Showing papers in "Signal Processing in 2005"

TL;DR: A methodology for model selection based on a penalized contrast is developed, and an adaptive choice of the penalty function for automatically estimating the dimension of the model, i.e., the number of change points, is proposed.

TL;DR: A new blind source separation (BSS) method called Time-Frequency Ratio Of Mixtures (TIFROM) which uses time-frequency information to cancel source signal contributions from a set of linear instantaneous mixtures of these sources.

TL;DR: The Savitzky-Golay smoothing and differentiation filter is extended for even number data to validate the feasibility of such an approach and some corresponding properties are discussed.

TL;DR: In this paper, the authors present methodologies for studying the quasi-stationary composition of both local EEGs/MEGs and the inherent synchrony between the structures in pairs of EEG and MEG channels.

TL;DR: This paper proposes to estimate change-points in the mean of a signal corrupted by an additive Gaussian noise with a method based on a penalized least-squares criterion, and chooses the penalty function such that the resulting estimator minimizes the quadratic risk.

TL;DR: The algorithms developed here extend standard active sensing methodology to dynamically evolving objects and continuous state spaces of high dimension and yield more than a ten fold gain in sensor efficiency when compared to periodic scanning.

TL;DR: The performances of four representative multivariate linear signal processing techniques in the time and frequency domain have been investigated and both methods have been shown to be most suitable for this application to brain neural networks based on the model systems investigated.

TL;DR: Methods of noise reduction have decisive influence on performance of all electrocardiographic (ECG) signal processing systems and this work presents a new class of nonlinear filters without signal distortion.

TL;DR: An improved scheme for estimating just-noticeable distortion (JND) is proposed in this paper and is proved to outperform the DCTune model, with the major contributions of a new formula for luminance adaptation adjustment and the incorporation of block classification for contrast masking.

TL;DR: After a critical review of the least-squares (LS) algorithms, a TLS estimation algorithm is developed based on the method of orthogonal vectors with the advantage of simplicity and reduced bias in the presence of bearing noise and observer position errors.

TL;DR: The present bibliography represents a comprehensive list of references on cyclostationarity and its applications by listing most of the existing references up to the year 2005 and by providing a detailed classification group.

TL;DR: A physical basis for the integration of the well-known Gabor filters with the measurement of color is given and the derived color-texture filter is demonstrated to be accurate in capturing texture statistics and compatible with the measurements of gray-value texture.

TL;DR: A new tensor approach for multidimensional data filtering is presented, which leads to some estimated n-mode filters which can be considered as an extension of the well-known Wiener filter in a particular mode.

TL;DR: The results suggest that professional training in music is able to elicit context-sensitive functional connectivity between multiple cortical regions resulting in different listening strategies to music.

TL;DR: Novel, frequency domain, approaches for TDOA calculation in a reverberant and noisy environment are presented based on the speech quasistationarity property, noise stationarity and on the fact that the speech and the noise are uncorrelated.

TL;DR: In this paper, the steady-state probabilities of the attractors of a Boolean network with perturbation were derived for a class of disordered dynamical systems called probabilistic Boolean networks (PBNs).

TL;DR: The results indicate that the additional computational effort required in shock-graph matching is worthwhile in the presence of large amount variations, in particular those involving the presenceof articulation or rearrangement of parts.

TL;DR: This work derives a new entropic expression with respect to higher-order moments of the modeled data, which yields a novel generalized source entropy matching estimator (G-EME), which is compared against a number of other popular statistical methods.

TL;DR: Simulation results show that particle-based multiple model filters are superior to IMM-based filters in maneuvering ground target tracking and their capability in handling the non-Gaussian problem is superior.

TL;DR: It is shown that the Telecom process can be interpreted as the workload of a network responding to job requests with stable infinite variance intensities and durations and that fractional Brownian motion (fBM) can beinterpreted in the same way but with finite variance intensITIES.

TL;DR: The technique is compared to previous entropy matching methods for a variety of continuous and discrete features sets including: single pixel gray levels: tag sub-image features: and independent component analysis (ICA) features.

TL;DR: This paper compares the performance of this symbolic-dynamics-based method with that of other existing pattern recognition techniques from the perspectives of early detection of small anomalies.

TL;DR: It is proposed that enhanced phase synchrony in the alpha frequency during meditation may improve functional integration and may have implications for performance and mind-body health.

TL;DR: Two general gradient based approaches for minimizing mutual information in a parametric model are presented, and they can be used in any mutual information minimization problem in blind source separation.

TL;DR: A new rigorous derivation of a whole class of multichannel adaptive filtering algorithms in the frequency domain based on a recursive least-squares criterion is presented, which inherently takes the coherence between all input signal channels into account.

TL;DR: A fast and accurate multivariate expectation-maximization (EM) algorithm for maximum likelihood estimation of the scalar, vector, and matrix parameters of the MNIG distribution is proposed.

TL;DR: This paper compares the performance of power features and phase locking values computed from broadband and narrowband filtered EEG signals for discriminating 3 mental tasks in the framework of a BCI and shows that band power features were more discriminative when they were computed in the narrower frequency band 8-12 Hz.

TL;DR: The paper considers a minimum-entropy parametric estimator that minimizes an estimate of the entropy of the distribution of the residuals of a regression problem and considers a direct approach that does not involve any preliminary √n-consistent estimator.

TL;DR: A simple recursive solution to passive tracking of maneuvering targets using time difference of arrival (TDOA) measurements is presented, and an iterative Gauss-Newton algorithm is developed for stationary target localization based on a constrained weighted least-squares criterion.

TL;DR: This paper introduces a new approach to BSS that can be applied to nonorthogonal signals, where the orthogonality requirement is replaced by a partial orthog onality and a nonnegativity constraint which are well-suited for many real-world signals.