This thesis concerns the development, estimation and investigation of a general anisotropic spatial correlation function, within model-based geostatistics, expressed as a Gaussian linear mixed model, and estimated using residual maximum likelihood (REML). The Matern correlation function is attractive because of its parameter which controls smoothness of the spatial process, and which can be estimated from the data. This function is combined with geometric anisotropy, with an extension permitting different distance metrics, forming a flexible spatial covariance model which incorporates as special cases many infinite- range spatial covariance functions in common use. Derivatives of the residual log-likelihood with respect to the four correlation-model parameters are derived, and the REML algorithm coded in Splus for testing and refinement as a precursor to its implementation into the software ASReml, with additional generality of linear mixed models. Suggestions are given regarding initial values for the estimation. A residual likelihood ratio test for anisotropy is also developed and investigated. Application to three soil-based examples reveals that anisotropy does occur in practice, and that this technique is able to fit covariance models previously unavailable or inaccessible. Simulations of isotropic and anisotropic data with and without a nugget effect reveal the following principal points. Inclusion of some closely-spaced locations greatly improves estimation, particularly of the Matern smoothness parameter, and of the nugget variance when present. The presence of geometric anisotropy does not adversely affect parameter estimation. Presence of a nugget effect introduces greater uncertainty into the parameter estimates, most dramatically for the smoothness parameter, and also increases the chance of non-convergence and decreases the power of the test for anisotropy. Estimation is more difficult with very “unsmooth" processes (Matern smoothness parameter 0.1 or 0.25) | non- convergence is more likely and estimates are less precise and/or more biased. However it is still often possible to fit the full model including both anisotropy and nugget effect using REML with as few as 100 observations. Additional simulations involving model misspecification reveal that ignoring anisotropy when it is present can substantially increase the mean squared error of prediction, but overfitting by attempting to model anisotropy when it is absent is less damaging. Further, plug-in estimates of prediction error variance are reasonable estimates of the actual mean squared error of prediction, regardless of the model fitted, weakening the argument requiring Bayesian approaches to properly allow for uncertainty in the parameter estimates when estimating prediction error variance. The most valuable outcome of this research is the implementation of an anisotropic Matern correlation function in ASReml, including the full generality of Gaussian linear mixed models which permits additional fixed and random effects, making publicly available the facility to fit, via REML estimation, a much wider range of variance models than has previously been readily accessible. This greatly increases the probability and ease with which a well-fitting covariance model can be found for a spatial data set, thus contributing to improved geostatistical spatial analysis. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1297562 / Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food and Wine, 2007

La synchronisation temporelle est la première opération effectuée par le démodulateur. Elle permet d'assurer que les échantillons transmis aux processus de démodulation puissent réaliser un taux d'erreurs binaires le plus faible.Dans cette thèse, nous proposons l'étude d'algorithmes innovants de synchronisation temporelle à haute performance.D'abord, nous avons proposé des algorithmes exploitant l'information souple du décodeur en plus du signal reçu afin d'améliorer l'estimation aveugle d'un retard temporel supposé constant sur la durée d'observation.Ensuite, nous avons proposé un algorithme original basé sur la synchronisation par lissage à faible complexité.Cette étape a consisté à proposer une technique opérant dans un contexte hors ligne, permettant l'estimation d'un retard aléatoire variable dans le temps via les boucles d'aller-retour sur plusieurs itération. Les performances d'un tel estimateur dépassent celles des algorithmes traditionnels.Afin d'évaluer la pertinence de tous les estimateurs proposés, pour des retards déterministe et aléatoire, nous avons évalué et comparé leurs performances à des bornes de Cramèr-Rao que nous avons développées pour ce cadre. Enfin, nous avons évalué les algorithmes proposés sur des signaux WBAN. / Time synchronization is the first function performed by the demodulator. It ensures that the samples transmitted to the demodulation processes allow to achieve the lowest bit error rate.In this thesis we propose the study of innovative algorithms for high performance time synchronization.First, we propose algorithms exploiting the soft information from the decoder in addition to the received signal to improve the blind estimation of the time delay. Next, we develop an original algorithm based on low complexity smoothing synchronization techniques. This step consisted in proposing a technique operating in an off-line context, making it possible to estimate a random delay that varies over time on several iterations via Forward- Backward loops. The performance of such estimators exceeds that of traditional algorithms. In order to evaluate the relevance of all the proposed estimators, for deterministic and random delays, we evaluated and compared their performance to Cramer-Rao bounds that we developed within these frameworks. We, finally, evaluated the proposed algorithms on WBAN signals.