Recursive information granulation: aggregation and interpretation issues.

This paper contributes to the conceptual and algorithmic framework of information granulation. We revisit the role of information granules that are relevant to several main classes of technical pursuits involving temporal and spatial granulation. A detailed algorithm of information granulation, regarded as an optimization problem reconciling two conflicting design criteria, namely, a specificity of information granules and their experimental relevance (coverage of numeric data), is provided in the paper.

Efficient 3-D medical image registration using a distributed blackboard architecture.

A major drawback of 3-D medical image registration techniques is the performance bottleneck associated with re-sampling and similarity computation. Such bottlenecks limit registration applications in clinical situations where fast execution times are required and become particularly apparent in the case of registering 3-D data sets. In this paper a novel framework for high performance intensity-based volume registration is presented.

Modeling complex environmental data.

Artificial neural networks (ANNs) are used to model the interactions that occur between ozone pollution, climatic conditions, and the sensitivity of crops and other plants to ozone. A number of generic methods for analysis and modeling are presented. These methods are applicable to the modeling and analysis of any data where an effect (in this case damage to plants) is caused by a number of variables that have a nonlinear influence.