The estimation of age at time of death is often an important step in the identification of human remains. If this age can be accurately estimated, it will significantly narrow the field of possible identities that will have to be compared to the remains in order to establish a positive identification. Some of the more accurate methods of age estimation, in the juvenile and younger adult, have been based on the assessment of the degree of dental development as it relates to chronological age. The purpose of this current study was to test the applicability of one such system, the Demirjian system, to a Sydney sample population, and to develop and test age prediction models using a large sample of Sydney children (1624 girls, 1637 boys). The use of the Demirjian standards resulted in consistent overestimates of chronological age in children under the age of 14 years by as much as a mean of 0.97 years, and underestimates of chronological age in children over 14 years by as much as a mean of 2.18 years in 16 year-old females. Of the alternative predictive models derived from the Sydney sample, those that provided the most accurate age estimates are applicable for the age ranges 2-14 years, with a coefficient of determination value of R-square=0.94 and a 95% confidence interval of �1.8 years. The Sydney based standards provided significantly different and more accurate estimates of age for that sample when compared to the published standards of Demirjian.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 112-121).

L'utilisation d'antennes multiple à la transmission et réception dans les communications mobiles, offre d'importantes perspectives pour accroître le débit et améliorer la qualité du lien. Cela peut être effectué en utilisant un plus important multiplexage spatial et en exploitant la diversité contenue dans le canal, tout en gardant une complexité acceptable. L'état de connaissance sur le canal a un impact important sur la conception de la chaîne de transmission. En effet, l'information sur l'état du canal (CSI) au transmetteur (Tx) a un impact sur le codage alors que la qualité du CSI au récepteur (Rx) a principalement un impact sur la détection et l'estimation du canal. Dans la première partie de cette thèse nous avons considéré le cas d'absence de CSI au Tx et un parfait CSI au Rx. On propose la dispersion spatio-temporelle (STS), qui est un schéma de codage spatio-temporel basé sur le précodage linéaire en utilisant un filtre multi-entrée multi sortie (MIMO). Le STS effectue un multiplexage de flux maximal, qui est optimisé pour exploiter une diversité maximale, atteindre un bon gain codage et conserver la capacité ergodique. Un autre avantage du STS est qu'il permet d'utiliser une variété de récepteurs de complexité réduite. Le Stripping MIMO avec égalisation a retour de décision, est un récepteur non-itératif qui détecte les flux d'une manière successive. Les performances du Stripping sont données en terme du compromis entre diversité et multiplexage. Un autre récepteur non-itératif est l'égaliseur à retour de décision appliqué au cas MIMO. Il permet la détection des symboles des différents flux d'une manière conjointe mais successivement dans le temps. Le troisième récepteur proposé est itératif. Il profite de la présence d'un codage canal binaire et itère entre l'égaliseur linéaire et le décodeur canal binaire. Des simulations sont présentées pour évaluer les performances. Dans la seconde partie on considère des canaux avec un CSI partiel au Tx et parfait au Rx. La connaissance partiale dans ces cas peut être le résultat de la décomposition du canal en paramètres lents et rapides. Elle peut aussi être le résultat d'une réciprocité du canal physique entre la liaison montante et descendante. A ces différents cas on présente des modèles de canaux adaptés et on étudie la capacité ergodique. Dans la dernière partie on traite du cas d'absence de CSI aux Tx et Rx. La capacité de deux modèles de canaux, évanescent par bloc et sélectif en temps, sont étudiées. A cause de l'absence de CSI au Rx le canal doit être estimé dans les systèmes utilisés en pratique. On propose des estimateurs semi-aveugle qui combinent l'information de la séquence d'apprentissage et celle de la partie aveugle. Les conditions d'identifiabilité sont obtenues et des simulations sont proposées pour évaluer les performances.

No description available.

Many ecological populations can be interpreted as response surfaces; the spatial patterns of the population vary in response to changes in the spatial patterns of environmental explanatory variables. Collection of a probability sample from the population provides unbiased estimates of the population parameters using design based estimation. When information is available for the environmental explanatory variables, model based procedures are available that provide more precise estimates of population parameters in some cases. In practice, not all of these environmental explanatory variables will be known. When the spatial coordinates of the population units are available, a spatial model can be used as a surrogate for the unknown, spatially patterned explanatory variables. Design based and model based procedures will be compared for estimating parameters of the population of Acid Neutralizing Capacity (ANC) of lakes in the Adirondack Mountains in New York. Results from the analysis of this population will be used to elucidate some general principles for model based estimation of parameters of spatial populations. Results indicate that using model based estimates of population parameters provide more precise estimates than design based estimates in some cases. In addition, including spatial information as a surrogate for spatially patterned missing covariates improves the precision of the estimates in some cases, the degree to which depends upon the model chosen to represent the spatial pattern. When the probability sample is selected from the spatial population is a stratified sample, differences in stratum variances need to be accounted for when residual spatial covariance estimation is desired for the entire population. This can be accomplished by scaling residuals by their estimated stratum standard deviation functions, and calculating the residual covariance using these scaled residuals. Results here demonstrate that the form of scaling influences the estimated strength of the residual correlation and the estimated correlation range. / Graduation date: 1997

Electricity markets around the world have gone through, or are currently in a deregulation phase. As a result, power companies that formerly enjoyed a monopoly are now facing risks. In order to cover (hedge) these risks, futures markets have emerged, in parallel with the spot price markets. Then, markets of more complex derived products have appeared to better hedge the risk exposures of power suppliers and consumers. An Affine Jump Diffusion (AJD) framework is presented here to coherently model the dynamics of the spot price of electricity and all the futures contracts. The non-storability of electricity makes it indeed impossible to use it in hedging strategies. Futures contracts, however, are standard financial contracts that can be stored and used in hedging strategies. We thus propose to consider the set of futures contracts as the primary commodities to be modelled and jointly estimate the parameters of the spot and futures prices based on their historical time series. The estimation is done by Maximum Likelihood, using a Kalman Filter recursive algorithm that has been updated to account for non-Gaussian errors. This procedure has been applied to the German European Energy index (EEX) based in Frankfurt for electricity, to the Brent for Crude oil, and to the NBP for natural gas. The AJD framework is very powerful because the characteristic function of the underlying stochastic variables can be obtained just by solving a system of complex valued ODEs. We took advantage of this feature and developed a novel approach to estimate expectations of arbitrary functions of random variables that does not require the probability density function of the stochastic variables, but instead, their characteristic function. This approach, relying on the Parseval Identity, provided closed form solutions for options with payoff functions that have an analytical Fourier transform. In particular, European calls, puts and spread options could be computed as well as the value of multi-fuel power plants that can be viewed as an option to exchange the most economic fuel of the moment against electricity. A numerical procedure has also been developed for options with payoff functions that do not have an analytical Fourier transform. This numerical approach is indeed using a Fast Fourier Transform of the payoff function, and can be used in Dynamic Programming algorithms to price contracts with endogenous exercise strategies. Finally, it is showed that the (mathematical) partial derivatives of these contracts, often referred to as the Greeks, could also be computed at low cost. This allows to build hedging strategies to shape the risk profile of a given producer, or consumer.

The determination of long-term goals for climate policy, or of near-term mitigation effort, requires a shared conception among nations of what is at stake. Unfortunately, because of different attitudes to risk, problems of valuing non-market effects, and disagreements about aggregation across rich and poor nations, no single benefit measure is possible that can provide commonly accepted basis for judgment. In response to this circumstance, a portfolio of estimates is recommended, including global variables that can be represented in probabilistic terms, regional impacts expressed in natural units, and integrated monetary valuation. Development of such a portfolio is a research task, and the needed program of work suggested. / Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/). / Results cited from the MIT Joint Program on the Science and Policy of Global Change were developed with the support of the US Department of Energy, Office of Biological and Environmental Research [BER] (DE-FG02-94ER61937) the US Environmental Protection Agency (X-827703-01-0), the Electric Power Research Institute, and by a consortium of industry and foundation sponsors.

Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 125-128). Also available on the World Wide Web.

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Kenneth E. Barner, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

A fault in the system operation is deemed to occur when the system practically experiences an abnormal condition, such as a malfunction in the actuators/sensors. Hence, detection and isolation of the faulty components is crucial in control applications. Effective control and monitoring of a system requires accurate information of internal behaviour of the system. This internal behaviour can be analyzed by system's states. Practically, in many real systems, state space variables are not fully available for measurements. The two critical problems stated have motivated significant research work in the area of robust state and fault estimation. Fault reconstruction and estimation is regarded as a stronger extension to fault detection and isolation (FDI) since accurate fault estimation automatically implies fault detection. It is well known that two promising control strategies to cope with uncertain control processes are H_infinity Control and Sliding Mode Control. Therefore, in this PhD thesis, we employ these tools and we propose observer based robust fault reconstruction (RFR) by integrating H_infinity filtering and Sliding Mode Control. We also employ adaptive control on the sliding motion to deal with faults with unknown bounds. Another open problem in the context of FDI and RFR is due to systems with multiple faults at different system's components since it is often the case where actuators and also sensors suffer from faults during the course of the system's operation. Both actuators and sensors can suffer from faults either alone, at separate times or simultaneously. The co-existence of unknown fault at both sensor(s) and actuator(s) has not been addressed in any earlier design of fault reconstruction schemes. In this Thesis, inspired by the theory of singular systems, we aim at solving this problem. A New structure for reduced-order unknown input observers (UIOs) with application to chaotic communication and sensor fault reconstruction is also proposed. / Controls