101. Wavelet based noise reduction of Liquid level system using minimum description length criterion
- Author
-
Anindita Sengupta and Rimi Paul
- Subjects
Discrete wavelet transform ,Lifting scheme ,Computer science ,Signal reconstruction ,Noise reduction ,Stationary wavelet transform ,Second-generation wavelet transform ,Speech recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Butterworth filter ,Wavelet transform ,Cascade algorithm ,Data_CODINGANDINFORMATIONTHEORY ,Filter (signal processing) ,Thresholding ,Wavelet packet decomposition ,Wavelet noise ,Wavelet ,Harmonic wavelet transform ,Algorithm ,Continuous wavelet transform - Abstract
The purpose of this paper is to employ wavelet based noise removal technique for Liquid level system (LLS) to remove the measurement noise from differential pressure transmitter (DPT) output indicating the level of a process tank via discrete wavelet transform (DWT). The choice of which wavelet to use for denoising is of critical importance because the wavelet affects reconstructed signal quality and the design of the system as a whole. The minimum description length criterion permits to select not only the suitable wavelet filter but also the best number of wavelet retained coefficients for signal reconstruction. The hard thresholding methods typically retain a very small number of coefficients, and the results are often smoothed. By retaining a slightly larger amount of coefficients and shrinking them, the soft thresholding methods usually give better results than the hard thresholding methods to de-noise any signal. The available response is compared with that of conventional Butterworth filtering method.
- Published
- 2012
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