Adding cognition to the existing Wireless Sensor Networks (WSNs) with a cognitive networking approach brings about many benefits. Cognitive networking deals with using cognition to the entire network protocol stack to achieve stack-wide as well as network-wide performance goals; unlike cognitive radios that apply cognition only at the physical layer to overcome the problem of spectrum scarcity....

In this paper, the concepts of somewhat fuzzy automata continuous functions and somewhat fuzzy automata open functions in fuzzy automata topological spaces are introduced and some interesting properties of these functions are studied. In this connection, the concepts of fuzzy automata resolvable spaces and fuzzy automata irresolvable spaces are also introduced and their properties are studied.

1. Introduction. In a probabilistic automaton, as defined e.g. in [2] and [l], the initial vector as well as the transition matrices are stochastic, and the final vector consists of O's and l's only. This paper is concerned with the so-called generalized automata where the elements of the initial vector, of the final vector and of the matrices are allowed to be arbitrary real numbers. Our purpo...

Temporal logics like LTL are frequently used for the specification and verification of reactive systems. For verification, LTL formulas are typically translated to generalized nondeterministic Büchi automata so that the verification problem is reduced to checking the emptiness of automata. While this can be done symbolically for nondeterministic automata, other applications require deterministi...

Learning automata have been found to be useful in the systems with incomplete knowledge. Therefore, it can be used as a tool to solve problems of Ad Hoc networks, where nodes aremobile and operatewithin a dynamic environment, which entails possibly unknown and time varying characteristics. In this paper, after a short review on the related works, learning automata and CEC algorithm, which is a ...

In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at...

We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating finite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can learn generic models for network entities in the form of automata templates. As is characteristic fo...

We study matrix product unitary operators (MPUs) for fermionic one-dimensional (1D) chains. In stark contrast with the case of 1D qudit systems, we show that (i) MPUs do not necessarily feature a strict causal cone and (ii) all Quantum Cellular Automata (QCA) can be represented as MPUs. then introduce natural generalization latter, obtained by allowing an additional operator acting on their aux...