Wavelet shrinkage and thresholding methods constitute a powerful way to carry out signal denoising, especially when the underlying signal has a sparse wavelet representation. They are computationally fast, and automatically adapt to the smoothness of the signal to be estimated. Nearly minimax properties for simple threshold estimators over a large class of function spaces and for a wide range o...

A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using crossvalidation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled flmctions widely used in the wavelet regressi...

Keypoints have become a fundamental feature in image and video processing. This article presents a wavelet-inspired structure to speed up the keypoint extraction process. The method restricts the extraction process to high variance areas solely, deemed likely to provide keypoints. The variance is calculated at different scales according to a hierarchical structure inspired from the Haar wavelet...

This paper presets a method including a combination of the wavelet transform and fuzzy function approximation (FFA) for high impedance fault (HIF) detection in distribution electricity network. Discrete wavelet transform (DWT) has been used in this paper as a tool for signal analysis. With studying different types of mother signals, detail types and feeder signal, the best case is selected. The...

The main goal of the present work is to decrease the computational burden for optimum design of steel frames with frequency constraints using a new type of neural networks called Wavelet Neural Network. It is contested to train a suitable neural network for frequency approximation work as the analysis program. The combination of wavelet theory and Neural Networks (NN) has lead to the developmen...

We propose a wavelet-based technique for the nonparametric estimation of functions contaminated with noise whose mean and variance are linked via a possibly unknown variance function. Our method, termed the data-driven wavelet-Fisz technique, consists of estimating the variance function via a Nadaraya-Watson estimator, and then performing a wavelet thresholding procedure which uses the estimate...