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Cochannel interference (CCI) is a major impairment in narrowband cellular systems. To increase the spectral efficiency of the narrowband systems, identical carrier frequencies are reused in distant cells. The interference rejection capability of the receiver determines this frequency reuse and is therefore critical. In this thesis, we propose an improved demodulation scheme, employing high-resolution frequency estimation techniques, for continuous phase modulated (CPM) signals in presence of CCI. Minimum shift keying (MSK), which is a special case of CPM, is a very popular modulation format around the world. Frequency detectors, such as the limiter-discriminator permit the non-coherent demodulation of MSK signals. High-resolution frequency estimation appears as a very attractive alternative to the conventional non-coherent frequency detectors. The frequency estimation methods that we have studied are based on autoregressive modeling. The contributions of this thesis include the implementation of various demodulation schemes employing parametric frequency estimation. The use of the Viterbi algorithm as a non-linear equalization technique to mitigate intersymbol interference is considered. We verified that the model-based sequence estimation schemes outperform the conventional non-coherent receivers for MSK with AWGN, flat fading, and CCI. Demodulator diversity is also investigated as a way to combat interference. An improved technique combining the proposed model-based receiver and the conventional coherent receiver is implemented and simulated in presence of CCI. / Master of Science

The rail dynamics modeling package NUCARS has been used extensively to model freight cars. We have found that it can also be used effectively to model freight locomotives. This thesis discusses the development of a NUCARS model to represent a six-axle freight locomotive equipped with steerable trucks. This includes separating it into a set of individual bodies, representing the suspension components as inter-body connections, and validation of the computer model. This model is then used to conduct a study of the impact on tangent track stability and curving performance of varying suspension parameters. It is found that the presence of damping in the system improves hunting stability, while increasing wheel conicity is harmful to stability and varying the flexicoiling stiffness has little effect. In curving, the clearances between the axles and truck frame are very important. Limiting these clearances in the steerable truck causes it to curve similar to a conventional straight locomotive truck and increases both the track force ratios and angles of attack. Increasing the wheel conicity increases an axle's tendency to align with the track and improves the locomotive's curving performance. The lateral stiffness of the inter-axle links and inter-motor links has little effect on the curving of the locomotive. / Master of Science

In clinical trials with failure-time primary outcomes, also known as "event-driven" designs, the statistical information is determined by total observed events. Examples of failure-time clinical trial endpoints include: time to death and time to disease progression. In trials with event-driven designs, the interim and final analyses are performed after a pre-specified number of events have been observed, based on a priori design considerations, rather than after observing patients for a pre-specified period of time. The timing of these analyses represent important milestones in the conduct of the study. In particular, if a trial requires review of interim analyses by a Data Monitoring Committee (DMC), convening the DMC members requires much advance planning and effort. In addition, advanced knowledge of when these milestones will occur can allow trial sponsors to make informed decisions regarding resources and financial planning. It is therefore of interest to predict when a pre-specified number of events will be observed based on accumulating data. Parametric and semi-parametric methods have been proposed for event prediction when data are right censored. In cases when the underlying failure time distribution is unknown or accumulated events are relatively sparse, these methods may not provide accurate or efficient prediction. We propose a method to predict the number of events that is a fully Bayesian non-parametric approach in modeling the survival probabilities that is more flexible and generalizes to interval censored data. We use a Gibbs sampler to sample from the posterior of the survival distribution to obtain point and interval estimates for the specified number of events. We compare the accuracy and precision of this approach to proposed parametric and semi-parametric methods under a variety of data generating mechanisms, beginning with right-censored data. We then extend the study to interval-censored data, comparing the methods under data generated from varying assessment intervals. Finally we consider the scenario in which we are blinded to treatment assignment, incorporating a Bayesian approach to determine the probability of membership to a particular treatment group. We demonstrate the proposed method offers greater flexibility and has the ability to match or outperform existing methods under multiple clinical trial scenarios.

Understanding failure mechanics of mechanical equipment is one of the most important aspects of structural and aerospace engineering. Crack growth being one of the major forms of failure in structural components has been studied for several decades to achieve greater reliability and guarantee higher safety standards. Conventional approaches using the finite element framework provides accurate solutions, yet they require extremely complicated numerical approaches or highly fine mesh densities which is computationally expensive and yet suffers from several numerical instabilities such as element entanglement or overly soften element behavior. The eXtended Finite Element Method (XFEM) is a relatively recent concept developed for modeling geometric discontinuities and singularities by introducing the addition of new terms to the classical shape functions in order to allow the finite element formulation to remain the same. XFEM does not require the necessity of computationally expensive numerical schemes such as active remeshing and allows for easier crack representation. In this work, verifies the validity of this new concept for quasi-static crack growth in tension with Abaqus' XFEM is employed. In the course of the work, the effect of various parameters that are involved in the modelling of the crack are parametrically analyzed. The load-displacement data and crack growth were used as the comparison criterion. It was found that XFEM is unable to accurately represent crack growth in the models in the elastic region without direct manipulation of the material properties. The crack growth in the plastic region is found to be affected by certain parameters allowing us to tailor the model to a small degree. This thesis attempts to provide a greater understanding into the parametric dependencies of XFEM crack growth. / Master of Science / Crack propagation is one of the major causes of failure in equipment in structural and aerospace engineering. The study of fracture and crack growth has been taking place for decades in an effort to increase quality of design and to ensure higher standards of safety. In the past, an accurate representation of crack growth within a specimen using conventional numerical analysis was computationally expensive. The eXtended Finite Element Method (XFEM) is a concept introduced that would reduce computational effort yet improving the fidelity of the analysis while allowing for easier representation of crack growth. This thesis, verifies the validity of XFEM in simulating crack growth in a specimen undergoing tension using a commercially available code, Abaqus. The various parameters involved in the modeling of this crack and their effects are studied. The study had shown that the inaccuracy of XFEM in its ability to model crack growth, however, it gives us some understanding into certain parameters that would allow us to tailor the model to better represent experimental data.