Abstract

Abstract
This paper proposes perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm and its application to the segmentation of dermoscopy images. A fuzzy-based homogeneity measure makes a fusion of the color features and the texture features. The proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. The effectiveness of the proposed method is confirmed through computer simulations that demonstrate the applicability of the proposed method to the segmentation of natural color imagesand dermoscopy images. ©2008 IEEE.

Abstract
The Watson distribution is frequently used for modeling axial data. We propose the two-way analysis of variance for a concentrated Watson distribution defined on the hypersphere in the girdle or bipolar form. We illustrate this technique with spherical data.

Abstract
The current need for flexibility, re-configurability and robustness are crucial reasons for moving to new distributed manufacturing paradigms. Approaches that inherit biological concepts, such as Holonic Manufacturing Systems and Reconfigurable Manufacturing Systems, address this challenge. The self-organization concept offers an alternative way of designing adaptive systems, in which autonomy, emergence and distributed functioning replace preprogramming and centralized control. This paper discusses the benefits that bio-inspired theories can bring to the manufacturing world, and analyzes why in spite of their promising perspective their adoption by industry is extremely rare.

Abstract
We exploit ideas of nonlinear dynamics in a non-deterministic dynamical setting. Our object of study is the observed riverflow time series of the Portuguese Paiva river whose water is used for public supply. The Takens delay embedding of the daily riverflow time series revealed an intermittent dynamical behaviour due to precipitation occurrence. The laminar phase occurs in the absence of rainfall. The nearest neighbour method of prediction revealed good predictability in the laminar regime but we warn that this method is misleading in the presence of rain. The correlation integral curve analysis, Singular Value Decomposition and the Nearest Neighbour Method indicate that the laminar regime of flow is in a small neighbourhood of a one-dimensional affine subspace in the phase space. The Nearest Neighbour method attested also that in the laminar phase and for a data set of 53 years the information of the current runoff is by far the most relevant information to predict future runoff. However the information of the past two runoffs is important to correct non-linear effects of the riverflow as the MSE and MRE criteria results show. The results point out that the Nearest Neighbours method fails when used in the irregular phase because it does not predict precipitation occurrence. © 2007, Birkhäuser Verlag Basel/Switzerland.

Abstract
The amount of data collected and stored in databases is growing considerably in almost all areas of human activity. In complex applications the data involves several relations and proposionalization is not a suitable approach. Multi-Relational Data Mining algorithms can analyze data from multiple relations, with no need to transform the data into a single table, but are computationally more expensive. In this paper a novel relational classification algorithm based on the k-nearest neighbour algorithm is presented and evaluated.