Network Calculus theory aims at evaluating worst-case performances in communication networks. It provides methods to analyze models where the traffic and the services are constrained by some minimum and/or maximum envelopes (service/arrival curves). While new applications come forward, a challenging and inescapable issue remains open: achieving tight analyzes of networks with aggregate multiplexing. The theory offers efficient methods to bound maximum end-to-end delays or local backlogs. However as shown recently, those bounds can be arbitrarily far from the exact worst-case values, even in seemingly simple feed-forward networks (two flows and two servers), under blind multiplexing (i.e. no information about the scheduling policies, except FIFO per flow). For now, only a network with three flows and three servers, as well as a tandem network called sink tree, have been analyzed tightly. We describe the first algorithm which computes the maximum end-to-end delay for a given flow, as well as the maximum backlog at a server, for any feed-forward network under blind multiplexing, with concave arrival curves and convex service curves. Its computational complexity may look expensive (possibly super-exponential), but we show that the problem is intrinsically difficult (NP-hard). Fortunately we show that in some cases, like tandem networks with cross-traffic interfering along intervals of servers, the complexity becomes polynomial. We also compare ourselves to the previous approaches and discuss the problems left open.

We focus on motion estimation using a block matching approach and suggest using a minimum-entropy criterion. Many entropy-based estimation procedures exist, such as plug-in estimators based on Parzen windowing. We consider here an alternative that is applicable to data of any dimension and that circumvents the critical issues raised by kernel-based methods. To the best of our knowledge, this criterion has not yet been considered for image processing problems. The inherent robustness property of entropy is expected to provide a robust and efficient estimation of the motion vector of a block of a video sequence. In particular, the minimum-entropy estimator should be robust to occlusions and variations of luminance, for which standard approaches like SSD usually meet their limitations.

The paper is a continuation of earlier work (Pronzato and Thierry, Proc. 20th Int. Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, p.169-80, 2001; Proc. ICASSP, 2001): we estimate parameters in a regression model, linear or not, by minimizing (an estimate of) the entropy of the symmetrized residuals, obtained by a kernel estimation of their distribution. The objective is to obtain efficiency in the absence of knowledge of the density, f, of the observation errors, which is called adaptive estimation (Stein, C., 1956; Stone, C.J., 1975; Bickel, P.J., 1982;. Manski, C.F, 1984). Connections and differences with previous work are indicated. Numerical results illustrate that asymptotic efficiency is not necessarily in conflict with robustness.

We consider the situation where the parameters /spl theta/ of a linear regression model have to be estimated from observations corrupted by an additive noise with unknown distribution f. Since maximum likelihood estimation cannot be used, we estimate /spl theta/ by minimizing the entropy of a kernel estimate of f, constructed from the residuals. An example of parameter estimation in the presence of interference with random binary signals is presented.

Prediction by kriging does not rely on any specific model structure, and is thus much more flexible than approaches based on parametric behavioural models. Since accurate predictions are obtained for extremely short training sequences, it generally performs better than prediction methods using parametric models. Application to nonlinear system inversion is considered.

The paper deals with the analytical evaluation of the asymptotic detection and false-alarm probabilities of multicycle and single-cycle detectors operating in additive white Gaussian noise with unknown and nonrandom spectral level, which are based on the cyclostationarity properties of the signal to be intercepted. The receiver operating characteristics are derived by using the asymptotic complex normality and the covariance expression of the sample average estimator of the cyclic covariance in an observed discrete-time series. A numerical example for interception of a binary phase-shift keying signal is considered.

The miniaturization of GSM handsets creates nonlinear acoustical echoes between the microphone and the loudspeaker when the signal level is high. Nonlinear adaptive filtering can tackle this problem but the computational complexity has to be reduced by restricting the number of coefficients introduced by the nonlinear models. This paper compares the performance of different nonlinear models. In a first training stage we use the OLS (orthogonal least squares) identification method to find models using the fewest coefficients along with a good fitting accuracy. In a second filtering stage these parsimonious models are used to adaptively filter the GSM signals.

This article presents a new approach to blind equalization of an FIR channel. It is based on a bounded-error assumption and takes into account the fact that the input signal is in a finite alphabet. We show that even in the noisy case, identifiability can be guaranteed in finite time, provided that the support of the noise density is suitably bounded.

The purpose of this paper is to evaluate theoretically in terms of receiver operating characteristics (ROC) the performance of a statistical test (based on the second order statistic) for the presence of cyclostationarity in a noisy observation. In order to obtain false alarm and detection probabilities, we consider the following specific situation: we choose a cycle frequency contained in a binary-phase-shift-keyed (BPSK) signal of interest (SOI) (BPSK signal is considered for its increasing attention in digital communications) and we test the presence of cyclostationarity of the SOI in a noisy observation. Two examples are considered, the SOI is buried either in a stationary Gaussian noise or in an another BPSK signal.

This paper deals with detection of weak cyclostationary signals embedded in colored Gaussian noise. We consider the normalized correlation function of the noise to be known and the noise power to be unknown. We propose a temporal structure of the single cycle detector which includes a prewhitening filter. We compare the performance of this detector to the classical radiometer and the modified radiometer. The performance is quantified in terms of receiver operating characteristics for two different noise power spectral densities. We compute the theoretical deflection, this measure gives a means to choose the best cyclic frequency used in the single cycle detector. We conclude that cyclic methods outperform radiometric methods when the noise and the signal power spectral densities strongly overlap and for a unknown noise power.