This paper presents a new wavelet-network-based technique for 1D and 2D functions’ approximation. Classical training algorithms start with a predetermined network structure which can be either insufficient or overcomplicated. Furthermore, the resolutions of wavelet networks training problems by gradient are characterized by their noticed inability to escape of local optima. The main feature of ...

This paper presents a statistical learning algorithm based on Support Vector Machines (SVMs) for the classification of Malayalam Consonant – Vowel (CV) speech unit in noisy environments. We extend SVM for multiclass classification using Decision Directed Acyclic Graph Support Vector Machine (DDAGSVM) algorithm. For classification, acoustical features are extracted using Wavelet Transform (WT) b...

Wavelet shrinking algorithms for image de-noising replace wavelet coeecients with absolute values below a certain threshold by zero and keep or shrink the other coeecients. This is basically a local procedure, since wavelet coeecients characterize the local regularity of a function. In spite of a careful choice of the threshold, this method does not take into account the geometrical structure o...

Image fusion deals with the integration of multi modal images from various sensors, with visual and infrared, to MMW images, aiming at achieving improved image quality to better support improved image classification, monitoring, surveillance and etc. The main goal of this paper is to introduce a new approach to fuse visual image and infrared images by complex wavelet taking depth of focus into ...

The soft thresholding function has been used in wavelet packet structure to efficiently remove background noises. However, it suffers from both serious residual noise and speech distortion. The classical method that estimates the threshold of wavelet coefficients assumes the corrupted noise to be white Gaussian noise. In fact, the noise is generally not white in practical environments. This pap...

due to the remarkable advantages of wavelet transformation, this technique is now very common in gravity analysis. in this research the green’s function occurring in the poisson potential field theory is used to construct non-orthogonal, non-compact, continuous wavelets. this kind of wavelet is directly corresponded to upward continuation procedure. simple geometrical forward models such as sph...

The concepts of basis and frame are studied in the classical literature of functional analysis, Fourier analysis, and wavelet theory in a wide range. In this paper, we consider an operator-theoretic approach to discrete frame theory on a separable Hilbert space. For this purpose, we define a special type of frames and bases, called wavelet-type frames and wavelet-type bases, obtained by acting ...

In this paper, we propose a 3D geometry compression technique for densely sampled surface meshes. Based on a 3D multiresolution analysis (performed by a 3D Discrete Wavelet Transform for semi-regular meshes), this scheme includes a model-based bit allocation process across the wavelet subbands and an efficient surface adapted weighted criterion for 3D wavelet coefficient coordinates. This permi...

Recently, several extensions of classical Shannon sampling theory to wavelet subspaces have been reported. This paper is devoted to uniform and periodic nonuniform oversampling in wavelet subspaces. Specifically, we provide a stability analysis and we introduce a technique for calculating the condition number of wavelet subspace sampling operators. It is shown that oversampling results in impro...

This note is devoted to an analysis of the so-called peeling algorithm in wavelet denoising. Assuming that the wavelet coefficients of the signal can be modeled by generalized Gaussian random variables, we compute a critical thresholding constant for the algorithm, which depends on the shape parameter of the generalized Gaussian distribution. We also quantify the optimal number of steps which h...