Electronic Theses and Dissertations

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0.3 THz CMOS Transceiver Pixels for Reflection Mode Active Imaging
(December 2022) Byreddy, Pranith Reddy 1993-; Thuraisingham, Bhavani; O, Kenneth K.; Lee, Mark; Henderson, Rashaunda; Saquib, Mohammad
Electromagnetic waves at frequencies ranging from 0.1 to 10THz, commonly referred to as THz waves have a wide variety of medical, security and industrial imaging applications. However, generation and detection of signals at these frequencies are quite challenging. The complementary metal-oxide semiconductor (CMOS) technology which is widely used in most of the modern consumer electronic devices is an affordable means for generation and detection of THz signals. Near-millimeter-wave and terahertz imagers are expected to complement visible light, IR, Radar and Light Detection and Ranging (LiDAR) imaging by providing a unique combination of angular resolution and a capability to image in visibly impaired conditions such as fog, rain and dust as well as for imaging through other materials. This research aims at the design of concurrent transceiver pixels operating at frequencies around 300GHz for reflection mode active imaging using a CMOS process technology. A 7-element array of 287-GHz CMOS transceiver pixels with pixel area smaller than (λ/2)2 housed in a QFN package is demonstrated. Each pixel concurrently performs transmission and coherent detection using a push-push VCO, that functions as a 287-GHz transmitter, a 143-GHz LO, and a sub-harmonic mixer at the same time. An effective isotropic radiated power (EIRP) of −2.5dBm and sensitivity of −88dBm for 1-kHz noise bandwidth are achieved. Link budget analyses suggest that it should be possible to perform reflection-mode active imaging at 10 m using the array, and a reflector with a diameter of 15-cm and a simulated near-field gain of 44.6dB. The packaged array exhibits a 2.5-dB higher EIRP and a 3-dB lower noise figure than the array without QFN packaging due to the antenna performance enhancement. This demonstrates that it is possible to package 300- GHz integrated circuits with an on-chip patch antenna using a low-cost technique. Lens-less short-range reflection-mode imaging through cardboard is demonstrated at 275GHz using a pair of concurrent CMOS transceiver pixels separated by ~5mm on a PCB. An isolation study employing EM simulations is performed to quantify the unwanted coupling. This is first such demonstration at frequencies above 100GHz. The separation between the imaged object and pixels is ~1cm and the operation at 275GHz allows the lateral resolution to be reduced to ~2mm due to a smaller wavelength. This pixel achieves an EIRP of -18.9dBm and a double-sided noise figure (NFDSB) of ~51dB in an area of 0.45×0.49 mm2. An 1x3 array of 296-GHz CMOS concurrent transceiver pixels incorporating circuits for baseband signal extraction in addition to the RF section in an area of (λ/2)2 is demonstrated. The EIRP of array is ~-6dBm and NFDSB is ~48dB. An E-shaped patch antenna to broaden the antenna bandwidth is used. Using a pair of these arrays, lens-less short-range reflection-mode imaging of a target ~1cm away through a cardboard is demonstrated. More importantly, use of the arrays improves isolation between the pair by ~10 dB to ~70 dB compared to that when single pixels are used. This work points to a path for incorporation of millimeter and sub-millimeter wave imaging capabilities in a handheld device.
200-400 GHz Antennas in Integrated Circuits Incorporating Packaging Effects
(2022-05) Bakshi, Harshpreet Singh Phull; O, Kenneth K.; Blanchard, Andrew J.; Sirsi, Shashank; Henderson, Rashaunda M.; Lee, Mark; Iyer, Mahadevan Krishna
There is an increasing demand for wider bandwidth in high data rate applications. Using frequency bands in the sub-millimeter wave and terahertz (THz) range allows for a significant improvement of fractional bandwidth. The rise in operational frequency translates to reduced wavelengths, which are comparable to the dimensions of the integrated circuits (ICs). This has made the integration of antennas on a chip or within a semiconductor package possible. Presented in this dissertation is a “simple” bond-wire antenna intended for operation at 180-GHz. At 178.3 GHz, a gold bond wire shaped in a half loop and terminated on a bond pad of a neighboring chip exhibits a measured peak gain of 2.5 dB at the 𝜙 = 90° plane. The antenna also exhibits > 20-GHz measured −10-dB |S11| bandwidth. These measured results compare favorably with the simulated results. This is the first bond-wire antenna (operating at 180-GHz) that can be used for wireless communication in the broadside direction (perpendicular to the IC). A 300-GHz on-chip patch antenna is also presented and the effects of semiconductor packaging on the antenna performance are explored. Full-wave simulations of the rectangular patch antenna, compliant with the metal stack and design rules of a 65-nm complementary metal– oxide–semiconductor (CMOS) process, are implemented. The simulated results show an increase of 13% in radiation efficiency, an increase of 1 dB in peak antenna gain, and a 7-GHz −10-dB |S11| bandwidth improvement, upon encapsulation within a quad-flat no-lead (QFN) package compared to one without encapsulation. Measured results (within a QFN package) of a 276-GHz CMOS signal generator with the same on-chip antenna, show ∼6 dB higher effective isotropic radiated power over that of an unpackaged chip, corroborating the simulation results. A technique to improve the efficiency, gain and impedance bandwidth of on-chip planar patch antennas, using the encapsulation material, is thus presented and design guidelines are suggested for future planar on-chip antenna implementation. An E-shaped patch geometry is simulated to demonstrate the improvement of impedance bandwidth and gain over a rectangular patch. The −10-dB |S11| bandwidth of 3 GHz for rectangular patch at 300 GHz, is improved four-fold to 12 GHz with this geometry. The peak gain also improves by ~1 dB. To further improve the gain of a rectangular patch antenna, a 420- GHz, eight-element on-chip series-fed patch antenna array is developed. This is the first array in this configuration to be implemented on chip. The measured gain at boresight matches the simulated gain of ~9 dB at 415.5 GHz. The overlapping measured and simulated radiation patterns of this antenna demonstrates the reliability of antenna designs using EM simulations at sub-millimeter wave and THz frequencies. Power input to on-chip antennas at 200-400 GHz cannot be accurately measured in the presence of probes. Preliminary simulation results presented show that root mean square voltage detectors along with a phase detector can be used to determine the antenna input impedance in-situ, thereby eliminating the use of probes for antenna measurements. The feasibility of this technique for determination of the antenna characteristics is documented.
2D Materials: Theoretical Study of Magnetic and Contact Properties
(December 2023) Reyntjens, Peter Dirk Jan 1994-; Liu, Jin; Vandenberghe, William; Kim, Moon J.; Fischetti, Massimo V.; Sorée, Bart
The integrated circuit is without a doubt one of the most influential inventions in all of human history. While every technological revolution has had massive impacts across human societies, modern electronic circuits have increased the rate of change by orders of magnitude and this process shows no signs of stopping. As society has become accustomed to the rapid pace of technological development, the expectations for further improvements are more and more demanding. The silicon transistor was the ideal vehicle for such a rapid development, as transistors typically become more powerful and less costly to make when their size is decreased. With the added bonus of being able to cram more transistors into the same chip, the electronics revolution started, and a snowball effect of increasingly complexity and performance was unleashed onto the market, leading to the highly interconnected society we live in today. However, the benefits of decreasing transistor dimensions cannot last forever. There are certain extremely fundamental limits, at the nanometer scale, to how far one can go in making smaller and smaller devices. At some point, transistors begin to suffer from all sorts of performance-degrading issues, such as short-channel effects, increased leakage, fabrication difficulties, etc. Even more fundamental issues arise once the device dimensions go down to only a few nanometers, where quantum effects can seriously degrade traditional silicon-based transistors. It is with these scaling limitations in mind that researchers started looking very seriously at a relatively new class of materials: two-dimensional (2D) materials. 2D materials are atomically thin materials, consisting of a single layer not bound covalently in the out-of-plane direction. The 2D nature of these materials is of course in stark contrast with more “normal” materials, such as silicon or iron, which have covalent bonds in three dimensions. It turns out that due to the special structure of 2D materials, the physical properties are also extremely interesting, and worth investigating seriously. At present, various classes of 2D materials have been found, and many 2D materials have corresponding stacked layered versions with their own special properties. Add in, for example, the fact that one can dope these materials of make heterostructures out of several different kinds, then one can start to appreciate the vast parameter space that can be explored in the search for interesting applications. In this work, I focus on the applications of 2D materials in logic and memory devices. More specifically, I discuss the studies done by myself and my collaborators on the magnetic properties of layered WSe2 and PtSe2, and the calculation of the contact resistance between a metal and a 2D semiconductor. In the first part of the thesis, I share our investigation on the nature and stability of magnetic phases of doped intercalated WSe2 and PtSe2. We showed that, depending on the dopant, the stable magnetic phase at low temperature can be drastically different in both stability and type (ferro- or antiferromagnetic). We further showed that the presence of W or Pt vacancies in the lattice can be used to control the thermodynamic stability of the intercalated structures. Finally, we investigated the effect of the Pt vacancies on the magnetism in intercalated PtSe2. We showed that even though the spin polarization around the Pt atoms is very small, the Pt electronic cloud mediates longer magnetic interactions. Therefore, the presence or absence of Pt vacancies has a strong impact on the magnetic phases in the intercalated PtSe2. In the second part of this thesis, transport properties at a metal-2D semiconductor contact are the main topic. More specifically, I, along with my collaborators, have created a flexible model that can be used to efficiently simulate metal-2D semiconductor contacts and extract key parameters, such as the contact resistance. We studied the effects of device parameters, such as backgate bias, but also simulation parameters, such as the size of the simulation domain used to solve the Poisson equation. Crucially, we found that the contact resistance can be underestimated by over an order of magnitude when the Poisson domain is too small. In the final chapter, I provide an overview of the main achievements of the thesis and discuss potential avenues for future research.
3-D Geometrical Reconstruction and Flexural Modeling of Colville Foreland Basin, Northern Alaska
(2020-05) Quddusi, Muhammad Hassan; Pirouz, Mortaza
Brooks Range orogeny initiated in response to the collision of Arctic Alaska with an oceanic arc in Jurassic to early Cretaceous, and the Colville basin formed as a result of loading from the range topography. In this study Colville basin geometry is constrained and spatiotemporal variations of deflection is modeled in northern Alaska in order to estimate the elastic thickness (Te) of the lithosphere beneath the Colville foreland basin. Previous studies show that the effective elastic thickness of the Colville Basin in the northern Alaska region is 65 km which seems overestimated. That is because, the depth of frequent earthquakes dramatically reduces at 25 km under the Brooks Range and Colville foreland and wavelength of the Colville foreland is shorter than what one can expect for a plate with 65 km elastic thickness. To address these contrasting observations, a 3D flexural model technique is used to provide an accurate elastic thickness of northern Alaska lithosphere. The geometry of the Colville basin is characterized by using subsurface data and available structure maps, where the maximum depth reaches to 8 km towards the southwest of the basin. Flexural deflection of the northern Alaskan plate is modeled by various parameters (e.g., density, subsurface load), and results are compared to the observed data to optimize modeling results. The applied loads include basin and topographic loads along with crustal root loads with a ratio of 4.5 times to modern topography. Calculated elastic thickness is about 16 km and an average misfit between the model and observation is less than 3% and spans 83000 km2 of the basin. The results of this study indicate that the Colville basin geometry is mainly controlled by the loads of the Brooks Range and basin deposits and any other additional loads or density anomalies in the crust are not required for deflection of the Colville foreland basin.
3D Printing Tough, Isotropic, and Sustainable Polythiourethanes for Fused Filament Fabrication
(2022-05-01T05:00:00.000Z) Perez, Benjamin Ryan; Voit, Walter; Lu, Hongbing; Bleris, Leonidas; Smaldone, Ronald A.; Auciello, Orlando; Di Prima, Matthew
3D printing, especially fused filament fabrication (FFF), has captivated the imagination of thinkers, hackers, doers, makers and manufacturers alike for its ability to rapidly convert digital designs into tangible objects.. Many industries such as biomedical, aerospace, and automotive have been early adopters of the technology because of FFF's ability to print complex geometries, its cost-saving ability for custom parts, prototypes and low lot manufacturing runs, and its potential to be a more sustainable manufacturing practice. However, despite these benefits, FFF is still limited to prototyping and non-functional parts in these industries because of its limited selection of engineering-grade filaments and its typically weak mechanical properties. It also shows limited ability to be recycled and reused multiple times, similar to other plastics. Past research has explored various ways to widen the material selection, improve the mechanical properties of FFF filament, and make it more recyclable, but research has yet to show a material with these properties in an easy-to-use filament. This work leverages a thiol-isocyanate click chemistry that produces tough, isotropic, and sustainable FFF filaments. These filaments are comparable to many polymers used in traditional manufacturing, and widely outperform common 3D printed materials in all print directions. This filament also retains its mechanical properties when recycled multiple times without significant changes to the polymer's mechanical or materials properties. While further development is needed for FFF printing to flourish across all industries, this work shows the potential of polythiourethane filaments to offer solutions to some of the most significant drawbacks of FFF technology.
3D Travel Techniques for Virtual Reality Cyberlearning Systems
(2019-08) Lai, Chengyuan; 0000-0003-4219-4803 (Lai, C); McMahan, Ryan P.
Cyberlearning is the use of new technology to create effective learning experiences that were never possible or practical before. Among those technologies, virtual reality (VR) attracts many people’s attention and interests in creating cyberlearning systems. The underlying simulations of VR can provide experiences that are realistic, controllable, flexible, and repeatable, which make VR an ideal tool for cyberlearning and confer a wide range of benefits. Since consumer-level VR equipment becomes accessible in recent years, allowing people to have easy access to immersive experiences at a much lower cost, it is more feasible for companies and entities to adopt VR for their training needs. Therefore, it is worthwhile to investigate how to make VR cyberlearning systems more effective and efficient. Previous research efforts have been made in reducing the cognitive load of using VR techniques and improving the training results by allowing the trainees to focus more on the training tasks. In this dissertation, a standardized methodology is proposed to evaluate 3D travel techniques to guide the design of VR cyberlearning systems. 3D travel techniques are often included in VR cyberlearning systems for navigating the virtual world. While different travel techniques vary in the level of difficulty to learn and use, the cognitive load to the users is also different. Since learning and using the travel technique is usually not related to the learning goal, the cognitive load of using 3D travel techniques should be reduced as much as possible to increase the effectiveness of VR cyberlearning. In addition to the cognitive load, there are also many other factors that affect a user’s subjective opinion and objective performance, such as simulator sickness, sense of presence and perceived usability. Therefore, it is necessary to evaluate different 3D travel techniques based on those factors and figure out what is essential to help to improve the effectiveness of VR cyberlearning systems. The goal of this dissertation is to provide a systematic approach of evaluating 3D travel techniques, gain a better understanding of how 3D travel interaction design affects cyberlearning systems in terms of effectiveness and usability, and develop guidelines for selecting 3D travel techniques for VR cyberlearning systems. A dual-task methodology has been proposed for comparing different 3D travel techniques, which includes a cognitive task based on the n-back test and a travel task based on the Fitts’ Law task. With this methodology, we’ve conducted a series of studies to evaluate 3D travel techniques from different categories. With these evaluations and results, we have validated the usefulness of the proposed methodology, gained knowledge about the pros and cons of many common 3D travel techniques, and summarized a set of design guidelines that can be helpful for future VR cyberlearning system design.
4D Printing Liquid Crystalline Actuators toward Assistive Devices
(2020-10-16) Ambulo, Cedric Paul; Schmidtke, David; Ware, Taylor
Liquid crystal elastomers (LCEs) are potential artificial muscle candidates within patientassistive devices. Their stimuli-responsive, shape-morphing properties can be controlled by various processes to produce a wide range of actuation behaviors. However, there are inherent processing limitations that inhibit their application within biomedical devices: 1) force and work constraints due to size restrictions, 2) high activation temperatures (≥ 100 °C) to induce actuation, and 3) incompatible power delivery. The aim of my research is to develop manufacturing processes to fabricate three-dimensional LCE artificial muscles compatible with patient-assistive devices. The developed 4D printing process enables control over geometry and liquid crystalline orientation to develop 3D LCE structures with improved actuation behaviors. The development of tunable, printable LCE chemistries allows for low-temperature activation suitable for human body interfacing. Incorporation of liquid metal fillers generates a multiresponsive LCE composite compatible with facile power delivery systems, i.e., current and light. The enabled processing freedom for this class of LCEs can be exploited within a myriad of assistive devices ranging from untethered, implantable dynamic valves to wearable, rehabilitative artificial muscles.
82 Reflections
(2021-05-07) Wilson, David; Cooley, Heidi
82 Reflections is a live streaming digital audio-visual experience shaped by and in response to deep social disruption caused by pandemic, ongoing demands for social justice, and the uncertainty, fear, and hope these concurrent tragedies elicit. 82 Reflections is a piece of Pandemic Art. To collect materials for this art the artist employed a socially distant documentation practice focused on the ongoing demand for an end to systemic racism and white supremacy in his community of downtown Dallas, Texas. Audio recordings, digital images, and digital video were collected between May and October 2020. The artist collected these materials in a socially distant manner due to his chronic lung conditions and risk for contracting covid19. The artist then constructed a small sculpture with household items and small reflective surfaces. Training a lo-fi webcam onto the sculpture, materials captured by live video web stream are glitched, reflected, and looped, as symbolic of complete disruption, uncertainty, and misunderstanding as a starting point for difficult transformation for social justice. Video projections reflecting from the sculpture create ambient lighting and patterns that symbolize the bringing down of barriers to change-making dialogue and action. They further signify that difficult dialogue has occurred and progress has been made, but more is needed to continue positive social justice action and sustainable change. Glitched, reflected, and looped audio of 82 recordings of protestors, law enforcement, the artist, and materials within the sculpture create instability, a necessary condition for meditation and reflection on difficult and transformational racial dialogue especially directed to White Americans. The work is situated within an emergent artistic movement, Pandemic Art, created in response to covid19 and social conditions. Pandemic Art is created through improvisation with readily available materials that are safe, practical, and feasible.
A 0.1 PS Resolution Coarse-Fine Time-To-Digital Converter with 2.21 PS Single-Shot Precision
(2018-05) Huang, Huihua; 0000-0002-6945-7469 (Huang, H); Sechen, Carl M.
This dissertation proposes a new type of time-to-digital converter based on a resistor-capacitor (RC) delay line that offers low power consumption, high speed and high resolution with error-correction circuitry. The 14-bit, 0.1 picosecond resolution interpolating coarse-fine time-to-digital converter (TDC) has been developed in 45 nm complementary metal–oxide–semiconductor (CMOS) technology. It is based on an asynchronous buffer delay line and an RC delay line. A lookup-table (LUT) based calibration scheme was developed to correct non-linearities due to process, voltage and temperature (PVT) variations. The root mean square (rms) single-shot precision of the TDC is 4.18 picosecond (ps) without the LUT but is 2.21 ps with the LUT. The power consumption is 2.05 mW at 500 MHz with a 1.3 V operating supply voltage. Compared to other high-resolution state-of-the-art TDCs, the proposed TDC achieves the best figure-of-merit (FOM) of 0.723 fJ per conversion step.
A 12-Bit Two-Step SAR ADC with Linearized Open-Loop Amplifier
(2017-05) Cai, Yongda; Chiu, Yun; K. O, Kenneth; Henderson, Rashaunda
Successive approximation register analog to digital converter (SAR ADC), which mostly consists of digital components, is becoming more and more popular in recent years as it is power efficient and friendly to process scaling. However, the two-step structure, which is widely used for medium or high-resolution SAR ADCs, usually contains a closed-loop residue amplifier that is not friendly to technology scaling. As transistors continue to scale, the intrinsic gain of transistors and supply voltage drop, which post challenges on the design of high gain amplifiers for the closed-loop residue amplifier. To ease the amplifier design in advanced processes, a linearized open-loop amplifier with expansive loading compensation is explored in this project as a residue amplifier. The incomplete settling technique is also employed in the design to lower the power consumption of the amplifier. Schematic simulation in GF65nm shows that the ADC achieves 65dB SNDR, 88dB SFDR while consuming 5mW at the sampling rate of 400MS/s.
A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies
(2018-08-31) Zhang, Lei; Biswas, Swati
The genome-wide association studies (GWAS) aim to identify genetic variants, typically single nucleotide polymorphisms (SNPs), associated with a disease/trait. A commonly used analytic strategy in GWAS is to test for association with one single SNP at a time. However, such a strategy lacks power to detect associations that are caused by joint effects of multiple variants, each with a modest effect of its own. Pathway analysis jointly tests the combined effects of all SNPs in all genes belonging to a molecular pathway. This analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. Moreover, due to biological functionality of pathways, a significant result lends itself more easily to interpretation. In this dissertation, we develop a Bayesian hierarchical model that fully models the natural three-level hierarchy inherent in pathway structure, namely SNP—gene—pathway, unlike most other methods that use ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. This joint modeling allows detection of not only the associated pathways but also testing for association with genes and SNPs within significant pathways and significant genes in a hierarchical manner, which can be useful for follow-up studies. To deal with the high dimensionality of such a unified model, we regularize the regression coefficients through an appropriate choice of priors. We fit the model using a combination of Iteratively Weighted Least Squares and Expectation-Maximization algorithms to estimate the posterior modes and their standard errors. The inference is carried out in a hierarchical manner from pathways to genes to SNPs. Hierarchical false discovery rate (FDR) is used for multiplicity adjustment of the entire inference procedure. We also explore the utility of effective number of parameters proposed in the Bayesian literature in our context of multiplicity adjustment using the hierarchical FDR. To study the proposed approach, we conduct simulations with samples generated under realistic linkage disequilibrium patterns obtained from the HapMap project. We find that our method has higher power than some standard approaches in several settings for identifying pathways that have multiple modest-sized variants. Moreover, it can also pinpoint associated genes once a pathway is implicated, a feature unavailable in other methods. We also find that the use of the effective number of parameters can boost the power to detect associated genes and helps in distinguishing them from the null genes. We apply the proposed method to two GWAS datasets on breast and renal cancer.
A Bayesian Modeling for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer
(2020-12-02) Bu, Yashi; Chen, Min
Many complex human diseases are associated with genetic factors. Identifying genetic markers is the key step to account for disease heritability, and develop disease diagnosis, risk prediction, prevention and therapeutic strategies. Genome-wide association study (GWAS) has emerged as a powerful tool to identify genetic variants that are associated with various cancers. The common statistical methodologies in GWAS focus on case-control data where cases and controls are sampled independently from the populations. Despite the success of GWAS in finding a number of genetic variants that are associated with cancers, the power of conventional GWAS is limited. Extensive research has shown that many tumors develop as a consequence of the progressive accumulation of somatic mutations over time. We focus on GWAS data from tumor and paired normal tissues to unravel the genetic association of somatic mutations. To address the limitation that conventional GWAS methods are not applicable to matched-paired data, we propose in this dissertation a framework that incorporates allelic relative risk, frequency and mutation rate to accommodate the structure of paired data. We first apply the penalized maximum likelihood estimation (MLE) to perform single marker analysis based on the framework. Simulation studies are carried out to assess the performance of penalized MLE. To further improve the estimation accuracy and power of single marker analysis, we develop a Bayesian hierarchical model that takes advantage of applying Bayesian shrinkage and making inferences based on the posterior distributions. The hierarchical Bayesian model has the flexibility to take into account the prior knowledge and extend to multiple marker analysis. We find that the single-marker Bayesian model has improved the estimation and power performance in most simulation scenarios. To identify DNA segments and SNP sets, rather than single genetic variants that are associated with the disease, we develop a multiple-SNP Bayesian model which considers SNP sets that are grouped together in a biologically meaningful way, such as genes or pathways. The multiple SNP analysis considers the joint effects of the SNP set, which improves the power to identify SNPs that have moderate marginal effects by themselves. Simulation studies show that the multi-marker Bayesian model has higher power to identify associated SNPs and lower type I error rates. Next, we apply the proposed methods to a breast cancer data set from The Cancer Genome Atlas (TCGA).We compare the most significant genes identified by single marker analysis and multiple marker analysis to external resources on somatic mutations of breast cancer. We find that both methods identify genes associated with breast cancer, and multiple marker analysis provides more consistent results with external resources.
A Big Data Framework for Unstructured Text Processing With Applications Towards Political Science and Healthcare
(2021-12-01T06:00:00.000Z) Salam, Sayeed; Khan, Latifur; Hu, Yang; Bastani, Farokh B.; Kim, Dohyeong; Wu, Weili
Machine learning and deep neural networks have soared in popularity in recent years, allowing us to enhance many aspects of everyday life. While these methods are intuitive, they are very reliant on the dataset being used to build the model. A high-quality dataset boosts the model’s accuracy and validates the model’s output in the context of a real-world scenario. Furthermore, continuous improvement on the dataset contributes in the tuning of the model in a time-consistent way and the mitigation of temporal inconsistencies. However, preparing datasets, particularly for text domains, is difficult due to the inherent unstructured nature of the data and the use of multiple languages. Furthermore, the amount of text produced in the form of news articles or social media posts is massive, necessitating large-scale processing. The velocity at which new texts are produced demands an elastic and scalable system that can accommodate any surge of inputs while remaining resource efficient while not in use. Texts are created in a variety of ways and must be preprocessed and analyzed in order to provide well-structured, consistent data. This can be accomplished through the use of a well-defined domain-specific ontology (rule-based approach) or machine learning approaches. While rule-based systems can provide information that are more precise and are preferred in a variety of circumstances, they lack flexibility as the ontologies are often fixed and does not respond well with the continuous changes in respective domains. We propose associated solutions to the challenges described above in this dissertation. First, we go over a scalable architecture for collecting news stories from around the world and utilizing a rule-based approach with the Conflict and Mediation Event Observation(CAMEO) ontology to generate political events. We present a summary of the generated dataset, as well as some basic analysis, to demonstrate how it relates to the real-world scenario. We present techniques to dynamically adding information to the ontology using a mining approach for discovering new political actors that works as a recommender system and retrieves more than 80% of the missing information including political figures and their roles. We discuss an extended data processing system for processing articles published in several languages, with a focus on translation methodologies and tools developed. In comparison to the English language, we demonstrate the efficacy of the coder in Spanish. When compared to equivalent events in English articles, the revised event coder with translated knowledge-base was able to recognize 83% of information in Spanish. For healthcare, we propose an alternative strategy in which we use several machine learning algorithms and social media, such as tweets, to extract the location and severity of Road Traffic Incidents (RTI). We highlight a pipeline that goes from collecting tweets to summarizing related tweets for an RTI. We also demonstrate how semi-automatic ontology learning can be useful in determining severity and offer a simplified example in which 100% of the target rules were identified using an iterative technique.
A Chemical Toolbox for Stimuli-responsive Photoresins in 3D Printing
(2022-05) Durand Silva, Alejandra; Smaldone, Ronald A.; Xuan, Zhenyu; Dodani, Sheel; Voit, Walter E.; Stefan, Mihaela C.
Three-dimensional printing (3DP) is an advanced manufacturing process that builds successive layers of materials to create objects from digital models. Without the need of molds, 3DP enables the rapid fabrication of complex and customizable products at low cost. Several 3DP techniques are available, being extrusion and vat photopolymerization some of the most used. Compared to extrusion, vat photopolymerization techniques stand out due to their efficiency in fabricating objects with minimal mechanical anisotropy and high resolution. These techniques are based on the spatiotemporal control of a polymerization reaction using light to build the polymer layers. However, most resins that are compatible with this manufacturing process are based on conventional acrylate monomers and have limited functionality. Inspired by the ability of natural systems to adapt to external conditions, several developments in polymer science have endowed polymers with the intrinsic ability to modify their properties under external stimuli, but it remains a challenge to incorporate this functionality into conventional 3D printable polymers. The objective of this research is to develop a chemical toolbox that expands the availability of stimuli-responsive photoprintable materials with improved processability and functionality. The first chapter provides a review of approaches in incorporating chemical interactions to improve the processability and functionality of 3D printable materials, including noncovalent interactions, dynamic covalent chemistry and click chemistry, and how this toolbox enables stimuli-responsive properties, such as self-healing. The second chapter describes the development of photoprintable polymer networks that include furan-maleimide Diels-Alder thermally reversible cycloadducts to enable self-healing in 3D printed objects, with up to a 99% self-healing efficiency, as observed in the recovery of mechanical strength, without affecting their detailed printed shape. The third chapter describes the use of ureas as pendant hydrogen bonding groups to provide noncovalent crosslinking, to enable fine tuning in the mechanical properties, and to endow the printed parts with self-healing properties. The fourth chapter describes the use of click and unclick reactions to facilitate the control in the integrity of a 3D printable polymer network, by photoprinting at 405 nm via thiol-ene, but enabling degradation at 365 nm via cleavage of ortho-nitrobenzyl ester. This research demonstrates the potential of using a chemical toolbox to develop advanced functionality in 3D photoprintable materials an enable delicate control of their properties.
A Class of Implicit Transmission Techniques for Throughput Enhancement
(2018-12) Rezaei, Elahe; 0000-0002-4363-5757 (Rezaei, E); Fonseka, John P.
Throughput enhancing techniques are very valuable to keep up with the fast increasing data rates in communication systems. Implicit transmission is particularly attractive as it can transmit information without physically transmitting them over a channel. In this study, two separate throughput enhancing techniques using implicit transmission are investigated. First a multi-constellation signaling (MCS) technique that selects one out of N(> 1) constellations based on a set of implicit bits during every interval is introduced. The overall constellation used by a MCS scheme is a NM-ary constellation formed by replacing every point of a M-ary constellation by a cluster with N constellation points. Further, the size of clusters is reduced by employing multi-dimensional mapping. It is demonstrated that a properly designed MCS scheme can double and triple the throughput and also perform better than a scheme that employs a single constellation, and MCS schemes can perform better than turbo coded signals in the long term evolution (LTE). In contrast to MCS schemes, throughput enhancing concatenated codes (TECCs) schemes transmit bits implicitly without expanding the overall constellation. In a TECC, the coded sequence of a code C transmitted over a channel is altered according to the coded bits of a second coded sequence of an implicit code C’. In this study, TECCs select one bit in every segment of n coded bits of C based on ns ≤ log2n coded bits of C’, and flip that selected coded bit of C before transmission. It is shown that using iterative decoding between codes C and C’, the receiver can decode the coded bits of both the explicit code C and the implicit code C’ jointly. TECCs that can increase the throughput of C by 25% to 37.5% without sacrificing the performance of C are reported, however, at the expense of increased complexity. TECCs are extended to form a new class of TECC-2 schemes to include a second uncoded implicit stream. It is shown here that TECC-2 schemes can significantly increase the throughput enhancing capability of TECCs at high signal to noise ratio (SNR) with only a modest increase in complexity. The tradeoff between the low SNR performance and throughput expansion is also discussed.
A Comprehensive in Vitro and in Vivo Failure Analysis of Titanium Dental Implant Systems
(2018-08) Sridhar, Sathyanarayanan; Rodrigues, Danieli C.
This dissertation provides a novel approach to understand failure modes and evaluate the surface performance of commercially pure (cp) titanium (Ti) dental implants (DI). It is based on the hypothesis that multiple oral factors can damage the titanium oxide (TiO2) layer leading to peri-implant dissolution of metal ions. This study is comprised of three aims to understand the effect of (i) surgical insertion in different bone qualities; (ii) bacteria colonization; (iii) occlusal forces on the surface of DI. In the aim 1, DI were inserted following surgical procedure in simulated bone materials of different densities to check the possibility of surface exfoliation and corrosion behavior. Powder x-ray diffraction (XRD) of ground specimen from the insertion site was performed to detect particle release. ASTM standard electrochemical corrosion tests were performed to evaluate the corrosion behavior of DI post-insertion. In the aim 2, surface analyses of in vivo failed retrievals were performed. DI were immersed in in vitro polyculture of early colonizers (Stretptococcus mutans, S. salivarius, S. sanguinis), and late colonizers (Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis) for 30 days. In the aim 3, cyclic occlusal forces were employed on DI immersed in (i) phosphate buffered saline (PBS) and in (ii) bacterial polyculture. Optical microscope, x-ray photoelectron spectroscopy (XPS), and electrochemical corrosion tests were performed to evaluate the surface-morphology, chemistry, and potential of (i) failed explanted retrievals, and (ii) DI obtained post-in vitro tests. Powder XRD results post-insertion test confirmed that particles were not released due to the insertion procedure irrespective of bone density. Electrochemical corrosion results post-insertion further corroborated that the surface integrity was not compromised due to insertion irrespective of bone quality. In vivo retrieval analyses suggested that both early- and late-colonizers degraded the surface- morphology (discoloration, pitting, scratches, and fractures) and chemistry (thinning/depletion of TiO2 layer with respect to the control). However, in vitro bacteria immersion tests showed that late- colonizers inflicted more damage to the surface chemistry compared to the early colonizers. Electrochemical corrosion results also indicated higher corrosion rate (not statistically significant) for DI immersed in late colonizers compared to early colonizers and their respective controls. The surface degradation due to bacteria adhesion was aggravated in the presence of mechanical forces. XPS analysis illustrated depleted TiO2 layer for DI exposed to cyclic loading in circulating broth containing bacteria compared to DI subjected to fatigue test in PBS. The overall findings of this study indicated that bacteria could degrade the surface which would be exacerbated by mechanical loading. This dissertation highlights the need to focus on the material surface, as particle release into the peri-implant tissue might trigger osteolysis and affect biological integration.
A computational thermal model of breast cancer validated by clinical data
(2020-04-06) Lozano, Adolfo; Hassanipour, Fatemeh
The present research study consisted of 1) a clinical study that collected data from female subjects with radiologic abnormalities associated with breast cancer, and 2) a computational thermal (or bioheat) modeling effort of breast cancer based on the data collected. The primary objective of this research was to construct an accurate thermal model of breast cancer, validated by real clinical data. The pilot clinical study enrolled eleven female subjects with radiologic breast abnormalities based on subjects’ routine diagnostic imaging procedures. Clinical data collected consisted of the following: High-resolution infrared (IR) images and video of subjects’ breasts; threedimensional (3D) breast surface scans of subjects’ breasts; and standard radiologic imaging data of subjects’ internal tumor definition (i.e., sizes and spatial locations within the breast). The clinical study received the appropriate Institutional Review Board (IRB) approval and informed consent was obtained from each subject prior to subjects’ enrollment in the study. The modeling effort aimed to construct a computational thermal model of the breast with cancer for a representative subject histologically diagnosed with breast cancer with the goal of quantifying the thermal characteristics of breast cancer, namely the blood perfusion rate and metabolic heat generation rate. IR images were used to validate the results of the thermal model, whereas 3D scans and radiologic imaging data served as geometric inputs to the model. Two modeling approaches were taken to model the cancerous breast: First, the “traditional” bioheat modeling approach, wherein blood perfusion and metabolic heat generation (or production) were considered; second, a novel and unique approach, herein referred to as “perfusion-driven modeling,” in which metabolic heat generation was not required to recreate surface temperatures obtained from infrared images, contrary to all prior modeling efforts. The motivation for implementing the latter modeling approach was the mathematical certainty of avoiding unrealistically high internal breast temperatures. Potential clinical applications for this modeling study of breast cancer include the following: Monitoring tumor response to cancer treatment over other imaging modalities; tracking tumor growth over time; parametrically simulating various tumor cases (i.e., size and location) in order to provide an approximation of expected breast temperatures; and simulating expected temperature distributions for hyperthermia and cryotherapy cancer treatments. The thermophysiology of breast cancer is surveyed, an extensive literature review of prior relevant clinical studies and thermal modeling efforts are outlined, data collected from the clinical study are presented, and finally modeling results using both approaches are presented and discussed.
A Conservation of Resources Approach to the Multiple Dimensions of Sleep in Organizational Behavior
(2021-05-04) Boncoeur, Olivier D; Takeuchi, Riki; Richard, Orlando C.
Sleep is an important organizational phenomenon with significant implications for employee attitude and behaviors at work. While the organizational literature on sleep has made remarkable theoretical strides regarding sleep quantity and quality (such as subjective assessments of insomnia), this literature has grown without examining the existence of different types of sleep. Models developed around sleep in the organizational studies appear to have evolved separately from the wider body of research on sleep. Notably, one of the primary tenets of this work asserts that sleep is not a single mode of consciousness but is multimodal. Sleep can be divided into non-rapid eye movement (NREM) and rapid eye movement (REM) sleep and they each are characterized by different neural activity and psychological functions. Therefore, given that the management literature has conceptualized and captured sleep as a unidimensional state, it appears at odds with the neuroscience of sleep which suggests that sleep is a complex unconscious behavior. Neglecting sleep states runs the risk of missing central underlying processes in understanding how sleep impacts employee responses to daily work demands. I address this problem by integrating the neurocognitive approach to sleep with the conservation of resources (COR) theory. In the first study, I examine the role of REM sleep for employees to feel recovered in order to tackle daily work demands, as well as the implication of daily resource expenditures affecting this process. In the second study, I further expand on the role of REM sleep to assess how it enhances positive self-referential thought which is subsequently associated with both in-role and extra-role daily performance behaviors, as well as the implication of resource substitution in high quality social exchanges at work. Finally, the third study integrates both REM and NREM sleep to suggest a dual pathway involving psychological and energetic processes that predict employees’ ability to engage in organizationally-desired behaviors without subverting their well-being. Across three experience sampling studies conducted in organizations across continents, I draw on the classification of sleep into two broad types, theorizing about the potential role of each sleep state and its implications for employees’ attitude and behavior at work. I test these hypotheses by integrating actigraphy with a Fitbit Charge 3, along with proximal measurement of employees’ daily lives using ExpiWell. Results from these three studies provide large support for key aspect of this dissertation, where REM sleep plays a central role is acquiring the necessary psychological resources necessary for employees to engage in behaviors that benefit organizations while not eroding their psychological well-being. Furthermore, it shows that central contingencies in resources permeating organizations can help ease the conservation process emanating from sleep. Taken together this work yields theoretical and practical implications about the significant influence that specific sleep states have on employees, and their organizations.
A Content Analysis of Sex Trafficking Between 2010 and 2019
(2020-05) Repeta, Ellen James; 0000-0001-7504-2940 (Repeta, EJ); Vieraitis, Lynne
Human trafficking is a crime that effects people all over the globe but due to the nature of human trafficking, questions about the magnitude of the problem and why such a problem occurs remain. The news plays an integral role on how people consume information. People’s views can unknowingly be influenced by how this news is presented with semantics, emotion, and different sources of information. The present study builds on previous research studies looking at media framing on human trafficking. Using the Dallas Morning News, a content analysis of articles published between 2010 and 2019 was conducted to see how media coverage on human trafficking has evolved since the passage of the Justice for Victims of Trafficking Act in 2015. Three frames were used to examine this evolution: a diagnostic frame, a prognostic frame, and the motivational frame. Results reveal that many variables are significantly related to the period after the Justice for Victims of Trafficking Act of 2015 was passed.
A Day in the Life: Kkandbabyj, An Exploration of Three-Dimensional Design Processes to Convey Character Dynamics
(May 2023) McClanahan, Sara; Fechter, Todd; McCord, Peter; Farrar, Eric
A Day in the Life: Kkandbabyj, An Exploration of Three-Dimensional Design Processes to Convey Character Dynamics is a sixteen-month Master’s Thesis project in which the artist explores the complex processes of 3D modeling and examines how a family of social media influencers’ image can be translated from the real world to 2D design, and result in stylized 3D characters that demonstrate a dynamic story between character models. This paper is designed to introduce readers to the three-dimensional design process and demonstrate one method in which storytelling can be told in a virtual space using a variety of techniques to imply motion with static digital figures. While this paper describes a detailed artistic methodology custom to the author’s workflow and creativity, it will not disclose the specifics of the digital software or tools without relevance to the overall storytelling agenda. The process of this thesis includes an analysis of preproduction, understanding shape language, setting dynamic poses through rigging techniques, creating atmosphere with texture and lighting, and using cinematography in a way that best strengthens the overall story.

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