DSpace Repository :: Browsing by Author "Frery, Alejandro C."

Browsing by Author "Frery, Alejandro C."

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Bandt-Pompe symbolization dynamics for time series with tied values: a data-driven approach
(2018-07) Traversaro Varela, Francisco; Redelico, Francisco; Risk, Marcelo; Frery, Alejandro C.; Rosso, Osvaldo A.
"In 2002, Bandt and Pompe [Phys. Rev. Lett. 88, 174102 (2002)] introduced a successfully symbolic encoding scheme based on the ordinal relation between the amplitude of neighboring values of a given data sequence, from which the permutation entropy can be evaluated. Equalities in the analyzed sequence, for example, repeated equal values, deserve special attention and treatment as was shown recently by Zunino and co-workers [Phys. Lett. A 381, 1883 (2017)]. A significant number of equal values can give rise to false conclusions regarding the underlying temporal structures in practical contexts. In the present contribution, we review the different existing methodologies for treating time series with tied values by classifying them according to their different strategies. In addition, a novel data-driven imputation is presented that proves to outperform the existing methodologies and avoid the false conclusions pointed by Zunino and co-workers."
Characterization of electric load with information theory quantifiers
(2017-01) Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro C.; Viana, Leonardo P.; Cavalcante, Tamer S. G.; Rosso, Osvaldo A.
"This paper presents a study of the electric load behavior based on the Causality Complexity–Entropy Plane.We use a public data set, namely REDD, which contains detailed power usage information from several domestic appliances. In our characterization, we use the available power data of the circuit/devices of all houses. The Bandt–Pompe methodology combined with the Causality Complexity–Entropy Plane was used to identify and characterize regimes and behaviors over these data. The results showed that this characterization provides a useful insight into the underlying dynamics that govern the electric load."
Comparing samples from the 𝒢0 distribution using a geodesic distance
(2020-06) Frery, Alejandro C.; Gambini, Juliana
"The 𝒢0 distribution is widely used for monopolarized SAR image modeling because it can characterize regions with different degrees of texture accurately. It is indexed by three parameters: the number of looks (which can be estimated for the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for comparing samples from the 𝒢0 distribution using a geodesic distance (GD) as a measure of dissimilarity between models. The objective is quantifying the difference between pairs of samples from SAR data using both local parameters (scale and texture) of the 𝒢0 distribution. We propose three tests based on the GD which combine the tests presented in Naranjo-Torres et al. (IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):987–997, 2017), and we estimate their probability distributions using permutation methods."
Evaluación del error en la detección de puntos de borde en imágenes SAR polarimétricas
(2017-04) Monferrán, Daniel; Gambini, Juliana; Frery, Alejandro C.
"El Radar de Apertura Sintética polarimétrico (PolSAR - Polarimentric Synthetic Aperture Radar) es ampliamente utilizado en teledetección porque permite capturar imágenes terrestres de alta resolución. La interpretación automática de imágenes PolSAR es una tarea muy difícil porque éstas contienen un gran volumen de información y además se encuentran contaminadas con ruido speckle. Las características de este ruido hacen necesario utilizar métodos estadísticos para el procesamiento digital de este tipo de imágenes. En esta línea de investigación se pretende evaluar el error que se comete al calcular las posiciones de los puntos de borde dentro de la imagen, utilizando la distribución Wishart compleja y experimentos de Montecarlo en imágenes PolSAR simuladas."
The geodesic distance between 𝒢I0 models and its application to region discrimination
(2017-03) Naranjo-Torres, José; Gambini, Juliana; Frery, Alejandro C.
"The 𝒢I0 distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter, and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the geodesic distance as a measure of dissimilarity between 𝒢I0 models. We derive geodesic distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of identifying edges between regions with different texture, and quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the geodesic distance when compared to stochastic distances."
Imágenes SAR polarimétricas: evaluación del error en la detección de puntos de borde
(2017) Monferrán, Daniel; Gambini, Juliana; Frery, Alejandro C.
"El Radar de Apertura Sintética polarimétrico (PolSAR - Polarimentric Synthetic Aperture Radar) es ampliamente utilizado en teledetección porque permite capturar imágenes terrestres de alta resolución. La interpretación automática de imágenes PolSAR es una tarea muy difícil porque éstas contienen un gran volumen de información y además se encuentran contaminadas con ruido speckle. Las características de este ruido hacen necesario utilizar métodos estadísticos para el procesamiento digital de este tipo de imágenes. En esta línea de investigación se pretende evaluar el error que se comete al calcular las posiciones de los puntos de borde dentro de la imagen, utilizando la distribución Wishart compleja y experimentos de Montecarlo en imágenes PolSAR simuladas."
Low-cost robust estimation for the single-look 𝒢I0 model using the Pareto distribution
(2020) Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C.
"The statistical properties of Synthetic Aperture Radar (SAR) image texture reveal useful target characteristics. It is well-known that these images are affected by speckle and prone to extreme values due to double bounce and corner reflectors. The G0 I distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: α, related to the texture, γ , a scale parameter, and L, the number of looks. Quality estimation of α is essential due to its immediate interpretability. In this letter, we exploit the connection between the G0 I and Pareto distributions. With this, we obtain six estimators that have not been previously used in the SAR literature. We compare their behavior with others in the noisiest case for monopolarized intensity data, namely single look case. We evaluate them using Monte Carlo methods for noncontaminated and contaminated data, considering convergence rate, bias, mean squared error, and computational time. We conclude that two of these estimators based on the Pareto law are the safest choices when dealing with actual data and small samples, as is the case of despeckling techniques and segmentation, to name just two applications. We verify the results with an actual SAR image."
Methods and frameworks for sampling 𝒢I0 data
(2017) Chan, Debora; Rey, Andrea; Gambini, Juliana; Cassetti, Julia; Frery, Alejandro C.
"The 𝒢I0 distribution is a competitive tool for SAR image description. This distribution is useful for describing speckled imagery because it models adequately areas with different degrees of texture. Data simulation is crucial for the development of new methods of automatic interpretation of this type of images. We compare four alternatives for generating data under the 𝒢I0 distribution. The experiments are performed on a variety of programming languages and, a number of criteria to test the fidelity of the generated data are applied."
Sampling from the 𝒢I0 distribution
(2018-12) Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C.
"Synthetic Aperture Radar (SAR) images are widely used in several environmental applications because they provide information which cannot be obtained with other sensors. The 𝒢I0 distribution is an important model for these images because of its flexibility (it provides a suitable way for modeling areas with different degrees of texture, reflectivity and signal-to-noise ratio) and tractability (it is closely related to the Snedekor-F, Pareto Type II, and Gamma distributions). Simulated data are important for devising tools for SAR image processing, analysis and interpretation, among other applications. We compare four ways for sampling data that follow the 𝒢I0 distribution, using several criteria for assessing the quality of the generated data and the consumed processing time. The experiments are performed running codes in four different programming languages. The experimental results indicate that although there is no overall best method in all the considered programming languages, it is possible to make specific recommendations for each one."
Speckle noise reduction in SAR images using information theory
(2020) Chan, Debora; Gambini, Juliana; Frery, Alejandro C.
"In this work, a new nonlocal means filter for single-look speckled data using the Shannon and Renyi entropies is proposed. The measure of similarity between a central window and patches of the image is based on a statistical test for comparing if two samples have the same entropy and hence have the same distribution. The results are encouraging, as the filtered image has better signal-to-noise ratio, it preserves the mean, and the edges are not severely blurred."
Statistical properties of the entropy from ordinal patterns
(2022) Chagas, Eduarda T. C.; Frery, Alejandro C.; Gambini, Juliana; Lucini, María M.; Ramos, Heitor S.; Rey, Andrea
"The ultimate purpose of the statistical analysis of ordinal patterns is to characterize the distribution of the features they induce. In particular, knowing the joint distribution of the pair entropy-statistical complexity for a large class of time series models would allow statistical tests that are unavailable to date. Working in this direction, we characterize the asymptotic distribution of the empirical Shannon’s entropy for any model under which the true normalized entropy is neither zero nor one. We obtain the asymptotic distribution from the central limit theorem (assuming large time series), the multivariate delta method, and a third-order correction of its mean value. We discuss the applicability of other results (exact, first-, and second-order corrections) regarding their accuracy and numerical stability. Within a general framework for building test statistics about Shannon’s entropy, we present a bilateral test that verifies if there is enough evidence to reject the hypothesis that two signals produce ordinal patterns with the same Shannon’s entropy. We applied this bilateral test to the daily maximum temperature time series from three cities (Dublin, Edinburgh, and Miami) and obtained sensible results."

Browsing by Author "Frery, Alejandro C."

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