An optimal adaptive wavelet method without coarsening of the iterands

An optimal adaptive wavelet method without coarsening of the iterands

In this paper, an adaptive wavelet method for solving linear operator equations is constructed that is a modification of the method from [Math. Comp, 70 (2001), pp. 27–75] by Cohen, Dahmen and DeVore, in the sense that there is no recurrent coarsening of the iterands. Despite this, it will be shown that the method has optimal computational complexity. Numerical results for a simple model proble...

An Optimal Adaptive Wavelet Method without Coarsening

In this paper, an adaptive wavelet method for solving linear operator equations is constructed that is a modification of the method from [Math. Comp, 70 (2001), pp.27–75] by Cohen, Dahmen and DeVore, in the sense that there is no recurrent coarsening of the approximate solutions. Despite of this, it will be shown that the method has optimal computational complexity. Numerical results in a simpl...

An Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform

In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

An Adaptive Segmentation Method Using Fractal Dimension and Wavelet Transform

In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

An optimal adaptive wavelet method - for strongly elliptic operator equations