1,100 research outputs found
Comments on “Detecting Outliers in Gamma Distribution” by M. Jabbari Nooghabi et al. (2010)
No full textThis note shows that the results presented by Jabbari Nooghabi et al (2010) do not hold in all expected cases. With this, the technique proposed by Kumar and Lalitha (2012) for detecting upper outliers in Gamma samples is also not valid.Fil: Lucini, María Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; ArgentinaFil: Frery, Alejandro César. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentin
When data do not bring information: A case study in markov random fields estimation
No full textThe Potts model is frequently used to describe the behavior of image classes, since it allows to incorporate contextual information linking neighboring pixels in a simple way. Its isotropic version has only one real parameter β, known as smoothness parameter or inverse temperature, which regulates the classes map homogeneity. The classes are unavailable and estimating them is central in important image processing procedures as, for instance, image classification. Methods for estimating the classes which stem from a Bayesian approach under the Potts model require to adequately specify a value for β. The estimation of such parameter can be efficiently made solving the pseudo maximum-likelihood (PML) equations in two different schemes, using the prior or the posterior model. Having only radiometric data available, the first scheme needs the computation of an initial segmentation, whereas the second uses both the segmentation and the radiometric data to make the estimation. In this paper, we compare these two PML estimators by computing the mean-square error (MSE), bias, and sensitivity to deviations from the hypothesis of the model. We conclude that the use of extra data does not improve the accuracy of the PML; moreover, under gross deviations from the model, this extra information introduces unpredictable distortions and bias.Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
How to successfully make a scientific contribution through IEEE geoscience and remote sensing letters
No full text116911671,5082,228Q1Q1SCI
Simulation of spatially correlated clutter fields
No full textCorrelated G distributions can be used to describe the clutter seen in images obtained with coherent illumination, as is the case of B-scan ultrasound, laser, sonar, and synthetic aperture radar (SAR) imagery. These distributions are derived using the square root of the generalized inverse Gaussian distribution for the amplitude backscatter within the multiplicative model. A two-parameter particular case of the amplitude G distribution, called, constitutes a modeling improvement with respect to the widespread KA distribution when fitting urban, forested, and deforested areas in remote sensing data. This article deals with the modeling and the simulation of correlated-distributed random fields. It is accomplished by means of the Inverse Transform method, applied to Gaussian random fields with spatial correlation. The main feature of this approach is its generality, since it allows the introduction of negative correlation values in the resulting process, necessary for the proper explanation of the shadowing effect in many SAR images. © 2009 Taylor & Francis Group, LLC.Fil: Bustos, Oscar Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Lucini, María Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentin
Structural changes in data communication in wireless sensor networks
No full textWireless sensor networks are an important technology for making distributed autonomous measures in hostile or inaccessible environments. Among the challenges they pose, the way data travel among them is a relevant issue since their structure is quite dynamic. The operational topology of such devices can often be described by complex networks. In this work, we assess the variation of measures commonly employed in the complex networks literature applied to wireless sensor networks. Four data communication strategies were considered: geometric, random, small-world, and scale-free models, along with the shortest path length measure. The sensitivity of this measure was analyzed with respect to the following perturbations: insertion and removal of nodes in the geometric strategy; and insertion, removal and rewiring of links in the other models. The assessment was performed using the normalized Kullback-Leibler divergence and Hellinger distance quantifiers, both deriving from the Information Theory framework. The results reveal that the shortest path length is sensitive to perturbations.Fil: Cabral, Raquel da Silva. Universidade Federal do Minas Gerais; BrasilFil: Aquino, Andre L. L.. Universidade Federal de Alagoas; BrasilFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Anibal. Universidade Federal de Alagoas; Brasil. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Computación. Laboratorio de Sistemas Complejos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ramirez, Javier Alberto. Universidade Federal do Minas Gerais; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
Characterization of electric load with Information Theory quantifiers
No full textThis 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.Fil: Aquino, Andre L.L.. Universidade Federal de Alagoas; BrasilFil: Ramos, Heitor S.. Universidade Federal de Alagoas; BrasilFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Viana, Leonardo P.. Universidade Federal de Alagoas; BrasilFil: Cavalcante, Tamer S.G.. Universidade Federal de Pernambuco; BrasilFil: Rosso, Osvaldo Aníbal. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Universidad de los Andes; Chil
Information theory perspective on network robustness
No full textA crucial challenge in network theory is the study of the robustness of a network when facing a sequence of failures. In this work, we propose a dynamical definition of network robustness based on Information Theory, that considers measurements of the structural changes caused by failures of the network's components. Failures are defined here as a temporal process defined in a sequence. Robustness is then evaluated by measuring dissimilarities between topologies after each time step of the sequence, providing a dynamical information about the topological damage. We thoroughly analyze the efficiency of the method in capturing small perturbations by considering different probability distributions on networks. In particular, we find that distributions based on distances are more consistent in capturing network structural deviations, as better reflect the consequences of the failures. Theoretical examples and real networks are used to study the performance of this methodology.Fil: Schieber, Tiago A.. Universidade Federal de Minas Gerais; Brasil. University of Florida; Estados UnidosFil: Carpi, Laura. Universidad Politécnica de Catalunya; EspañaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Universidade Federal de Alagoas; Brasil. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Pardalos, Panos M.. University of Florida; Estados UnidosFil: Ravetti, Martín G.. Universidade Federal de Minas Gerais; Brasil. Universidad de Barcelona; Españ
Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach
No full textIn 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.Fil: Traversaro Varela, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Lanús; ArgentinaFil: Redelico, Francisco Oscar. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; ArgentinaFil: Risk, Marcelo. Hospital Italiano; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Hospital Italiano; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Los Andes; Chil
- …
