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: [email protected]
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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 - https://www.scopus.com/pages/publications/77951547934
UR - https://www.mendeley.com/catalogue/70be615a-4889-3192-a843-b53746100bd5/
U2 - 10.2174/1874476111003010066
DO - 10.2174/1874476111003010066
M3 - Journal articles
SN - 1874-4761
VL - 3
SP - 66
EP - 76
JO - Recent Patents on Electrical and Electronic Engineering
JF - Recent Patents on Electrical and Electronic Engineering
IS - 1
ER -
SDG 9 Industry, Innovation, and Infrastructure