Eliana S. de Almeida

Enhancing the experience of 3D virtual worlds with a cartographic generalization approach

In this work we propose a new approach for fast visualization and exploration of virtual worlds based on the use of cartographic concepts and techniques. Versions of cartographic maps with different levels of details can be created by using a set of operations named cartographic generalization. Cartographic generalization employs twelve operators and domain-specific knowledge, being the contribution of this work their transposition to 3D virtual worlds. The architecture of a system for 3D generalization is proposed and the system is implemented. Differently from traditional cartographic processes, we use artificial intelligence for both selecting the key objects and applying the operators. As a case study, we present the simplification of the historical quarter of Recife (Brazil).

How good are MatLab, Octave and Scilab for computational modelling?

In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS). We submitted them to numerical tests using standard data sets and using the functions provided by each platform. A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input. We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known. We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions. The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits. Mathematical subject classification: Primary: 06B10; Secondary: 06D05.

Image formation in vibro-acoustography with sector array transducers

This paper presents the image formation process in vibro-acoustography for systems based on sector array transducers. These transducers are an alternative to annular concave transducers. They represent an innovative technique deserving detailed assessment in vibro-acoustography applications. The system point-spread function (PSF) is defined in terms of the acoustic emission of a point-target in response to the dynamic ultrasound radiation force. This force is produced by two overlapping ultrasound beams. We calculate the radiation force on the target in a nonviscous fluid using the plane wave approximation for the ultrasound beams. The beamforming of sector array transducers is analyzed through linear acoustics. An expression for the velocity potential produced by sector arrays is derived, and the vibro-acoustography PSF is evaluated numerically. An experimental setup is design to validate the theory; the comparison is made using location and amplitude of sidelobes and spatial resolution defined by the PSF. Results show that the computed PSF is in full agreement with the PSF obtained experimentally.

Evaluation of Deep Feedforward Neural Networks for Classification of Diffuse Lung Diseases

Diffuse Lung Diseases (DLDs) are a challenge for physicians due their wide variety. Computer-Aided Diagnosis (CAD) are systems able to help physicians in their diagnoses combining information provided by experts with Machine Learning (ML) methods. Among ML techniques, Deep Learning has recently established itself as one of the preferred methods with state-of-the-art performance in several fields. In this paper, we analyze the discriminatory power of Deep Feedforward Neural Networks (DFNN) when applied to DLDs. We classify six radiographic patterns related with DLDs: pulmonary consolidation, emphysematous areas, septal thickening, honeycomb, ground-glass opacities, and normal lung tissues. We analyze DFNN and other ML methods to compare their performance. The obtained results show the high performance obtained by DFNN method, with an overall accuracy of 99.60%, about 10% higher than the other studied ML methods.