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We present a time-domain method to detect and correct spectral alterations ofsignals by employing statistical characterization of waveforms and apattern-recognition procedure using simple Artificial Neural Networks. Theproposed strategy implements very-fast routines with a computational costproportional to the number of signal samples, being convenient for applicationsin embedded environments with limited computational capabilities or fastreal-time control tasks. We use the proposed algorithms to correct spectralamplitude attenuations in a pulse-like waveform with a sinc profile as anapplication example.

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