Wavelets and regularization of the Cauchy problem for the Laplace equation

In this paper, a Cauchy problem for two-dimensional Laplace equation in the strip 0<x?1 is considered again. This is a classical severely ill-posed problem, i.e., the solution (if it exists) does not depend continuously on the data, a small perturbation in the data can cause a dramatically large error in the solution for 0<x?1. The stability of the solution is restored by using a wavelet regularization method. Moreover, some sharp stable estimates between the exact solution and its approximation in Hr(R)-norm is also provided.     

Polygon Recognition and Symmetry Detection

We introduce an approach based on moving frames for polygon recognition and symmetry detection. We present detailed algorithms for the recognition of polygons in R² modulo the special Euclidean, Euclidean, equi-affine, skewed-affine, and similarity Lie groups. We also solve the case of polygons in the Poincar\'e half-plane under the action of SL(2) and explain a method applicable to Lie group actions in general. The time complexity of our algorithms is linear in the number of vertices and they are noise resistant. The signatures used allow the detection of partial, as well as approximate, equivalences.     

区间小波在奇异性探测中的应用

本文利用满足一定边界条件的区间小波,对一类边界层问题进行了数值探测,不但求出了问题的数值解,而且进一步确定了边界层的位置。     

非参数回归中方差变点的小波检测(英文)

本文主要研究了非参数回归模型中方差函数的变点, 利用小波方法构造的检验量来检测方差中的变点,建立了这些检验量的渐近分布, 并且运用这些检验量构造了方差变点的位置和跳跃幅度的估计, 给出了这些估计的渐近性质, 并进一步通过随机模拟验证了本文方法在有限样本下的性质.     

Directional Spectra-Based Clustering for Visualizing Patterns of Ocean Waves and Winds

The energy distribution of wind-driven ocean waves is of great interest in marine science. Discovering the generating process of ocean waves is often challenging and the direction is the key for a better understanding. Typically, wave records are transformed into a directional spectrum which provides information about the wave energy distribution across different frequencies and directions. Here, we propose a new time series clustering method for a series of directional spectra to extract the spectral features of ocean waves and develop informative visualization tools to summarize identified wave clusters. We treat directional distributions as functional data of directions and construct a directional functional boxplot to display the main directional distribution of the wave energy within a cluster. We also trace back when these spectra were observed, and we present color-coded clusters on a calendar plot to show their temporal variability. For each identified wave cluster, we analyze wind speed and wind direction hourly to investigate the link between wind data and wave directional spectra. The performance of the proposed clustering method is evaluated by simulations and illustrated by a real-world dataset from the Red Sea. Supplementary materials for this article are available online.     

Using Wavelets for the Detection of Discrete Events in Time Series of Hybrid Systems

This contribution deals with the use of wavelets for the analysis of time series of systems which are hybrid in the sense that they contain discrete and continuous dynamics. We focus on the detection of discrete events which is an important step in the identification of hybrid systems. A brief overview of the characteristics of the wavelet transform is given, which shows that the wavelet transform is an appropriate method for the analysis of time series of hybrid systems. By the combination of two wavelet-based analysis techniques, a two-step procedure is obtained which allows the detection of switching points in the presence of weak noise. In this context, emphasis is given to the problems which arise when the theoretical results are used to detect discrete events in real time series. The procedure is demonstrated for a time series obtained from the simulation of a nonlinear laboratory plant.     

Band-limited Wavelets and Framelets in Low Dimensions

In this paper, we study the problem of constructing non-separable band-limited wavelet tight frames, Riesz wavelets and orthonormal wavelets in $\mathbb {R}^{2}$ and $\mathbb {R}^{3}$ . We first construct a class of non-separable band-limited refinable functions in low-dimensional Euclidean spaces by using univariate Meyer’s refinable functions along multiple directions defined by classical box-spline direction matrices. These non-separable band-limited definable functions are then used to construct non-separable band-limited wavelet tight frames via the unitary and oblique extension principles. However, these refinable functions cannot be used for constructing Riesz wavelets and orthonormal wavelets in low dimensions as they are not stable. Another construction scheme is then developed to construct stable refinable functions in low dimensions by using a special class of direction matrices. The resulting stable refinable functions allow us to construct a class of MRA-based non-separable band-limited Riesz wavelets and particularly band-limited orthonormal wavelets in low dimensions with small frequency support.     

Statistical Methods for the Detection of a Centre of Symmetry in a Crystal

It is important in the determination of crystal structures to establish whether a centre of symmetry is present, since this will affect the choice of space group, the equations used for structure‐factor and electron‐density calculations? the size of the asymmetric unit, and whether molecules occupy ‘special’ or ‘general’ positions in the unit cell. As a centre of symmetry does not give rise to a systematic pattern of absent reflections in the X‐ray diffraction data, evidence for its presence or absence must be sought by other methods such as pyroelectric and piezoelectric effects, or statistical tests applied to the intensities of the X‐ray reflections. The theory underlying statistical methods applied to this problem is outlined. Practical tests suggested by several authors are reviewed. These illustrate the application of statistical methods in the field of crystallography and solid state chemistry. A computer program is described which performs the N(z)test and calculates the Wilson ratio; operating instructions and a FORTRAN IV listing are given.     

Symmetry and Localization in Periodic Crystals: Triviality of Bloch Bundles with a Fermionic Time-Reversal Symmetry

Acta Applicandae Mathematicae - We describe some applications of group- and bundle-theoretic methods in solid state physics, showing how symmetries lead to a proof of the localization of electrons...     

Wavelets and Bidemocratic Pairs in Weighted Norm Spaces

A complete characterization of weight functions for which the higher-rank Haar wavelets are greedy bases in weighted L^p spaces is given. The proof uses the new concept of a bidemocratic pair for a Banach space and also pairs (Φ, Φ), where Φ is an orthonormal system of bounded functions in the spaces L^p, p≠2.     

Packet Models Revisited: Tandem and Priority Systems

We examine two extensions of traditional single-node packet-scale queueing models: tandem networks and (strict) priority systems. Two generic input processes are considered: periodic and Poisson arrivals. For the two-node tandem, an exact expression is derived for the joint distribution of the total queue length, and the queue length of the first queue, implicitly determining the distribution of the second queue. Similarly we derive the distribution of the low-priority queue in a two-class priority system. We also provide explicit approximations based on the Brownian bridge.     

Quadrature Formulas for Refinable Functions and Wavelets II: Error Analysis

This paper is concerned with the construction and the analysis of Gauss quadrature formulas for computing integrals of (smooth) functions against refinable functions and wavelets. The main goal of this paper is to develop rigorous error estimates for these formulas. For the univariate setting, we derive asymptotic error bounds for a huge class of weight functions including spline functions. We also discuss multivariate quadrature rules and present error estimates for specific nonseparable refinable functions, i.e., for some special box splines.     

Norms Concerning Subdivision Sequences and Their Applications in Wavelets

Let a:=(a(α))_{α ^s} be a finitely supported sequence of r×r matrices and M be a dilation matrix. The subdivision sequence {(a_n(α))_{α ^s}:n } is defined by a₁=a and

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Let 1≤p≤∞ and f=(f¹,…,f^r)^T be a vector of compactly supported functions in L_p( ^s). The stability is not assumed for f. The purpose of this paper is to give a formula for the asymptotic behavior of the L_p-norms of the combinations of the shifts of f with the subdivision sequence coefficients: Such an asymptotic behavior plays an essential role in the investigation of wavelets and subdivision schemes. In this paper we show some applications in the convergence of cascade algorithms, construction of inhomogeneous multiresolution analyzes, and smoothness analysis of refinable functions. Some examples are provided to illustrate the method.     

Cauchy problem and averaging in Fock space

We investigate a Cauchy problem in the Fock space for a system consisting of a two-level atom, a quantum field, and a classical field. A solution estimate is obtained for the Cauchy problem with initial data from a special class. This class is invariant with respect to the dynamic semigroup of the system. We propose an averaging method for solving the Cauchy problem in the case where the Hamiltonian parameters differ greatly in the order of magnitude. An estimate of the averaging error is obtained. Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 117, No. 1, pp. 92–106, October, 1998.     

Cauchy structures in closure and proximity spaces

Research supported by Hungarian National Foundation for Scientific Research, grant no. 2114.     

Symmetry and Unimodality in Linear Inference

Distribution-free results beyond Gauss-Markov theory are found under weak assumptions regarding the errors. Symmetry, unimodality, and location-scale families are studied in estimation; nonstandard versions of Gauss–Markov results are given; and distribution-free confidence sets are tightened under symmetry and unimodality of errors. Normal-theory approximate tests are seen to exhibit monotone power in certain classes of symmetric unimodal errors.     

Symmetry and optimal control in economics

This paper provides a new simple version of Noether's theorem. From symmetries of dynamic optimal programs, this theorem gives invariant quantities along optimal paths. It is suited to optimal control programs especially for economic models. Applications in growth economics are given.