Wavelet transform is a one of the most powerful concept used in image processing. Wavelet transform can divide a given function into different scale components and can find out frequency information without losing temporal information. Wavelet Transform is more suitable technique as compared to fourier transform because it is not possible with fourier transform to observe varying frequencies wi...

We show that the ‘centered’ Battle-Lemarié scaling function and wavelet of order n converge in L(2 ≤ q ≤ ∞), uniformly in particular, to the Shannon scaling function and wavelet as n tends to the infinity. AMS 2000 Subject Classification: 41A60, 42A20, 42C40.

The purpose of this paper is to represent the integral Hankel transform as a series. If one uses Bspline wavelet this series is a linear combination of the hypergeometrical functions. Numerical evaluation of the test function with known analytical Hankel transform of the null kind illustrates the proposed results. Key-Words: the Hankel transform, hypergeometrical function, B-spline wavelet.

In our previous work, we showed empirically that independent (Haar) wavelet models were parsimonious, computationally efficient and accurate in modeling heterogeneous network traffic measured by both autocovariance functions and buffer loss rate. We also proved analytically that such models were capable of capturing any decay rate of auto-covariance functions at large lags. In this work, we foc...

Denoising of images corrupted by Gaussian noise using wavelet transform is of great concern in the past two decades. In wavelet denoising method, detail wavelet coefficients of noisy image are thresholded using a specific thresholding function by comparing to a specific threshold value, and then applying inverse wavelet transform, results in denoised image. Recently, an effective image denoisin...

We consider Bayesian approach to wavelet decomposition. We show how prior knowledge about a function's regularity can be incorporated into a prior model for its wavelet coeecients by establishing a relationship between the hyperparameters of the proposed model and the parameters of those Besov spaces within which realizations from the prior will fall. Such a relation may be seen as giving insig...

In this paper we study the usage of wavelet in inverse problem multiplayer soil.We put forward a function and prove it is a wavelet function. Then we do theory analysis in detail about the application in computing soil parameters. At the same time, we do numerical experiments with two and three levels soil structure. The results indicate the valid of method

AbstmctIn our previous work, we showed empirically that independent wavelet models were parsimonious, computationally efficient, and accurate in modeling heterogeneous network traffic measured by both auto-covariance functions and buffer loss rate. In this work, we focus on auto-covariance functions, to establish a theory of independent wavelet models as unified models for heterogeneous network...

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-...

For various types of noise (exponential, normal mixture, compactly supported, ...) wavelet tresholding methods are studied. Problems linked to the existence of optimal thresholds are tackled, and minimaxity properties of the methods also analyzed. A coefficient dependent method for choosing thresholds is also briefly presented. 1. Introduction. A common underlying assumption in non-parametric c...