Showing papers by "Kamala Krithivasan published in 2011"

On string languages generated by spiking neural p systems with anti-spikes

Abstract: An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules ac → a and ac → ā, c ϵ ℕ, thus help to generate languages which cannot be generated using simple SN P systems.

Real time cross layer design using particle swarm optimization

Abstract: Cross Layer Optimization (CLO) strategies are currently being incorporated in network operating system for efficient utilization of resources to enable effective information management. In wireless adhoc networks real time optimizations need to be performed and hence CLO strategies that have faster response time are required. In this paper we propose a Cross Layer Optimization strategy that uses a variant of the Particle Swarm Optimization (PSO) for real time cross layer design of the network. The variant of the PSO used in this research work uses digital pheromones for improved performance. The proposed PSO-CLO strategy can be used for delay sensitive, bandwidth intensive and loss-tolerant wireless multimedia transmissions that have an ever demanding need for better Quality of Service. Our experimental results show that the proposed PSO-CLO strategy has significantly faster response time in comparison with the classical CLO solutions.

On the ambiguity of insertion systems

Abstract: Gene insertion and deletion are the operations that occur commonly in DNA processing and RNA editing. Based on these evolutionary transformations, a computing model has been formulated in formal language theory known as insertion-deletion systems. In this paper, we study about the ambiguity issues of insertion systems. First, we define six levels of ambiguity for insertion systems based on the components used in the derivation such as axiom, contexts and strings. Next, we show that there are inherently i-ambiguous insertion languages which are j-unambiguous for the combinations (i, j) ∈ {(5,0), (5,4), (4,3), (4,2), (3,1),(2,1), (1,0), (0,1)}. Finally, we prove an important result that the ambiguity problem of insertion systems is undecidable.

Clique-Detection Algorithm Using Clique-Self-Assembly

Abstract: Self-assembly is a process in which simple objects autonomously combine themselves into larger objects. It is considered as a promising technique in nano-technology. Two simple graphs G1 and G2 with a clique of same size overlap and a new self-assembled graph is formed. Besides studying the properties of self assembled graphs on cliques, we answer the question: Can a given set of graphs be generated through the self-assembly of cliques? If so, how to find the generator that could generate the given set of graphs by the process of clique-self-assembly. The question of the existence of minimal generator is also discussed. The necessary and sufficient condition for a graph H to be obtained by the iterated clique-self-assembly of the graph G is also answered. We also conclude that the problem of finding the generator is decidable. We note the importance of our work with respect to several closely related clique finding problem.

An Adaptive e-Learning Environment Using Distributed Spiking Neural P Systems

Abstract: The motivation behind the proposed research work is the need for an innovative e-learning system that can adapt to the learning capability of every individual. Adaptive e-learning systems create new opportunities and at the same time have several research challenges that need to be addressed. The primary requirement of such adaptive systems is the need to create and represent adaptable content effectively. This paper presents a membrane computing model to demonstrate how adaptable content can be represented and used efficiently. The Spiking Neural P System (SNP) is a membrane computing model inspired by the way neurons communicate by means of spikes. This paper proposes the Distributed Spiking Neural P System (DSNP), a variant of the existing Distributed P System, that can be used to represent dynamic and distibuted systems. Temporal relations captured on a time line during authoring of the ecourse, can be automatically converted into an SNP system using the algorithm presented in the paper. An algorithm for the automatic generation of the DSNP from the e-course compositions represented using a linked list of SNPs is also presented in the paper along with experimental results to prove the efficiency and scalability of the proposed model.