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  • The equations of polyconvex thermoelasticity

    Galanopoulou, Myrto Maria (2020-11-25) [Dissertation]
    Advisor: Tzavaras, Athanasios
    Committee members: Hoteit, Ibrahim; Markowich, Peter A.; Christoforou, Cleopatra; Dafermos Constantine, M.
    In my Dissertation, I consider the system of thermoelasticity endowed with poly- convex energy. I will present the equations in their mathematical and physical con- text, and I will explain the relevant research in the area and the contributions of my work. First, I embed the equations of polyconvex thermoviscoelasticity into an aug- mented, symmetrizable, hyperbolic system which possesses a convex entropy. Using the relative entropy method in the extended variables, I show convergence from ther- moviscoelasticity with Newtonian viscosity and Fourier heat conduction to smooth solutions of the system of adiabatic thermoelasticity as both parameters tend to zero and convergence from thermoviscoelasticity to smooth solutions of thermoelasticity in the zero-viscosity limit. In addition, I establish a weak-strong uniqueness result for the equations of adiabatic thermoelasticity in the class of entropy weak solutions. Then, I prove a measure-valued versus strong uniqueness result for adiabatic poly- convex thermoelasticity in a suitable class of measure-valued solutions, de ned by means of generalized Young measures that describe both oscillatory and concentra- tion e ects. Instead of working directly with the extended variables, I will look at the parent system in the original variables utilizing the weak stability properties of certain transport-stretching identities, which allow to carry out the calculations by placing minimal regularity assumptions in the energy framework. Next, I construct a variational scheme for isentropic processes of adiabatic polyconvex thermoelasticity. I establish existence of minimizers which converge to a measure-valued solution that dissipates the total energy. Also, I prove that the scheme converges when the limit- ing solution is smooth. Finally, for completeness and for the reader's convenience, I present the well-established theory for local existence of classical solutions and how it applies to the equations at hand.
  • Quantile Function Modeling and Analysis for Multivariate Functional Data

    Agarwal, Gaurav (2020-11-25) [Dissertation]
    Advisor: Sun, Ying
    Committee members: Ombao, Hernando; Tester, Mark A.; He, Xuming
    Quantile function modeling is a more robust, comprehensive, and flexible method of statistical analysis than the commonly used mean-based methods. More and more data are collected in the form of multivariate, functional, and multivariate functional data, for which many aspects of quantile analysis remain unexplored and challenging. This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions. Firstly, it proposes bivariate quantile analysis methods that can predict the joint distribution of bivariate response and improve on conventional univariate quantile regression. The proposed robust statistical techniques are applied to examine barley plants grown in saltwater and freshwater conditions providing interesting insights into barley’s responses, informing future crop decisions. Secondly, it proposes modeling and visualization of bivariate functional data to characterize the distribution and detect outliers. The proposed methods provide an informative visualization tool for bivariate functional data and can characterize non-Gaussian, skewed, and heavy-tailed distributions using directional quantile envelopes. The radiosonde wind data application illustrates our proposed quantile analysis methods for visualization, outlier detection, and prediction. However, the directional quantile envelopes are convex by definition. This feature is shared by most existing methods, which is not desirable in nonconvex and multimodal distributions. Thirdly, this challenge is addressed by modeling multivariate functional data for flexible quantile contour estimation and prediction. The estimated contours are flexible in the sense that they can characterize non-Gaussian and nonconvex marginal distributions. The proposed multivariate quantile function enjoys the theoretical properties of monotonicity, uniqueness, and the consistency of its contours. The proposed methods are applied to air pollution data. Finally, we perform quantile spatial prediction for non-Gaussian spatial data, which often emerges in environmental applications. We introduce a copula-based multiple indicator kriging model, which makes no distributional assumptions on the marginal distribution, thus offers more flexibility. The method performs better than the commonly used variogram approach and Gaussian kriging for spatial prediction in simulations and application to precipitation data.
  • BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

    Tang, Meini (2020-11-25) [Thesis]
    Advisor: Ombao, Hernando
    Committee members: Sun, Ying; Laleg-Kirati, Taous-Meriem; Ting, Chee-Ming
    Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.
  • Imitation Learning based on Generative Adversarial Networks for Robot Path Planning

    Yi, Xianyong (2020-11-24) [Thesis]
    Advisor: Michels, Dominik L.
    Committee members: Wonka, Peter; Moshkov, Mikhail
    Robot path planning and dynamic obstacle avoidance are defined as a problem that robots plan a feasible path from a given starting point to a destination point in a nonlinear dynamic environment, and safely bypass dynamic obstacles to the destination with minimal deviation from the trajectory. Path planning is a typical sequential decision-making problem. Dynamic local observable environment requires real-time and adaptive decision-making systems. It is an innovation for the robot to learn the policy directly from demonstration trajectories to adapt to similar state spaces that may appear in the future. We aim to develop a method for directly learning navigation behavior from demonstration trajectories without defining the environment and attention models, by using the concepts of Generative Adversarial Imitation Learning (GAIL) and Sequence Generative Adversarial Network (SeqGAN). The proposed SeqGAIL model in this thesis allows the robot to reproduce the desired behavior in different situations. In which, an adversarial net is established, and the Feature Counts Errors reduction is utilized as the forcing objective for the Generator. The refinement measure is taken to solve the instability problem. In addition, we proposed to use the Rapidly-exploring Random Tree* (RRT*) with pre-trained weights to generate adequate demonstration trajectories in dynamic environment as the training data, and this idea can effectively overcome the difficulty of acquiring huge training data.
  • Efficient Ensemble Data Assimilation and Forecasting of the Red Sea Circulation

    Toye, Habib (2020-11-23) [Dissertation]
    Advisor: Hoteit, Ibrahim
    Committee members: Knio, Omar; Al-Naffouri, Tareq Y.; Iskandarani, Mohamad
    This thesis presents our efforts to build an operational ensemble forecasting system for the Red Sea, based on the Data Research Testbed (DART) package for ensemble data assimilation and the Massachusetts Institute of Technology general circulation ocean model (MITgcm) for forecasting. The Red Sea DART-MITgcm system efficiently integrates all the ensemble members in parallel, while accommodating different ensemble assimilation schemes. The promising ensemble adjustment Kalman filter (EAKF), designed to avoid manipulating the gigantic covariance matrices involved in the ensemble assimilation process, possesses relevant features required for an operational setting. The need for more efficient filtering schemes to implement a high resolution assimilation system for the Red Sea and to handle large ensembles for proper description of the assimilation statistics prompted the design and implementation of new filtering approaches. Making the most of our world-class supercomputer, Shaheen, we first pushed the system limits by designing a fault-tolerant scheduler extension that allowed us to test for the first time a fully realistic and high resolution 1000 ensemble members ocean ensemble assimilation system. In an operational setting, however, timely forecasts are of essence, and running large ensembles, albeit preferable and desirable, is not sustainable. New schemes aiming at lowering the computational burden while preserving reliable assimilation results, were developed. The ensemble Optimal Interpolation (EnOI) algorithm requires only a single model integration in the forecast step, using a static ensemble of preselected members for assimilation, and is therefore computationally significantly cheaper than the EAKF. To account for the strong seasonal variability of the Red Sea circulation, an EnOI with seasonally-varying ensembles (SEnOI) was first implemented. To better handle intra-seasonal variabilities and enhance the developed seasonal EnOI system, an automatic procedure to adaptively select the ensemble members through the assimilation cycles was then introduced. Finally, an efficient Hybrid scheme combining the dynamical flow-dependent covariance of the EAKF and a static covariance of the EnOI was proposed and successfully tested in the Red Sea. The developed Hybrid ensemble data assimilation system will form the basis of the first operational Red Sea forecasting system that is currently being implemented to support Saudi Aramco operations in this basin.
  • Leveraging Graph Convolutional Networks for Point Cloud Upsampling

    Qian, Guocheng (2020-11-16) [Thesis]
    Advisor: Ghanem, Bernard
    Committee members: Wonka, Peter; Pottmann, Helmut
    Due to hardware limitations, 3D sensors like LiDAR often produce sparse and noisy point clouds. Point cloud upsampling is the task of converting such point clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling using deep neural networks. The effectiveness of a point cloud upsampling neural network heavily relies on the upsampling module and the feature extractor used therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle. NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves the performance of previous upsampling methods. I also propose a new GCN-based multi-scale feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, Inception DenseGCN learns a hierarchical feature representation and enables further performance gains. I combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling network called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.
  • Spatio-Temporal Statistical Modeling with Application to Wind Energy Assessment in Saudi Arabia

    Chen, Wanfang (2020-11-08) [Dissertation]
    Advisor: Genton, Marc G.
    Committee members: Huser, Raphaël G.; Stenchikov, Georgiy L.; Zhang, Hao
    Saudi Arabia has been trying to change its long tradition of relying on fossil fuels and seek renewable energy sources such as wind power. In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area ofWestern Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia. Under the Bayesian framework, I measure the uncertainty of return levels and produce risk maps of wind extremes, which show that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations. In order to perform spatial predictions of the bivariate wind random field for efficient turbine control, I propose parametric variogram matrix (function) models for cokriging, which have the advantage of allowing for a smooth transition between a joint second-order and intrinsically stationary vector random field. Under Gaussianity, the covariance function is central to spatio-temporal modeling, which is useful to understand the dynamics of winds in space and time. I review the various space-time covariance structures and models, some of which are visualized with animations, and associated tests. I also discuss inference issues and a case study based on a high-resolution wind-speed dataset. The Gaussian assumption commonly made in statistics needs to be validated, and I show that tests for independently and identically distributed data cannot be used directly for spatial data. I then propose a new multivariate test for spatial data by accounting for the spatial dependence. The new test is easy to compute, has a chi-square null distribution, and has a good control of the type I error and a high empirical power.
  • Spatio-Temporal Prediction and Stochastic Simulation for Large-Scale Nonstationary Processes

    Li, Yuxiao (2020-11-04) [Thesis]
    Advisor: Sun, Ying
    Committee members: McCabe, Matthew; Wikle, Christopher K.; Zhang, Xiangliang
    There has been an increasing demand for describing, predicting, and drawing inferences for various environmental processes, such as air pollution and precipitation. Environmental statistics plays an important role in many related applications, such as weather-related risk assessment for urban design and crop growth. However, modeling the spatio-temporal dynamics of environmental data is challenging due to their inherent high variability and nonstationarity. This dissertation is composed of four signi cant contributions to the modeling, simulation, and prediction of spatiotemporal processes using statistical techniques and machine learning algorithms. This dissertation rstly focuses on the Gaussian process emulators of the numerical climate models over a large spatial region, where the spatial process exhibits nonstationarity. The proposed method allows for estimating a rich class of nonstationary Mat ern covariance functions with spatially varying parameters. The e cient estimation is achieved by local-polynomial tting of the covariance parameters. To extend the applicability of this method to large-scale computations, the proposed method is implemented by developing software with high-performance computing architectures for nonstationary Gaussian process estimation and simulation. The developed software outperforms existing ones in both computational time and accuracy by a large margin. The method and software are applied to the statistical emulation of high-resolution climate models. The second focus of this dissertation is the development of spatio-temporal stochastic weather generators for non-Gaussian and nonstationary processes. The proposed multi-site generator uses a left-censored non-Gaussian vector autoregression model, where the random error follows a skew-symmetric distribution. It not only drives the occurrence and intensity simultaneously but also possesses nice interpretations both physically and statistically. The generator is applied to 30-second precipitation data collected at the University of Lausanne. Finally, this dissertation investigates the spatial prediction with scalable deep learning algorithms to overcome the limitations of the classical Kriging predictor in geostatistics. A novel neural network structure is proposed for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. The proposed method, called DeepKriging, has multiple advantages over Kriging and classical neural networks with spatial coordinates as features. The method is applied to the prediction of ne particulate matter (PM2:5) concentrations in the United States.
  • A Novel HVDC Architecture for Offshore Wind Farm Applications

    Dezem Bertozzi Junior, Otávio José (2020-11) [Thesis]
    Advisor: Ahmed, Shehab
    Committee members: Salama, Khaled N.; Lima, Ricardo
    The increasing global participation of wind power in the overall generation ca- pacity makes it one of the most promising renewable resources. Advances in power electronics have enabled this market growth and penetration. Through a literature review, this work explores the challenges and opportunities presented by offshore wind farms, as well as the different solutions proposed concerning power electron- ics converters, collection and transmission schemes, as well as control and protection techniques. A novel power converter solution for the parallel connection of high power offshore wind turbines, suitable for HVDC collection and transmission, is presented. For the parallel operation of energy sources in an HVDC grid, DC link voltage con- trol is required. The proposed system is based on a full-power rated uncontrolled diode bridge rectifier in series with a partially-rated fully-controlled thyristor bridge rectifier. The thyristor bridge acts as a voltage regulator to ensure the flow of the desired current through each branch, where a reactor is placed in series for filtering of the DC current. AC filters are installed on the machine side to mitigate harmonic content. The mathematical modeling of the system is derived and the control design procedure is discussed. Guidelines for equipment and device specifications are pre- sented. Different setups for an experimental framework are suggested and discussed, including a conceptual application for hardware-in-the-loop real-time simulation and testing.
  • Control Theoretic Approaches to Computational Modeling and Risk Mitigation for Large Crowd Management

    Alrashed, Mohammed (2020-11) [Dissertation]
    Advisor: Shamma, Jeff S.
    Committee members: Shamma, Jeff S.; Feron, Eric; Knio, Omar; Bamieh, Bassam
    We develop a computational framework for risk mitigation in high population density events. With increased global population, the frequency of high population density events is naturally increased. Therefore, risk-free crowd management plans are critical for efficient mobility, convenient daily life, resource management and most importantly mitigation of any inadvertent incidents and accidents such as stampedes. The status-quo for crowd management plans is the use of human experience/expert advice. However, most often such dependency on human experience is insufficient, flawed and results in inconvenience and tragic events. Motivated by these issues, we propose an agent-based mathematical model describing realistic human motion and simulating large dense crowds in a wide variety of events as a potential simulation testbed to trial crowd management plans. The developed model incorporates stylized mindset characteristics as an internal drive for physical behavior such as walking, running, and pushing. Furthermore, the model is combined with a visualisation of crowd movement. We develop analytic tools to quantify crowd dynamic features. The analytic tools will enable verification and validation to empirical evidence and surveillance video feed in both local and holistic representations of the crowd. This work addresses research problems in computational modeling of crowd dynamics, specifically: understanding and modeling the impact of a collective mindset on crowd dynamics versus mixtures of heterogeneous mindsets, the effect of social contagion of behaviors and decisions within the crowd, the competitive and aggressive pushing behaviors, and torso and steering dynamics.
  • Theoretical Kinetic Study of Gas Phase Oxidation of Nicotine by Hydroxyl Radical

    Chavarrio Cañas, Javier Eduardo (2020-11) [Thesis]
    Advisor: Sarathy, Mani
    Committee members: Mishra, Himanshu; Castaño, Pedro
    Cigarette smoke is suspected to cause diverse illnesses in smokers and people breathing second- and third-hand smoke. Although different studies have been done to elucidate the impact on health due to smoking, there is a lack of kinetic information regarding the degradation of nicotine under different environmental conditions. As a consequence, currently it is not possible to determine thoroughly the risk due to exposure to nicotine and the compounds derived from its decomposition. With the aim of contributing to clarify the different degradation paths followed by nicotine during and after the consumption of cigarettes, this work presents a theoretical study of the hydrogen atom abstraction reaction by hydroxyl radical at four sites in the nicotine molecule in a broad range of temperature, specifically be-tween 200-3000 K. The site-specific kinetic rate constants were computed by means of the multi-structural torsional variational transition state theory with small curvature tunneling contribution, performing ab initio calculations at the level M06-2X/aug-cc-pVQZ//M06-2X/cc-pVTZ. According to our computations, the dependence on temperature of the studied rate constants exhibited a non-Arrhenius fashion, with a minimum at 873 K. A negative temperature dependence was observed at temperatures lower than 873 K, indicating more prolonged exposure to nicotine in warmer environments. On the other hand, the opposite behavior was observed at higher temperatures; this non-Arrhenius be-havior results of interest in tobacco cigarette combustion, inducing different reaction mechanisms depending on the burning conditions of the different smoking devices. The results indicate that multi-structural and torsional anharmonicity is an im-portant factor in the computation of accurate rate constants, especially at high tem-peratures where the higher-energy conformers of the different species exert a larger influence. The anharmonicity factors suggest that disregarding the anharmonic de-viations leads to overestimation of the rate constant coefficients, by a factor between four and six. Our computed overall kinetic rate constant at 298 K exhibited very good agreement with the only experimental value meausred by Borduas et al. [1], af-fording certainty about our calculated site-specific rate constants, which are currently inaccessible to experiments. However, further experimental studies are necessary to validate our kinetic studies at other temperatures.
  • Design and topological optimization of nanophotonic devices

    Lin, Ronghui (2020-11) [Dissertation]
    Advisor: Li, Xiaohang
    Committee members: Li, Xiaohang; Fratalocchi, Andrea; Liberale, Carlo; Lu, Tien-Chang
    A central topic in the research of nanophotonics is the geometrical optimization of the nanostructures since the geometries are deeply related to the Mie resonances and the localized surface plasmon resonances in dielectric and metallic nanomaterials. When many nanostructures are assembled to form a metamaterial, the tuning of the geometrical parameters can bring even more profound effects, such as bound states in the continuum (BIC) with infinite quality factors (Q factors). Moreover, with the development of nanofabrication technologies, there is a trend of integrating nanostructures in the vertical direction, which provides more degrees of freedom for controlling the device performance and functionality. The main topic of this dissertation is to explore some of the abovementioned tuning possibilities to enhance the performance of nanophotonic devices. The dissertation contains two major parts: In chapters 2 and 3, the vertical integration of metalenses is studied. We discover a phenomenon similar to the Moiré effect in the bilayer Pancharatnam-Berry phase metalenses and reveal the role of geometrical imperfections on the focusing performance of reflective metalenses. Novel multifocal and reflective metalenses, with smaller footprints and enhanced performance compared to their bulky conventional counterparts, are designed based on the theoretical findings. The study of geometrical imperfections also provides guidelines for analyzing and compensating the fabrication errors, which is vital for large scale production and commercialization of metalenses. In chapters 4 and 5, we use machine learning to harness the full tuning power of the complicated geometries, which is challenging with conventional design methods. Plasmonic metasurfaces with on-demand optical responses are designed by manipulating the coupling of multiple nanodisks using neural networks. An accuracy of ± 8 nm is achieved, which is higher than previous reports and close to the fabrication limits of nanofabrication technologies. We also demonstrate, for the first time, the control of multiple BIC states using freeform geometries with predefined symmetry. It is a new method to exploit the untapped potential of freeform photonics structures. The discoveries we have made in both dielectric and plasmonic nanophotonic devices could benefit applications such as imaging, sensing, and light-emitting devices.
  • Increasing the Top Brine Temperature of Multi-Effects Distillation-MED to Boost Its Performance through Controlling the Formation of Scale by Nanofilteration and Antiscalants

    Alharthi, Khalid (2020-11) [Thesis]
    Advisor: Ng, Kim Choon
    Committee members: Ghaffour, NorEddine; Lai, Zhiping
    Thermal desalination technology especially, multi-effect distillation MED is of great importance to oil producing countries such as those in the gulf region owing to its efficacy in processing seawater with the minimum pre-treatment of the feed and cheap energy input available from waste heat. One of the main drawback of the current MED processes is the susceptibility of scaling when operate above 70 ºC. This limitation deprives the technology to be energy efficient and reduce its optimal productivity. An optimized pre-treatment of the seawater feed by NF membranes can enhance its efficiency significantly. In this work, the possibility of applying a tailored feed quality using thermodynamic speciation chemistry of the feed water and prediction of the scale propensity based on the Saturation Index of the scale minerals was investigated. Different NF membranes with different properties were used and compared experimentally with each other and theoretically with predictions that are based the saturation index. Moreover, new generation of the polymeric antiscalants promoted by the main manufacturers of the inhibitors industry to control scale formation has been investigated. In addition, as part of planned work for future studies, design and construction of a pilot scale based on NF membrane process was carried out and meant to be a potential extension of this work.
  • The Influence of pH and Temperature on the Encapsulation of Quinine by Alpha, Beta, and Gamma Cyclodextrins as Explored by NMR Spectroscopy

    Poulson, Benjamin Gabriel (2020-11) [Thesis]
    Advisor: Jaremko, Mariusz
    Committee members: Arold, Stefan T.; Saikaly, Pascal; Gao, Xin
    Cyclodextrins are well known for their ability to encapsulate molecules and have captured the attention of scientists for many years. This ability alone makes cyclodextrins attractive for study, research, and applications in many fields including food, cosmetics, textiles, and the pharmaceutical industry. In this thesis, we specifically look at the ability of the three native cyclodextrins, alpha, beta, and gamma cyclodextrin (α-CD, β-CD, and γ-CD, respectively), to encapsulate the drug molecule, quinine, a small hydrophobic, lipophilic molecule used to treat malaria, leg cramps, and other similar conditions. This encapsulation process is driven by the molecular interactions, which have been studied by NMR techniques at different temperatures (288 K, 293 K, 298 K, 303 K, 308 K) and pH values (7.4, 11.5). These factors (temperature and pH) influence these molecular interactions, which in turn significantly affects the entire encapsulation process. Detailed studies of the influences of temperature and pH on the interactions that drive the encapsulation may suggest some new directions into designing controlled drug release processes. Results obtained throughout the course of this work indicate that β-CD is the best native cyclodextrin to bind quinine, and that binding is best at pH = 11.5. It was found that temperature does not significantly affect the binding affinity of quinine to either α-CD, β-CD, or γ-CD.
  • Micro-electromechanical Resonator-based Logic and Interface Circuits for Low Power Applications

    Ahmed, Sally (2020-11) [Dissertation]
    Advisor: Fariborzi, Hossein
    Committee members: Fariborzi, Hossein; Shamim, Atif; Younis, Mohammad I.; Weinstein, Dana
    The notion of mechanical computation has been revived in the past few years, with the advances of nanofabrication techniques. Although electromechanical devices are inherently slow, they offer zero or very low off-state current, which reduces the overall power consumption compared to the fast complementary-metal-oxide-semiconductor (CMOS) counterparts. This energy efficiency feature is the most crucial requirement for most of the stand-alone battery-operated gadgets, biomedical devices, and the internet of things (IoT) applications, which do not require the fast processing speeds offered by the mainstream CMOS technology. In particular, using Micro-Electro-Mechanical (MEM) resonators in mechanical computing has drawn the attention of the research community and the industry in the last decade as this technology offers low power consumption, reduced circuit complexity compared to conventional CMOS designs, run-time re- programmability and high reliability due to the contactless mode of operation compared to other MEM switches such as micro-relays. In this thesis, we introduce digital circuit design techniques tailored for clamped-clamped beam MEM resonators. The main operation mechanism of these circuit blocks is based on fine-tuning of the resonance frequency of the micro-resonator beam, and the logic function performed by the devices is mainly determined by factors such as input/output terminal arrangement, signal type, resonator operation regime (linear/non-linear), and the operation frequency. These proposed circuits include the major building blocks of any microprocessor such as logic gates, a full adder which is a key block in any arithmetic and logic operation units (ALU), and I/O interface units, including digital to analog (DAC) and analog to digital (ADC) data converters. All proposed designs were first simulated using a finite element software and then the results were experimentally verified. Important aspects such as energy per operation, speed, and circuit complexity are evaluated and compared to CMOS counterparts. In all applications, we show that by proper scaling of the resonator’s dimensions, MHz operation speeds and energy consumption in the range of femto-joules per logic operation are attainable. Finally, we discuss some of the challenges in using MEM resonators in digital circuit design at the device level and circuit level and propose solutions to tackle some of them.
  • An Unexplored Genome Insulating Mechanism in Caenorhabditis Elegans

    Alkhaldi, Faisal (2020-11) [Thesis]
    Advisor: Frøkjær-Jensen, Christian
    Committee members: Krattinger, Simon G.; Tegner, Jesper
    Caenorhabditis Elegans genome maintains active H3K36me3 chromatin domains interspersed with repressive H3K27me3 domains on the autosomes’ distal ends. The mechanisms stabilizing these domains and the prevention of position-effect variegation remains unknown as no insulator elements have been identified in C. elegans. De-novo motif discovery applied on mes-4 binding sites links the H3K36me3-specific methyltransferase to a class of non-coding DNA known as Periodic An/Tn Clusters (PATCs). PATCs display characteristics of insulator elements such as local nucleosome depletion and their restriction to genes with specific expression profiles and chromatin marks. Finally, I describe a set of experiments to further investigate the role of PATCs and mes-4 in the maintenance of stable chromatin domains using a synthetic biology approach.

    Wackerow, Wiebke (2020-11) [Dissertation]
    Advisor: Basset, Jean-Marie
    Committee members: Huang, Kuo-Wei; Ruiz-Martinez, Javier; Astruc, Didier
    The general objective of this thesis is the analysis of selective reactions for group VI grafted metal complexes via methods and principles of SOMC. For this objective, three approaches have been chosen. The first chapter is an introduction to the topic of selectivity in catalysis, emphasizing heterogeneous catalysis and more specifically the different approaches to support catalysts on surfaces. The concept of catalysis by design is introduced as a new way to use the surface as a ligand. Chapter 2 presents the results of a library of well-defined catalysts of group VI with identical catalytic functionality, but different ligand environment. The results reveal, that metal-carbynes are able to switch their catalytic reactivity based on the substrate that they are contacted with. The difference in reaction mechanisms and the differing reactivities towards the substrates are presented. It can be concluded that the classical ROMP is selectively achieved with cyclic alkene substrates leading to polymers whereas cyclic alkanes yield exclusively higher and lower homologues of the substrate without polymeric products. Chapter 3 presents the study of olefin metathesis of cis-2-pentene with metal-carbynes of group VI, where the selectivity of the catalyst library towards yield of cis-/trans products is analyzed. It is presented, that the ligand environment of the catalysts is showing an influence in the selectivity. Rates of cis/trans isomerization of the products are high and are approaching thermodynamic equilibrium at high conversion. Product isomerization, thermodynamic equilibrium and reactivity differences between liquid phase and gas phase products are analyzed. Chapter 4 presents the full characterization of tungsten-hydrides by selective transformation into tungsten-hydroxides. These newly discovered well-defined tungstenhydroxides are fully characterized by ICP, TEM, DRIFT, double quantum and triple quantum solid-state NMR. The presented results allow to predict that tungsten-hydrides on KCC-1700 are present as two distinct species. Catalysis results with cyclooctane show, that due to burial of the complexes in the KCC-1700 surface the tungsten-hydrides are less active towards cyclic alkane metathesis reactions with bulky cyclooctane than the metalcarbyne complexes. Chapter 5 is giving a conclusion of results and an outlook for catalytic applications of the generated tungsten-hydroxides of chapter 4.
  • Towards Structured Prediction in Bioinformatics with Deep Learning

    Li, Yu (2020-11-01) [Dissertation]
    Advisor: Gao, Xin
    Committee members: Hoehndorf, Robert; Arold, Stefan T.; Ma, Jian
    Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classi cation or regression problems, whose outputs are simple vectors or scalars, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classi cation but has much broader applications, especially in bioinformatics, considering the fact that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-speci c constraints and dependency within the labeling space, the straightforward application of existing deep learning models on the problems can lead to unsatisfactory results. In this dissertation, we argue that the following two ideas can help resolve a wide range of structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design and train problem-speci c deep learning architectures or methods by considering the structured labeling space and problem constraints, either explicitly or implicitly. We demonstrate our ideas with six projects from four bioinformatics sub elds, including sequencing analysis, structure prediction, function annotation, and network analysis. The structured outputs cover 1D electrical signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. With the help of the above ideas, all of our methods can achieve state-of-the-art performance on the corresponding problems. The success of these projects motivates us to extend our work towards other more challenging but important problems, such as health-care problems, which can directly bene t people's health and wellness. We thus conclude this thesis by discussing such future works, and the potential challenges and opportunities.
  • Molecular Modeling of Interfacial, Sorptive, and Diffusive Properties of Systems for Carbon Capture and Storage Applications

    Yang, Yafan (2020-11) [Dissertation]
    Advisor: Sun, Shuyu
    Committee members: Stenchikov, Georgiy L.; Cavallo, Luigi; Kumar, Arun; Nair, Narayanan; Smit, Berend
    Carbon capture and storage has been considered as a promising way to mitigate global warming by reducing greenhouse gas emissions. Understanding of the interfacial, sorptive, and diffusive properties of related systems are of significant importance. For example, interfacial tension controls the capillary forces in the caprock, which act to avoid upward migration of the stored fluid and play an important role in related enhanced oil recovery processes. The optimal design of many carbon capture and storage processes requires understanding the properties of porous media, e.g., clay and kerogen. The capability of porous media for storing carbon dioxide depends on its adsorption properties, while the separation timescale of porous media for capturing carbon dioxide can be dictated by their transport properties. The objective of this dissertation is to enhance the understanding of the processes mentioned above. Molecular simulation techniques and theoretical methods are applied in this dissertation to gain molecular insights on three types of relevant systems: fluid mixtures, fluids in amorphous porous media, and fluids in ordered porous media.
  • UAV Enabled IoT Network Designs for Enhanced Estimation, Detection, and Connectivity

    Bushnaq, Osama (2020-11) [Dissertation]
    Advisor: Al-Naffouri, Tareq Y.
    Committee members: Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim; Shamma, Jeff S.; Shihada, Basem; Gesbert, David
    The Internet of Things (IoT) is a foundational building block for the upcoming information revolution. Particularly, the IoT bridges the cyber domain to anything within our physical world which enables unprecedented monitoring, connectivity, and smart control. The utilization of Unmanned Aerial Vehicles (UAVs) can offer an extra level of flexibility which results in more advanced and efficient connectivity and data aggregation. In the first part of the thesis, we focus on the optimal IoT devices placement and, the spectral and energy budgets management for accurate source estimation. Practical aspects such as measurement accuracy, communication quality, and energy harvesting are considered. The problem is formed such that a set of cheap and expensive sensors are placed to minimize the estimation error under limited system cost. The IoT revolution relies on aggregating big data from massive numbers of devices that are widely scattered in our environment. These devices are expected to be of low- complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. Aerial data transmission offers strong line-of-sight links and flexible/instant deployment. The UAV-enabled IoT networks can, for instance, offer solutions to avoid and manage natural disasters such as forest fire. We investigate in this thesis the aerial data aggregation for field estimation, wildfire detection, and connection coverage enhancement via UAVs. To accomplish the network task, the field of interest is divided into several subregions over which the UAVs hover to collect samples from the underlying nodes. To this end, we formulate and solve optimization problems to minimize total hovering and traveling times. This goal is fulfilled by optimizing the UAV hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. Finally, we propose the utilization of the tethered UAV (T-UAV) to assist the terrestrial network, where the tether provides power supply and connects the T-UAV to the core network through a high capacity link. The T-UAV however has limited mobility due to the limited tether length. A stochastic geometry-based analysis is provided for the optimal coverage probability of T-UAV-assisted cellular networks.

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