Most independent component analysis (ICA) algorithms use mutual information (MI) measures based on Shannon entropy as a cost function, but Shannon entropy is not the only measure in the literature. In this paper, instead of Shannon entropy, Tsallis entropy is used and a novel ICA algorithm, which uses kernel density estimation (KDE) for estimation of source distributions, is proposed. KDE is di...

The paper examines relationships between the conditional Shannon entropy and the expectation of `α-norm for joint probability distributions. More precisely, we investigate the tight bounds of the expectation of `α-norm with a fixed conditional Shannon entropy, and vice versa. As applications of the results, we derive the tight bounds between the conditional Shannon entropy and several informati...

Integral imaging systems performance has been previously investigated with regard to different parameters such as lateral resolution, field of view, and depth of view. Those parameters are linked to one another, and, since the information capacity of an integral imaging system is finite, there are always trade-offs among them. We use the Shannon number and information capacity limit as figures ...

Quantifiers were introduced to convert recurrence plots into a statistical analysis of dynamical properties. It is shown that the Shannon entropy, if properly computed, increases as the chaotic regime is developed as expected. Recurrence plots and a new estimator for the Shannon entropy are then used to identify asynchronisms in non-invasive mechanical ventilation. It is thus shown that the pha...

This paper reports a new reading for wavelets, which is based on the classical ’De Broglie’ principle. The waveparticle duality principle is adapted to wavelets. Every continuous basic wavelet is associated with a proper probability density, allowing defining the Shannon entropy of a wavelet. Further entropy definitions are considered, such as Jumarie or Renyi entropy of wavelets. We proved tha...