Preparation and structural characterization of rare-earth doped lead lanthanum zirconate titanate ceramics

A new preparation route towards rare-earth (RE) doped polycrystalline lead lanthanum zirconate titanate (PLZT) ceramics (RE = Y(3+), Nd(3+), Yb(3+)), based on the use of doped lanthanum oxide or zirconia, is reported. Structural characterization by X-ray powder diffraction reveals that secondary phase formation can be substantially diminished in comparison to conventional preparation methods. The distribution of the rare-earth dopants was investigated as a function of concentration by static (207)Pb spin echo NMR spectra, using Fourier Transformation of Carr-Purcell-Meiboom-Gill spin echo trains. For the Nd- and Yb-doped materials, the interaction of the (207)Pb nuclei with the unpaired electron spin density results in significant broadening and shifting of the NMR signal, whereas these effects are absent in the diamagnetic Y(3+) doped materials. Based on different concentration dependences of the NMR lineshape parameters, we conclude that the structural role of the Nd(3+) dopants differs significantly from that of Yb(3+). While the Nd(3+) ions appear to be statistically distributed in the PLZT lattice, incorporation of Yb(3+) into PLZT appears to be limited by the appearance of doped cubic zirconia as a secondary phase. (C) 2009 Elsevier Masson SAS. All rights reserved.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Gevrey solvability near the characteristic set for a class of planar complex vector fields of infinite type

We study the Gevrey solvability of a class of complex vector fields, defined on Omega(epsilon) = (-epsilon, epsilon) x S(1), given by L = partial derivative/partial derivative t + (a(x) + ib(x))partial derivative/partial derivative x, b not equivalent to 0, near the characteristic set Sigma = {0} x S(1). We show that the interplay between the order of vanishing of the functions a and b at x = 0 plays a role in the Gevrey solvability. (C) 2008 Elsevier Inc. All rights reserved.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.