Classical finite mixture regression is useful for modeling the relationship between scalar predictors and scalar responses arising from subpopulations defined by the di ering associations between those predictors and responses. The classical finite mixture regression model is extended to incorporate functional predictors by taking a wavelet-based approach in which both the functional predictors...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

Classical discrete wavelet packet transforms are sensitive to changes in image orientation and translation. Therefore, it is hardly possible to extract rotation invariant features from images in the transform domain. This paper proposes several algorithms for invariant discrete wavelet decomposition to produce an invariant representation for an image. The procedure can be divided into several s...

This paper presents a novel wavelet-based denoising and compression statistical model for 3D hippocampus shapes. Shapes are encoded using spherical wavelets and the objective is to remove noisy coefficients while keeping significant shape information. To do so, we develop a non-linear wavelet shrinkage model based on a data-driven Bayesian framework. We threshold wavelet coefficients by locally...

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...

This paper reports a new reading for wavelets, which is based on the classical ’De Broglie’ principle. The waveparticle duality principle is adapted to wavelets. Every continuous basic wavelet is associated with a proper probability density, allowing defining the Shannon entropy of a wavelet. Further entropy definitions are considered, such as Jumarie or Renyi entropy of wavelets. We proved tha...

The quantum Fourier transform (QFT), a quantum analog of the classical Fourier transform, has been shown to be a powerful tool in developing quantum algorithms. However, in classical computing there is another class of unitary transforms, the wavelet transforms, which are every bit as useful as the Fourier transform. Wavelet transforms are used to expose the multi-scale structure of a signal an...

The image de-noising naturally corrupted by noise is a classical problem in the field of signal or image processing. Additive random noise can easily be removed using simple threshold methods. De-noising of natural images corrupted by Gaussian noise using wavelet techniques are very effective because of its ability to capture the energy of a signal in few energy transform values. The wavelet de...

introduction: spect is a diagnostic imaging technique the main disadvantage of which is the existence of poisson noise. so far, different methods have been used by scientists to improve spect images. the wavelet transform is a new method for de-noising which is widely used for noise reduction and quality enhancement of images. the purpose of this paper is evaluation of noise reduction in spect ...