E. Gonzalez-Ramirez
Autonomous University of Zacatecas
24 Papers
35 Citations
E. Gonzalez-Ramirez is an academic researcher from Autonomous University of Zacatecas. The author has contributed to research in topics: Linear combination & Basis function. The author has an hindex of 6, co-authored 24 publications.
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Papers
Analysis of a multiclass classification problem by Lasso Logistic Regression and Singular Value Decomposition to identify sound patterns in queenless bee colonies
Antonio Robles-Guerrero,Tonatiuh Saucedo-Anaya,E. Gonzalez-Ramirez,José Ismael De la Rosa-Vargas +3 more
TL;DR: The results show that it is possible to detect the queenless state by monitoring bee sound in two possible cases; a strong and healthy colony that lost its queen and a reduced population queenless colony.
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Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier
TL;DR: A novel technique to monitor continuously the azimuthal pointing accuracy of a weather radar antenna by cross-correlating between modelled and measured echoes from ground clutter in real-time at low elevation angles under precipitation and non-precipitation conditions is presented.
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An alternative approach to the tomographic reconstruction of smooth refractive index distributions
E. de la Rosa-Miranda,L. R. Berriel-Valdos,E. Gonzalez-Ramirez,Daniel Alaniz-Lumbreras,Tonatiuh Saucedo-Anaya,J. J. de la Rosa-Vargas,Jesús Villa-Hernández,Vianey Torres-Arguelles,Victor M. Castaño +8 more
TL;DR: In this paper, a linear combination of Gaussian basis functions is used to estimate the temperature distribution of an actual soldering tip, which can be used to get a fast and accurate estimation of the temperature distributions of a real tip.
A method and software solution for classifying clast roundness based on the radon transform
TL;DR: An algorithm for clast roundness classification based on the Radon transform is presented, which correctly classifies the roundness classes of the visual graph and proposes Gaussian models, which are useful to classify the particles into the five classes.
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MAP entropy estimation: Applications in robust image filtering
J. I. de la Rosa,Jesús Villa-Hernández,E. M. de la Rosa,E. Gonzalez-Ramirez,Osvaldo Gutiérrez,Nivia Escalante,R. Ivanov,G. Fleury +7 more
TL;DR: A new approach for image filtering in a Bayesian framework where the probability density function of the likelihood function is approximated using the concept of non-parametric or kernel estimation, based on the generalized Gaussian Markov random fields.
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