The concept of entropy plays a major part in communication theory. The Shannon entropy is a measure of uncertainty with respect to a priori probability distribution. In algorithmic information theory the information content of a message is measured in terms of the size in bits of the smallest program for computing that message. This paper discusses the classical entropy and entropy rate for dis...

Many studies have noted significant differences among human electroencephalograph (EEG) results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that...

This paper presents an exploratory study of related concepts across information theory-based measures and functional size measures. Information theory-based software measurement has been used in the design of an entropy-based measure of functional complexity in terms of an amount of information based on some abstraction of the interactions among software components. As a functional size measure...

Many studies have noted significant differences among human electroencephalograph (EEG) results when participants or patients are exposed to different stimuli, undertaking different tasks, or being affected by conditions such as epilepsy or Alzheimer's disease. Such studies often use only one or two measures of complexity and do not regularly justify their choice of measure beyond the fact that...

Complexity measures for sequences over finite fields, such as the linear complexity and the k-error linear complexity, play an important role in cryptology. Recent developments in stream ciphers point towards an interest in word-based stream ciphers, which require the study of the complexity of multisequences. We introduce various options for error linear complexity measures for multisequences....

The joint linear complexity and k error joint linear complexity of an m fold 2n periodic multisequence can be efficiently computed using Modified Games Chan algorithm and Extended Stamp Martin Algorithm respectively. In this paper we derived an algorithm for finding the joint linear complexity of periodic binary multisequence with the help of Modified Games Chan algorithm. Here we derived the m...

In this paper, we systematically analyse the connecting architectures of recurrent neural networks (RNNs). Our main contribution is twofold: first, we present a rigorous graphtheoretic framework describing the connecting architectures of RNNs in general. Second, we propose three architecture complexity measures of RNNs: (a) the recurrent depth, which captures the RNN’s over-time nonlinear compl...

A number of different measures of complexity have been described, discussed, and applied to the logistic map. A classification of these measures has been proposed, distinguishing homogeneous and generating partitions in phase space as well as structural and dynamical elements of the considered measure. The specific capabilities of particular measures to detect particular types of behavior of dy...