This paper discusses the numerical precision of five spreadsheets (Calc, Excel, Gnumeric, NeoOffice and Oleo) running on two hardware platforms (i386 and amd64) and on three operating systems (Windows Vista, Ubuntu Intrepid and Mac OS Leopard). The methodology consists of checking the number of correct significant digits returned by each spreadsheet when computing the sample mean, standard deviation, first-order autocorrelation, F statistic in ANOVA tests, linear and nonlinear regression and distribution functions. A discussion about the algorithms for pseudorandom number generation provided by these platforms is also conducted. We conclude that there is no safe choice among the spreadsheets here assessed: they all fail in nonlinear regression and they are not suited for Monte Carlo experiments.

The dengue viral infection is one of the most relevant vector-borne diseases in the world. The disease can manifest in a variety of forms, from asymptomatic to a condition of dengue hemorrhagic fever (DHF). The last reported cases in Brazil correspond to 80% of the cases reported in the Americas, which emphasizes the magnitude of the problem. This study was conducted using Geographic Information System (GIS) techniques, in order to evaluate the spatial distribution of the disease in the urban area of Mossoró, Rio Grande do Norte. In the period between 2001 and 2007, 867 new cases were listed. About 85.7% of the addresses were georeferenced, with a larger number of cases, 14.8%, in the neighborhoods of Santo Antônio and Santa Delmira (north region), and 11.7% in the neighborhoods of Conjunto Vingt-Rosado and Alto de São Manoel (east region). There were 18 confirmed cases of dengue hemorrhagic fever associated with regions with the highest incidence of classic cases of the disease. The use of Geographic Information System (GIS) proved a great benefit for better visualization of the endemic, especially in elucidating the actual distribution of dengue cases in the county and providing an effective tool for planning the monitoring of the disease at a local level.

Abstract. 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.

Remote Sensing is both an active research area and the source of valuable information for decision-making. Many actors play a fundamental role in Remote Sensing, from industry (public or private) to large or small research groups. From that intensive activity, methods, algorithms, and techniques are continuously published or broadcasted through papers, conference presentations, repositories, patents, standards, and other means. The consumers of that information need it to be readily available and dependable. Reproducible research can handle those needs. In this paper, we discuss two concepts: reproducibility and replicability in the context of Remote Sensing research. We propose a badging system suited to the specifics of the Remote Sensing community. Such a system aims at both recognizing the level of the reproducibility of the research, and to help increase its visibility. We show examples of reproducible research and provide clues to make easier the transition to the inevitable new times that embrace contemporary Science and Technology.

Abstract. Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an important source of information in remote sensing applications. The most complete format this type of imaging produces consists of complex-valued Hermitian matrices in every image coordinate and, as such, their visualization is challenging. They also suffer from speckle noise which reduces the signal-to-noise ratio. Smoothing techniques have been proposed in the literature aiming at preserving different features and, analogously, projections from the cone of Hermitian positive matrices to different color representation spaces are used for enhancing certain characteristics. In this work we propose the use of stochastic distances between models that describe this type of data in a Nagao-Matsuyamatype of smoothing technique. The resulting images are shown to present good visualization properties (noise reduction with preservation of fine details) in all the considered visualization spaces.

Abstract.A new generalized Statistical Complexity Measure (SCM) was proposed by Rosso et al in 2010. It is a functional that captures the notions of order/disorder and of distance to an equilibrium distribution. The former is computed by a measure of entropy, while the latter depends on the definition of a stochastic divergence. When the scene is illuminated by coherent radiation, image data is corrupted by speckle noise, as is the case of ultrasound-B, sonar, laser and Synthetic Aperture Radar (SAR) sensors. In the amplitude and intensity formats, this noise is multiplicative and non-Gaussian requiring, thus, specialized techniques for image processing and understanding. One of the most successful family of models for describing these images is the Multiplicative Model which leads, among other probability distributions, to the G 0 law. This distribution has been validated in the literature as an expressive and tractable model, deserving the "universal" denomination for its ability to describe most types of targets. In order to compute the statistical complexity of a site in an image corrupted by speckle noise, we assume that the equilibrium distribution is that of fully developed speckle, namely the Gamma law in intensity format, which appears in areas with little or no texture. We use the Shannon entropy along with the Hellinger distance to measure the statistical complexity of intensity SAR images, and we show that it is an expressive feature capable of identifying many types of targets.

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