This paper presents a geostatistical model as a new approach to the linear prediction analysis of speech. The autocorrelation method of autoregressive modeling, which is widely applied in the linear predictive coding of speech, is used as a benchmark for comparison with the present algorithm. Before discussing the proposed model, we will briefly describe the concepts of linear prediction analys...

This paper discusses advantages and shortcomings of the S environment for multivariable geostatistics, in particular when extended with the gstat package, an extension package for the S environments (R, S-Plus). The gstat S package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. In particular, it makes the calculation, simu...

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Title of dissertation: SCALABLE LEARNING FOR GEOSTATISTICS AND SPEAKER RECOGNITION Balaji Vasan Srinivasan Doctor of Philosophy, 2011 Thesis directed by: Professor Ramani Duraiswami Department of Computer Science With improved data acquisition methods, the amount of data that is being collected has increased several fold. One of the objectives in data collection is to learn useful underlying pa...

Thin irregularly-shaped surfaces such as clay drapes often have a major control on flow and transport in heterogeneous porous media. Clay drapes are often complex curvilinear 3dimensional surfaces and display a very complex spatial distribution. Variogram-based stochastic approaches are often also not able to describe the spatial distribution of clay drapes since complex, curvilinear, continuou...

Differences in spatial supports (shape, size, etc.) among spatial data often complicate spatial data analyses, and spatial support conversions, such as aggregation, disaggregation, and interpolation, are often applied to cope with them. However, in general, spatial support conversions present the following problems: (i) how to convert data accurately? and (ii) how to reduce biases caused by the...