Noise Level Estimation and Detection
Affiliation: University of Applied Sciences Wolfsburg, Faculty of Automotive Engineering, Robert Koch Platz 12, D-38440 Wolfsburg, Germany.
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
Keywords: Noise detection, wavelets, wavelets' packets, variance, Haar functions, signal processing, fault detection, data reconciliation
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