Signal classification through multifractal analysis and complex domain neural networks

This paper describes a system capable of classifying stochastic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.     

Synthesis and characterization of a novel proton‐exchange membrane for fuel cells operating at high temperatures and low humidities

The synthesis of a p‐toluidine/formaldehyde (PTF) resin was performed, and the effects of the molar ratio of the individual monomers and the polymerization conditions on the structure of the PTF resin were studied. Fourier transform infrared and ¹³C‐NMR spectra were used to characterize the PTF. Wide‐angle X‐ray diffraction patterns revealed the crystalline structures of various PTFs. Polarized optical microscopy revealed that the molar ratio of the monomers had a strong effect on the crystalline morphologies. A longer polymerization time turned out a polymer with a higher intrinsic viscosity and molecular weight, which led to differences in the proton conductivity. All of the PTFs showed a higher proton conductivity than a commercial Nafion membrane at 90–100°C and 0% relative humidity. The proton conductivity of the PTF series could be improved by sulfonation with sulfuric acid and could be maintained after blending with polyurethane. Pure methanol could be used as a fuel source because of the insolubility and nonwetting properties of PTF in methanol to increase the output current density for a PTF membrane electrode assembly. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2008     

Cross-Border Migration of Hong Kong Residents Under the 'One Country, Two Systems' Policy

Hong Kong is known as a migrant city because its population was sourced from mainland China, and because there has always been outflows of people to overseas countries, especially from the mid-1980s to mid-1990s. Emigration to mainland China has become a significant recent trend. This article discusses this phenomenon in the theoretical contexts of migration and trans-border residential development. It contends that the trend will intensify more quickly than other cross-border movements because the two systems are separated by a political border within one country, and because there are common cultural ties and rapid economic integration.     

Class Based Contextual Logic for DOOD

Cntextual logic provides a mechanism to reason about modules.In this paper,this theory of modules if modules is extended to a context theory of classes where class is in the true spirit of object-oriented databases.The logic,referred to as CLOG,is class-based.CLOG supports class,object identity,multiple role of object, monotonic and non-monotonic inheritance of data and method,method factoring,views,derived and query classes.Views and derived classes are queries in themselves.Objects are pure data terms representing the ground instances of facts in the class.Object identity is a first class term in the logic.Inheritance is handled through delegation.     

Application of the spline finite strip to the analysis of shear-deformable plates

A spline finite strip is proposed to analyse thick isotropic or laminated composite plates. The formulation is based upon the principle of virtual work and the third-order plate theory developed by Reddy. The variational functional requires the satisfaction of C₁,-continuity of the assumed vertical deflection variable which can be easily fulfilled by the present method. The proposed spline finite strip is a conforming element with a smaller number of unknowns at each node compared to other existing elements based on the third-order theory. For the analysis of thin isotropic or laminated plates, the present element shows no sign of shear locking. A number of computational examples are given to demonstrate the efficiency and the accuracy of the present method.     

Two-dimensional phase unwrapping using a minimum spanning treealgorithm

Phase unwrapping refers to the determination of phase from modulo 2pi data, some of which may not be reliable. In 2D, this is equivalent to confining the support of the phase function to one or more arbitrarily shaped regions. A phase unwrapping algorithm is presented which works for 2D data known only within a set of nonconnected regions with possibly nonconvex boundaries. The algorithm includes the following steps: segmentation to identify connectivity, phase unwrapping within each segment using a Taylor series expansion, phase unwrapping between disconnected segments along an optimum path, and filling of phase information voids. The optimum path for intersegment unwrapping is determined by a minimum spanning tree algorithm. Although the algorithm is applicable to any 2D data, the main application addressed is magnetic resonance imaging (MRI) where phase maps are useful.     

Rival-Model Penalized Self-Organizing Map

As a typical data visualization technique, self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. In a conventional adaptive SOM, it needs to choose an appropriate learning rate whose value is monotonically reduced over time to ensure the convergence of the map, meanwhile being kept large enough so that the map is able to gradually learn the data topology. Otherwise, the SOM's performance may seriously deteriorate. In general, it is nontrivial to choose an appropriate monotonically decreasing function for such a learning rate. In this letter, we therefore propose a novel rival-model penalized self-organizing map (RPSOM) learning algorithm that, for each input, adaptively chooses several rivals of the best-matching unit (BMU) and penalizes their associated models, i.e., those parametric real vectors with the same dimension as the input vectors, a little far away from the input. Compared to the existing methods, this RPSOM utilizes a constant learning rate to circumvent the awkward selection of a monotonically decreased function for the learning rate, but still reaches a robust result. The numerical experiments have shown the efficacy of our algorithm     

Optical appearance of alpha particle tracks in CR-39 SSNTD

A ray tracing method based on geometrical optics was used to study the tracks from alpha particles with different energies and with an incident angle of 50°. The transmission operation mode of the microscope is simulated. Considering the distribution of light intensities from the tracks, the mean and the 80% percentile gray levels from real experiments are proposed as quantitative variables to differentiate among tracks. The gray level properties for the same track for different exposures can vary to large extents. We introduce three variables, κ, λ and ε, to make empirical corrections. It is interesting to see that these coefficients are very consistent for the same alpha particle track despite the very different gray level properties. Gray level results have been obtained for tracks from alpha particles with 50° incident angle and different incident energies. However, the track depths cannot be predicted by any one of the coefficients κ, λ and ε. Multivariate analyses can help separate the tracks corresponding to different alpha energies. By using discriminant analysis with κ, λ and ε as independents, effectively all alpha energies can be determined with an accuracy of ±0.5 MeV.     

Modeling and experiments of waveform effects on oxide charging inplasma immersion ion implantation [MOS ICs]

An analytical model of the time-dependent currents of a plasma allows the determination of the physical mechanisms and dependencies of charging damage in Plasma Immersion Ion Implantation (PIII). From the model, we determine that the plasma requires several ms to equilibrate after the application of an implantation voltage pulse. Although the individual pulses only change the surface potential by a fraction of a volt, the long equilibration time means that the pulse repetition rate perturbs the time-averaged surface potential. A physical relation describing the dependence of the time-averaged surface potential on plasma and voltage bias parameters predicts that a shorter pulse width, lower electron temperature, and rectangular pulse shape maximizes the allowable implantation rate and minimizes the charging damage. For a given pulse shape and plasma condition, a maximum pulse repetition rate exists. Beyond this threshold frequency, the oxide fails because the oxide field exceeds its breakdown value. Experimental results corroborate the simulation's predictions, showing the dependence of the pulsing repetition rate on charging damage     

Experimental demonstration of multiwavelength optical network with microwave subcarriers

The authors use the unique multiwavelength filtering capability of the acousto-optic tunable filter to demonstrate multiple broadband services with only one fixed-wavelength transmitter and receiver per user in the first experimental demonstration of a multiwavelength subcarrier network.<>