We derive a method of approximating a nonstationary process by a time-varying autoregressive model of order (TVAR( )). This method is based on the Dym–Gohberg band matrix extension technique, and for an -variate arbitrary nondegenerate covariance matrix it gives the unique TVAR( ) model. For adaptive applications, this method requires a sample size that is comparable with the TVAR( ) model orde...

Time-varying autoregressive (TVAR) modeling approach for the representation of complex non-stationary process is presented, and applied to the classiication of High Range Resolution (HRR) radar signatures. HRR radar signatures are multi-channel nonstationary complex processes, and features extracted under the stationary assumption often result in unsatisfactory performance. In a TVAR modeling a...

Time Variance (TVAR) and Maximum Time Interval Error (MTIE) are historically the main time-domain quantities for the specification of network synchronization performance in telecommunications standards. Nevertheless, plain computation of the TVAR and MTIE standard estimators proves cumbersome in most cases of practical interest, due to their heavy computational weight. In this paper, TVAR and M...

Real-life decisions are usually made in the state of uncertainty or risk. In this article we present the risk measuring techniques value at risk (VaR) and tail value at risk (TVaR) under uncertainty. Firstly, we introduce the VaR concept of uncertain variable based on uncertainty theory and examine its fundamental properties. Then, the TVaR concept is evolved and some fundamental properties of ...

Das Konstrukt der Transaktions-Variablen in Concurrent Haskell erleichtert die Entwicklung von nebenläufigen Anwendungen, indem es Deadlocks verhindert und erlaubt Programmteile einfach zu komponieren. In einer vorherigen Arbeit haben wir bereits eine Haskell-Version als Alternative zur existierenden lowlevel GHC-Bibliothek implementiert, die das optimistische verify-und-commit Konzept durch da...

A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The timevarying modelling problem is then redu...

Biomedical signals are non-stationary and a research topic of practical interest as the signal has time varying statistics. The problem of time varying is usually circumvented by assuming local stationary over a short time interval, where stationary techniques are applied. However, features extracted from these methods are not always suitable and methods for non-stationary process are needed. T...

In this paper we propose a method for the estimation of time-varying autoregressive processes. The approach is essentially to regularize the heavily underdetermined unconstrained prediction equations with a smoothness priors type side constraint. The implementation of nonhomogenous smoothness properties is straightforward. The method is compared to the usual determistic regression approach (TVA...

In this paper, recent applications of autoregressive (AR) and adaptive autoregressive (TVAR) models to EEG signals for detection of epileptic seizures are addressed. First of all, AR/TVAR models based the complexity measure with the order of AR model and the spectrum estimation with online AR model are introduced and employed to analyze the EEG signals with epileptic seizures. Then, three new a...