262,629 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
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 vehicle behavior with information theory
No full textThis work proposes the use of Information Theory for the characterization of vehicles behavior through their velocities. Three public data sets were used: (i) Mobile Century data set collected on Highway I-880, near Union City, California; (ii) Borlänge GPS data set collected in the Swedish city of Borlänge; and (iii) Beijing taxicabs data set collected in Beijing, China, where each vehicle speed is stored as a time series. The Bandt-Pompe methodology combined with the Complexity-Entropy plane were used to identify different regimes and behaviors. The global velocity is compatible with a correlated noise with f − k Power Spectrum with k ≥ 0. With this we identify traffic behaviors as, for instance, random velocities (k ≃ 0) when there is congestion, and more correlated velocities (k ≃ 3) in the presence of free traffic flow.Fil: Aquino, Andre L. L.. Universidade Federal de Alagoas; BrasilFil: Cavalcante, Tamer S. G.. Universidade Federal de Alagoas; BrasilFil: Almeida, Eliana S.. Universidade Federal de Alagoas; BrasilFil: 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; Argentin
Improving Estimation in Speckled Imagery
No full textthis paper is to compare the performance, in terms of bias, of four di#erent estimators for the homogeneity parameter of a distribution that proves to be very useful in modelling image data contaminated by speckle noise, as SAR (Synthetic Aperture Radar), sonar, ultrasound-B and laser imagery. Those data are generated by systems that employ coherent illumination, which generate a stochastic multiplicative noise along with the information. The statistical properties of this noise have been extensively studied in the last thirty years (see Goodman 1976, Oliver & Quegan 1998). Most of the proposed models for image data can be considered as variations of a general framework, the multiplicative model. The multiplicative model assumes the resulting image to be the product of two statistically independent random variables which are related, respectively, to the target information and the speckle noise. Within this framework, a particularly sucessful distribution that was proposed as a model for data coming from images of extremely heterogeneous regions (e.g., urban areas in SAR imagery) is that one called the I distribution. This distribution was introduced by Frery, Muller, Yanasse & Sant'Anna (1997a) and practical applications have shown its outstanding performance in fitting that type of data (for an application to statistical classification see Mejail, Jacobo-Berlles, Frery & Bustos 2003). Recent results propose it as a universal model for speckled data (see Mejail, Frery, Jacobo-Berlles & Bustos 2001). Robust procedures have been also studied for particular cases of this law (see Frery, Sant'Anna, Mascarenhas & Bustos 1997b, Bustos, Lucini & Frery 2002), and improved inference using resampling is presented in Cribari-Neto, Frery & Silva (2002
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
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
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
On the Numerical Accuracy of Spreadsheets
No full textThis 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.
Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks
No full textThis article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns
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