Despu\es de discutir las principales características del histograma y de un número de variables en el boxplot, se presento una herramienta de visualisación específicamente diseñada para el tratamiento de datos. La idea es usar varios tipos de boxplots (el clásico, el cual es adaptado para la consideración de sesgo de los datos, el boxplot trasladado, y el gráfico de cajas de percentiles), el gráfico violin, y el histograma con un estimador no paramétrico de la densidad. Los gráficos son presentados de forma que faciliten la extracción de información adicional. Se muestra como una buena cantidad de información que puede ser extraída a través de ejemplos de imágenes de radar de apertura sintética. Se presenta su implementacón en R basada en funciones actualmente disponibles.

After discussing the main characteristics of the histogram and of a number of variations in the boxplot, this work presents a visualization tool specifically tailored to deal with skewed data. The idea is to use various types of boxplots (the classical one, which is tuned for skewness of the data, the shifting boxplot, and the box-percentile plot), the violin plot, and the histogram with a nonparametric estimate of the density overlay. The plots are presented in such a way that they facilitate the extraction of additional information from each one. We show that a good deal of information can be extracted from the inspection of the output using example data from synthetic aperture radar images. We provide an implementation in R based on functions already available.

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.

Ultrasonics is a widely used technique in medical imaging, due to its noninvasive nature and its capability of forming real-time portraits of hidden targets. In this technique, pulse-echo ultrasonic waves are sent to the investigated medium and the backscattered waves provide the information for image formation. However, the coherent nature of the waves results in the appearance of speckle noise. This phenomenon is common to laser, sonar and synthetic aperture radar (SAR) imagery, and is the result of interference between the scattered waves. Many filters have been proposed for alleviating this noise, especially in remote sensing applications. Some of these filters (Mean, Median, Lee, Frost and Gaussian MAP) are reviewed and applied to ultrasound B-scan images, along with a non-linear technique based on computing the median on the binary slices of the data. The performance of these techniques is assessed both quantitatively (regarding noise suppression and radiometric preservation) and qualitatively (through perceptual contrast and edge preservation).