Exploration and inspection of dynamic environments using mobile robots are applications that benefit immensely from novelty detection algorithms. In this paper we propose the use of a new approach for on-line novelty detection based on incremental Principal Component Analysis and compare its performance and functionality with a previously studied technique based on a GWR neural network. We have...

This paper presents experiments with an autonomous inspection robot, whose task was to highlight novel features in its environment from camera images. The experiments used two different attention mechanisms — saliency map and multi-scale Harris detector — and two different novelty detection mechanisms — Grow-When-Required (GWR) neural network and an incremental Principal Component Analysis (PCA...

Abstract. Groundwater recharge (GWR) is a strategic hydrologic variable, and its estimate necessary to implement sustainable groundwater management. This especially true in global warming context that highly impacts key winter conditions cold humid climates. For this reason, long-term simulations are particularly useful for understanding past changes GWR associated with changing climatic condit...

This paper examines the association between religious adherence and fertility rates across counties in the United States (1998-2002), controlling for other demographic and socioeconomic variables. Employing geographically weighted regression (GWR) analysis, this study finds that the relationship between religion and fertility differs remarkably over space, illustrating different spatial pattern...

Geographically Weighted Regression (GWR) is a method of spatial analysis that can be used to perform by assigning weights based on the geographical distance each observation location and assumption having heterogeneity. The result this an equation model whose parameter values apply only are different from other locations. However, when there outliers at location, more robust estimation needed. ...