Logical Entropy of Information Sources.

In this paper, we present the concept of the logical entropy of order , logical mutual information, and the logical entropy for information sources. We found upper and lower bounds for the logical entropy of a random variable by using convex functions. We show that the logical entropy of the joint distributions X1 and X2 is always less than the sum of the logical entropy of the variables X1 and X2.

Complexities of information sources.

Calculating the entropy for complex systems is a significant problem in science and engineering problems. However, this calculation is usually computationally expensive when the entropy is computed directly. This paper introduces three classes of information sources that for all members of each class, the entropy value is the same.

Entropy of Iterated Function Systems and Their Relations with Black Holes and Bohr-Like Black Holes Entropies.

In this paper we consider the metric entropies of the maps of an iterated function system deduced from a black hole which are known the Bekenstein-Hawking entropies and its subleading corrections. More precisely, we consider the recent model of a Bohr-like black hole that has been recently analysed in some papers in the literature, obtaining the intriguing result that the metric entropies of a black hole are created by the metric entropies of the functions, created by the black hole principal quantum numbers, i.e.