Vidakovic, B., Nonlinear wavelet shrinkage with bayes rules and bayes factors (1998) J Am Stat Assoc, 93 (441), pp. 137-179; Wu, B.S., Cai, C.Z., Wavelet denoising and its implementation in labVIEW (2009) CISP09, pp. 1-4; Vonesch, C., Unser, M., A fast thresholded landweber algorithm for wavelet-regularized multidimensional deconvolution (2008) IEEE Trans Image Proc, 17 (4), pp. 539-549; Smith, C.B., Agaian, S., Akopian, D.A., Wavelet-denoising approach using polynomial threshold operators (2008) IEEE Single Proc Lett, 15 (1), pp. 906-909; Mohcak, M.K., Kozintsev, I., Ramchandran, K., Moulin, P., Low-complexity image denoising based on statistical modeling of wavelet coefficients (1999) IEEE Single Proc Lett, 6 (12), p. 300; Fan, G.L., Xia, X.G., Image denoising using a local contextual hidden markov model in the wavelet domain (2001) SP Lett, 8 (5), pp. 125-128; Beheshti, S., Dahleh, M.A., Noise variance and signal denoising (2003) Proc. IEEE Int Conf Acustic Speech Signal Proc (ICASSP), , Hong Kong; Rissanen, J., Minimum description length denoising (2000) IEEE Trans Inform Theory, 46 (7), pp. 2537-2543; Gruenwald, P., Myung, I.J., Pitt, M.A., Advances in minimum description lenght (2005) Theory Applications, , MIT Press; Kumar, V., Heikkonen, J., Rissanen, J., Kaski, K., Minimum description length denoising with histogram models (2006) IEEE Trans Signal Proc, 54 (8), pp. 2922-2928; Rissanen, J., (2007) Information and Complexity In Statistical Modelling, 1, p. 144. , Springer: USA; Rissanen, J., Roos, T., Conditional NML universal models (2007) Information Theory Applications Workshop, pp. 337-341. , Springer: USA; Donoho, D.L., Denoising and soft Thresholding (1995) IEEE Trans Inform Theory, 41 (3), pp. 613-627; Ojanen, J., Heikkonen, J., A soft thresholding approach for MDL denoising (2007) Proc 15th European Signal Process Conf (EU-SIPCO 2007), pp. 1083-1087; Donoho, D.L., Johnstone, I.M., Adapting to unknown smoothness via wavelet shrinkage (1994) J Am Stat Assoc, 90 (432), pp. 1220-1224; Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., Picard, D., Density estimation by wavelet thesholding (1996) Ann Stat, 24 (2), pp. 508-539; Menold, P.H., Pearson, R.K., Allgöwer, F., Online outlier detection and removal (1999) Proc Mediterranean Control Conf, , Israel; Isermann, R., Modellgestützte Überwachung und Fehlerdiagnose Technischer Systeme (1996) Automatisierungstechnische Praxis, 5, pp. 9-20; Ishii, Y., Saito, T., Komatsu, T., Denoising via nonlinear image decomposition for a digital color camera (2007) ICIP07, pp. 309-312; Daubechies, I., (1995) Ten Lectures On Wavelets, , publisher society for industrial and applied mathematics. Philadelphia (Pennsylvania); Mercorelli, P., Frick, A., (2007) Industrial Applications Using Wavelet Packets For Gross Error Detection, pp. 89-127. , Springer Berlin/Heidelberg: SCI Series

PY - 2010/1

Y1 - 2010/1

N2 - The noise detection and the data cleaning find application in data compressions for images and voice as well as in their analysis and recognition, data transmission, data reconciliation, fault detection and in general in all application area of the signal processing and measurements. This paper presents a short overview of some very recent inventions and other correlated literature which utilize methods of noise detection which are characterized by thresholding techniques. Moreover, the paper concentrates its attention on an innovative invention. 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 technique 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 second 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 incoherent part of the measured signal. It 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 general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though it is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers because of their easy structure. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB's (Asea Brown Boveri) industry division. The article presented some promising patents on the Noise Level Estimation and Detection Using Haar Wavelet Packet Trees and their Applications.

AB - The noise detection and the data cleaning find application in data compressions for images and voice as well as in their analysis and recognition, data transmission, data reconciliation, fault detection and in general in all application area of the signal processing and measurements. This paper presents a short overview of some very recent inventions and other correlated literature which utilize methods of noise detection which are characterized by thresholding techniques. Moreover, the paper concentrates its attention on an innovative invention. 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 technique 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 second 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 incoherent part of the measured signal. It 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 general properties of the wavelet packets on which the proposed procedure is based. The developed algorithm is totally general even though it is applied by using Haar wavelet packets and it is present in some industrial software platforms to detect sensor outliers because of their easy structure. More, it is currently integrated in the inferential modeling platform of the Advanced Control and Simulation Solution Responsible Unit within ABB's (Asea Brown Boveri) industry division. The article presented some promising patents on the Noise Level Estimation and Detection Using Haar Wavelet Packet Trees and their Applications.

KW - Data reconciliation

KW - Fault detection

KW - Haar functions

KW - Noise detection

KW - Signal processing

KW - Variance

KW - Wavelets

KW - Wavelets' packets

KW - Advanced control

KW - Application area

KW - Basic idea

KW - Data cleaning

KW - Data transmission

KW - Denoising methods

KW - Haar wavelets

KW - Industrial software

KW - Measured signals

KW - Median value

KW - Modeling platforms

KW - Noise level estimation

KW - Noise variance estimation

KW - Running-in

KW - S/N ratio

KW - State of the art

KW - Thresholding

KW - Thresholding methods

KW - Thresholding techniques

KW - Wavelet components

KW - Wavelet Packet

KW - Wavelet tree

KW - Data compression

KW - Estimation

KW - Feature extraction

KW - Patents and inventions

KW - Signal detection

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=77951547934&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/70be615a-4889-3192-a843-b53746100bd5/

U2 - 10.2174/1874476111003010066

DO - 10.2174/1874476111003010066

M3 - Journal articles

VL - 3

SP - 66

EP - 76

JO - Recent Advances in Electrical and Electronic Engineering

JF - Recent Advances in Electrical and Electronic Engineering

SN - 2352-0965

IS - 1

ER -