Data mining is a collection of analytical techniques to uncover new trends and patterns in massive databases. These data mining techniques stress visualization to thoroughly study the structure of data and to check the validity of the statistical model fit which leads to proactive decision making. Discriminant analysis is one of the data mining tools used to discriminate a single classification...

Digital compression of audio signals is one of the technological fields that has always been tightly linked to detailed knowledge on human perception. Recent advances have extended the sources for bit-rate reduction from a predominantly masking-oriented approach towards additional exploitation of spatial perceptual irrelevancies. New techniques aim at modeling of perceptually-relevant spatial c...

Many current geometric modeling systems use the rational parametric form to represent surfaces. Although the parametric representation is useful for tracing, rendering and surface tting, many operations like surface intersection desire one of the surfaces to be represented implicitly. Moreover, the implicit representation can be used for testing whether a point lies on the surface boundary and ...

A topic of interest in demographic literature is the graduation of the age-specific fertility pattern. A classical graduation technique extensively used by demographers is to fit parametric models that accurately reproduce it. Standard non parametric statistical methodology, as kernels and splines, might alternately be used for this graduation purpose. Support Vector Machines (SVM) is an innova...

Inverse analysis of nonlinear dynamic systems is an important area of research in the field of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensys...

Consider the semi-parametric linear regression model Y = ′X+ , where has an unknown distribution F0. The semi-parametric MLE
̃ of under this set-up is called the generalized semi-parametric MLE(GSMLE).Although the GSML estimation of the linear regression model is statistically appealing, it has never been attempted due to difficulties with obtaining the GSML estimates of and F until recent work...

We analyze the convergence of compressive sensing based sampling techniques for the efficient evaluation of functionals of solutions for a class of high-dimensional, affine-parametric, linear operator equations which depend on possibly infinitely many parameters. The proposed algorithms are based on so-called “non-intrusive” sampling of the high-dimensional parameter space, reminiscent of Monte...

(Received 00 Month 200x; In final form 00 Month 200x) We study experimentally parametric amplification in the continuous regime using a transverse-degenerate type-II Optical Parametric Oscillator operated below threshold. We demonstrate that this device is able to amplify either in the phase insensitive or phase sensitive way first a single mode beam, then a multimode image. Furthermore the tot...

Prediction error techniques for joint identification of parametric transfer functions and noise models from measured data are considered. The time domain and frequency domain prediction error methods are presented and compared and their close connection with corresponding maximum-likelihood method is explored. Conditions are established under which the two techniques are asymptotically equivalent.

Detection of the relationship between two time series is so important in environmental and hydrological studies. Several parametric non-parametric approaches can be applied to detect relationships. These techniques are usually sensitive stationarity assumptions. In this research, a new copula-based method introduced cylostationary with fractional Brownian motion (fBm) errors. The numerical stud...