The objective of this dissertation is to better understand the impact of the range extension and interference effects of opportunistic large arrays (OLAs), in the context of cooperative routing in multi-hop ad hoc networks. OLAs are a type of concurrent cooperative transmission (CCT), in which the number and location of nodes that will participate in a particular CCT cannot be known a priori. The motivation of this research is that the previous CCT research simplifies or neglects significant issues that impact the CCT-based network performance. Therefore, to develop and design more efficient and realistic OLA-based protocols, we clarify and examine through experimentation and analysis the simplified or neglected characteristics of CCT, which should be considered in the network-level system design. The main contributions of this research are (i) intra-flow interference analysis and throughput optimization in both disk- and strip-shaped networks, for multi-packet OLA transmission, (ii) CCT link modeling focusing on path-loss disparity and link asymmetry, (iii) demonstration of CCT range-extension and OLA-based routing using a software-defined radio (SDR) test-bed, (iv) a new OLA-based routing protocol with practical error control algorithm. In the throughput optimization in presence of the intra-channel interference, we analyze the feasibility condition of spatially pipelined OLA transmissions using the same channel and present numerical results with various system parameters. In the CCT link model, we provide the impact of path-loss disparity that are inherent in a virtual multiple-input-single-output (VMISO) link and propose an approximate model to calculate outage rates in high signal-to-noise-ratio (SNR) regime. Moreover, we present why link asymmetry is relatively more severe in CCT compared to single-input-single-output (SISO) links. The experimental studies show actual measurement values of the CCT range extension and realistic performance evaluation of OLA-based routing. Lastly, OLA with primary route set-up (OLA-PRISE) is proposed with a practical route recovery technique.

Le développement et l'utilisation de modèles intégrés transport-urbanisme sont devenus une norme pour représenter les interactions entre l'usage des sols et le transport de biens et d'individus sur un territoire. Ces modèles sont souvent utilisés comme outils d'aide à la décision pour des politiques de planification urbaine.Les modèles transport-urbanisme, et plus généralement les modèles mathématiques, sont pour la majorité conçus à partir de codes numériques complexes. Ces codes impliquent très souvent des paramètres dont l'incertitude est peu connue et peut potentiellement avoir un impact important sur les variables de sortie du modèle.Les méthodes d'analyse de sensibilité globales sont des outils performants permettant d'étudier l'influence des paramètres d'un modèle sur ses sorties. En particulier, les méthodes basées sur le calcul des indices de sensibilité de Sobol' fournissent la possibilité de quantifier l'influence de chaque paramètre mais également d'identifier l'existence d'interactions entre ces paramètres.Dans cette thèse, nous privilégions la méthode dite à base de plans d'expériences répliqués encore appelée méthode répliquée. Cette méthode a l'avantage de ne requérir qu'un nombre relativement faible d'évaluations du modèle pour calculer les indices de Sobol' d'ordre un et deux.Cette thèse se focalise sur des extensions de la méthode répliquée pour faire face à des contraintes issues de notre application sur le modèle transport-urbanisme Tranus, comme la présence de corrélation entre paramètres et la prise en compte de sorties multivariées.Nos travaux proposent également une approche récursive pour l'estimation séquentielle des indices de Sobol'. L'approche récursive repose à la fois sur la construction itérative d'hypercubes latins et de tableaux orthogonaux stratifiés et sur la définition d'un nouveau critère d'arrêt. Cette approche offre une meilleure précision sur l'estimation des indices tout en permettant de recycler des premiers jeux d'évaluations du modèle. Nous proposons aussi de combiner une telle approche avec un échantillonnage quasi-Monte Carlo.Nous présentons également une application de nos contributions pour le calage du modèle de transport-urbanisme Tranus. / Land Use and Transportation Integrated (LUTI) models have become a norm for representing the interactions between land use and the transportation of goods and people in a territory. These models are mainly used to evaluate alternative planning scenarios, simulating their impact on land cover and travel demand.LUTI models and other mathematical models used in various fields are most of the time based on complex computer codes. These codes often involve poorly-known inputs whose uncertainty can have significant effects on the model outputs.Global sensitivity analysis methods are useful tools to study the influence of the model inputs on its outputs. Among the large number of available approaches, the variance based method introduced by Sobol' allows to calculate sensitivity indices called Sobol' indices. These indices quantify the influence of each model input on the outputs and can detect existing interactions between inputs.In this framework, we favor a particular method based on replicated designs of experiments called replication method. This method appears to be the most suitable for our application and is advantageous as it requires a relatively small number of model evaluations to estimate first-order or second-order Sobol' indices.This thesis focuses on extensions of the replication method to face constraints arising in our application on the LUTI model Tranus, such as the presence of dependency among the model inputs, as far as multivariate outputs.Aside from that, we propose a recursive approach to sequentially estimate Sobol' indices. The recursive approach is based on the iterative construction of stratified designs, latin hypercubes and orthogonal arrays, and on the definition of a new stopping criterion. With this approach, more accurate Sobol' estimates are obtained while recycling previous sets of model evaluations. We also propose to combine such an approach with quasi-Monte Carlo sampling.An application of our contributions on the LUTI model Tranus is presented.

近年來，隨著消費意識的提升，食品業者正面臨最嚴格的考驗，品質 產 生變異，將會反應在銷售量上，嚴重影響企業的經營。而目前台灣食品 造業者，有許多廠家仍停留在經驗掛帥、老式的檢驗品質階段，對於源流 瑊z的觀念仍不甚了解，致使品質提昇的效率不盡理想。有鑑於此，本研 究B用田口玄一博士生產線外品質工程理念架構，靈活搭配各種工具（如 系統洈k、直交表），以使田口方法更臻完善。同時利用S/N比（signal- to-Noes Ratio）變異數分析兩種方法進行資料解析，提供食品製造業者 最佳生ㄡ捰X。最後，乃以本人所親自參與之食品製造業品質改善研究實 驗為例，憭什ㄟ筆嗾膋熊痕G報告外，另詳述記載寶貴的實驗經歷，可進 一步提供相鶻t商作為改善品質的借鏡。

The thesis focuses on the development of statistical theory in experimental design with applications in global optimization. It consists of four parts. In the first part, a criterion of design efficiency, under model uncertainty, is studied with reference to possibly nonregular fractions of general factorials. The results are followed by a numerical study and the findings are compared with those based on other design criteria. In the second part, optimal designs are dentified using Bayesian methods. This work is linked with response surface methodology where the first step is to perform factor screening, followed by response surface exploration using different experiment plans. A Bayesian analysis approach is used that aims to achieve both goals using one experiment design. In addition we use a Bayesian design criterion, based on the priors for the analysis approach. This creates an integrated design and analysis framework. To distinguish between competing models, the HD criterion is used, which is based on the pairwise Hellinger distance between predictive densities. Mixed-level fractional factorial designs are commonly used in practice but its aliasing relations have not been studied in full rigor. These designs take the form of a product array. Aliasing patterns of mixed level factorial designs are discussed in the third part. In the fourth part, design of experiment ideas are used to introduce a new global optimization technique called SELC (Sequential Elimination of Level Combinations), which is motivated by genetic algorithms but finds the optimum faster. The two key features of the SELC algorithm, namely, forbidden array and weighted mutation, enhance the performance of the search procedure. Illustration is given with the optimization of three functions, one of which is from Shekel's family. A real example on compound optimization is also given.

An Opportunistic Large Array (OLA) is a form of cooperative diversity in which a large group of simple, inexpensive relays operate without any mutual coordination, but naturally fire together in response to the energy received from a single source or another OLA. The main contributions of this thesis are the introduction of two OLA-based routing protocols: OLA Concentric Routing Algorithm (OLACRA), which is an upstream routing algorithm suitable for static wireless sensor networks (WSNs), and OLA Routing On-Demand (OLAROAD), which is a robust reactive routing scheme suitable for mobile ad hoc networks (MANETs). In fixed multi-hop wireless sensor networks with a single sink, where energy conservation is often a concern, simulations of the new algorithms show as much as 80% of the transmit energy required to broadcast data can be saved, relative to existing OLA-based broadcasting approaches. In MANETs, where robustness of the routes is an important performance indicator, OLAROAD-based cooperative routes last much longer compared to their state-of-art multi-hop non-cooperative transmission (CT)-based counterparts. However, OLACRA and OLAROAD have higher node participation, and thereby lower throughput, in comparison with the non-CT schemes. To improve the throughput, and thereby bandwidth utilization, the properties of uplink OLAs and their suppression regions are carefully studied. Based on the observations, Hop-Optimized OLACRA (HOLA), which is a variant of OLACRA, and has the maximum bandwidth utilization amongst all the OLA unicast schemes studied, is proposed. HOLA routes have bandwidth utilization comparable to non-CT schemes, but a much lower (~10 dB less) transmit power per node. The last section of this thesis treats the MAC design for OLA-based networks. In contrast to non-CT networks, a 802.11-based RTS/CTS MAC scheme is shown to reduce the reliability in OLA unicast schemes. A distributed cluster-head-based MAC scheme for channel reservation and OLA Size Adaptation Mechanism for link repair/maintenance are proposed for OLA-based networks. The performances of these protocols are shown to be comparable to a non-CT multihop scheme using the RTS/CTS/DATA/ACK handshake-based link layer design.

An opportunistic large array (OLA) is a form of cooperative diversity in which a large group of simple, inexpensive relays or forwarding nodes operate without any mutual coordination, but naturally fire together in response to energy received from a single source or another OLA. When used for broadcast, OLAs form concentric rings around the source, and have been shown to use less energy than conventional multi-hop protocols. This simple broadcasting scheme, which is already known, is called Basic OLA. The OLA Concentric Routing Algorithm (OLACRA), which is our contribution, takes advantage of the concentric ring structure of broadcast OLAs to limit flooding on the upstream connection. By limiting the node participation, OLACRA saves over 80% of the energy compared to Basic OLA, without requiring GPS, individual node addressing, or inter-node interaction. This thesis analyzes the performance of OLACRA over 'deterministic channels' where transmissions are on non-faded orthogonal channels and on 'diversity channels' where transmissions are on Rayleigh flat fading limited orthogonal channels. The performance of diversity channels is shown to approach the deterministic channel at moderate orders of diversity. Enhancements to OLACRA to further improve its efficiency by flooding in the initial upstream level and limiting the downlink 'step sizes' are also considered. The protocols are tested using Monte Carlo evaluation.

The objective of this work was to employ artificial neural networks (NN) to solve inverse problems in different engineering fields, overcoming various obstacles in applying NN to different problems and benefiting from the experience of solving different types of inverse problems. The inverse problems investigated are: 1) damage detection in structures, 2) detection of an anomaly in a light-diffusive medium, such as human tissue using optical imaging, 3) structural optimization of fiber optic sensor design. All of these problems require solving highly complex inverse problems and the treatments benefit from employing neural networks which have strength in generalization, pattern recognition, and fault tolerance. Moreover, the neural networks for the three problems are similar, and a method found suitable for solving one type of problem can be applied for solving other types of problems. Solution of inverse problems using neural networks consists of two parts. The first is repeatedly solving the direct problem, obtaining the response of a system for known parameters and constructing the set of the solutions to be used as training sets for NN. The next step is training neural networks so that the trained neural networks can produce a set of parameters of interest for the response of the system. Mainly feed-forward backpropagation NN were used in this work. One of the obstacles in applying artificial neural networks is the need for solving the direct problem repeatedly and generating a large enough number of training sets. To reduce the time required in solving the direct problems of structural dynamics and photon transport in opaque tissue, the finite element method was used. To solve transient problems, which include some of the problems addressed here, and are computationally intensive, the modal superposition and the modal acceleration methods were employed. The need for generating a large enough number of training sets required by NN was fulfilled by automatically generating the training sets using a script program in the MATLAB environment. This program automatically generated finite element models with different parameters, and the program also included scripts that combined the whole solution processes in different engineering packages for the direct problem and the inverse problem using neural networks. Another obstacle in applying artificial neural networks in solving inverse problems is that the dimension and the size of the training sets required for the NN can be too large to use NN effectively with the available computational resources. To overcome this obstacle, Principal Component Analysis is used to reduce the dimension of the inputs for the NN without excessively impairing the integrity of the data. Orthogonal Arrays were also used to select a smaller number of training sets that can efficiently represent the given system. / Ph. D.

The use of computer modeling is fast increasing in almost every scientific, engineering and business arena. This dissertation investigates some challenging issues in design, modeling and analysis of computer experiments, which will consist of four major parts. In the first part, a new approach is developed to combine data from approximate and detailed simulations to build a surrogate model based on some stochastic models. In the second part, we propose some Bayesian hierarchical Gaussian process models to integrate data from different types of experiments. The third part concerns the development of latent variable models for computer experiments with multivariate response with application to data center temperature modeling. The last chapter is devoted to the development of nested space-filling designs for multiple experiments with different levels of accuracy.

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