Waterflooding is currently the most commonly used method to improve oil recovery after primary depletion. The reservoir heterogeneity such as permeability distribution could negatively affect the performance of waterflooding. The presence of high permeability streaks could lead to an early water breakthrough at the producers and thus reduce the sweep efficiency in the field. One approach to counteract the impact of heterogeneity and to improve waterflood sweep efficiency is through optimal rate allocation to the injectors and producers. Through optimal rate control, we can manage the propagation of the flood front, delay water breakthrough at the producers and also increase the sweep and hence, the recovery efficiency. The arrival time optimization method uses a streamline-based method to calculate water arrival time sensitivities with respect to production and injection rates. It can also optimize sweep efficiency on multiple realizations to account for geological uncertainty. To extend the scope of this optimization method for more general conditions, this work utilized a finite difference simulator and streamline tracing software to conduct the optimization. Apart from sweep efficiency, another most widely used optimization method is to maximize the net present value (NPV) within a given time period. Previous efforts on optimization of waterflooding used optimal control theorem to allocate injection/production rates for fixed well configurations. The streamline-based approach gives the optimization result in a much more computationally efficient manner. In the present study, we compare the arrival time optimization and NPV optimization results to show their strengths and limitations. The NPV optimization uses a perturbation method to calculate the gradients. The comparison is conducted on a 4- spot synthetic case. Then we introduce the accelerated arrival time optimization which has an acceleration term in the objective function to speed up the oil production in the field. The proposed new approach has the advantage of considering both the sweep efficiency and net present value in the field.

In this thesis the direction of arrival estimation of wideband signals using frequency invariant beamforming method is examined. The difficulty with the direction of arrival estimation of wideband signals is that it is not possible to obtain a single covariance matrix valid for the whole frequency spectrum of the signal. There are various methods proposed in the literature to overcome this difficulty. The common aim of all the methods is to obtain a composite covariance matrix for the overall band of the signal. In this thesis, we concentrate on a method in [12]. This method is based on a beamforming technique that provides frequency invariant beams in the band of interest. Therefore there is no need for frequency decomposition as it is done with the other wideband methods. A comparison of the frequency invariant beamforming method with coherent signal subspace methods and narrow band methods is also given.

Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results. Because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also prediction intervals. With the ANN models, the prediction intervals of bus arrival time were calculated. Because the ANN models are non parametric models, conventional techniques for prediction intervals can not be used. Consequently, a newly developed computer-intensive method, the bootstrap technique was used to obtain prediction intervals of bus arrival time. On-time performance of a bus is very important to transit operators to provide quality service to transit passengers. To measure the on-time performance, the probability of a bus being on time is required. In addition to the prediction interval of bus arrival time, the probability that a given bus is on time was calculated. The probability density function of schedule adherence seemed to be the gamma distribution or the normal distribution. To determine which distribution is the best fit for the schedule adherence, a chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well the schedule adherence. With the normal distribution, the probability of a bus being on time, being ahead schedule, and being behind schedule can be estimated.

Estimating the quantity of water that reaches the water table following an infiltration event is vital for modeling and management of water resources. Estimating the time scale of groundwater recharge after a rainfall event is difficult because of the dependence on nonlinear soil characteristics and variability in antecedent conditions. Modeling the flow of water through the variably saturated zone is computationally intensive since it requires simulation of Richards' equation, a nonlinear partial differential equation without a closed-form analytical solution, with parametric relationships that are difficult to approximate. Hence, regional scale coupled (surface water - groundwater) hydrological models make simplistic assumptions about the quantity and timing of recharge following infiltration. For simplicity, such models assume the quantity of recharge to be a fraction of the total rainfall and the time to recharge the saturated groundwater is scaled proportionally to the depth to water table, in lieu of simulating computationally intensive flow in the variably saturated zone. In integrated or coupled (surface water - groundwater) regional scale hydrological models, better representation of the timing and quantity of groundwater recharge is required and important for water resources management. This dissertation presents a practical groundwater recharge estimation method and relationships that predict the timing and volume accumulation of groundwater recharge to moderate to deep water table settings. This study combines theoretical, empirical, and simulation techniques to develop a relatively simple model to estimate the propagation of the soil moisture wetting front through variably saturated soil. This model estimates the time scale and progression of recharge following infiltration for a specified depth to water table, saturated hydraulic conductivity and equilibrium moisture condition. High-resolution soil moisture data from a set of experiments conducted in a laboratory soil column were used to calibrate the HYDRUS-1D model. The calibrated model was used to analyze the time scale of recharge by varying soil hydraulic properties and simulating the application of rainfall pulses of varying volume and intensities. Modeling results were used to develop an equation that relates the non-dimensional travel time of the wetting front to excess moisture moisture content above equilibrium. This research indicates that for a soil with a known retention curve, the wetting front arrival time at a given depth can be described by a power law, where the power is a function of the saturated hydraulic conductivity. This equation relates the non-dimensional travel time of the wetting front to excess moisture content above the equilibrium moisture content. Since the equilibrium moisture content is dependent on the water retention curve, the powers in the equation governing the timing of recharge depend on the saturated hydraulic conductivity for a large variation in water retention curve. Also, the power law relates recharge (normalized by applied pulse volume) to time (normalized by the time of arrival of wetting front at that depth). The resulting equations predicted the model simulated normalized (relative) recharge with root mean square errors of less than 14 percent for the tested cases.

Millimeter wave (mm-Wave) systems have become very attractive recently as lower frequency spectrums used for mobile device communications have been experiencing a “spectral crunch” due to the dissemination of smartphones. Channel characterization of the outdoor urban environment, where networks for mobile devices require the highest data capacity, has been quite scarce and even non-existent for cellular (rooftop to ground) setting measurements. Our project characterizes the urban environment at 38 GHz in a cellular setting and 38 and 60 GHz in a peer-to-peer setting. A sliding correlator channel sounder with an 800 MHz RF bandwidth at 38 GHz and 1.5 GHz RF bandwidth at 60 GHz was constructed to measure the channel using a bandwidth that is larger than the expected bandwidths of future mm-Wave channels. Directional antennas were utilized during the measurements to imitate mm-Wave systems using beam steering antenna arrays, which also allowed for AOA characterization. Path loss and RMS delay spread statistics are provided. Finally, an outage study was performed to test the outage likelihood in an urban environment with many multi-story buildings. / text

We consider the problem of staffing large-scale service centers with multiple customer classes and agent types operating under quality-of-service (QoS) constraints. We introduce formulations for a class of staffing problems, minimizing the cost of staffing while requiring that the long-run average QoS achieves a certain pre-specified level. The queueing models we use to define such service center staffing problems have random inter-arrival times and random service times. The models we study differ with respect to whether the arrival rates are deterministic or stochastic. In the deterministic version of the service center staffing problem, we assume that the customer arrival rates are known deterministically. It is computationally challenging to solve our service center staffing problem with deterministic arrival rates. Thus, we provide an approximation and prove that the solution of our approximation is asymptotically optimal in the sense that the gap between the optimal value of the exact model and the objective function value of the approximate solution shrinks to zero as the size of the system grows large. In our work, we also focus on doubly stochastic service center systems; that is, we focus on solving large-scale service center staffing problems when the arrival rates are uncertain in addition to the inherent randomness of the system's inter-arrival times and service times. This brings the modeling closer to reality. In solving the service center staffing problems with deterministic arrival rates, we provide a solution procedure for solving staffing problems for doubly stochastic service center systems. We consider a decision making scheme in which we must select staffing levels before observing the arrival rates. We assume that the decision maker has distributional information about the arrival rates at the time of decision making. In the presence of arrival-rate uncertainty, the decision maker's goal is to minimize the staffing cost, while ensuring the QoS achieves a given level. We show that as the system scales large in size, there is at most one key scenario under which the probability of waiting converges to a non-trivial value, i.e., a value strictly between 0 and 1. That is, the system is either over- or under-loaded in any other scenario as the size of the system grows to infinity. Exploiting this result, we propose a two-step solution procedure for the staffing problem with random arrival rates. In the first step, we use the desired QoS level to identify the key scenario corresponding to the optimal staffing level. After finding the key scenario, the random arrival-rate model reduces to a deterministic arrival-rate model. In the second step, we solve the resulting model, with deterministic arrival rate, by using the approximation model we point to above. The approximate optimal staffing level obtained in this procedure asymptotically converges to the true optimal staffing level for the random arrival-rate problem. The decision making scheme we sketch above, assumes that the distribution of the random arrival rates is known at the time of decision making. In reality this distribution must be estimated based on historical data and experience, and needs to be updated as new observations arrive. Another important issue that arises in service center management is that in the daily operation in service centers, the daily operational period is split into small decision time periods, for example, hourly periods, and then the staffing decisions need to be made for all such time periods. Thus, to achieve an overall optimal daily staffing policy, one must deal with the interaction among staffing decisions over adjacent time periods. In our work, we also build a model that handles the above two issues. We build a two-stage stochastic model with recourse that provides the staffing decisions over two adjacent decision time periods, i.e., two adjacent decision stages. The model minimizes the first stage staffing cost and the expected second stage staffing cost while satisfying a service quality constraint on the second stage operation. A Bayesian update is used to obtain the second-stage arrival-rate distribution based on the first-stage arrival-rate distribution and the arrival observations in the first stage. The second-stage distribution is used in the constraint on the second stage service quality. After reformulation, we show that our two-stage model can be expressed as a newsvendor model, albeit with a demand that is derived from the first stage decision. We provide an algorithm that can solve the two-stage staffing problem under the most commonly used QoS constraints. This work uses stochastic programming methods to solve problems arising in queueing networks. We hope that the ideas that we put forward in this dissertation lead to other attempts to deal with decision making under uncertainty for queueing systems that combine techniques from stochastic programming and analysis tools from queueing theory. / text

ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV / In this paper, a new technique utilizing a scatterer of high dielectric constant in between electrically small antennas to achieve good Direction of arrival (DOA) estimation performance is demonstrated. The phase information of the received signal at the antennas is utilized for direction estimation. The impact of the property of the scatterer on the directional sensitivity and the output signal to noise ratio (SNR) level are studied. Finally the DOA estimation accuracy is analyzed with the proposed technique under the consumption of white Gaussian noise environment.

Ο κύριος σκοπός της παρούσας εργασίας είναι να συγκεντρωθούν συγκεκριμένες θεωρήσεις που χρησιμοποιούν τον φορμαλισμό των συνεπών ιστοριών σε βασικά προβλήματα της κβαντικής θεωρίας και κβαντικής κοσμολογίας. Ο φορμαλισμός αυτός είναι πολλά υποσχόμενος για τον τομέα της κανονικής κβαντικής βαρύτητας. Ο λόγος που θα κάνουμε αυτή την ανασκόπηση είναι για να δώσουμε μία ενοποιημένη εικόνα στα ζητήματα αυτά και να μπορέσουμε να τα συγκρίνουμε. Συγκεκριμένα, το πρώτο μέρος αφορά δύο διαφορετικές προσεγγίσεις για το πρόβλημα του χρόνου άφιξης στην κβαντομηχανική, εκ των οποίων και οι δύο χρησιμοποιούν φορμαλισμό ιστοριών. Η πρώτη έγινε από τους Halliwell και Yearsly (2009) και η δεύτερη από τους Anastopoulo και Saviddou (2012). Από την σύγκριση αυτών καταλήγουμε στο συμπέρασμα ότι και οι δύο δίνουν μία αδρομερή μορφή της εξίσωσης του Kijowski. Το δεύτερο μέρος αφορά την κβαντική κοσμολογία. Σε αυτό αρχικά παρουσιάζεται μία προσέγγιση με συνεπείς ιστορίες για την πυκνότητα πιθανότητας στην κβαντική κοσμολογία η οποία έγινε από τον Halliwell (2009). Στην συνέχεια παρουσιάζεται μία προσέγγιση με ιστορίες για μοντέλα μίνι-υπερχώρου από τους Anastopoulo and Savidou (2005). Σε αυτή κατασκευάζονται μοντέλα μίνι-υπερχώρου με όρους προβολικών τελεστών ιστοριών (HPO). Η σπουδαιότητα αυτού του φορμαλισμού έγκειται στο γεγονός ότι η γενική σχετικότητα σε αυτή την μορφή ικανοποιεί και τους χωροχρονικούς διαφορομορφισμούς και την άλγεβρα Dirac, με αποτέλεσμα να είναι εύκολα κβαντίσιμη. / The major purpose of this study is to consecrate specific approaches to some problems of quantum theory and quantum cosmology, in terms of decoherence histories formalism which is a very promising formalism for the canonical quantum gravity theories. The reason is to give a unified picture to these issues in order to be possible to compare them. Specifically, the first part contains two different approaches to the time of arrival in quantum mechanics, both of these use a histories formalism. The first is from Halliwell and Yearsly (2009) and the second from Anastopoulos and Saviddou (2012). By comparing them we deduce that both of them first gives a coarse-grain form of the Kijowski' s probability distribution. The second part concerns quantum cosmology. In this, we presented a decoherent histories approach to quantum cosmological probabilities, in which was used a complex potential, from Halliwell (2009). After that we present a histories approach to minisuperspace models by Anastopoulos and Savidou (2005). In this, minisuperspace models is written in terms of histories projector operator (HPO) formalism. The spectacular of this is that in that form general relativity satisfies both spacetime diffeomorfisms and Dirac algebra, which is very important because it is easier to be quantized.

The cell transmission model (CTM) based single destination system optimal dynamic traffic assignment (SD-SO-DTA) model has been widely applied to situations such as mass evacuations on a transportation network. Although formulated as a linear programming (LP) model, embedded multi-period cell network representation yields an extremely large model for real-size networks. As a result, most of these models are not solvable using existing LP solvers. Solutions obtained by LP also involve holding vehicles at certain locations, violating CTM flow dynamics. This doctoral research is aimed at developing innovative algorithms that overcome both computational efficiency and solution realism issues. We first prove that the LP formulation of the SD-SO-DTA problem is equivalent to the earliest arrival flow (EAF), and then develop efficient algorithms to solve EAF. Two variants of the algorithm are developed under different model assumptions and network operating conditions. For the case of time-varying network parameters, we develop a network flow algorithm on a time-expanded network. The main challenge in this approach is to address the issue of having backward wave speed lower than forward wave speed. This situation leads to non-typical constraints involving coefficients with value of less than 1. In this dissertation we develop a new network algorithm to solve this problem in optimal, even with coefficients of value less than 1. Additionally, the developed approach solves for optimal flows that exhibit non-vehicle-holding properties, which is a major breakthrough compared to all existing solution techniques for SD-SODTA. For the case of time-invariant network parameters, we reduce the SD-SO-DTA to a standard EAF problem on a dynamic network, which is constructed on the original roadway network without dividing it into cells. We prove that the EAF under free flow status is one of the optimal solutions of SD-SO-DTA, if cell properties follow a trapezoidal/triangular fundamental diagram. We use chain flows obtained on a static network to induce dynamic flows, an approach applicable to large-scale networks. Another contribution of this research is to provide a simple and practical algorithm solving the EAF with multiple sources, which has been an active research area for many years. Most existing studies involve submodular function optimization as subroutines, and thus are not practical for real-life implementation. This study’s contribution in this regard is the development of a practical algorithm that avoids submodular function optimization. The main body of the given method is comprised of |S⁺| iterations of earliest arrival s - t flow computations, where |S⁺| is the number of sources. Numerical results show that our multi-source EAF algorithm solves the SD-SO-DTA problem with time-invariant parameters to optimum.

As wireless devices become more common, the ability to position a wireless device has become a topic of importance. Accurate positioning through technologies such as the Global Positioning System is possible for outdoor environments. Indoor environments pose a different challenge, and research continues to position users indoors. Due to the prevalence of wireless local area networks (WLANs) in many indoor spaces, it is prudent to determine their capabilities for the purposes of positioning. Signal strength and time based positioning systems have been studied for WLANs. Direction or angle of arrival (AOA) based positioning will be possible with multiple antenna arrays, such as those included with upcoming devices based on the IEEE 802.11n standard. The potential performance of such a system is evaluated. The positioning performance of such a system depends on the accuracy of the AOA estimation as well as the positioning algorithm. Two different maximum-likelihood (ML) derived algorithms are used to determine the AOA of the mobile user: a specialized simple ML algorithm, and the space- alternating generalized expectation-maximization (SAGE) channel parameter estimation algorithm. The algorithms are used to determine the error in estimating AOAs through the use of real wireless signals captured in an indoor office environment. The statistics of the AOA error are used in a positioning simulation to predict the positioning performance. A least squares (LS) technique as well as the popular extended Kalman filter (EKF) are used to combine the AOAs to determine position. The position simulation shows that AOA- based positioning using WLANs indoors has the potential to position a wireless user with an accuracy of about 2 m. This is comparable to other positioning systems previously developed for WLANs.