3 resultados para SPECTRAL TRANSITION


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La tesis se ha centrado en la síntesis y caracterización estructural de materiales tipo perovskita: SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni,Fe) y ALn2CuTi2O9 (A=Ca,Ba; Ln=La,Pr,Nd,Sm). El estudio de las estructuras de los materiales se ha realizado mediante el análisis de los patrones de difracción en polvo de rayos-X, sincrotrón y/o neutrones. En el refinamiento por el método de Rietveld de las estructuras se han sustituido las coordenadas atómicas (el método más común), por coordenadas colectivas: las amplitudes de los modos que describen la distorsión de la fase prototipo. Los resultados generales para la serie SrLnMRuO6 (Ln=La,Pr,Nd; M=Zn,Co,Mg,Ni) a temperatura ambiente se ha recogido en un diagrama en el que se han indicado las amplitudes de los modos que transforman de acuerdo a las irreps en función del factor de tolerancia, ya que todos ellos cristalizan en la misma fase monoclínica (P21/n); y a temperaturas altas se ha construido un diagrama de fase. Los materiales SrLnFeRuO6 ( Ln=La,Pr,Nd) y CaLn2CuTi2O9 cristalizan en la fase ortorrómbica Pbnm a temperatura ambiente; mientras que BaLn2CuTi2O9 tienen una estructura más simétrica, I4/mcm. A altas temperaturas se han identificado las transiciones de fase inducidas por el cambio de temperatura.A temperaturas bajas se han analizado las estructuras magnéticas de algunos de los compuestos mediante difracción de neutrones.

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Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification

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In this work, a study of the nematic (N)-isotropic (I) phase transition has been made in a series of odd non-symmetric liquid crystal dimers, the alpha-(4-cyanobiphenyl-4'-yloxy)-omega-(1-pyrenimine-benzylidene-4'-oxy) alkanes, by means of accurate calorimetric and dielectric measurements. These materials are potential candidates to present the elusive biaxial nematic (N-B) phase, as they exhibit both molecular biaxiality and flexibility. According to the theory, the uniaxial nematic (N-U)-isotropic (I) phase transition is first-order in nature, whereas the N-B-I phase transition is second-order. Thus, a fine analysis of the critical behavior of the N-I phase transition would allow us to determine the presence or not of the biaxial nematic phase and understand how the molecular biaxiality and flexibility of these compounds influences the critical behavior of the N-I phase transition.