Ever faster data transmission in wireless communication is desired to satisfy emerging markets for various media services, such as voice, picture and video calls, multimedia messaging, music and video downloads, and even television. With the explosive increase in the use of mobile devices such as cellular phones, PDAs, GPS, and laptop computers, power consumption has become a prime consideration in the design of mobile communication systems. In order to reliably maintain a high rate of transmission and low power consumption, it is imperative that the receiver obtains as much knowledge as possible about the current state of the channel. A more accurate model of wireless communication channels will indisputably help in obtaining more knowledge about the transient channel state, providing a more accurate and efficient reproduction of the transmitted signal, and decreased power consumption by the receiver. With careful choice and consideration of the channel model, systemic optimization based on the selected channel model will improve the system performance of the transmitter and receiver through better encoding and decoding, as well as through better control of transmitted signal's power level. This thesis focuses on understanding the physical and statistical characteristics of wireless channels, and investigates how to represent wireless channels using simple mathematical models. This thesis initially studied a simple time-varying stationary channel, i.e.a multipath fiat fading channel without terminal motion, which is typically used for indoor wireless communication. With an introduction of stochastic differential equations, we derived a first-order AR stochastic process to represent this stationary channel. For a general multipath fiat fading channel with terminal motion, the traditional Clarke's model was then extended by incorporating the effects of fluctuations in the component phases and analyzed statistically. The resulting theoretical power spectrum was shown to fit practical measured spectra, in contrast to the traditional theoretical fiat fading channel spectra (Jakes' spectrum in [19]) . Finally, we developed a state-space model that represents a wireless channel using these modified spectral characteristics. This was achieved by developing a relationship between the state-space model and the theory of a rational transfer function. A novel method for designing a rational transfer function for linear systems was then proposed. In this method, the rational transfer function is represented via the Observable Canonical Form (OCF) to obtain the state-space model, which can be used to represent and simulate a fiat fading wireless channel. The presented state-space approach is simple and provides rapid computation. The present AR and state-space models provide valuable contributions that can be integrated with other algorithms for better system optimization of wireless communication networks. / Thesis / Doctor of Philosophy (PhD)

In decentralized control, agents have only a partial view and partial control of the system and must cooperate to achieve the control objective. In order to synthesize a decentralized control solution, a specification must satisfy the co-observability property. Existing co-observability verification methods require the possibly intractable construction of the complete system. To address this issue, we introduce an incremental verification of co-observability approach. Selected subgroups of the system are evaluated individually, until verification is complete. The new method is potentially much more efficient than the monolithic approaches, in particular for systems composed of many subsystems, allowing for some intractable problems to be manageable. Properties of this new strategy are presented, along with a corresponding algorithm and an example. To further increase the scalability of decentralized control, we wish to adapt the existing Hierarchical Interface-Based Supervisory Control (HISC) to support it. We introduce the Hierarchical Interface-Based Decentralized Supervisory Control (HIDSC) framework that extends HISC to decentralized control. To adapt co-observability for HIDSC, we propose a per-component definition of co-observability along with a verification strategy that requires only a single component at a time in order to verify co-observability. Finally, we provide and prove the necessary and sufficient conditions for supervisory control existence in the HIDSC framework and illustrate our approach with an example. As the entire system model never needs to be constructed, HIDSC potentially provides significant savings. / Thesis / Doctor of Philosophy (PhD)

The U.S. forest products industry has witnessed an unprecedented period of mergers and acquisitions in the last decades. The overall goal of this thesis is to examine the impact of these activities on the financial performance and market power of the U.S. forest products industry in the last several decades. The first part of this thesis evaluated the mergers by event study. The results revealed that the equity market reacted positively to these mergers; the position of a firm and the relative transaction size explained most of the variations of the cumulative abnormal returns; and the risk for most of the selected 14 acquiring firms had changed after the mergers. The second part examined the market power of the U.S. paper industry by the new empirical industrial organization approach. The results indicated that the oligopoly power remained significant at the 1% level over the whole sample period; whereas the oligopsony power had dropped dramatically and become insignificant at the 5% level in recent 30 years.

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<p>The main theme of this dissertation is statistical estimation and information theory. There are three related topics including "distributed estimation", "an information geometric approach to ML estimation with incomplete data" and "joint identification and estimation in non-linear state space using Bayesian filters". The expectationmaximization (EM) algorithm, as an iterative estimation technique for dealing with incomplete data is the common bond that binds these three topics together.</p> <p>1. <em>Distributed estimation</em></p> <p>Distributed estimation involves the study of estimation theory in an information theoretic framework. This field concerns the following question: "What if the purpose of communications in a distributed environment is parameter estimation rather than source reconstruction?" The first part of this thesis is dedicated to designing low-complexity iterative algorithms for distributed estimation. The algorithm design, in this case, involves transmission of statistics via communication systems. Therefore, the first question raised is "whether the code rates in distributed estimation are different from those in conventional communications?" Surprisingly, under certain conditions, the answer is found to be negative. It is shown that for fixed parameters, the achievable rates coincide with rates in conventional distributed coding of correlated sources (i.e. Slepian-Wolf region). In order to prove the main theorem, we also devise a novel distributed binning scheme and a new theorem in Large deviation theory that are used for proving our distributed coding theorem. The proof of the converse is implemented by a generalized <em>Fano's inequality</em> for distributed estimation.</p> <p>Determination of the region of achievable rates for efficient estimation of a general source is an extremely difficult problem. This fact is the motivation for proving a theorem that provides a method for determining the region of achievable rates for a large class of sources with a convex mutual information with respect to the unknown parameters.</p> <p>With a given set of rates, an efficient implementation of universal coding schemes for distributed estimation based on the expectation maximization (EM) technique is presented. Since the correlation channel between the sources is assumed to be unknown at the joint decoder, previously proposed distributed coding schemes are not useful for this purpose. Therefore, LDPC-based coset-coding schemes are extended to the case where the correlation channel is unknown at the decoder. The basic idea is to implement a low-complexity version of the EM algorithm on a factor~graph that includes an LDPC decoding mechanism.</p> <p>2. <em>Information geometric approach to ML estimation with incomplete data</em></p> <p>The stochastic maximum likelihood estimation of parameters with incomplete data is cast in an information geometric framework. In this vein we develop the information geometric identification (IGID) algorithm, that provides an alternative iterative solution to the incomplete-data estimation problem. The algorithm consists of iterative alternating projections on two sets of probability distributions (PD); i.e., likelihood PD's and data empirical distributions. A Gaussian assumption on the source distribution permits a closed form lowcomplexity solution for these projections. The method is applicable to a wide range of problems; however the emphasis is on semi-blind identification of unknown parameters in a multi-input multi-output (MIMO) communications system.</p> <p>3. <em>Joint identification and estimation in non-linear state space using Bayesian filters</em></p> <p>There are situations in estimation where nonlinear state-space models where the model parameters or the model structure itself are not known a priori or are known only partially. In these scenarios, standard estimation algorithms like the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. The nonlinear state estimation problem with possibly non-Gaussian noise in the presence of measurement model uncertainty is modeled as a special case of maximum likelihood estimation with incomplete data. The EM algorithm is used to solve the problem. The expectation (E) step is implemented by a particle filter that is initialized by a Monte-Carlo Markov chain algorithm. In the maximization (M) step, a nonlinear regression method, here using a mixture of Gaussians (MoG), is used to approximate (identify) the uncertain model equations. The proposed procedure is used to solve a highly nonlinear bearing-only tracking problem, as well as the sensor registration problem in a multi-sensor fusion scenario.</p> / Doctor of Philosophy (PhD)

Our data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the sharks' behavior, and behavioral interaction between them. Our method uses particle filters, a computational Bayesian technique designed to sequentially model a dynamic system. We discuss how resampling is used to approximate arbitrary densities, and illustrate its use in a simple example of a particle filter implementation of a state-space model. We then introduce a particular model formulation that uses conditioning to introduce unobserved parameters for the shark's behaviors. We show how the irregularly-observed shark locations can be modeled by interpolation as a set of movements at constant-length time intervals. We use a spline method for generating approximations of the ground truth at these intervals for comparison with our model. Finally, we demonstrate our model's estimates of the sharks' behavioral and ecological parameters of interest on a subset of the observed data. / Statistics

No / In this paper, a new three-dimensional spline finite strip method (spline FSM) is introduced. This is done by combining the classical spline finite strip method [1] and the state space approach. According to the traditional spline FSM, a laminated plate is divided into strips. Within each strip, the spline FSM calls for the use of simple polynomials and a continuously differentiable spline function, respectively, in the transverse and in-plane directions. In the through-thickness direction, the state space method is used to compute the distribution of displacements and stresses. The combination of the in-plane spline FSM and the out-of-plane state space formulations results in a global state space equation that is solved numerically by the precise time step integration method [2,3]. Apart from obtaining a three-dimensional solution, the new method has a unique feature that the final algebra equation system is independent of the number of material layers of a laminate. The main aim of this work is to establish the new solution procedure and validate the method. To this end, the work reported in the paper focus on laminated plates with arbitrary boundary conditions. Thus, the spline FSM is more flexible than the FSM in imposing boundary conditions. Future development is expected to extend the solution to more practical applications. From the numerical validation included, it can be seen clearly that the newly developed method can provide accurate three dimensional solutions for laminated composites, particularly, with continuous transverse stress distributions across material interfaces. This is normally difficult to obtain if a traditional three dimensional finite element is used, where only continuity of displacements across material boundaries are guaranteed. Apart from the above new feature, the new three-dimensional formulation always ends up with a global matrix whose dimension depends only on the number of strips and knots that a plate has been divided into, and is completely independent of the number of material layers of the plate.

The advent of synchronized wide-area frequency measurements obtained from frequency disturbance recorders and phasor measurement units has presented the power industry with special opportunities to study power system dynamics. I propose the use of wide-area frequency measurements in identifying system disturbances based on power system post-event modal properties. In this work, power system dynamics are examined from an internal system energy viewpoint. Since an electric power system is composed of coupled rotating machines (large generators) which have air gap magnetic fields that are essentially static, or quasi-static, the power system may be modeled as a system with energy stored in quasi-static magnetic fields. The magnetic fields in the machines do change with time but may be modeled as static as far as wave propagation is concerned. The dynamic model that I develop treats this magnetic energy specifically as potential energy. Each rotating machine also contains an inertia due to the mass and motion of its rotor train and so each machine contains a rotational kinetic energy. Thus the internal system energy for a power system dynamic model may be considered to be contained in potential (magnetic) and kinetic (rotating mass) energies. This notion of internal energy lends itself to the use of a state-space model where each system state is associated with either a kinetic energy or a potential energy. An n-machine system would have a total of 2n states and would thus be a 2n-th order system. For many power system disturbances, I postulate that a linearized version of this model may be used to examine system natural response in terms of frequency and phasor measurements. The disturbances that I will investigate include generator and line outages. For any particular outage, the power system exhibits a very specific natural response in terms of its kinetic and potential energies. Kinetic energy in the system is directly related to each specific machine's rotational speed. I propose that the kinetic energy corresponds directly with bus frequencies through a linear transformation. Likewise magnetic field energy in each machine corresponds directly with a torque angle. The potential energy in the system thus corresponds directly with bus angles through a linear transformation. The primary focus of this work is on frequency deviation modal characteristics – specifically damped oscillation frequencies, mode shapes, and damping ratios. This work presents how specific disturbances on a power system will lead to specific oscillation frequencies in the deviation quantities and that these oscillation frequencies may be used to identify the disturbance. The idea of disturbance identification stems out of previous work done in locating disturbances by using a distributed parameter (DP) model of an electric power system. This DP model, which assumes a wave-like motion of frequency and phase quantities, was used to locate disturbances via a triangulation method. This present work, instead of using a DP model of the power system, assumes lumped parameters and focuses on disturbance identification strictly via modal characteristics – particularly oscillation frequency in the frequency deviations. This model is not concerned with geographic location but focuses on system topology, loading, and machine mass as lumped parameters. Advantages of disturbance identification include mainly reliability enhancements but can also be used in marketing applications. The state-space model used to realize this theory is verified via simulation using small, "academic" systems which should prove useful in classroom settings. Additionally the model is verified on a larger test system in order prove its validity and potential usefulness on large power systems. / Ph. D.

Hand transmitted vibration (HTV) is one of the most common hazards faced by workers in the construction industry. A major source of HTV is hand held percussion tools, such as pneumatically driven chipping hammers and rock drills. This thesis presents a new approach to measuring the vibration from these tools using an experimental hand arm model to which the tools are attached. The experimental hand-arm model has been designed to have similar dynamic characteristics to that of a human hand-arm system. This approach addresses the issue of repeatability as HTV measurements suffer from variability between cases. The measured acceleration of the hand-arm system is in range or close to range of the measured accelerations of the test subjects with superior repeatability. Further, the thesis presents a nonlinear numerical model of a pneumatic impact hammer. Fundamentally, the numerical model was made up of two different sub-models, 1) a fluid flow model and 2) a structural dynamic model. The fluid flow model was based on the equations for mass flow rate of air though a bleed orifice assuming an isentropic process. The second sub-model deals with modeling the structural components of the impact hammer consisting of the major hammer like the center body, handle, piston and chisel as well as the human hand and the ground. Time domain simulations of the hammer were carried out by using a state space formulation to get displacements, velocities and accelerations of the each component as well as the exhaust jet velocities. Experiments were carried out to measure the handle response and exhaust jet velocities as well as pressure profiles. The results obtained from the numerical model were then validated using these experimental results. Finally, a parametric study using the numerical model was carried out to explore different vibration control techniques. / Master of Science