Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE)" that employs hybrid kernel (i.

Image analysis by Krawtchouk moments.

In this paper, a new set of orthogonal moments based on the discrete classical Krawtchouk polynomials is introduced. The Krawtchouk polynomials are scaled to ensure numerical stability, thus creating a set of weighted Krawtchouk polynomials. The set of proposed Krawtchouk moments is then derived from the weighted Krawtchouk polynomials.

A model for the prediction of Oncomelania hupensis in the lake and marshland regions, China.

A model has been developed for predicting the density of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. The model takes into account different environmental factors, including elevation, air and soil temperature, type of vegetation, mean height of preponderant vegetation and soil humidity. Deviance and Akaike information criteria were used to determine the best model fits.

Image analysis using hahn moments.

This paper shows how Hahn moments provide a unified understanding of the recently introduced Chebyshev and Krawtchouk moments. The two latter moments can be obtained as particular cases of Hahn moments with the appropriate parameter settings, and this fact implies that Hahn moments encompass all their properties. The aim of this paper is twofold: 1) To show how Hahn moments, as a generalization of Chebyshev and Krawtchouk moments, can be used for global and local feature extraction, and 2) to show how Hahn moments can be incorporated into the framework of normalized convolution to analyze local structures of irregularly sampled signals.