Showing papers by "Kamala Krithivasan published in 2014"

On Controlled P Systems

Abstract: We introduce and briefly investigate P systems with controlled computations. First, P systems with label restricted transitions are considered in each step, all rules used have either the same label, or, possibly, the empty label, λ, then P systems with the computations controlled by languages as in context-free controlled grammars. The relationships between the families of sets of numbers computed by the various classes of controlled P systems are investigated, also comparing them with length sets of languages in Chomsky and Lindenmayer hierarchies characterizations of the length sets of ET0L and of recursively enumerable languages are obtained in this framework. A series of open problems and research topics are formulated.

Spiking Neural P Systems with Cooperating Rules

Abstract: The concept of cooperation and distribution as known from grammar systems is introduced to spiking neural P systems (in short, SN P systems) in which each neuron has a finite number of sets (called components) of rules. During computations, at each step only one of the components can be active for the whole system and one of the enabled rules from this active component of each neuron fires. The switching between the components occurs under different cooperation strategies. This paper considers the terminating mode, in which the switching occurs when no rule is enabled in the active component of any neuron in the system. By introducing this new mechanism, the computational power of asynchronous and sequential SN P systems with standard rules is investigated. The results are that asynchronous standard SN P systems with two components and strongly sequential unbounded SN P systems with two components are Turing complete.

Small Universal Spiking Neural P Systems with Cooperating Rules as Function Computing Devices

Abstract: The paper considers spiking neural P systems (SN P systems) with cooperating rules where each neuron has the same number of sets of rules, labelled identically. Each set is called a component (maybe empty). At each step only one of the components can be active for the whole system, and only the rules from the active component are enabled. Each neuron with enabled rules from this active component can fire. By using 59 neurons, a small universal SN P system with two components, working in the terminating mode, is constructed for computing functions.

An entropy maximization problem in shortest path routing networks

Abstract: In the context of an IP network, we investigate an interesting case of the inverse shortest path problem using the concept of network centrality. For a given network, the centrality distribution associated with the links of a network can be determined based on the number of shortest paths passing through each link. An entropy measure for this distribution is defined, and we then forumulate the inverse shortest problem in terms of maximizing this entropy. We then obtain a centrality distribution that is as broadly distributed as possible subject to the topology constraints. An appropriate change in the weight of a link alters the number of shortest paths that pass through it, thereby modifying the centrality distribution. The idea is to obtain a centrality distribution that maximizes the entropy. This problem is shown to be NP-hard, and a heuristic approach is proposed. An application to handling link failure scenarios in Open Shortest Path First routing is discussed.

Tree topology inference from leaf-to-leaf path lengths using Prüfer sequence

Abstract: In this paper, we propose an algorithm to reconstruct the set of nodes and their topology relationships, particularly in case of a topological tree where all edge weights are 1, given the set of terminal vertices or leaves and their pairwise distances or path lengths. Prufer encoding and decoding techniques showed a one-to-one correspondence between n-labelled trees and n − 2-tuples of vertex labels. We consider Prufer encoding technique in discovering the intermediate vertices from the end-to-end path length between all pairs of terminal vertices. The results are useful in inferring graph characteristics, topology in particular, in the field of network tomography where the internal structure and link-level performance are inferred from end-to-end measurements.