The effectiveness of partial isolation is evaluated in this study. The static design procedures for linear and nonlinear partially isolated bridges are developed. Results from the static analysis of linear and nonlinear partially isolated bridges, compared with conventional and fully isolated bridges, demonstrate that the effectiveness of nonlinear partial isolation is close to full isolation for reducing the yield force and displacement of the columns in some parameter ranges. However, increased seismic demands on the abutment displacement in the bridge are observed. Nonlinear time history analysis of the different bridge models under earthquake excitations are carried out to investigate the accuracy of the design procedure for nonlinear partial isolation. In addition, an example shows the application of nonlinear partial isolation to a practical bridge.

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering, 1956. / "June, 1956." / Includes bibliographical references (leaf 113). / Introduction: A physically realizable nonlinear system, like a linear one, is a system whose present output is a function of the past of its input. We may regard the system as a computer that operates on the past of one time function to yield the present value of another time function. Mathematically we say that the system performs a transformation on the past of its input to yield its present output. When this transformation is linear (the case of linear systems) we can take advantage of the familiar convolution integral to obtain the present output from the past of the input and the system is said to be characterized by its response to an impulse. That is, the response of a linear system to an impulse is sufficient to determine its response to any input. When the transformation is nonlinear we no longer have a simple relation like the convolution integral relating the output to the past of the input and the system can no longer be characterized by its response to an impulse since superposition does not apply. Wiener has shown, however, that we can characterize a nonlinear system by a set of coefficients and that these coefficients can be determined from a knowledge of the response of the system to shot noise excitation. Thus, shot noise occupies the same position as a probe for investigating nonlinear systems that the impulse occupies as a probe for investigating linear systems. The first section of this thesis is devoted to the Wiener theory of nonlinear system characterization. Emphasis is placed on important concepts of this theory that are used in succeeding chapters to develop a theory for determining optimum nonlinear systems. / by Amar Gopal Bose. / Sc.D.

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Taking inspiration from a variety of creatures found in nature, this thesis demonstrates a new class of materials designed for light capture and guidance. Through the facile method of waveguide self-inscription developed herein, the arrangement of these self-generated light channels can be influenced to produce complex architectures. Inspired by the arrangement of ommatidia found in arthropodal eyes, this was first demonstrated through the fabrication of a radial arrangement of waveguides. This resulted in a thin, polymer film which demonstrated a continuous, panoramic field of view (FOV) able to successfully control the light of a light emitting diode (LED). Moving to more complex architecture, waveguides self-generated in a conical geometry were fabricated. More closely reminiscent of the geometry seen in arthropodal eyes, this waveguide architecture demonstrated a seamless omnidirectional FOV and enhanced imaging capabilities in conjunction with a CMOS camera chip. Lastly, using the method of waveguide self-inscription with an electroactive hydrogel precursor, remote controllable light guiding architectures, as inspired by deep sea creatures, are designed and fabricated. The application of an electric field, in conjunction with the stimuli-responsive waveguides, allows for precise control of the waveguide structures and therefore control over the waveguided light. / Thesis / Doctor of Philosophy (PhD)

In this work, we apply a nonlinear mixture autoregressive (MixAR) model to supplant the Gaussian mixture model for speaker verification. MixAR is a statistical model that is a probabilistically weighted combination of components, each of which is an autoregressive filter in addition to a mean. The probabilistic mixing and the datadependent weights are responsible for the nonlinear nature of the model. Our experiments with synthetic as well as real speech data from standard speech corpora show that MixAR model outperforms GMM, especially under unseen noisy conditions. Moreover, MixAR did not require delta features and used 2.5x fewer parameters to achieve comparable or better performance as that of GMM using static as well as delta features. Also, MixAR suffered less from overitting issues than GMM when training data was sparse. However, MixAR performance deteriorated more quickly than that of GMM when evaluation data duration was reduced. This could pose limitations on the required minimum amount of evaluation data when using MixAR model for speaker verification.

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Optimal control design and implementation for nonlinear systems is a topic of much interest. However, unlike for linear systems, for nonlinear systems explicit analytical solution for optimal feedback control is not available. Numerical techniques, on the other hand, can be used to approximate the solution of the HJB equation to find the optimal control. In this research, a computational approach is developed for finding the optimal control for nonlinear systems with polynomial vector fields based on sum of squares technique. In this research, a numerical technique is developed for optimal control of polynomial nonlinear systems. The approach follows a four-step procedure to obtain both local and approximate global optimality. In the first step, local optimal control is found by using the linearization method and solving the Algebraic Riccati equation with respect to the quadratic part of a given performance index. Next, we utilize the density function method to find a globally stabilizing polynomial nonlinear control for the nonlinear system. In the third step, we find a corresponding Lyapunov function for the designed control in the previous steps based on the Hamilton Jacobi inequality by using semidefinite programming. Finally, to achieve global optimality, we iteratively update the pair of nonlinear control and Lyapunov function based on a state-dependent polynomial matrix inequality. Numerical examples illustrate the effectiveness of the design approach.