iosr-JCE   Volume-16 ~ Issue-6 ~ Nov-Dec 2014

Abstract: The focus of this paper is towards developing an application specific design methodology for low power solutions in recursive coding system in which filtration process is performed by using MAC approach. For the realization of this MAC operation a recursive multiplication and addition operation is carried out. Theconventional recursion results in multiple accumulation operation in successive clocks of duration amounting to that of an addition-time, so that the accumulation of 𝑁 successive input words are performed in 𝑁clock cycles. For large values of 𝑁, a computational latency of 𝑁addition-time over head and high power consumption and accumulation process to obtain an accumulated output could be too high to meet the timing requirement in real-time application. so in order to over this problem in this paper a new scalable Repetitive multiply accumulates (MACs) is proposed.From the simulation results the proposed MAC has less delay complexity and low power consumption when compared to the conventional approaches.

Keyword: Recursive coding, VLSI, Application specific integrated circuits, digital signal processing chips , digital arithmetic.

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Abstract: Clustering is the process of assigning data objects into a set of disjoint groups called clusters so that objects in each cluster are more similar to each other than objects from different clusters. We try to exploit computational power from the multicore processors. We need a new design on existing algorithms and software. Firefly algorithm is one of the metaheuristic algorithms which are used for solving optimization problems. The existing clustering algorithms either handle different data types with inefficiency in handling large data or handle large data with limitations in considering numeric attributes. Hence, parallel clustering has come into picture to provide crucial contribution towards clustering large data. In this paper, we have developed a scalable parallel clustering algorithm using FA and genetic algorithm to cluster large data. Modified FA algorithm does not handle the large data effectively. So, our ultimate aim is to design and develops an algorithm in parallel way by considering data. The experimental analysis will be carried out to evaluate the feasibility of the new combined clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy and time. Most of the programming languages doesn't provide multiprocessing facilities and hence wastage of processing resources. In order to utilize the intrinsic capabilities of a multi-core processor the software application must be able to execute tasks in parallel using all available CPUs. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance.

Keywords: Parallel clustering, large data, Firefly, genetic algorithm.

[1] X. S. Yang, "Nature-Inspired metaheuristic Algorithms". Luniver Press, 2008.

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[3] X. S. Yang, "Firefly algorithm for multimodal optimization." In: Stochastic Algorithms: foundations and applications ،SAGA ،lecture notes in computer sciences, pp. 169-178, 2009.

[4] S. M. Elsayed, R. A. Sarker, and D. L. Essam, "A new genetic algorithm for solving optimization problems," Engineering Applications of Artificial Intelligence, vol. 27, pp. 57-69, 2014.
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Abstract: Wireless communication between mobile users is becoming more popular than ever before. This is due to recent technological advances in laptop computers and wireless data communication devices, such as wireless modems and wireless LANs. This has lead to lower prices and higher data rates, which are the two main reasons why mobile computing continues to enjoy rapid growth. With current advances in technology, wireless networks are increasing in popularity. Wireless networks allow users the freedom to travel from one location to another without interruption of their computing services. However, wireless networks require the existence of a wired base station (BS) in order for the wireless user to send/receive messages. Ad-hoc networks, a subset of wireless networks, allow the formation of a wireless network without the need for a BS. All participating users in an Ad-hoc network agree to accept and forward messages, to and from each other. With this flexibility, wireless networks have the ability to form anywhere, at any time, as long as two or more wireless users are willing to communicate. This chapter will introduce the mobile ad hoc networking in general, provide background on the nature and problems of this type of networking and give an overview of the current state of research. Mobile networking is one of the most important technologies supporting pervasive computing. During the last decade, advances in both hardware and software techniques have resulted in mobile hosts and wireless networking common and miscellaneous. Generally there are two distinct approaches for enabling wireless mobile units to communicate with each other.

Keywords: Mobile ad hoc networks (MANETS) , Quality of services (QoS) , AODV, ZRP, DSDV routing

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