Characterizing Complex Spatiotemporal Patterns from Entropy Measures.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements.

Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia.

Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning.

Asymptotic Distribution of Certain Types of Entropy under the Multinomial Law.

We obtain expressions for the asymptotic distributions of the Rényi and Tsallis of order entropies and Fisher information when computed on the maximum likelihood estimator of probabilities from multinomial random samples. We verify that these asymptotic models, two of which (Tsallis and Fisher) are normal, describe well a variety of simulated data. In addition, we obtain test statistics for comparing (possibly different types of) entropies from two samples without requiring the same number of categories.

Dual views of the generalized degree of purity.

Several approaches and descriptors have been proposed to characterize the purity of coherency or density matrices describing physical states, including the polarimetric purity of 2D and 3D partially polarized waves. This work introduces two interpretations of the degree of purity: one derived from statistics and another from algebra. In the first one, the degree purity is expressed in terms of the mean and standard deviation of the eigenvalue spectrum of the density or coherency matrix of the corresponding state.

Entropy Estimators in SAR Image Classification.

Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, with the advantage of being less sensitive to adverse atmospherical conditions and of carrying their own source of illumination. On the one hand, new generations and constellations of Synthetic Aperture Radar (SAR) sensors provide images with high spatial and temporal resolution and excellent coverage.

Modeling dengue vector population with earth observation data and a generalized linear model.

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses.

A detailed characterization of complex networks using Information Theory.

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric.

Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases.

Unlabelled: Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration BACKGROUND:  Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods.

Bandt-Pompe symbolization dynamics for time series with tied values: A data-driven approach.

In 2002, Bandt and Pompe [Phys. Rev. Lett.

Modeling Dengue vector population using remotely sensed data and machine learning.

Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns.

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.

We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier.

Classification of complex Wishart matrices with a diffusion-reaction system guided by stochastic distances.

We propose a new method for polarimetric synthetic aperture radar (PolSAR) imagery classification based on stochastic distances in the space of random matrices obeying complex Wishart distributions. Given a collection of prototypes [Formula: see text] and a stochastic distance d(.,.

Distinguishing noise from chaos: objective versus subjective criteria using horizontal visibility graph.

A recently proposed methodology called the Horizontal Visibility Graph (HVG) [Luque et al., Phys. Rev.

Deconvolution of vibroacoustic images using a simulation model based on a three dimensional point spread function.

Vibro-acoustography (VA) is a medical imaging method based on the difference-frequency generation produced by the mixture of two focused ultrasound beams. VA has been applied to different problems in medical imaging such as imaging bones, microcalcifications in the breast, mass lesions, and calcified arteries. The obtained images may have a resolution of 0.

Data driven performance evaluation of Wireless Sensor Networks.

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment.

Using fuzzy logic to enhance stereo matching in multiresolution images.

Stereo matching is an open problem in computer vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors.

Image formation in vibro-acoustography with depth-of-field effects.

We study the image formation of vibro-acoustography systems based on a concave sector array transducer taking into account depth-of-field effects. The system point-spread function (PSF) is defined in terms of the acoustic emission of a point-target in response to the dynamic radiation stress of ultrasound. The PSF on the focal plane and the axis of the transducer are presented.

Stress field forming of sector array transducers for vibro-acoustography.

This paper presents a study of the stress field forming of sector array transducers for vibro-acoustography applications. The system point-spread function (PSF) is given in terms of the dynamic radiation stress exerted on a point target by a dual ultrasound beam with slightly different frequencies. The radiation stress is calculated by assuming that the resulting ultrasound beam is a plane wave.

User satisfaction through empathy and orientation in three-dimensional worlds.

This paper describes a methodology for navigation and exploration assistance intended to enhance user satisfaction when exploring three-dimensional virtual environments. The complexity of such environments often makes navigation and information retrieval difficult, making it necessary to add assistance components to the world in order to turn it easier to manipulate. This methodology uses three-dimensional "intelligent" avatars as interactive guides, along with information-based navigation strategies.