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Spectral domain analysis of double screen frequency selective surfaces
(SBMO/SBMag, 2012-06)
Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD
(Institute of Electrical and Electronics Engineers, 2020-03-27)
This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, ...
A spectral envelope approach towards effective SVM-RFE on infrared data
(Elsevier Science, 2016-02)
Infrared spectroscopy data is characterized by the presence of a huge number of variables. Applications of infrared spectroscopy in the mid-infrared (MIR) and near-infrared (NIR) bands are of widespread use in many fields. ...
Outer Factor 2-D MA Models for Purely Random Fields and Fields and Wold-Tipe Texture Decompositions
(2007-06-29)
In this paper, we propose a new method to compute the parameters of finite approximations of 2-D MA infinite models associated with purely indeterministic stationary fields, by using spectral factorizations. This method ...
On confidence bands for time series problems in the time and frequency domains
(Institute of Mathematical Statistics, 2003-12)
The construction of (asymptotic) simultaneous confidence bands for some time series problems is studied, typically for the sample autocorrelogram and windowed spectral density estimate. The following approaches are explored: ...
Spectral domain approach applied to the rigorous determination of the microwave field in planar structures with asymmetric electrodes distribution
(Wiley-Blackwell, 2020-12-19)
Among several methods used for designing microstrip-like microwave and millimetre-wave waveguides, we have the spectral domain approach (SDA), which allows determining parameters such as effective permittivity and ...
Spectral mixture kernels for Multi-Output Gaussian processes
(Universidad de Chile, 2017)
Multi-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression ...