3 resultados para Fourier transformation
em Repositório Científico do Instituto Politécnico de Lisboa - Portugal
Resumo:
In this paper we consider the monoid OR(n) of all full transformations on a chain with n elements that preserve or reverse the orientation, as well as its submonoids OD(n) of all order-preserving or order-reversing elements, OP(n) of all orientation-preserving elements and O(n) of all order-preserving elements. By making use of some well known presentations, we show that each of these four monoids is a quotient of a bilateral semidirectproduct of two of its remarkable submonoids.
Resumo:
The bifunctional transformation of n-hexane was carried out over Pt/MCM-22 based catalysts. MCM-22 was synthesized and submitted to ion exchange with rare earth nitrate solutions of La, Nd and Yb, followed by Pt introduction. Three different methods were used to introduce about 1 wt% of Pt in the zeolite: ion exchange, incipient wetness impregnation and mechanical mixture with Pt/Al(2)O(3). The bifunctional catalysts were characterized by transmission electron microscopy and by the model reaction of toluene hydrogenation. These experiments showed that, in the ion exchanged sample, Pt is located both within the inner micropores and on the outer surface, whereas in the impregnated one, the metal is essentially located on the outer surface under the form of large particles. The presence of RE elements increases the hydrogenating activity of Pt/MCM-22 since the location of these species at the vicinity of metal particles causes modification on its electronic properties. Whatever the mode of Pt introduction, a fast initial decrease in conversion is observed for n-hexane transformation, followed by a plateau related to the occurrence of the catalytic transformations at the hemicages located at the outer surface of the crystals. The effect of rare earth elements on the hydrogenating function leads to a lower selectivity in dibranched isomers and increased amounts of light products.
Resumo:
In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.