Metastability and front propagation in the first-order optical Fréedericksz transition

A general dynamical model for the first-order optical Fréedericksz transition incorporating spatial transverse inhomogeneities and hydrodynamic effects is discussed in the framework of a time-dependent Ginzburg-Landau model. The motion of an interface between two coexisting states with different director orientations is considered. A uniformly translating front solution of the dynamical equations for the motion of that interface is described.

Local character of magnetic coupling in ionic solids

Magnetic interactions in ionic solids are studied using parameter-free methods designed to provide accurate energy differences associated with quantum states defining the Heisenberg constant J. For a series of ionic solids including KNiF3, K2NiF4, KCuF3, K2CuF4, and high- Tc parent compound La2CuO4, the J experimental value is quantitatively reproduced. This result has fundamental implications because J values have been calculated from a finite cluster model whereas experiments refer to infinite solids. The present study permits us to firmly establish that in these wide-gap insulators, J is determined from strongly local electronic interactions involving two magnetic centers only thus providing an ab initio support to commonly used model Hamiltonians.

Neutral atoms in ionic lattices: Excited states of KCl:Ag(0)

The optical-absorption spectrum of a cationic Ag0 atom in a KCl crystal has been studied theoretically by means of a series of cluster models of increasing size. Excitation energies have been determined by means of a multiconfigurational self-consistent field procedure followed by a second-order perturbation correlation treatment. Moreover results obtained within the density-functional framework are also reported. The calculations confirm the assignment of bands I and IV to transitions of the Ag-5s electron into delocalized states with mainly K-4s,4p character. Bands II and III have been assigned to internal transitions on the Ag atom, which correspond to the atomic Ag-4d to Ag-5s transition. We also determine the lowest charge transfer (CT) excitation energy and confirm the assignment of band VI to such a transition. The study of the variation of the CT excitation energy with the Ag-Cl distance R gives additional support to a large displacement of the Cl ions due to the presence of the Ag0 impurity. Moreover, from the present results, it is predicted that on passing to NaCl:Ag0 the CT onset would be out of the optical range while the 5s-5p transition would undergo a redshift of 0.3 eV. These conclusions, which underline the different character of involved orbitals, are consistent with experimental findings. The existence of a CT transition in the optical range for an atom inside an ionic host is explained by a simple model, which also accounts for the differences with the more common 3d systems. The present study sheds also some light on the R dependence of the s2-sp transitions due to s2 ions like Tl+.

Advanced optical microscopies for materials: new trends

This article summarizes the new trends of Optical Microscopy applied to Materials, with examples of applications that illustrate the capabilities of thetechnique.

Magnetism of isolated Mn12 single-molecule magnets detected by magnetic circular dichroism: Observation of spin tunneling with a magneto-optical technique

We report magnetic and magneto-optical measurements of two Mn12 single-molecule magnet derivatives isolated in organic glasses. Field-dependent magnetic circular dichroism (MCD) intensity curves (hysteresis cycles) are found to be essentially identical to superconducting quantum interference device magnetization results and provide experimental evidence for the potential of the optical technique for magnetic characterization. Optical observation of magnetic tunneling has been achieved by studying the decay of the MCD signal at weak applied magnetic field

Line search multilevel optimization as computational methods for dense optical flow

We evaluate the performance of different optimization techniques developed in the context of optical flow computation with different variational models. In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we de- velop the use of efficient multilevel schemes for computing the optical flow. More precisely, we evaluate the performance of a standard unidirectional mul- tilevel algorithm - called multiresolution optimization (MR/OPT), to a bidrec- tional multilevel algorithm - called full multigrid optimization (FMG/OPT). The FMG/OPT algorithm treats the coarse grid correction as an optimiza- tion search direction and eventually scales it using a line search. Experimental results on different image sequences using four models of optical flow com- putation show that the FMG/OPT algorithm outperforms both the TN and MR/OPT algorithms in terms of the computational work and the quality of the optical flow estimation.

Composition, nanostructure, and optical properties of silver and silver-copper lusters

Lusters are composite thin layers of coinage metal nanoparticles in glass displaying peculiar optical properties and obtained by a process involving ionic exchange, diffusion, and crystallization. In particular, the origin of the high reflectance (golden-shine) shown by those layers has been subject of some discussion. It has been attributed to either the presence of larger particles, thinner multiple layers or higher volume fraction of nanoparticles. The object of this paper is to clarify this for which a set of laboratory designed lusters are analysed by Rutherford backscattering spectroscopy, transmission electron microscopy, x-ray diffraction, and ultraviolet-visible spectroscopy. Model calculations and numerical simulations using the finite difference time domain method were also performed to evaluate the optical properties. Finally, the correlation between synthesis conditions, nanostructure, and optical properties is obtained for these materials.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

Application of the Mutual Information Minimization to speaker recognition / identification improvement

In this paper we propose the inversion of nonlinear distortions in order to improve the recognition rates of a speaker recognizer system. We study the effect of saturations on the test signals, trying to take into account real situations where the training material has been recorded in a controlled situation but the testing signals present some mismatch with the input signal level (saturations). The experimental results for speaker recognition shows that a combination of several strategies can improve the recognition rates with saturated test sentences from 80% to 89.39%, while the results with clean speech (without saturation) is 87.76% for one microphone, and for speaker identification can reduce the minimum detection cost function with saturated test sentences from 6.42% to 4.15%, while the results with clean speech (without saturation) is 5.74% for one microphone and 7.02% for the other one.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Empirical mode decomposition-based face recognition system

In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

Magnetism of isolated Mn12 single-molecule magnets detected by magnetic circular dichroism: Observation of spin tunneling with a magneto-optical technique

We report magnetic and magneto-optical measurements of two Mn12 single-molecule magnet derivatives isolated in organic glasses. Field-dependent magnetic circular dichroism (MCD) intensity curves (hysteresis cycles) are found to be essentially identical to superconducting quantum interference device magnetization results and provide experimental evidence for the potential of the optical technique for magnetic characterization. Optical observation of magnetic tunneling has been achieved by studying the decay of the MCD signal at weak applied magnetic field

Trust region versus line search for computing the optical flow

We consider the numerical treatment of the optical flow problem by evaluating the performance of the trust region method versus the line search method. To the best of our knowledge, the trust region method is studied here for the first time for variational optical flow computation. Four different optical flow models are used to test the performance of the proposed algorithm combining linear and nonlinear data terms with quadratic and TV regularization. We show that trust region often performs better than line search; especially in the presence of non-linearity and non-convexity in the model.