%ARMAQS Estimates ARMA parameters via the q-slice algorithm. % [avec, bvec] = armaqs(y,p,q, norder,maxlag,samp_seg,overlap,flag) % y : time-series (vector or matrix) % p : AR order % q : MA order % norder: cumulant order: 3 or 4 [def ault = 3 ] % ma
This monograph primarily concerns the use of power method polynomials in the context of simulating univariate and multivariate nonnormal distributions with specified cumulants and correlation matrices.
Mutual Information (MI) has been extensively used as a similarity measure in image registration and motion esti- mation, and it is particularly robust for 3D multimodal medical image registration. However, MI estimators are known i) to have a high v
Chapter 1. Introduction 1.1 Is Pattern Recognition Important? 1.2 Features, Feature Vectors, and Classifiers 1.3 Supervised, Unsupervised, and Semi-Supervised Learning 1.4 MATLAB Programs 1.5 Outline of the Book Chapter 2. Classifiers Based on Bayes
The performance of ground-based surveillance radars strongly depends on the distribution and spectral characteristics of ground clutter. To design signal processing algorithms that exploit the knowledge of clutter characteristics, a preliminary stat
There is much more information in a stochastic non-Gaussian or deterministic signal than is conveyed by its autocorrelation and power spectrum. Higher-order spectra which are defined in terms of the higher-order moments or cumulants of a signal, con
A method based on fourth-order cumulants (FOC) for direction-of-arrival (DOA) estimation in the presence of sensor gain-phase errors is presented. This method can be applied in the scenario that the signals are non-Gaussian and the noises are Gaussia
