Computing the covariance matrix of a population of shapes is essential for establishing shape correspondence, identifying shape variation across the population, and building statistical shape models. The covariance matrix is usually computed from a discrete set of points (a.k.a. landmarks) sampled on each shape. The distribution and density of the sampled points thus greatly influence the covar...

We define new measures of sequence similarity for oligonucleotide probe design. These new measures incorporate the nearest neighbor k-stem motifs in their definition, but can be efficiently computed by means of a bit-vector method. They are not as computationally costly as algorithms that predict nearest neighbor hybridization potential. Our new measures for sequence similarity correlate signif...

This paper describes an enhanced clustering method for 3D point cloud data which is acquired from a laser range scanning system. The proposed method overcomes the instinctive problems of the vertical or horizontal scanning system. The acquired 3D laser range data for an autonomous vehicle has a disadvantage in that the point cloud data from the system is not equally distributed based on distanc...

With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While a...

A technique is described for collecting reef interstitial water using manually-driven well points. This method does not require the use of hydraulic drills, and eliminates the problems associated with the casing of boreholes. The technique allows collection of samples from discrete depths within reef frameworks, and produces samples unaffected by atmospheric or surface seawater contamination. D...

Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted near...

Shape deformations preserving the intrinsic properties of a surface are called isometries. An isometry deforms a surface without tearing or stretching it, and preserves geodesic distances. We present a technique for matching point set surfaces, which is invariant with respect to isometries. A set of reference points, evenly distributed on the point set surface, is sampled by farthest point samp...

To have high performance scheduling mechanisms in grid computing, we need accurate methods for estimating parameters like jobs' wait time and run time. In this paper, we consider wait time prediction problem. Different regression techniques are examined on AuverGrid data set to predict wait time. To improve the quality of prediction, some extra features are proposed. Simulation results show tha...

Article history: Received 9 November 2010 Received in revised form 4 February 2012 Accepted 6 February 2012 Available online 21 February 2012 A large spectrum of applications such as location based services and environmental monitoring demand efficient query processing on uncertain databases. In this paper, we propose the probabilistic Voronoi diagram (PVD) for processing moving nearest neighbo...

Hausdorff metrics are used in geometric settings for measuring the distance between sets of points. They have been used extensively in areas such as computer vision, pattern recognition and computational chemistry. While computing the distance between a single pair of sets under the Hausdorff metric has been well studied, no results were known for the Nearest Neighbor problem under Hausdorff me...