By locally measuring changes on arterial wall thickness as a function of pressure, the related Young modulus can be evaluated. This physical magnitude has shown to be an important predictive factor for cardiovascular diseases. For evaluating those changes, imaging segmentation or time correlations of ultrasonic echoes, coming from wall interfaces, are usually employed. In this paper, an alterna...

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and s...

This paper develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use latent process distributional transformation to construct with very flexible correlation features that can have any pre-specified marginal distribution, including classical Poisson, generalized negative binomial, binomial structures. pseudo-likelihood implied Y...

In this work, two methods for blind separation of independent sources from the output of a convolutive multi-dimensional system are presented. The proposed methods assume a multiple-input multiple-output (MIMO) system, in which a multi-dimensional auto-regressive (AR) model relates the input and the output signals. The proposed algorithms exploit non-Gaussianity of the independent sources by mo...

We develop a version of the Corrected Akaike Information Criterion (AICC) suitable for selection of an h-step-ahead linear predictor for a weakly stationary time series in discrete time. A motivation for this criterion is provided in terms of a generalized Kullback-Leibler information which is minimized at the optimal h-step predictor, and which is equivalent to the ordinary Kullback-Leibler in...

The prediction of visual field deterioration in patients who are suffering from normal tension glaucoma plays an important role in the management of the disease. The Vector Auto-Regressive (VAR) process appears to be an appropriate way of modelling the multivariate time series data from the visual fields. However, standard parameterisation techniques such as the Yule-Walker equations for buildi...

In this article, a new kind of stationary zero-inflated Pegram’s operator based integer-valued time series process of order p with Poisson marginal or ZIPPAR(p) is constructed for modelling a count time series consisting a large number of zeros compared to standard Poisson time series processes. Estimates of the model parameters are studied using three methods, namely Yule-Walker, conditional l...

We deal with singular multivariate AR systems and the corresponding AR processes. An AR system is called singular if the variance of the white noise innovation is singular. AR processes are the stationary solutions of AR systems. In the singular case AR processes consist of a linearly regular and a linearly singular component. The corresponding Yule-Walker equations and in particular the possib...