This paper considers the numerical simulation of 2D electromagnetic wave scattering problems and describes the construction of a reduced–order approximation which enables the rapid prediction of the scattering width distribution for a range of incident wave directions. Associated certainty bounds ensure confidence in the results of the computed approximation. Numerical examples are included to ...

We present a new tool, GPA, that can generate key performance measures for very large systems. Based on solving systems of ordinary differential equations (ODEs), this method of performance analysis is far more scalable than stochastic simulation. The GPA tool is the first to produce higher moment analysis from differential equation approximation, which is essential, in many cases, to obtain an...

The parameter estimation of damped sinusoidal signals is an important issue in spectral analysis and many applications. The existing algorithms, such as the KT algorithmm8] and the TLS algorithmm13], are based on the low-rank approximation of prediction matrix, which ignores the Hankel property of the prediction matrix, We will prove in this paper that the performance of parameter estimation ca...

This paper proposes a new parameter estimation algorithm for damped sinusoidal signals. Parameter estimation for damped sinusoidal signals with additive white noise is a problem of signiicant interest in many signal processing applications, like analysis of NMR data and system identiication. The new algorithm estimates the signal parameters using a matrix pencil constructed from the measured da...

We apply to the observed seismicity of Lesser Antilles a short term earthquake precursor which has been recently found by analysis of synthetic seismicity. The latter was generated by a lattice-type “Colliding Cascades” model of interacting elements. Precursor named ROC depicted premonitory increase of the earthquakes correlation range. Here, this precursor is used as a second approximation to ...

We extend XCS with computed prediction by replacing the usual linear prediction used in XCSF with a feedforward multilayer neural network. In XCSF with neural prediction, XCSFNN, classifier exploits a neural network to approximate the payoff surface associated to the target problem while the genetic algorithm adapts both classifier conditions, classifier actions, and the network structure. We c...

One of the most important open problems in computational biology is the prediction of the conformation of a protein based on its amino acid sequence. In this paper, we design approximation algorithms for protein structure prediction in the so-called HP side chain model. The major drawback of the standard HP side chain model is the bipartiteness of the cubic lattice. To eliminate this drawback, ...

New Optimal Observer Design Based on State Prediction for a Class of Non-linear Systems Through Approximation

The computation required for Gaussian process regression with n training examples is about O(n) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.