Spectral analysis of signals by time-domain statistical characterization and neural network processing: Application to correction of spectral amplitude alterations in pulse-like waveforms

We present a time-domain method to detect and correct spectral alterations of signals by employing statistical characterization of waveforms and a pattern-recognition procedure using simple Artificial Neural Networks. The proposed strategy implements very-fast routines with a computational cost prop...

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Bibliographic Details
Published in:	2022 IEEE Biennial Congress of Argentina (ARGENCON) pp. 1 - 8
Main Authors:	Bustos, Guillermo H., Segnorile, Hector H.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 07-09-2022
Subjects:	Artificial neural networks Attenuation Computational efficiency Real-time systems signal processing Signal processing algorithms statistical characterization Task analysis Time-domain analysis
Online Access:	Get full text
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Summary:	We present a time-domain method to detect and correct spectral alterations of signals by employing statistical characterization of waveforms and a pattern-recognition procedure using simple Artificial Neural Networks. The proposed strategy implements very-fast routines with a computational cost proportional to the number of signal samples, being convenient for applications in embedded environments with limited computational capabilities or fast real-time control tasks. We use the proposed algorithms to correct spectral amplitude attenuations in a pulse-like waveform with a sinc profile as an application example.
DOI:	10.1109/ARGENCON55245.2022.9939817