The objective of this research is to develop large signal modeling and optimization methods for Capacitive Micromachined Ultrasonic Transducers (CMUTs), especially when they are used in an array configuration. General modeling and optimization methods that cover a large domain of CMUT designs are crucial, as many membrane and array geometry combinations are possible using existing microfabrication technologies. Currently, large signal modeling methods for CMUTs are not well established and nonlinear imaging techniques utilizing linear piezoelectric transducers are not applicable to CMUTs because of their strong nonlinearity. In this work, the nonlinear CMUT behavior is studied, and a feedback linearization method is proposed to reduce the CMUT nonlinearity. This method is shown to improve the CMUT performance for continuous wave applications, such as high-intensity focused ultrasound or harmonic imaging, where transducer linearity is crucial. In the second part of this dissertation, a large signal model is developed that is capable of transient modeling of CMUT arrays with arbitrary electrical terminations. The developed model is suitable for iterative design optimization of CMUTs and CMUT based imaging systems with arbitrary membrane and array geometries for a variety of applications. Finally, a novel multi-pulse method for nonlinear tissue and contrast agent imaging with CMUTs is presented. It is shown that the nonlinear content can be successfully extracted from echo signals in a CMUT based imaging system using a multiple pulse scheme. The proposed method is independent of the CMUT geometry and valid for large signal operation. Experimental results verifying the developed large signal CMUT array model, proposed gap feedback and multi-pulse techniques are also presented.

Microstructure determines fracture toughness of materials through the activation of different fracture mechanisms. To tailor the fracture toughness through microstructure design, it is important to establish relations between microstructure and fracture toughness. To this end, systematic characterization of microstructures, explicit tracking of crack propagation process and realistic representation of deformation and fracture at different length scales are required. A cohesive finite element method (CFEM) based multiscale framework is proposed for analyzing the effect of microstructural heterogeneity, phase morphology, texture, constituent behavior and interfacial bonding strength on fracture toughness. The approach uses the J-integral to calculate the initiation/propagation fracture toughness, allowing explicit representation of realistic microstructures and fundamental fracture mechanisms. Both brittle and ductile materials can be analyzed using this framework. For two-phase Al₂O₃/TiB₂ ceramics, the propagation fracture toughness is improved through fine microstructure size scale, rounded reinforcement morphology and appropriately balanced interphase bonding strength and compliance. These microstructure characteristics can promote interface debonding and discourage particle cracking induced catastrophic failure. Based on the CFEM results, a semi-empirical model is developed to establish a quantitative relation between the propagation toughness and statistical measures of microstructure, fracture mechanisms, constituent and interfacial properties. The analytical model provides deeper insights into the fracture process as it quantitatively predicts the proportion of each fracture mechanism in the heterogeneous microstructure. Based on the study on brittle materials, the semi-analytical model is extended to ductile materials such as AZ31 Mg alloy and Ti-6Al-4V alloy. The fracture resistance in these materials not only depends on the crack surfaces formed during the failure process, but also largely determined by the bulk plastic energy dissipation. The CFEM simulation permits surface energy release rate to be quantified through explicit tracking of crack propagation in the microstructure. The plastic energy dissipation rate is evaluated as the difference between the predicted J value and the surface energy release rate. This method allows competition between material deformation and fracture as well as competition between transgranular and intergranular fracture to be quantified. The methodology developed in this thesis is potentially useful for both the selection of materials and tailoring of microstructure to improve fracture resistance.

This paper investigates a new class of database systems loosely referred to as "non-relational databases," which offer a subset of traditional relational database functionality, in exchange for improved scalability, performance, and / or simplicity. We explore the differences in conceptual modeling techniques, and examine both the advantages and limitations of several classes of currently available systems, using running examples of real-world problems as implemented in both a traditional relational database model, as well as several non-relational models. / text

Block copolymers have been subject of tremendous research interest owing to their capability of undergoing self-assembly which allows them to tailor their electrical, optical, and mechanical properties. Statistical mechanics of flexible block copolymers is well understood. However, there are many unresolved issues with confinement of block copolymers as well as structure formation in block copolymers having non-flexible polymer blocks. We develop mean field theory models to address the issues arising in thermodynamics of such complex block copolymers. Also, we develop theoretical formalisms to understand the link between morphology and macroscopic properties in these block copolymers. We study the stability and ordering in thin films of flexible diblock copolymer in the presence of compressible solvent using a combined polymer mean field theory and lattice gas model for binary fluid mixtures. We utilize mean field theory model to understand the self-assembly behavior in side-chain liquid crystalline block copolymers which involve interplay between microphase separation and liquid crystalline ordering of side chain mesogenic units. We extend the field theoretic models for block copolymer to account for self-assembly in semicrystalline block copolymers. The semicrystalline chain is modeled as a semiflexible chain having non-bonded attractions between parallel bonds. We characterize the structure formation in such block copolymers as a function of the rigidity of the semicrystalline chain. Then we extend the formalism to study semicrystalline triblock and pentablock copolymers and evaluate bridging fractions in different sequences of semicrystalline multiblock copolymers. Rod-coil block copolymers have a flexible polymer covalently linked to rigid polymer. Such polymers have potential applications as organic LEDs and photovoltaic devices. We study the self-assembly of such block copolymer under confinement. To make these block copolymers viable as photovoltaic devices, we performed the photovoltaic modeling of devices based on self-assembly of block copolymers. We characterize the interplay between self-assembly and anisotropy of charge transport (arising due to rigid polymer chains) in determining the eventual photovoltaic properties. / text

The field of category learning is replete with theories that detail how similarity and comparison based processes are used to learn categories, but these theories are limited to cases in which item and category representations consist of feature vectors. This precludes these methods from learning relational categories, where membership is determined by the structured relations binding the features of a stimulus together. Fortuitously,  researchers within the analogy literature have developed theories of comparison that account for this structure.  This thesis bridges the two approaches, describing a theory of category learning that utilizes the representational frameworks provided by the analogy literature to learn categories that may only be described through the appreciation of the structured relations within their members. This theory is formalized in a model, Building Relations through Instance Driven Gradient Error Shifting (BRIDGES), that shows how relational categories can be learned through attention-driven analogies between concrete exemplars.  This approach is demonstrated through several simulations that compare similarity-based learning and alternatives, such as rule-based abstractions and re-representation.  We then present a series of experiments that explore the reciprocal impact of relational comparison on category structure and category structure on relational comparison.  This work provides a theoretical framework and formal model suggesting that feature-based and relation-based categories are a continuum that are learned through selective attention and similarity-based comparison. / text

This thesis presents different methods that improve the ability to relate the flow properties of heterogeneous reservoirs to equivalent anisotropic flow properties in order to predict the performance of the Steam Assisted Gravity Drainage (SAGD) process. Process simulation using full scale heterogeneous reservoirs are inefficient and so the need arises to develop equivalent anisotropic reservoirs that can capture the effect of reservoir heterogeneity. Since SAGD is highly governed by permeability in the reservoir, effective permeability values were determined using different upscaling techniques. First, a flow-based upscaling technique was employed and a semi-analytical model, derived by Azom and Srinivasan, was used to determine the accuracy of the upscaling. The results indicated inadequacy of flow-based upscaling schemes to derive effective direction permeabilities consistent with the unique flow geometry during the SAGD process. Subsequently, statistical upscaling was employed using full 3D models to determine relationships between the heterogeneity variables: k[subscript italic v]⁄k[subscript italic h] , correlation length and shale proportion. An iterative procedure coupled with an optimization algorithm was deployed to determine optimal k[subscript italic v] and k[subscript italic k] values. Further regression analysis was performed to explore the relationship between the variables of shale heterogeneity in a reservoir and the corresponding effective properties. It was observed that increased correlation lengths and shale proportions both decrease the dimensionless flow rates at given dimensionless times and that the semi-analytical model was more accurate for cases that contained lower shale proportions. Upscaled heterogeneous values inputted into the semi-analytical model resulted in underestimation of oil flow rate due to the inability to fully account for the impact of reservoir barriers and the configuration of flow streamlines during the SAGD process. Statistical upscaling using geometric averaging as the initial guess was used as the basis for developing a relationship between correlation length, shale proportion and k[subscript italic v]⁄k[subscript italic h]. The initial regression models did not accurately predict the anisotropic ratio because of insufficient data points along the regression surface. Subsequently a non-linear regression model that was 2nd order in both length and shale proportion was calibrated by executing more cases with varying levels of heterogeneity and the regression model revealed excellent matches to heterogeneous models for the prediction cases. / text

This thesis reports the development of potent and selective inhibitors of m-calpain for the treatment of cataract. SJA6017 has been proven to prevent lens opacity in rat and has been our lead compound. A series of Val-Leu peptidyl aldehyde inhibitors (33a-e, 33g, 33i and 35) have been designed, synthesized, and tested for therapeutic potential as cataract inhibitors. Chapter 1 is an introduction to calpain and the diseases associated with it's over activation. A review of the literature on calpain inhibition is given. Structure activity relationship (SAR) theory is presented. The techniques that have been applied in our research group to drug design include molecular modeling, synthesis, assay and animal studies which are all briefly discussed. The importance of a -strand conformation for an inhibitor to bind to calpain is discussed. Chapter 2 describes the synthesis of m-calpain inhibitors. This comprises the preparation of the Val-Leu dipeptide core 29, Val-Leu dipeptidyl alcohols 31a-g and 31i, and the synthesis of dipeptidyl aldehydes 33a-e, 33g, 33i and 35. The choice of coupling regents and conditions in the coupling reactions is investigated. Sulfur trioxide pyridine oxidation for the conversion of Val-Leu dipeptidyl alcohols to aldehydes is discussed. The molecular modeling and biological assay results are presented.

By a dominant account, memory is composed of multiple storage systems, each operating according to unique principles. By an alternative account, memory is a single storage system and operates according to a single set of principles. Selective memory impairments in amnesia serve as the primary evidence for the multiple-system perspective. This thesis reports a critical appraisal of the multiple-system perspective using a combination of computational and empirical methods. In the computational analysis, I adopt the Holographic Exemplar Model, a single-system model of memory based on Hintzman’s (1986) classic MINERVA2 model. I simulate amnesia by manipulating the quality with which items are encoded in memory. In the empirical analysis, I simulate amnesia by manipulating peoples’ quality of encoding by limiting the time given to study stimuli. Simulations 1-2 and Experiments 1-2 simulate a dissociation between classification and recognition. All four analyses are consistent with the original results. Simulation 3 and Experiment 3 simulate single and double dissociations between tachistoscopic identification and recognition. The analyses were consistent with the single but not double dissociation. Simulation 4 and Experiment 4 simulate a dissociation among word-stem completion, cued recall, and recognition. Both analyses were only partially consistent with the original results, representing a failure overall. Simulation 5 and Experiment 5 derived a novel prediction from artificial grammar learning, predicting a non-dissociation between string completion and recognition. The mixed results provide some support for a single-system account of memory and opens opportunities for future work. I argue that the analysis is best considered in convergence with previous work moving toward a more integrated account of memory / February 2017

Maintaining IAQ (Internal Air Quality) and thermal comfort of occupants in buildings have been a challenge to overcome satisfying the two ends: criteria for sustainability and cost effectiveness. Although there was a movement for mechanical ventilation systems in the recent past, in addition to the cost involved, they are found to not deliver the desired air quality, lead to social consequences such as sick building syndrome, contribute to environmental consequences related to ozone-depleting substances with increasing energy consumption, generate noise and having difficulties in cleaning and maintaining. These consequences compelled research on natural ventilation systems, which were used in ancient buildings. Although it has been found that natural ventilation of buildings can become a substantial architectural design tool that leads to “breathing architecture,” fluctuations in indoor temperature and air quality makes depending entirely on natural ventilation less effective. The combination of natural and mechanical ventilation, the hybrid ventilation or mixed-mode ventilation, systems utilizes advantages and eliminates drawbacks from both mechanical and entirely dependent natural ventilation systems. Hybrid ventilation systems, which have been utilized in historical buildings, with less investment cost and reduction of energy usage have been found to be a solution to provide acceptable standards of IAQ and thermal comfort through natural air circulation in buildings. This research study was carried out to verify the effectiveness of a hybrid ventilation system in an auditorium built around 60 years back for its effectiveness as a provider of thermal comfort to its occupants. Computational Fluid Dynamic (CFD) modeling was carried out on a Finite Element (FE) model owing to its capability of offering a wide range of flexible analytical solutions, lower realization time and comparative cost effectiveness to experimental methods of modeling. This verification of the system has revealed that hybrid ventilation systems could provide effective thermal comfort in buildings designed specifically to allow circulation of air through the system. The results of the study were in agreement with measured data and the expected flow of air through the building when the thermal load due to metabolism of occupants was not included in the analysis. In addition, the expected results complied with similar studies on natural/hybrid ventilation systems. With the addition of the thermal load, as a uniform heat flux from the flow of the auditorium, it was observed that the conditioning of the air throughout the space was better than the without thermal load scenario. In the case modeling people as cylinders, with a convective heat flux, it was observed that the air flow direction changes and the seating level of the auditorium do not get sufficient air flow to maintain a comfortable air quality.  Ineffective simulation of the inlet louver was assumed to be the primary reason for this scenario and other reasons such as the seating arrangement modeling too could have effects on the result. As conclusions of the study it was found that the whole building system properties have to be selected, as the control component to produce operating commands, to circulate air through the building in accordance with the air flow: both velocity and patterns, required to maintain thermal comfort of all occupants. Air inflow could be through windows as acquisition components to collect indoor and outdoor climatic parameters and air outflow could be mechanically controlled through exhausted fans turning on or off as the operating component in the system. The result of the study ensures the method of solutions through CFD to be utilized to provide effective and less costly path to verify systems such as natural or hybrid air flow systems through buildings.  The whole system studied could be applied with suitable contextual modifications to any new location, with similar cost effective modeling, to produce less fuel consuming building systems leading to sustainability of built environment.

This thesis work studied the behavioral modeling, estimation of parameters, model performance and linearization of power amplifier (PA) using Digital Pre Distortion (DPD) technique. PAs are one of the fundamental block in communication systems and also one of the main sources of nonlinearities in the system, as these devices are frequently subjected to signals characterized by considerable bandwidths and non-constant envelopes due to use of modern modulation technique. Moreover, PAs have high efficiency level at its nonlinear region. So, to operate the PA at its high efficiency region, linearization operation needs to be done. This has been investigated in this thesis work with the help of behavioral modeling and DPD. An essential initial step in designing a linearizer for a PA is to model the PA nonlinearity accurately. Behavioral modeling has been used for PA model for its computational efficiency, which means by relating input and output signals without addressing to the physical analysis of the device. DPD technique has been chosen for linearizing the performance of PA based on their low requirement of resources for implementation. In this thesis, five different PA models with memory effect, based on Volterra series, are studied and compared for three different PAs selected by Ericsson. These PAs are designed for third and fourth generation telecommunication system. Two different signals with different peak to average ratios and different bandwidths have been used as input signals of PA for this study. The main result in this thesis work includes the comparison of all five forward behavioral modeling results for all three PA’s. The results also describe that; two of the given PA’s can be linearized by using the DPD technique within the 3GPP standard regulations for ACPR.