NDLI: Adaptive FIR filter use for signal noise cancelling

Content Provider	IEEE Xplore Digital Library
Author	Kolinova, M. Prochazka, A. Mudrova, M.
Copyright Year	1998
Description	Author affiliation: Dept. of Comput. & Control Eng., Prague Univ. of Chem. Technol., Prague, Czech Republic (Kolinova, M.)
Abstract	Noise reduction, together with signal modelling, classification and prediction, forms a basic problem in many engineering, biomedical and econometric systems. The paper summarizes basic adaptive methods based upon the application of artificial neural networks simplified in many cases to an adaptive linear element only. Adaptive noise cancelling algorithms by such finite impulse response (FIR) filters are studied in the first part of the paper both for the case that a reference signal is known and for reference-free structures. Resulting signals are compared with those obtained by noise rejection in the wavelet transform domain either based upon signal decomposition and reconstruction using properly chosen threshold levels for signal modification or incorporating adaptive FIR filtering. Signals preprocessed in this way are then used for their segmentation and classification to verify the effect of signal denoising. Methods presented in the paper are used for simulated signals at first and then applied to the processing of real signals representing physiological signals and technological time series.
Starting Page	496
Ending Page	505
File Size	529575
Page Count	10
File Format	PDF
ISBN	078035060X
ISSN	10893555
DOI	10.1109/NNSP.1998.710680
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-09-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Finite impulse response filter Noise cancellation Signal processing Adaptive filters Noise reduction Predictive models Biomedical engineering Econometrics Artificial neural networks Noise level
Content Type	Text
Resource Type	Article

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