On the exact evaluation of integrals of wavelets

Wavelet expansions are a powerful tool for constructing adaptive approximations. For this reason, they find applications in a variety of fields, from signal processing to approximation theory. Wavelets are usually derived from refinable functions, which are the solution of a recursive functional equation called the refinement equation. The analytical expression of refinable functions is known in only a few cases, so if we need to evaluate refinable functions we can make use only of the refinement equation. This is also true for the evaluation of their derivatives and integrals. In this paper, we detail a procedure for computing integrals of wavelet products exactly, up to machine precision. The efficient and accurate evaluation of these integrals is particularly required for the computation of the connection coefficients in the wavelet Galerkin method. We show the effectiveness of the procedure by evaluating the integrals of pseudo-splines

Shear stress fluctuations in the granular liquid and solid phases

We report on experimentally observed shear stress fluctuations in both granular solid and fluid states, showing that they are non-Gaussian at low shear rates, reflecting the predominance of correlated structures (force chains) in the solidlike phase, which also exhibit finite rigidity to shear. Peaks in the rigidity and the stress distribution's skewness indicate that a change to the force-bearing mechanism occurs at the transition to fluid behaviour, which, it is shown, can be predicted from the behaviour of the stress at lower shear rates. In the fluid state stress is Gaussian distributed, suggesting that the central limit theorem holds. The fibre bundle model with random load sharing effectively reproduces the stress distribution at the yield point and also exhibits the exponential stress distribution anticipated from extant work on stress propagation in granular materials.Comment: 11 pages, 3 figures, latex. Replacement adds journal reference and addresses referee comment

Statistical properties of acoustic emission signals from metal cutting processes

Acoustic Emission (AE) data from single point turning machining are analysed in this paper in order to gain a greater insight of the signal statistical properties for Tool Condition Monitoring (TCM) applications. A statistical analysis of the time series data amplitude and root mean square (RMS) value at various tool wear levels are performed, �nding that ageing features can be revealed in all cases from the observed experimental histograms. In particular, AE data amplitudes are shown to be distributed with a power-law behaviour above a cross-over value. An analytic model for the RMS values probability density function (pdf) is obtained resorting to the Jaynes' maximum entropy principle (MEp); novel technique of constraining the modelling function under few fractional moments, instead of a greater amount of ordinary moments, leads to well-tailored functions for experimental histograms.Comment: 16 pages, 7 figure

A comparison of iterative thresholding algorithms for the MEG inverse problem

The magnetoencephalography (MEG) aims at reconstructing the unknown electric activity in the brain from the measurements of the magnetic field in the outer space. The MEG inverse problem is ill-posed and/or ill-conditioned thus further constraints are needed to guarantee a unique and stable solution. Assuming that neural sources are confined in small regions of the brain, the sparsity assumption can be used as a regularization term. Thus, the solution of the inverse problem can be approximated by iterative thresholding algorithms. In order to identify an efficient inversion method for the MEG problem, we compare the performance -efficiency, accuracy, computational load- of some thresholding algorithms when localizing a single neural source. The numerical tests will give some suggestions on the construction of an efficient algorithm to be used in real life applications

Computation of quadrature rules for integration with respect to refinable functions on assigned nodes

Integrals involving refinable functions are of interest in several applications ranging from discretization of PDEs to wavelet analysis. We present a procedure to construct quadrature rules with assigned nodes for these integrals. The process requires in input the refinement mask coefficients and the sequence of nodes only. The corresponding weights are computed by an iterative procedure that does not involve the solution of linear systems. The proposed approach is deeply based on the strong connection between balanced measures and integrals of refinable functions

Approximation of the Riesz–Caputo derivative by cubic splines

Differential problems with the Riesz derivative in space are widely used to model anomalous diffusion. Although the Riesz–Caputo derivative is more suitable for modeling real phenomena, there are few examples in literature where numerical methods are used to solve such differential problems. In this paper, we propose to approximate the Riesz–Caputo derivative of a given function with a cubic spline. As far as we are aware, this is the first time that cubic splines have been used in the context of the Riesz–Caputo derivative. To show the effectiveness of the proposed numerical method, we present numerical tests in which we compare the analytical solution of several boundary differential problems which have the Riesz–Caputo derivative in space with the numerical solution we obtain by a spline collocation method. The numerical results show that the proposed method is efficient and accurate

Totally positive functions through non stationary subdivision schemes

In this paper a new class of nonstationary subdivision schemes is proposed to construct functions having all the main properties of B-splines, namely compact support, central symmetry and total positivity. We show that the constructed nonstationary subdivision schemes are asympotically equivalent to the stationary subdivision scheme associated with a B-spline of suitable degree, but the resulting limit function has smaller support than the B-spline although keeping its regularity

Some recent results on a new class of bivariate refinable functions

In this paper a new class of bivariate refinable functions is presented and some of its properties are investigated. The new class is constructed by convolving a tensor product refinable function of special type with χ[0,1], the characteristic function of the interval [0, 1]. As in the case of box splines, the convolution product here used is the directional convolution product