Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI

In this paper we address the "skull-stripping" problem in 3D MR images. We propose a new method that employs an efficient and unique histogram analysis. A fundamental component of this analysis is an algorithm for partitioning a histogram based on the position of the maximum deviation from a Gaussian fit. In our experiments we use a comprehensive image database, including both synthetic and real MRI. and compare our method with other two well-known methods, namely BSE and BET. For all datasets we achieved superior results. Our method is also highly independent of parameter tuning and very robust across considerable variations of noise ratio.

Effective Properties Evaluation for Smart Composite Materials

The purpose of this article is to present a method which consists in the development of unit cell numerical models for smart composite materials with piezoelectric fibers made of PZT embedded in a non-piezoelectric matrix (epoxy resin). This method evaluates a globally homogeneous medium equivalent to the original composite, using a representative volume element (RVE). The suitable boundary conditions allow the simulation of all modes of the overall deformation arising from any arbitrary combination of mechanical and electrical loading. In the first instance, the unit cell is applied to predict the effective material coefficients of the transversely isotropic piezoelectric composite with circular cross section fibers. The numerical results are compared to other methods reported in the literature and also to results previously published, in order to evaluate the method proposal. In the second step, the method is applied to calculate the equivalent properties for smart composite materials with square cross section fibers. Results of comparison between different combinations of circular and square fiber geometries, observing the influence of the boundary conditions and arrangements are presented.

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.

Effective properties evaluation for smart composite materials

The purpose of this article is to present a method which consists in the development of unit cell numerical models for smart composite materials with piezoelectric fibers made of PZT embedded in a non-piezoelectric matrix (epoxy resin). This method evaluates a globally homogeneous medium equivalent to the original composite, using a representative volume element (RVE). The suitable boundary conditions allow the simulation of all modes of the overall deformation arising from any arbitrary combination of mechanical and electrical loading. In the first instance, the unit cell is applied to predict the effective material coefficients of the transversely isotropic piezoelectric composite with circular cross section fibers. The numerical results are compared to other methods reported in the literature and also to results previously published, in order to evaluate the method proposal. In the second step, the method is applied to calculate the equivalent properties for smart composite materials with square cross section fibers. Results of comparison between different combinations of circular and square fiber geometries, observing the influence of the boundary conditions and arrangements are presented.