in this paper some new applications of empirical mode decomposition (emd) and hilbert spectrum in seismic ground-roll attenuation, random noise attenuation and spectral decomposition are introduced. hilbert spectrum is a time-frequency representation for hilbert-huang transform which is obtained by combination of instantaneous frequency (if) concept and intrinsic mode function of empirical mode...

electrocardiogram (ecg) is one of the most common biological signals which plays a significant role in diagnosis of heart diseases. one of the most important parts of ecg signal processing is interpretation of qrs complex and obtaining its characteristics. r wave is one of the most important sections of this complex which has an essential role in diagnosis of heart rhythm irregularities and als...

The Hilbert transform plays an important role in the theory and practice of signal processing. A generalization of the Hilbert transform, the fractional Hilbert transform, was recently proposed, and it presents physical interpretation in the definition. In this paper, we develop the discrete fractional Hilbert transform, and apply the proposed discrete fractional Hilbert transform to the edge d...

The peak value of a signal is a characteristic that has to be controlled in many applications. In this paper we analyze the peak value of the Hilbert transform for the space B∞ π of bounded bandlimited signals. It is known that for this space the Hilbert transform cannot be calculated by the common principal value integral, because there are signals for which it diverges everywhere. Although th...

The p-norm of the Hilbert transform is the same as the p-norm of its truncation to any Lebesgue measurable set with strictly positive measure. This fact follows from two symmetry properties, the joint presence of which is essentially unique to the Hilbert transform. Our result applies, in particular, to the finite Hilbert transform taken over ( — 1,1), and to the one-sided Hilbert transform tak...

In this paper, those splendid characters of the Hilbert transform let the processes that taking wavelet transform after taking Hilbert transform for the statistic self-similarity processes FBM [ ) (t BH ] become another processes, that firstly taking Hilbert transform for the wavelet function ) (t φ and forming a new wavelet function ) (t ψ , secondly taking the wavelet transform for ) (t BH . ...