iosr-JCE   Volume-18 ~ Issue-4 ~ July-August 2016

Abstract: The increasing rate of data growth has led to finding techniques for faster processing of data. Big Data analytics has recently emerged as a promising field for examining huge volume of datasets containing different data types. It is a known fact that image processing and retrieval involves high computation especially with a large dataset. We present a scalable method for face recognition based on sparse coding and dictionary learning. Sparse representation has closer resemblance with a cortex like image representation and thus more closer to human perception. The proposed method parallelizes the computation of image similarity for faster recognition.

Keyword: Dictionary learning, Image classification, Parallel computation, Sparse representation.

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[3]. Cebrian Manuel,Alofonseca Manuel, Ortega Alfonso, Common pitfalls using the normalized compression distance : what to watch out for in a compressor?, Communications in Information & Systems, 2005, 5:367-384
[4]. Elad Michael, on the role of sparse and redundant representations in image processing, IEEE proceedings, 2010, 15: 3736-3745
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Abstract: Dynamic load balancing with decentralized load balancer using PSO technique: Cloud consists of multiple resources and various clients request to the cloud for allocation of shared resources. Each request will be allotted to the virtual machines. In different situation different machines get different load. So to balance the load amongst different virtual machines decentralized load balancer is enhanced using particle swarm algorithm. The main objective is reducing the energy and increasing the throughput in comparison to centralized and simple decentralized load balancer using particle swarm optimization.

Keyword: Centralized, Decentralized, Energy, PSO, Throughput

[1]. Michael Pantazoglou, Gavriil Tzortzakis, and Alex Delis, "Decentralized and Energy-Efficient Workload Management in Enterprise Clouds", in press, IEEE 2015.
[2]. Gulshan Soni and Mala Kalra, "A Novel Approach for Load Balancing in Cloud Data Center", IEEE International Conference on Intelligent Computing and Integrated Systems (ICISS), Guilin,vol.14, pp. 807-812, 2014.
[3]. Cristian Mateos, Elina Pacini & Carlos Garc Garino, An ACO-inspired algorithm for minimizing weighted flow time in cloud-based parameter sweep experiments, 2013.
[4]. Hongsheng Su, Ying Qi and Xi Song, "The Available Transfer Capability Based On a Chaos Cloud Particle Swarm Algorithm ", IEEE ninth International Conference on Natural Computation (ICNC), vol 13, pp.574-579, 2013.
[5]. Rajkumar Buyya,"A Particle Swarm Optimization-based Heuristic for Scheduling Workflow A", Cloud Computing and Distributed Systems Laboratory, Department of Computer.

Abstract: Wireless sensor network is a rapidly growing area for research and commercial development. Wireless Sensor Network (WSN) is a major and very interesting technology, which consists of small battery powered sensor nodes with limited power resources. The sensor nodes are inaccessible to the user once they are deployed. Replacing the battery is not possible every time. K-means algorithm will provide better results in terms of network lifetime enhancement and connectivity. A new cost function has been defined in K- means algorithm to minimize the intra-cluster distance by dynamically updating cluster head to enhance connectivity of the network.

Keyword: Cluster, Connectivity, K-Means algorithm, Lifetime, Wireless sensor network.

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