On the nonminimal vector coupling in the Duffin-Kemmer-Petiau theory and the confinement of massive bosons by a linear potential

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Spinless bosons embedded in a vector Duffin-Kemmer-Petiau oscillator

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

On the hardy space H¹ on products of half-spaces

We show that the Hardy space H¹ anal (R2+ x R2+) can be identified with the class of functions f such that f and all its double and partial Hubert transforms Hk f belong to L¹ (R2). A basic tool used in the proof is the bisubharmonicity of |F|q, where F is a vector field that satisfies a generalized conjugate system of Cauchy-Riemann type.

Sliding Vector Fields via Slow-Fast Systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Electromagnetic form factor of the pion in the space- and time-like regions within the front-form dynamics

The pion electromagnetic form factor is calculated in the space- and time-like regions from -10 (GeV/c)2 up to 10 (GeV/c)2, within a front-form model. The dressed photon vertex where a photon decays in a quark-antiquark pair is depicted generalizing the vector meson dominance ansatz, by means of the vector meson vertex functions. An important feature of our model is the description of the on-mass-shell vertex functions in the valence sector, for the pion and the vector mesons, through the front-form wave functions obtained within a realistic quark model. The theoretical results show an excellent agreement with the data in the space-like region, while in the time-like region the description is quite encouraging. © 2003 Elsevier B.V. All rights reserved.

Absence of Klein's paradox for massive spinless bosons coupled by a nonminimal vector interaction

A few properties of the nonminimal vector interaction in the Duffin-Kemmer-Petiau theory in the scalar sector are revised. In particular, it is shown that the nonminimal vector interaction has been erroneously applied to the description of elastic meson-nucleus scatterings and that the space component of the nonminimal vector interaction plays a peremptory role for the confinement of bosons whereas its time component contributes to the leakage. Scattering in a square step potential is used to show that Klein's paradox does not manifest in the case of a nonminimal vector coupling. Copyright © owned by the author(s) under the terms of the Creative Commons Attribution- NonCommercial-ShareAlike Licence.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Scattering and bound states of fermions in a mixed vector-scalar smooth step potential

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Generalized interval vector spaces and interval optimization

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.