连续切波变换反演公式的级数表示

我们主要研究连续切波变换反演公式的级数表示.首先引入两类由切波变换反演公式定义的无穷级数和有限级数,并研究了由Kittipoom等人介绍的切波生成空间,得到这个切波生成空间的一些重要性质.其次利用这些结果显示:对于这个切波生成空间,当采样密度趋于无穷时由我们定义的无穷级数按L~2-范数收敛于重构函数;对于可允许函数空间,当采样密度趋于无穷时由我们定义的有限级数按L~2-范数收敛于重构函数.     

平均采样定理

采样定理是信号分析中的基本工具之一,广泛应用于数字信号处理、无线通信等很多领域.近年来,经典的Shannon采样定理被从频谱有限函数空间推广到更一般的平移不变子空间,采样方法也从逐点取值推广到平均采样和多通道采样等.本文简要回顾采样定理的发展过程,重点介绍一些最新研究进展,包括平均采样、多通道采样和随机过程采样等,以及混淆误差和截断误差等重构误差估计.     

主平移不变子空间的平移整采样

给出主平移不变子空间的一个平移整采样定理,其采样公式不仅在L2(R)收敛意义下成立,而且在适当的1周期集上一致收敛的意义下成立.此采样定理包含了经典的Shannon采样公式,Walter在1992年的采样定理以及由紧支函数生成的主平移不变子空间的采样.最后给出了大量例子说明定理应用的广泛性.     

l~p-值Wiener过程在Hlder范数下的泛函重对数定律（英文）

在Hlder范数生成的强拓扑下,基于l~2-值Wiener过程的大偏差公式,本文得到了Hlder范数意义下,l~2-值Wiener过程的泛函重对数定律,也得到了l~p-值Wiener过程的泛函重对数定律,在这里1≤p∞.     

某些Mercer核矩阵的权范数

借助整函数插值研究由函数的广义平移所生成的Mercer核矩阵及其逆矩阵权范数的上、下界估计问题,将定义在无限区间上整函数的广义平移所生成的Mercer核矩阵权范数界的估计转化为其Fourier-Bessel变换来估计.     

Morrey空间上Marcinkiewicz积分与■交换子

本文建立了Marcinkiewicz积分M与具离散系数的正则有界平均振荡空间■生成的交换子M_b在非齐性度量测度空间上的有界性.在控制函数λ满足∈-弱反双倍条件的假设下,当p∈(1,∞)时,证明了M_b在L~P(μ)上是有界的.另外,还得到了M_b在Morrey空间上的有界性.     

Wiener过程在Hlder范数下的泛函重对数定律(英文)

本文借助于Hlder范数在函数空间中诱导出的强拓扑下的大偏差公式,得到了Wiener过程在Hlder范数下的泛函重对数定律.     

Wiener过程在H(o)lder范数下的泛函重对数定律

本文借助于H(o)lder范数在函数空间中诱导出的强拓扑下的大偏差公式,得到了Wiener过程在H(o)lder范数下的泛函重对数定律.     

Lagrange插值和Hermite-Fejér插值在Wiener空间下的平均误差

在L_q-范数逼近的意义下,确定了基于Chebyshev多项式零点的Lagrange插值多项式列和Hermite-Fejér插值多项式列在Wiener空间下的p-平均误差的弱渐近阶.从我们的结果可以看出,当2≤q<∞,1≤p<∞时,基于第一类Chebyshev多项式零点的Lagrange插值多项式列和Hermite-Fejér插值多项式列的p-平均误差弱等价于相应的最佳逼近多项式列的p-平均误差.在信息基计算复杂性的意义下,如果可允许信息泛函为计算函数在固定点的值,那么当1≤p,q<∞时,基于第一类Chebyshev多项式零点的Lagrange插值多项式列和Hermite-Fejér插值多项式列在Wiener空间下的p-平均误差弱等价于相应的最小非自适应p-平均信息半径.     

关于赋Luxemburg范数广义Orlicz序列空间的(J)非方点

本文研究了赋Luxemburg范数广义Orlicz序列空间的(J)非方点.通过分析、综合经典Orlicz空间与广义Orlicz空间的结构,给出了用生成函数表达该性质的充分必要条件.该结果不仅推广了经典空间的结果,也是十分好用的.     

Robustness of sampling and reconstruction and Beurling–Landau-type theorems for shift-invariant spaces

Beurling–Landau-type results are known for a rather small class of functions limited to the Paley–Wiener space and certain spline spaces. Here, we show that the sampling and reconstruction problem in shift-invariant spaces is robust with respect to two classes of probing measures as well as to the underlying shift-invariant space. As an application we enlarge the class of functions for which Beurling–Landau-type results hold.     

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

In our recent work, the sampling and reconstruction of non-decaying signals, modeled as members of weighted-\(L_p\) spaces, were shown to be stable with an appropriate choice of the generating kernel for the shift-invariant reconstruction space. In this paper, we extend the Strang–Fix theory to show that, for d-dimensional signals whose derivatives up to order L are all in some weighted-\(L_p\) space, the weighted norm of the approximation error can be made to go down as \(O(h^L)\) when the sampling step h tends to 0. The sufficient condition for this decay rate is that the generating kernel belongs to a particular hybrid-norm space and satisfies the Strang–Fix conditions of order L. We show that the \(O(h^L)\) behavior of the error is attainable for both approximation schemes using projection (when the signal is prefiltered with the dual kernel) and interpolation (when a prefilter is unavailable). The requirement on the signal for the interpolation method, however, is slightly more stringent than that of the projection because we need to increase the smoothness of the signal by a margin of \(d/p+\varepsilon \), for arbitrary \(\varepsilon >0\). This extra amount of derivatives is used to make sure that the direct sampling is stable.

     

Phaseless Sampling and Reconstruction of Real-Valued Signals in Shift-Invariant Spaces

Sampling in shift-invariant spaces is a realistic model for signals with smooth spectrum. In this paper, we consider phaseless sampling and reconstruction of real-valued signals in a high-dimensional shift-invariant space from their magnitude measurements on the whole Euclidean space and from their phaseless samples taken on a discrete set with finite sampling density. The determination of a signal in a shift-invariant space, up to a sign, by its magnitude measurements on the whole Euclidean space has been shown in the literature to be equivalent to its nonseparability. In this paper, we introduce an undirected graph associated with the signal in a shift-invariant space and use connectivity of the graph to characterize nonseparability of the signal. Under the local complement property assumption on a shift-invariant space, we find a discrete set with finite sampling density such that nonseparable signals in the shift-invariant space can be reconstructed in a stable way from their phaseless samples taken on that set. In this paper, we also propose a reconstruction algorithm which provides an approximation to the original signal when its noisy phaseless samples are available only. Finally, numerical simulations are performed to demonstrate the robustness of the proposed algorithm to reconstruct box spline signals from their noisy phaseless samples.

     

ON THE WEIGHTED VARIABLE EXPONENT AMALGAM SPACE W（L^p（x）, Lm^q）

In [4], a new family W（L^p（x）, Lm^q） of Wiener amalgam spaces was defined and investigated some properties of these spaces, where local component is a variable exponent Lebesgue space L^p（x） （R） and the global component is a weighted Lebesgue space Lm^q （R）. This present paper is a sequel to our work [4]. In Section 2, we discuss necessary and sufficient conditions for the equality W （L^p（x）, Lm^q） = L^q （R）. Later we give some characterization of Wiener amalgam space W （L^p（x）, Lm^q）.In Section 3 we define the Wiener amalgam space W （FL^p（x）, Lm^q） and investigate some properties of this space, where FL^p（x） is the image of L^p（x） under the Fourier transform. In Section 4, we discuss boundedness of the Hardy- Littlewood maximal operator between some Wiener amalgam spaces.     

Oversampling and reconstruction functions with compact support

Assume that a sequence of samples of a filtered version of a function in a shift-invariant space is given. This paper deals with the existence of a sampling formula involving these samples and having reconstruction functions with compact support. This is done in the light of the generalized sampling theory by using the oversampling technique. A necessary and sufficient condition is given in terms of the Smith canonical form of a polynomial matrix. Finally, we prove that the aforesaid oversampled formulas provide nice approximation schemes with respect to the uniform norm.     

Invariance of a Shift-Invariant Space

A shift-invariant space is a space of functions that is invariant under integer translations. Such spaces are often used as models for spaces of signals and images in mathematical and engineering applications. This paper characterizes those shift-invariant subspaces S that are also invariant under additional (non-integer) translations. For the case of finitely generated spaces, these spaces are characterized in terms of the generators of the space. As a consequence, it is shown that principal shift-invariant spaces with a compactly supported generator cannot be invariant under any non-integer translations.     

Local Sampling and Reconstruction in Shift-Invariant Spaces and Their Applications in Spline Subspaces

The local reconstruction from samples is one of the most desirable properties for many applications in signal processing. Local sampling is practically useful since we need only to consider a signal on a bounded interval and computer can only process finite samples. However, the local sampling and reconstruction problem has not been given as much attention. Most of known results concern global sampling and reconstruction. There are only a few results about local sampling and reconstruction in spline subspaces. In this article, we study local sampling and reconstruction in general shift-invariant spaces and multiple generated shift-invariant spaces with compactly supported generators. Then we give several applications in spline subspaces and multiple generated spline subspaces.     

Multivariate generalized sampling in shift-invariant spaces and its approximation properties

Nowadays the topic of sampling in a shift-invariant space is having a significant impact: it avoids most of the problems associated with classical Shannon's theory. Under appropriate hypotheses, any multivariate function in a shift-invariant space can be recovered from its samples at Zd. However, in many common situations the available data are samples of some convolution operators acting on the function itself: this leads to the problem of multivariate generalized sampling in shift-invariant spaces. This extra information on the functions in the shift-invariant space will allow to sample in an appropriate sub-lattice of Zd. In this paper an L²(Rd) theory involving the frame theory is exhibited. Sampling formulas which are frame expansions for the shift-invariant space are obtained. In the case of overcomplete frame formulas, the search of reconstruction functions with prescribed good properties is allowed. Finally, approximation schemes using these generalized sampling formulas are included.     

Local reconstruction for sampling in shift-invariant spaces

The local reconstruction from samples is one of most desirable properties for many applications in signal processing, but it has not been given as much attention. In this paper, we will consider the local reconstruction problem for signals in a shift-invariant space. In particular, we consider finding sampling sets X such that signals in a shift-invariant space can be locally reconstructed from their samples on X. For a locally finite-dimensional shift-invariant space V we show that signals in V can be locally reconstructed from its samples on any sampling set with sufficiently large density. For a shift-invariant space V(? ₁, ..., ? _N ) generated by finitely many compactly supported functions ? ₁, ..., ? _N , we characterize all periodic nonuniform sampling sets X such that signals in that shift-invariant space V(? ₁, ..., ? _N ) can be locally reconstructed from the samples taken from X. For a refinable shift-invariant space V(?) generated by a compactly supported refinable function ?, we prove that for almost all \((x_0, x_1)\in [0,1]^2\), any signal in V(?) can be locally reconstructed from its samples from \(\{x_0, x_1\}+{\mathbb Z}\) with oversampling rate 2. The proofs of our results on the local sampling and reconstruction in the refinable shift-invariant space V(?) depend heavily on the linear independent shifts of a refinable function on measurable sets with positive Lebesgue measure and the almost ripplet property for a refinable function, which are new and interesting by themselves.