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On sampling theorem, wavelets, and wavelet transforms

机译:关于采样定理,小波和小波变换

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摘要

The classical Shannon sampling theorem has resulted in many applications and generalizations. From a multiresolution point of view, it provides the sine scaling function. In this case, for a band-limited signal, its wavelet series transform (WST) coefficients below a certain resolution level can be exactly obtained from the samples with a sampling rate higher than the Nyquist rate. The authors study the properties of cardinal orthogonal scaling functions (COSF), which provide the standard sampling theorem in multiresolution spaces with scaling functions as interpolants. They show that COSF with compact support have and only have one possibility which is the Haar pulse. They present a family of COSF with exponential decay, which are generalizations of the Haar function. With these COSF, an application is the computation of WST coefficients of a signal by the Mallat (1989) algorithm. They present some numerical comparisons for different scaling functions to illustrate the advantage of COSF. For signals which are not in multiresolution spaces, they estimate the aliasing error in the sampling theorem by using uniform samples.
机译:经典的Shannon采样定理导致了许多应用和推广。从多分辨率的角度来看,它提供了正弦缩放功能。在这种情况下,对于带宽受限的信号,可以从采样率高于奈奎斯特速率的样本中准确获取低于某个分辨率级别的小波序列变换(WST)系数。作者研究了基数正交缩放函数(COSF)的属性,该函数为缩放函数作为内插值提供了多分辨率空间中的标准采样定理。他们表明,在紧凑支撑下的COSF仅有一种可能性是Haar脉冲。他们提出了具有指数衰减的COSF族,这是Haar函数的推广。有了这些COSF,应用程序就是用Mallat(1989)算法计算信号的WST系数。他们针对不同的缩放函数提供了一些数值比较,以说明COSF的优势。对于不在多分辨率空间中的信号,它们通过使用统一样本来估计采样定理中的混叠误差。

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