非均匀子波空间采样定理

本文从非均匀采样出发，详细研究了非均匀采样的Ｗａｌｔｅｒ子波空间采样定理的存在条件，给出了Ｓｈａｎｎｏｎ采样定理的一类非均匀采样形式，对紧支尺度函数张成的子波空间，给出了一类非均匀采样方法，使得对该空间中任意紧支信号，非均匀采样成为可能，文中还指出了非均匀紧支子汉空间采样的优点，数值实例验证了理论的正确性。     

关于小波空间的紧支集采样函数

余越、柯有安讨论了紧支集小波于空间内具紧支集的采样函数。本文证明了除Haar小波外没有任何紧支集正交小波空间具有紧支集采样函数。     

一种基于FIR滤波器的非均匀小波周期采样方法

针对Shannon采样定理只能处理带限信号和要求采样率不低于Nyquist率的缺陷,研究了小波空间中的一种非均匀周期采样理论,给出了定理成立的条件及其突破Nyquist率限制的理论依据,将采样理论扩展到了非带限信号领域。对于紧支尺度函数张成的子波空间中的任意信号,可以利用非均匀周期采样所得的样本以及正交镜像滤波器理论求出其小波系数的估计值,进而得到信号的重建表达式。该方法在信号重建的过程中用到的全是有限冲击响应滤波器,避免了无限冲击响应滤波器的出现,降低了实际物理实现的难度。计算机仿真结果表明该方法是切实有效的,信号重建的相对误差小于1%。     

M带Walter子波抽样定理

本文把2带的Walter子波抽样定理扩展到M带子波空间,构造出一类M带紧支撑正交插值尺度函数,并在一定范围内将其参数化。举例构造并证明了一个2阶3带插值尺度函数不仅是正交的,而且也是连续的。具有这样性质的Walter抽样定理(1992)在对多分辨空间的信号进行D/A转换时,除了计算机的有限字长误差外,没有任何截断误差。     

小波空间中采样理论研究

本文研究了小波理论在非带限信号采样中的应用，分析了Walter子波空间采样定理及其突破Nyquist率限制的理论依据；介绍了在信号恢复的过程中可以避免无限冲激响应滤波器出现的周期性非均匀采样及导数采样理论，并以紧支信号为例分别进行了仿真验证。     

一种基于小波的导数采样算法

研究了小波空间中的一种导数采样理论,给出了定理成立的条件及其突破Nyquist率限制的理论依据,将采样理论扩展到了非带限信号领域。对于紧支尺度函数张成的子波空间中的任意信号,可以利用对信号及其导数采样所得的样本求出其小波系数的估计值,进而得到信号的重建表达式。在信号重建的过程中用到的全是有限冲击响应滤波器,降低了实际物理实现的难度。计算机仿真结果表明该方法是切实有效的,信号重建的相对误差小于0．11％。     

M带Walter子波抽样定理

本文把２带的Ｗａｌｔｅｒ子波抽样定理扩展到Ｍ带子波空间，构造出一类Ｍ带紧支撑正交插值尺度函数，并在一定范围内将其参数化，举例构造并证明了一个２阶３带插值尺度函数不仅是正交的，而且也是连续的，具有这样性质的Ｗａｌｔｅｒ抽样定理（１９９２）在对多分辨空间的信号进行Ｄ／Ａ转换时，除了计算机的有限字长误差外，没有任何截断误差。     

多小波子空间采样定理

该文基于再生核Hilbert空间理论,把小波子空间的Walter采样定理推广到多小波子空间,建立了多小波子空间的均匀采样定理,利用Zak变换给出了由尺度函数构造重构函数的公式。进一步针对采样点不均匀的情况,建立了多小波子空间的不规则采样定理。最后给出数值算例。     

用于信号逼近的最佳正交子波选择方法

离散子波变换将离散时间信号分解为一系列分辨率下的离散逼近和离散细节，紧支的正交规范子波与完全重建正交镜象滤波器组相对应。本文提出一种用于信号最佳逼近的正交子波选择方法，即选择满足一定条件的滤波器的方法。通过对滤波器参数化，可以将带约束的最佳化问题转化为无约束最优化问题，通过对参数在一定范围内的搜索，得到最优解，文中给出了计算机模拟的结果。     

带通信号的数字正交采样定理及优化设计

在数字正交采样中,带通信号存在频谱迁移问题,虽然满足带通信号的采样定理,仍可能产生频谱混叠。该文证明了带通信号的数字正交采样定理,并讨论了这种采样系统的优化设计。     

Three-band compactly supported orthogonal interpolating scalingfunction

The authors construct a three-band compactly supported orthogonal scaling function with an integer-shifted sampling property. Waiter's sampling theorem for wavelet subspaces corresponding to this scaling function has a compactly supported interplant. Therefore, the signals in multiresolution spaces can be reconstructed quickly and accurately without any truncation errors     

具有抽样性质的双正交子波的逼近性能及其子波抽样的计算

本文主要讨论在具有抽样性质的双正交子波基下Mallat算法的逼近性能及其子波系数的计算。当尺度较小以及尺度较大时,我们依次得到了Mallat算法逼近误差的渐近公式和比较精确的定量估计。结果表明:在这样的子波基下,直接用均匀抽样点代替子波抽样点而无需进行预先滤波,其逼近速度可以达到K阶,这里K是综合尺度函数的阶。模拟实验也显示出它的优点。     

正交插值多子波理论和构造

该文根据多子波采样定理,构造了正交插值多子波,从而可直接用信号的采样值作为初始值,使离散多子波变换的预滤波算子简化为单位算子。     

Interpolating multiwavelet bases and the sampling theorem

This paper considers the classical sampling theorem in multiresolution spaces with scaling functions as interpolants. As discussed by Xia and Zhang (1993), for an orthogonal scaling function to support such a sampling theorem, the scaling function must be cardinal (interpolating). They also showed that the only orthogonal scaling function that is both cardinal and of compact support is the Haar function, which is not continuous. This paper addresses the same question, but in the multiwavelet context, where the situation is different. This paper presents the construction of compactly supported orthogonal multiscaling functions that are continuously differentiable and cardinal. The scaling functions thereby support a Shannon-like sampling theorem. Such wavelet bases are appealing because the initialization of the discrete wavelet transform (prefiltering) is the identity operator     

Generalized sampling theorems in multiresolution subspaces

It is well known that under very mild conditions on the scaling function, multiresolution subspaces are reproducing kernel Hilbert spaces (RKHSs). This allows for the development of a sampling theory. In this paper, we extend the existing sampling theory for wavelet subspaces in several directions. We consider periodically nonuniform sampling, sampling of a function and its derivatives, oversampling, multiband sampling, and reconstruction from local averages. All these problems are treated in a unified way using the perfect reconstruction (PR) filter bank theory. We give conditions for stable reconstructions in each of these cases. Sampling theorems developed in the past do not allow the scaling function and the synthesizing function to be both compactly supported, except in trivial cases. This restriction no longer applies for the generalizations we study here, due to the existence of FIR PR banks. In fact, with nonuniform sampling, oversampling, and reconstruction from local averages, we can guarantee compactly supported synthesizing functions. Moreover, local averaging schemes have additional nice properties (robustness to the input noise and compression capabilities). We also show that some of the proposed methods can be used for efficient computation of inner products in multiresolution analysis. After this, we extend the sampling theory to random processes. We require autocorrelation functions to belong to some subspace related to wavelet subspaces. It turns out that we cannot recover random processes themselves (unless they are bandlimited) but only their power spectral density functions. We consider both uniform and nonuniform sampling     

Wavelets with convolution-type orthogonality conditions

Wavelets with free parameters are constructed using a convolution-type orthogonality condition. First, finer and coarser scaling function spaces are introduced with the help of a two-scale relation for scaling functions. An inner product and a norm having convolution parameters are defined in the finer scaling function space, which becomes a Hilbert space as a result. The finer scaling function space can be decomposed into the coarser one and its orthogonal complement. A wavelet function is constructed as a mother function whose shifted functions form an orthonormal basis in the complement space. Such wavelet functions contain the Daubechies' compactly supported wavelets as a special case. In some restricted cases, several symmetric and almost compactly supported wavelets are constructed analytically by tuning free convolution parameters contained in the wavelet functions