Abstract: This paper describes the clustering analysis of microarray gene expression data. Microarray basically consists of large number of gene sequences under multiple conditions. This microarray technology has made it possible to concurrently monitor the expression levels of thousands of genes and across collection of related samples. The most important area of microarray technology is the data clustering analysis. Cluster analysis refers to partitioning a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. Many conventional clustering algorithms like K-means, FCM, hierarchical techniques are used for gene expression data clustering. But PSO based K-means gives better accuracy than these existing algorithms. In this paper, a Particle Swarm Optimization (PSO)-based K-means clustering algorithm has been proposed for clustering microarray gene expression data.

• Publication Date: 03-Jun-2011
• DOI: 10.15224/978-981-07-1847-3-1027
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Abstract: The growing demand for high density VLSI circuits result in scaling of supply voltage and an exponential increase of leakage or static power in deep sub-micron technology. Therefore reducing static power consumption of portable devices such as cell phones and laptop computers is highly desirable for a longer battery life. In this paper we propose two power reduction techniques such as reverse body bias and transistor stacking for reducing the static power of Pseudo NMOS logic circuits that have very high static power consumption. The simulation results show that the static power decreases with both the methods and the combined effect of reverse body bias and stack method gives the least static current. The simulations are done at 65nm and 45nm process technologies using HSPICE at a temperature of 27C with two different supply voltages of 1v and 0.3v.

• Publication Date: 14-Jul-2012
• DOI: 10.15224/978-981-07-2969-1-123
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