On a Degenerate Parabolic Problem in Holder Spaces

In this paper, we study some degenerate parabolic equation with Cauchy-Dirichlet boundary conditions. This problem is considered in little Holder spaces. The optimal regularity of the solution v is obtained and is specified in terms of those of the second member when some conditions upon the Holder exponent with respect to the degeneracy are satisfied. The proofs mainly use the sum theory of linear operators with or without density of domains and the results of smoothness obtained in the study of some abstract linear differential equations of elliptic type.

Pattern Recognition With Weighted Complex Networks

In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.

A Simplified Evaluation Procedure of the Scale of Fluctuation Combined Recurrence Averaging and Weighted Linear Regression

The divergence of properties from one location to another within a soil mass is termed spatial variability, which traditionally includes three parameters the mean, the standard deviation, and the scale of fluctuation, in order to stochastically describe a soil property. Among them, determining the scale of fluctuation in the evaluation of spatial variability of soil profiles is not easy due to soil condition complexity. A simplified procedure is presented in the paper to determine the scale of fluctuation combined recurrence averaging and weighted linear regression. The alternative approach utilizes widely usable spreadsheet to solve the problem more directly and efficiently.

Weighted least-squares phase unwrapping algorithm based on derivative variance correlation map

Among different phase unwrapping approaches, the weighted least-squares minimization methods are gaining attention. In these algorithms, weighting coefficient is generated from a quality map. The intrinsic drawbacks of existing quality maps constrain the application of these algorithms. They often fail to handle wrapped phase data contains error sources, such as phase discontinuities, noise and undersampling. In order to deal with those intractable wrapped phase data, a new weighted least-squares phase unwrapping algorithm based on derivative variance correlation map is proposed. In the algorithm, derivative variance correlation map, a novel quality map, can truly reflect wrapped phase quality, ensuring a more reliable unwrapped result. The definition of the derivative variance correlation map and the principle of the proposed algorithm are present in detail. The performance of the new algorithm has been tested by use of a simulated spherical surface wrapped data and an experimental interferometric synthetic aperture radar (IFSAR) wrapped data. Computer simulation and experimental results have verified that the proposed algorithm can work effectively even when a wrapped phase map contains intractable error sources. (c) 2006 Elsevier GmbH. All rights reserved.

Growth-Parameter Spaces and Optical Properties of Cubic Boron Nitride Films on Si(001)

Cubic boron nitride (c-BN) films were deposited on Si(001) substrates in an ion beam assisted deposition (IBAD) system under various conditions, and the growth parameter spaces and optical properties of c-BN films have been investigated systematically. The results indicate that suitable ion bombardment is necessary for the growth of c-BN films, and a well defined parameter space can be established by using the P/a-parameter. The refractive index of BN films keeps a constant of 1.8 for the c-BN content lower than 50%, while for c-BN films with higher cubic phase the refractive index increases with the c-BN content from 1.8 at chi(c) = 50% to 2.1 at chi(c) = 90%. Furthermore, the relationship between n and rho for BN films can be described by the Anderson-Schreiber equation, and the overlap field parameter gamma is determined to be 2.05.

Determining of the delay time for a heating ventilating and air-conditioning plant using two weighted neural network approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

Application of multi-weighted neuron for iris recognition

In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.

Design, fabrication, and characterization of an ultracompact low-loss photonic crystal corner mirror

An ultracompact, low-loss, and broad-band corner mirror, based on photonic crystals, is investigated in this paper. Based on the theoretical analysis of the loss mechanism, the boundary layers of the photonic crystal region are revised to improve the extra losses, and the transmission characteristics are evaluated by using the 3-D finite-difference time-domain method. The device with optimized structure was fabricated on silicon-on-insulator substrate by using electron-beam lithography and inductively coupled plasma etching. The measured extra losses are about 1.1 +/- 0.4 dB per corner mirror for transverse-electronic polarization for the scanning wavelength range of 1510-1630 nm. Dimensions of the achieved PC corner mirror are less than ;7 x 7 mu m(2), which are only about one tenth of conventional wave-guide corner mirrors.

Realization of an ultracompact low-loss photonic crystal corner mirror

We report on the realization and characterization of an ultracompact, low-loss, and broadband corner mirror based on photonic crystals (PCs). By modifying the boundary layers of the PC region, extra losses of 1.1 +/- 0.4 dB per corner mirror are achieved for transverse-electronic polarization for silicon-on-insulator ridge waveguides fabricated by electron beam lithography and inductively coupled plasma etching. Dimensions of the PC corner mirror are less than 7 x 7 mu m(2), which are only about one tenth of conventional waveguide corner mirrors.

Novel folding 8x8 silicon-based optical matrix switch with tapered waveguides and self-aligned corner mirrors

Novel folding 8 x 8 matrix switches based on silicon on insulator were demonstrated. In the design, single-mode rib waveguides and multimode interferences are connected by optimized tapered waveguides to reduce the mode coupling loss between the two types of waveguides. The self-aligned method was applied to the key integrated turning mirrors for perfect positions and low loss of them. A mixed etching process including inductively coupled plasma and chemical etching was employed to etch waveguides and mirrors, respectively. The compact size of the device is only 20 x 3.2 mm(2). The switch element with high switching speed and low power consumption is presented in the matrix. The average insertion loss of the matrix is about -21 dB, and the excess loss of one mirror is measured of -1.4 dB. The worst crosstalk is larger than 21 dB. Experimental results illuminate that some of the main characteristics of optical matrix switches are. developed in the modified design, which is in accord with theoretic analyses.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Storage of photoexcited electron-hole pairs in an AlAs/GaAs heterostructure created by electron transfer in real and kappa spaces

The storage of photoexcited electron-hole pairs is experimentally carried out and theoretically realized by transferring electrons in both real and k spaces through resonant Gamma - X in an AlAs/GaAs heterostructure. This is proven by the peculiar capacitance jump and hysteresis in the measured capacitance-voltage curves. Our structure may be used as a photonic memory cell with a long storage time and a fast retrieval of photons as well.

Structure approximate based on double weighted neural network

Double weighted neural network; is a kind of new general used neural network, which, compared with BP and RBF network, may approximate the training samples with a move complicated geometric figure and possesses a even greater approximation. capability. we study structure approximate based on double weighted neural network and prove its rationality.

Theoretical and Experimental Studies of an Ultra-compact Photonic Crystal Corner Mirror Based on Silicon-On-Insulator

An ultra-compact silicon-on-insulator based photonic crystal corner mirror is designed and optimized. A sample is then successfully fabricated with extra losses 1.1 +/- 0.4dB for transverse-electronic (M) polarization for wavelength range of 1510-1630nm.