Reliable short-term prediction of ship motions improves the safety in ship motion related special operations, where autoregressive (AR) model is extensively used as its advantages like convenient in real-time identification and high adaptive nature. Order selection is the most critical and difficult part in the identification of AR model. Conventionally, the model order is determined by applyin...

Autoregressive (AR) models with finite variance errors have been well studied. This paper is concerned with AR models with heavy-tailed errors, which is useful in various scientific research areas. Statistical estimation for AR models with infinite variance errors is very different from those for AR models with finite variance errors. In this paper, we consider a weighted quantile regression fo...

Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase an...

In this paper, a model-based approach to pel-recursive motion estimation is presented. The derivation of the algorithm is similar to the Wiener-based pel-recursive motion estimation algorithm. However, the proposed algorithm utilizes the spatiotemporal correlations in an image sequence by considering an autoregressive model for the motion compensated frames. Therefore, depending on the support ...

© 2013 Mustapha Djeddou et al. 757 This paper deals with the estimation of the time of arrival (TOA) of ultra-wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses ...

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approa...

For a zero-mean Gaussian process, the covariances of zero crossings can be expressed as the sum of quadrivariate normal orthant probabilities. In this paper, we demonstrate the evaluation of zero crossing covariances using one-dimensional integrals. Furthermore, we provide asymptotics of zero crossing covariances for large time lags and derive bounds and approximations. Based on these results, ...

Discrete-time signal processing (DSP) tools have been used to analyze numerous optical filter configurations in order to optimize their linear response. In this paper, we propose a DSP approach to design nonlinear optical devices by treating the desired nonlinear response in the weak perturbation limit as a discrete-time filter. Optimized discrete-time filters can be designed and then mapped on...

This contribution addresses the basic properties of the Bayesian Autoregressive Changepoint Detector (BACD) and its use for speech segmentation. The principle of the BACD consists in the identification of changes in both the voice excitation and vocal tract parameters. Thus different piecewise autoregressive (AR) models with changes in the order and coefficients are used to describe speech unit...

Nonlinear autoregressive processes constitute a potentially important class of nonlinear signal models for a wide range of signal processing applications involving both natural and man-made phenomena. A state space characterization is used to develop algorithms for modeling and estimating signals as nonlinear autoregressive processes from noise-corrupted measurements. Special attention is given...