Clustering is one of the important topics in pattern recognition. Since only the structure of the data dictates the grouping (unsupervised learning), information theory is an obvious criteria to establish the clustering rule. This paper describes a novel valley seeking clustering algorithm using an information theoretic measure to estimate the cost of partitioning the data set. The information theoretic criteria developed here evolved from a Renyi's entropy estimator that was proposed recently and has been successfully applied to other machine learning applications. An improved version of the k-change algorithm is used in optimization because of the stepwise nature of the cost function and existence of local minima. Even when applied to nonlinearly separable data, the new algorithm performs well, and was able to find nonlinear boundaries between clusters. The algorithm is also applied to the segmentation of magnetic resonance imaging data (MRI) with very promising results.

Ensembles of widely distributed, heterogeneous resources, or Grids, have emerged as popular platforms for largescale scientific applications. In this paper we present the Virtual Instrument project, which provides an integrated application execution environment that enables end-users to run and interact with running scientific simulations on Grids. This work is performed in the specific context of MCell, a computational biology application. While MCell provides the basis for running simulations, its capabilities are currently limited in terms of scale, ease-of-use, and interactivity. These limitations preclude usage scenarios that are critical for scientific advances. Our goal is to create a scientific "Virtual Instrument" from MCell by allowing its users to transparently access Grid resources while being able to steer running simulations. In this paper, we motivate the Virtual Instrument project and discuss a number of relevant issues and accomplishments in the area of Grid software development and application scheduling. We then describe our software design and report on the current implementation. We verify and evaluate our design via experiments with MCell on a real-world Grid testbed.

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Information retrieval from high resolution remotely sensed images is a challenging issue due to the inherent complexity and the curse of dimensionality of data under study. This paper presents an approach for building detection in high resolution remotely sensed images incorporating structural information of spatial data into spectral information. The proposed approach moves along eliminating irrelevant areas in a hierarchical manner. As a first step, pan-sharpened image is obtained from multi-spectral and panchromatic bands of Quickbird image. Vegetation and shadow regions are masked out by using Normalized Difference Vegetation Index (NDVI) and ratio of hue to intensity in YIQ model, respectively. Then, panchromatic band is filtered by mean shift filtering for smoothing structures while preserving the discontinuities near boundaries. Next, differential morphological profile (DMP) is calculated for each pixel and a relative measure of structure size is recorded as the first maximum value of DMP which generates a labeled image representing connected components according to sizes of structures. However, there appear some connected components which are irrelevant to buildings in shape. To eliminate those connected components, their skeletons are obtained via thinning to get a relative length measure along with measuring areas of connected components. These measures are compared to a threshold individually, which provides a cue for a candidate building structure.