Mode theory and analysis of planar array waveguides

Bloch modes can be excited in planar array due to its periodic lateral refractive index. The power coupled into each eigenmode of the array waveguides is calculated through the overlap integrals of the input field with the eigenmode fields of the coupled infinite array waveguides projected onto the x-axis. Low losses can be obtained if the transition from the array to the free propagation region is adiabatic. Due to the finite resolution of lithographic process the gap between the waveguides will stop abruptly, however, when the waveguides come into too close together. Calculation results show that losses will occur at this discontinuity, which are dependent on the ratio of the gap between the waveguides and grating pitch and on the confinement of field in the array waveguides. Tapered waveguides and low index contrast between the core and cladding layers can lower the transmitted losses.

Enhancing robustness and immunization in geographical networks

We find that different geographical structures of networks lead to varied percolation thresholds, although these networks may have similar abstract topological structures. Thus, strategies for enhancing robustness and immunization of a geographical network are proposed. Using the generating function formalism, we obtain an explicit form of the percolation threshold q(c) for networks containing arbitrary order cycles. For three-cycles, the dependence of q(c) on the clustering coefficients is ascertained. The analysis substantiates the validity of the strategies with analytical evidence.

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry. (C) 2003 Elsevier Science B.V All rights reserved.

Correlation analysis of the structures and stability constants of gadolinium(III) complexes

To simplify the abstraction of descriptors, for the correlation analysis of the stability constants of gadolinium(III) complexes and their ligand structures, aiming at gadolinium(III) complexes, we only considered the ligands and ignored the common parts of the structures, i.e., the metal ions. Quantum-chemical descriptors and topological indices were calculated to describe the structures of the ligands. Multiple regression analysis and neural networks were applied to construct the models between the ligands and the stability constants of gadolinium(III) complexes and satisfactory results were obtained.

Novel hydrogen-bonded three-dimensional networks encapsulating one-dimensional covalent chains: [M(4,4 '-bipy)(H2O)4](4-abs)2 center dot nH(2)O (4,4 '-bipy=4,4 '-Bipyridine; 4-abs=4-aminobenzenesulfonate) (M=Co, n=1; M=Mn, n=2)

Two novel compounds, [Co(4,4'-bipy)(H2O)(4)](4-abS)(2).H2O (1) and [Mn(4,4'-bipy)(H2O)(4)](4-abs)(2).2H(2)O (2) (4,4'-bipy = 4,4'-bipyridine; 4-abs = 4-aminobenzenesulfonate), have been synthesized in aqueous solution and characterized by single-crystal X-ray diffraction, elemental analyses, UV-vis and IR spectra, and TG analysis. X-ray structural analysis revealed that 1 and 2 both possess unusual hydrogen-bonded three-dimensional (3-D) networks encapsulating one-dimensional (1-D) covalently bonded infinite [M(4,4'-bipy)(H2O)(4)](2+) (M = Co, Mn) chains. The 4-abs anions in 1 form 1-D zigzag chains through hydrogen bonds. These chains are further extended through crystallization water molecules into 3-D hydrogen-bonded networks with 1-D channels, in which the [Co(4,4'-bipy)(H2O)(4)](2+) linear covalently bonded chains are located. Crystal data for 1: C22H30CoN4O11S2, monoclinic P2(1), a = 11.380(2) Angstrom, b = 8.0274(16) Angstrom, c = 15.670(3) Angstrom, alpha = gamma = 90degrees, beta = 92.82(3)degrees, Z = 2. Compound 2 contains interesting two-dimensional (2-D) honeycomb-like networks formed by 4-abs anions and lattice water molecules via hydrogen bonding, which are extended through other crystallization water molecules into three dimensions with 1-D hexagonal channels. The [Mn(4,4'-bipy)(H2O)(4)](2+) linear covalent chains exist in these channels. Crystal data for 2: C22H32WN4O12S2, monoclinic P2(1)/c, a = 15.0833(14) Angstrom, b = 8.2887(4) Angstrom, c = 23.2228(15) Angstrom, alpha = gamma = 90degrees, beta = 95.186(3)degrees, Z = 4.

Prediction of molar absorptivities of color reagents and their color reactions with yttrium by artificial neural networks

The new topological indices A(x1)-A(x3) suggested in our laboratories were applied to the study of structure-property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in-color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

Correlation analysis of the structures and gas-liquid chromatographic retention indices of amines

Quantitative structure-retention relationship(QSRR) was studied for amines to gas-liquid chromatography on three stationary phases of different polarities with the topological indices A(m) (A(m1), A(m2), A(m3)) and gravitational index GI. The algorithm of "Leaps and Bounds" was performed for selection of the variables. And the multi-regression and the quasi-Newton neural networks were employed for the calculation with better results.

Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

Quantitative structure-property relationships for colour reagents and their colour reactions with cerium using computational neural networks

Quantitative structure-activity/property relationships (QSAR/QSPR) studies have been exploited extensively in the designs of drugs and pesticides, but few such studies have been applied to the design of colour reagents. In this work, the topological indices A(x1)-A(x3) suggested in this laboratory were applied to multivariate analysis in structure-property studies. The topological indices of 43 phosphone bisazo derivatives of chromotropic acid were calculated. The structure-property relationships between colour reagents and their colour reactions with cerium were studied using A(x1-Ax3) indices with satisfactory results. The purpose of this work was to establish whether QSAR can be used to predict the contrasts of colour reactions and in the longer term to be a helpful tool in colour reagent design.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.