<p> This thesis presents an optimal design method for MRI (Magnetic Resonance Imaging) teardrop gradient waveforms in two and three dimensions. Teardrop in two dimensions was introduced at ISMRM 2001 by Anand et al. to address the need for a high efficiency balanced k-space trajectory for real-time cardiac SSFP (Steady State Free Precession) imaging.</p> <p> We have modeled 2D and 3D teardrop gradient waveform design as nonlinear convex optimization problems with a variety of constraints including global constraints (e.g., moment nulling for motion insensitivity). Commercial optimization solvers can solve the models efficiently. The implementation of AMPL models and numerical testing results with the solver MOSEK are provided. This optimal design procedure produces physically realizable teardrop waveforms which enable real-time cardiac imaging with equipment otherwise incapable of doing it, and optimally achieves the maximum resolution and motion artifact reduction goals. The research may encompass other waveform design problems in MRI and has built a good foundation for further research in this area.</p> / Thesis / Master of Science (MSc)

Rough sets have been studied for over 30 years, and the basic concepts of lower and upper approximations have been analysed in detail, yet nowhere has the idea of an `optimal' rough approximation been proposed or investigated. In this thesis, several concepts are used in proposing a generalized definition: measures, rough sets, similarity, and approximation are each surveyed. Measure Theory allows us to generalize the definition of the `size' for a set. Rough set theory is the foundation that we use to define the term `optimal' and what constitutes an `optimal rough set'. Similarity indexes are used to compare two sets, and determine how alike or different they are. These sets can be rough or exact. We use similarity indexes to compare sets to intermediate approximations, and isolate the optimal rough sets. The historical roots of these concepts are explored, and the foundations are formally defined. A definition of an optimal rough set is proposed, as well as a simple algorithm to find it. Properties of optimal approximations such as minimum, maximum, and symmetry, are explored, and examples are provided to demonstrate algebraic properties and illustrate the mechanics of the algorithm. / Thesis / Doctor of Philosophy (PhD) / Until now, in the context of rough sets, only an upper and lower approximation had been proposed. Here, an concept of an optimal/best approximation is proposed, and a method to obtain it is presented.

This dissertation studies hyperbolic partial differential equations for Conservation Laws motivated by traffic control problems. New traffic models for multi-directional flow in two dimensions are derived and their properties studied. Control models are proposed where the control variable is a multiplicative term in the flux function. Control models are also proposed for relaxation type systems of hyperbolic PDEs. Existence of optimal control for the case of constant controls is presented. Unbounded and bounded feedback control designs are proposed. These include advective, diffusive, and advective-diffusive controls. Existence result for the bounded advective control is derived. Performance of the relaxation model using bounded advective control is analyzed. Finally simulations using Godunov scheme are performed on unbounded and bounded feedback advective controls. / Ph. D.

Microarray experiments are used to perform gene expression profiling on a large scale. E- and A-optimality of mixed designs was established for experiments with up to 26 different varieties and with the restriction that the number of arrays available is equal to the number of varieties. Because the IBD setting only allows for a single blocking factor (arrays), the search for optimal designs was extended to the Row-Column Design (RCD) setting with blocking factors dye (row) and array (column). Relative efficiencies of these designs were further compared under analysis of variance (ANOVA) models. We also compared the performance of classification analysis for the interwoven loop and the replicated reference designs under four scenarios. The replicated reference design was favored when gene-specific sample variation was large, but the interwoven loop design was preferred for large variation among biological replicates. We applied mixed model methodology to detection and estimation of gene differential expression. For identification of differential gene expression, we favor contrasts which include both variety main effects and variety by gene interactions. In terms of t-statistics for these contrasts, we examined the equivalence between the one- and two-step analyses under both fixed and mixed effects models. We analytically established conditions for equivalence under fixed and mixed models. We investigated the difference of approximation with the two-step analysis in situations where equivalence does not hold. The significant difference between the one- and two-step mixed effects model was further illustrated through Monte Carlo simulation and three case studies. We implemented the one-step analysis for mixed models with the ASREML software. / Ph. D.

Sensitivity analysis is one of the most prominent gradient based optimization techniques for mechanical systems. Model sensitivities are the derivatives of the generalized coordinates defining the motion of the system in time with respect to the system design parameters. These sensitivities can be calculated using finite differences, but the accuracy and computational inefficiency of this method limits its use. Hence, the methodologies of direct and adjoint sensitivity analysis have gained prominence. Recent research has presented computationally efficient methodologies for both direct and adjoint sensitivity analysis of complex multibody dynamic systems. Multibody formulations with joint friction were developed in the recent years and these systems have to be modeled by highly non-linear differential algebraic equations (DAEs) that are difficult to solve using numerical methods. The sensitivity analysis of such systems and the subsequent design optimization is a novel area of research that has been explored in this work. The contribution of this work is in the development of the analytical methods for computation of sensitivities for the most commonly used multibody formulations incorporated with joint friction. Two different friction models have been studied, capable of emulating behaviors of stiction (static friction), sliding friction and viscous drag. A case study has been conducted on a spatial slider-crank mechanism to illustrate the application of this methodology to real-world systems. The Brown and McPhee friction model has been implemented using an index-1 formulation for computation of the dynamics and sensitivities in this case study. The effect of friction on the dynamics and model sensitivities has been analyzed by comparing the sensitivities of slider velocity with respect to the design parameters of crank length, rod length, and the parameters defining the friction model. Due to the highly non-linear nature of friction, it can be concluded that the model dynamics are more sensitive during the transition phases, where the friction coefficient changes from static to dynamic and vice versa. / Master of Science / Mechanisms have been in existence since the earliest days of technology and are more relevant than ever in this age of robotics, artificial intelligence and space exploration. Innovations like myoelectric and neural prosthetics, legged robotics, robotic surgeries, advanced manufacturing, extra-terrestrial vehicles and so on are the modern day manifestations of the traditional mechanisms that formed the backbone of the industrial revolution. All of these innovations implement precision controlled multibody dynamic systems as part of their function. This thesis explores the modelling of such dynamic systems using different mathematical formulations. The contribution of this work is the incorporation of friction in the formulation of such systems. The performance of any dynamical system depends on certain parameters, which can be optimized to meet a certain objective criteria. This is achieved by performing a sensitivity analysis with respect to those parameters on the mathematical formulation of the mechanism. The derivation of this approach has been explored in this thesis. For the benefit of the reader, the application of this method has been discussed using a case study of a simple 3-dimensional slider crank mechanism.

Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero in two scenarios: first in the context of general light-tailed distributions, and second in the specific context in which the objective function and constraints may be observed together as multivariate normal random variates. In the context of general light-tailed distributions, we present the optimal allocation as the result of a concave maximization problem for which the optimal solution is the result of solving one of two nonlinear systems of equations. The first result of its kind, the optimal allocation is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed, e.g., normal, Bernoulli. A consistent estimator for the optimal allocation and a corresponding sequential algorithm for implementation are provided. Various numerical examples demonstrate where and to what extent the proposed allocation differs from competing algorithms. In the context of multivariate normal distributions, we present an exact, asymptotically optimal allocation. This allocation is the result of a concave maximization problem in which there are at least as many constraints as there are suboptimal systems. Each constraint corresponding to a suboptimal system is a convex optimization problem. Thus the optimal allocation may easily be obtained in the context of a "small" number of systems, where the quantifier "small" depends on the available computing resources. A consistent estimator for the optimal allocation and a fully sequential algorithm, fit for implementation, are provided. The sequential algorithm performs significantly better than equal allocation in finite time across a variety of randomly generated problems. The results presented in the general and multivariate normal context provide the first foundation of exact asymptotically optimal sampling methods in the context of "stochastically" constrained simulation optimization on finite sets. Particularly, the general optimal allocation model is likely to be most useful when correlation between the objective and constraint estimators is low, but the data are non-normal. The multivariate normal optimal allocation model is likely to be useful when the multivariate normal assumption is reasonable or the correlation is high. / Ph. D.

The exact controllability problems for systems modeled by linear parabolic differential equations and the Burger's equations are considered. A condition on the exact controllability of linear parabolic equations is obtained using the optimal control approach. We also prove that the exact control is the limit of appropriate optimal controls. A numerical scheme of computing exact controls for linear parabolic equations is constructed based on this result. To obtain numerical approximation of the exact control for the Burger's equation, we first construct another numerical scheme of computing exact controls for linear parabolic equations by reducing the problem to a hypoelliptic equation problem. A numerical scheme for the exact zero control of the Burger's equation is then constructed, based on the simple iteration of the corresponding linearized problem. The efficiency of the computational methods are illustrated by a variety of numerical experiments. / Ph. D.

We consider the analysis of the scattering problem. Assume that an incoming time harmonic wave is scattered by a surface of an impenetrable obstacle. The reflected wave is determined by the surface impedance of the obstacle. In this paper we will investigate the problem of choosing the surface impedance so that a desired scattering amplitude is achieved. We formulate this control problem within the framework of the minimization of a Tikhonov functional. In particular, questions of the existence of an optimal solution and the derivation of the optimality conditions will be addressed. / Master of Science

The optimal projection approach to solving the reduced order model problem produces two coupled, highly nonlinear matrix equations with rank conditions as constraints. Due to the resemblance of these equations to standard matrix Lyapunov equations, they are called modified Lyapunov equations. The proposed algorithms utilize probability-one homotopy theory as the main tool. It is shown that there is a family of systems (the homotopy) that make a continuous transformation from some initial system to the final system. With a carefully chosen initial problem a theorem guarantees that all the systems along the homotopy path will be asymptotically stable, controllable and observable. One method, which solves the equations in their original form, requires a decomposition of the projection matrix using the Drazin inverse of a matrix. It is shown that the appropriate inverse is a differentiable function. An effective algorithm for computing the derivative of the projection matrix that involves solving a set of Sylvester equations is given. Another class of methods considers the equations in a modified form, using a decomposition of the pseudogramians based on a contragredient transformation. Some freedom is left in making an exact match between the number of equations and the number of unknowns, thus effectively generating a family of methods. Three strategies are considered for balancing the number of equations and unknowns. This approach proved to be very successful on a number of examples. The tests have shown that using the ‘best’ method practically always leads to a solution. / Master of Science

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability. / Ph. D.