Efficient design of polarization insensitive polymer optical waveguide devices considering stress-induced effects

We present an approach for the efficient design of polarization insensitive polymeric optical waveguide devices considering stress-induced effects. In this approach, the stresses induced in the waveguide during the fabrication process are estimated first using a more realistic model in the finite element analysis. Then we determine the perturbations in the material refractive indices caused by the stress-optic effect. It is observed that the stresses cause non-uniform optical anisotropy in the waveguide materials, which is then incorporated in the modal analysis considering a multilayer structure of waveguide. The approach is exploited in the design of a Bragg grating on strip waveguide. Excellent agreement between calculated and published experimental results confirms the feasibility of our approach in the accurate design of polarization insensitive polymer waveguide devices.

Reducing performance bias by intrinsically insensitive learning for unbalanced text mining

This thesis proposes three effective strategies to solve the significant performance-bias problem in imbalance text mining: (1) creation of a novel inexact field learning algorithm to overcome the dual-imbalance problem; (2) introduction of the one-class classification-framework to optimize classifier-parameters, and (3) proposal of a maximal-frequent-item-set discovery approach to achieve higher accuracy and efficiency.

Structural characterization of conducting polypyrrole using 13C cross-polarization/magic-angle spinning solid-state nuclear magnetic resonance spectroscopy

¹³C nuclear magnetic resonance (n.m.r.) has been used to study polypyrrole and N-substituted polypyrrole in the solid state. The extent of oxidation appears to be counterion-dependent; in particular, the quinoid structure appears favoured in the films prepared with dodecyl sulfate. Resonances associated with the quinoid unit are lost upon reduction of the polypyrrole film, which supports the idea that the quinoid structure is associated with the oxidized form of polypyrrole. N-substituted polypyrroles have a more distinct resonance at 110 ppm, which is linked to lower degrees of oxidation or charge delocalization in these systems. The decrease in conductivity of polypyrrole upon thermal ageing in air is associated with both the loss of counterion (‘thermal dedoping’) and the decomposition of the quinoid structure in the polymer backbone. There is no indication of carbonyl formation in the solid-state n.m.r. spectra obtained in the present study.

On the components of the dielectric constants of ionic liquids : ionic polarization?

According to dielectric spectroscopy measurements, ionic liquids (ILs) have rather modest dielectric constants that reflect contributions from distortion and electronic polarization caused by the molecular polarizability as well as the orientation polarization caused by the permanent dipole moment of the ions. To understand the relative importance of these various contributions, the electronic polarizabilities of 27 routinely used ionic liquid ions of different symmetry and size were calculated using ab initio-based methods such as HF and MP2. Using the Clausius–Mossotti equation, these polarizabilities were then used to obtain the electronic polarization contribution (ε_op) to the dielectric constants of six ionic liquids, [C₂mim][BF₄], [C₂mpyr][N(CN)₂], [C₂mim][CF₃SO₃], [EtNH₃][NO₃], [C₂mim][NTf₂] and [C₂mim][EtSO₄]. Theoretical ε_op values were compared to experimental refractive indices of these ionic liquids as well as to those of traditional molecular solvents such as water, tetrahydrofuran (THF), dimethylsulfoxide (DMSO) and formamide. The dipole moments of the ions were also calculated, and from these it is shown that the molecular reorientation component of the dielectric constants of the ionic liquids consisting of ions with small or negligible dipole moments is quite small. Thus it is concluded that a contribution from a form of “ionic polarization” must be present.

A hybrid ART-GRNN online learning neural network with a ε-insensitive loss function

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.