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Title: NOVEL 8-T CNFET SRAM CELL DESIGN FOR FUTURE ULTRA-LOW POWER MICROELECTRONICS
Creator: Kim, Youngbae
Date: 2016, 2016-05
Description: In deep sub-micron technology, leakage power consumption has become a major concern in VLSI circuits, especially for SRAM, which is used to...
Show moreIn deep sub-micron technology, leakage power consumption has become a major concern in VLSI circuits, especially for SRAM, which is used to build the cache in System-on-Chip (SOC). In this paper, a low power 8-T SRAM cell, based on carbon nanotube field effect transistor (CNFET), is proposed to circumvent the leakage power issue. Experiment datas show that the proposed SRAM cell can save 97.94% static power consumption compared to existing 6T CNFET SRAM cell. In case of writing, the proposed SRAM cell consumes 39.27% less power than the traditional SRAM cell for writing 0 and 58.79% less for writing 1. Also, because of the adoption of a colaborated voltage sense amplifier and independent read component, our 8T SRAM shows much improved delay performance, the delay is observed to reduce by approximate 30% in write operation and approximate 90% in read operation.
M.S. in Electrical Engineering, May 2016
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Title: LAM MUTANT LARVAE WITH MELANOTIC MASSES DO NOT SURVIVE WITH THEIR SIBLINGS THAT DO NOT DEVELOP MASSES
Creator: Cui, Yi
Date: 2012-04-28, 2012-05
Description: Melanotic masses are black nodules that form inside larvae and can be found in multiple mutants (Minakhina & Steward, 2006). There are two...
Show moreMelanotic masses are black nodules that form inside larvae and can be found in multiple mutants (Minakhina & Steward, 2006). There are two types of melanotic masses, the immune-related masses and the non-hemocyte-associated masses (Minakhina & Steward, 2006). Immune-related melanotic masses form in lam mutants (Markovic et.al, 2009). We now show all lam mutant larvae survived and 33% of the them developed melanotic masses when grown on fresh food, however, only 66% of them survived and none of them formed melanotic masses when living in the frassfood; the mixture of poo medium collected from wild type larvae vials. We find that frassfood lethality is not recipe specific, since the results were similar on normal yeast mashed potato medium and Nutrifly food. Our results also identified that it is bacteria in frassfood that kills lam mutant larvae with melanotic masses because the survival and melanotic mass incidence of lam mutant larvae were similar to fresh food when antibiotics were added to frassfood. However lam mutant larvae with melanotic masses are not generally sensitive to bacteria because all lam mutant larvae showed sensitivity to S. marcescens. Besides, since there is no frassfood lethality observed on Relish mutants, we propose that the humoral response was not affected in the lam mutants. The frassfood lethality to larvae with melanotic masses was also observed in neither cactus mutants nor the hopTum-l mutants, so the immune defects that caused melanotic masses in these two mutants do not lead to the frassfood lethality. Finally, we show that the frassfood lethality is not exactly the same in lam overexpressing larvae with melanotic masses and lam C overexpressing larvae with melanotic masses to the lam/Df larvae with melanotic masses. Thus it is possible that the gut immunity was affected in lam mutants with melanotic masses so that they were killed by the bacteria in frassfood.
M.S. in Biology, May 2012
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Title: MEASUREMENT MODEL OF IONOSPHERIC ELECTRON CONTENT WITH CYGNSS
Creator: Zhang, Jordi Xing
Date: 2013, 2013-12
Description: Global ionospheric imaging is currently limited by a lack of significant quantities of observations over the oceans. This deficiency of...
Show moreGlobal ionospheric imaging is currently limited by a lack of significant quantities of observations over the oceans. This deficiency of measurements arises because coverage from Global Navigation Satellite System (GNSS) remote sensing tools is typically limited to regions over land by using ground-based receivers. This dissertation presents the opportunity to estimate ionospheric data over the oceans by using reflected GNSS signals received by the upcoming Low-Earth Orbit (LEO) satellite constellation Cyclone Global Navigation Satellite System (CYGNSS). CYGNSS is currently being developed by NASA primarily for hurricane predictions. Its eight micro-satellites will have a single-frequency Global Positioning System (GPS) receiver onboard and zenith and nadir antennas that collect GPS signals reflecting from the ocean surface of Earth. This study investigates the possibility of leveraging CYGNSS for a secondary science mission, using GNSS-Reflectometry for ionospheric remote sensing. A mathematical model is developed that retrieves ionospheric electron content by using ranging measurements from direct and ocean-reflected GPS signals onboard CYGNSS satellites. The measurement model terms can be grouped as geometric raypath and signal refraction components due to the troposphere and the ionosphere. Each term of the model is analyzed and quantified by applying fundamental physical principles and empirical models. The implementation of a multi-orbit simulation estimates the magnitudes of anticipated electron content measurements with CYGNSS.
M.S. in Mechanical and Aerospace Engineering, December 2013
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Title: THE VAPORIZATION PHENOMENA OF FUEL DROPLETS EXPOSED TO ASYMMETRIC RADIANT HEATING USING PLANAR LASER-INDUCED FLUORESCENCE
Creator: Ammigan, Kavin
Date: 2012-04-17, 2012-05
Description: Droplet vaporization under asymmetric conditions is prevalent in many combustion related devices where fuel droplets may either experience...
Show moreDroplet vaporization under asymmetric conditions is prevalent in many combustion related devices where fuel droplets may either experience asymmetric thermal radiant heating or travel in velocity and temperature gradients. Asymmetric radiant heating is particularly common in spray flames, counter-flow diffusion flames, regions close to the walls of conventional combustion chambers and more importantly in liquid-fueled microcombustors. In this study, experiments are carried out to observe how droplets vaporize when exposed to asymmetric radiant heating. The experimental set-up consists of applying radiant heating, through a radiant panel heater, to one face of a monodisperse droplet stream while using the planar-laser induced fluorescence (PLIF) diagnostic tool to reveal the spatial vapor distribution around vaporizing droplets. Since most fuels are made up of multiple components, bicomponent droplets are also investigated. Pure acetone droplets as well as mixtures of acetone/alkanes (octane and hexane) and acetone/alcohols (ethanol and 2-propanol) droplets are investigated. Results in the form of PLIF images, reveal asymmetric vapor distributions around the droplets with the apparent induction of Stefan flow from the irradiated droplet surface. Such phenomena have not previously been reported in the literature and have relevance to the overall fuel vaporization process as well as subsequent ignition and pollutant formation processes. To further investigate the experimental results, a convective and radiative heat transfer model is employed to simulate the droplets under corresponding experimental conditions. Results from the model show convective cooling and a strong thermal radiation absorption near the droplets’ surface. The induced asymmetric Stefan flow observed experimentally is therefore a consequence of the high thermal radiation absorption at the droplets’ surface. This study gives both experimental and theoretical results of the vaporization phenomena of asymmetrically irradiated fuel droplets with varying compositions, diameters and irradiation temperatures.
Ph.D. in Mechanical and Aerospace Engineering, May 2012
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Title: Towards the Robust Situation Awareness in Distribution Management System
Creator: Yao, Yiyun
Date: 2019
Description: In distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system...
Show moreIn distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system operating conditions. This motivates the establishment of modern distribution management system (DMS) for real-time net-work monitoring, resource optimization, and demand management. Three subproblems are mainly discussed when establishing the robust situation awareness in DMS. A measurement placement problem is proposed to decide the optimal locations and types of measurements to be placed in the distribution systems that minimize the worst-case estimation errors for DSSE over different system operating conditions. Four indices of the estimation error covariance matrix are chosen as the criteria of accuracy. The proposed measurement placement problem is formulated as a mixed-integer sem-idefinite programming (MISDP) problem. To avoid the combinatorial complexity, a con-vex relaxation, followed by a local optimization method, is employed to solve the MISDP problem. The proposed problem and the effectiveness of the proposed solution method are numerically demonstrated on the 33-bus distribution system.Distribution system state estimation (DSSE) is one of the vital components in the next-generation distribution management system (DMS), which allows the operators to monitor the entire system’s operating conditions. Due to the lack of real-time measurements, DSSE has to process measurements whose quality varies significantly across different sources, which causes convergence issue to the Gauss-Newton solver. In this chapter, a semidefinite programming (SDP) framework is developed to reformulate the DSSE problem into a rank- constrained SDP problem. One challenge of this technique is the nonconvex rank-one constraint, which is generally relaxed. However, the relaxed SDP-DSSE problem cannot guarantee a rank-one solution and hence lose optimality. Therefore, we propose two solution approaches, namely the rank reduction approach and the convex iteration approach, to obtain rank-one solutions for the SDP-DSSE problem. The proposed model and the effectiveness of the proposed solution approaches are numerically demonstrated on the IEEE 13-, 34-bus, and 123-bus distribution systems.A SE algorithm based on random measurements selection, which is inspired by the concept of moving target defense (MTD), is developed to prevent and mitigate stealthy cyber-attacks. With the proposed SE, a library of selected measurements scenarios is first generated offline given the available measurements and network topology. During online operation, multiple weighted least square (WLS) based SEs are processed in parallel with randomly picked scenarios from the library. The final solution is selected based on the largest normalized residuals with regard to individual scenarios. The effectiveness of the proposed SE is examined by attack-defense experiments on IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems.
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Title: Statistical Experimental Design and Modeling for Complex Data
Creator: Huang, Xiao
Date: 2018
Description: The ability to handle complex data is essential for new research findings and business success today. With increased complexity, data can...
Show moreThe ability to handle complex data is essential for new research findings and business success today. With increased complexity, data can either be difficult to collect with designed experiments or be difficult to analyze with statistical models. Both kinds of difficulties are addressed in this dissertation.The first part of this dissertation (Chapter 2 and 3) addresses the issue of complex data collection by considering two design of experiment problems. In chapter 2, we consider Bayesian A-optimal design problem under a hierarchical probabilistic model involving both quantitative and qualitative response variables. The objective function was derived and an efficient optimization algorithm was developed. In chapter 3, we consider the A/B-testing problem and propose a novel discrepancy-based approach for designing such an experiment. As the numerical examples show, the A/B-testing experiments designed in this way achieve better group balance and parametric estimation results.In the second part of this dissertation (Chapter 4 and 5), we focus on analyzing complex data with Gaussian process (GP) models. Gaussian process model is widely used for analyzing data with highly nonlinear relationships and emulating complex systems. In Chapter 4, we apply and extend GP model to analyze the in-cylinder pressure data resulted from experiments on a newly-developed dual fuel engine. The resulted model incorporates different data types and achieves good prediction accuracy. In Chapter 5, a generalized functional ANOVA GP model is proposed to tackle the difficulty resulted from high-dimensional feature space, and we develop an efficient algorithm for building such a model from the perspective of multiple kernel learning. The proposed approach outperforms traditional MLE-based GP models on both computational efficiency and prediction accuracy.
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Title: CHARACTERIZATION OF NOVEL PULSED UV-LIGHT SYSTEMS FOR INACTIVATION OF LISTERIA MONOCYTOGENES IN APPLE JUICE AND ON APPLE SURFACE
Creator: Malik, Sargun
Date: 2018
Description: Pulsed light processing can effectively inactivate microorganisms from the surface of foods or in transparent liquid foods. Pulsed light...
Show morePulsed light processing can effectively inactivate microorganisms from the surface of foods or in transparent liquid foods. Pulsed light systems currently available in the market operate at a fixed pulse duration and frequency and might not be optimized for microbial inactivation. A novel pulsed light system (Model X 1100; Xenon Corporation, USA) enables the researchers to adjust various parameters including pulse duration (100-7000μsec), voltage(1000-3000V), frequency (0.1-20Hz),% of energy (0-100%), and energy (up to 9J /cm2 / pulse of optical energy or 2433J / pulse of electrical energy ) . This study evaluated the effect of various parameters (treatment time, voltage, frequency, energy / pulse) on inactivation of Listeria monocytogenes in buffered peptone water (BPW), apple juice, and apple surface. For liquids, a 4-mL of sample (4-mmdepth) artificially inoculated with Listeria monocytogenes was treated in a quartz Petri dish (5.5-cmdiameter). For solid food, the top surface (skin side) of a slice of apple (1×1×0.5cm）was inoculated and exposed to various pulsed light treatment conditions. The results indicated that the impact of these factors vary as many of these factors are inter-related. In general, increasing the frequency, input voltage, pulse duration, and percentage of energy, increased the microbial reduction at the tested conditions (p<0.05). For instance, reductions of 1.21and 5.47 log10 CFU/mL were obtained in BPW and reductions of 1.35 and 4.70 log10 CFU/mL was acquired in apple juice, at 0.1and 0.82Hz, respectively, for a 20-sec treatment at 2500V (50% energy,700 μsec pulse width). Increased energy per pulse resulted in increased microbial reduction. For example, reductions of 2.30, 5.59, 6.69, and 6.69 log10 CFU/mL were obtained at 645, 1241, 1837, and 2433J/ pulse of electrical energy, respectively, in apple juice. Similarly, reductions of 5.34, 6.45, 6.02, and 6.56 log10CFU/mL were obtained at 645, 1241, 1837, and 2433J/ pulse, respectively in BPW. Lower reduction was obtained from the skin surface of the apple, for instance, reductions of 0.70 and 1.19 log10 CFU/ slice were obtained at 0.10 and 0.82 Hz, respectively, after a 10 seconds treatment at 2500V (50%energy). Similarly, reductions of 2.44, 2.43, 3.39, and 3.48 log10 CFU/ slice were obtained at 645, 1241, 1837, and 2433J/ pulse of electrical energy, respectively, after a 15 seconds treatment at 3000V (0.2Hz). The results were similar to the pulsed light treatment with RC 800 system (Xenon Corporation, USA). Absorption of pulsed light energy resulted in temperature increase in the products. Temperature increase of up to 11°C was observed at the treated conditions. The results suggest that this novel pulsed light system can potentially be used for inactivation of Listeria monocytogenes.
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Title: ANALYSIS AND OPTIMIZATION OF VIBRATION AND ACOUSTIC NOISE IN SWITCHED RELUCTANCE MACHINES
Creator: Yaman, Selin
Date: 2019
Description: One of the main drawbacks of switched reluctance machines (SRM) is the vibration and high acoustic noise compared to other electrical motors....
Show moreOne of the main drawbacks of switched reluctance machines (SRM) is the vibration and high acoustic noise compared to other electrical motors. The root cause of the high level of acoustic noise is radial forces with high harmonic content. These harmonics may trigger resonant modes in the stator and cause the machine to create high vibration and acoustic noise. To better understand the factors influencing vibration and acoustic noise in an SRM, this dissertation first develops a multi-physics model in ANSYS Workbench environment and carries out a comprehensive analysis of multiple variations in stator and rotor geometries. Based on this understanding, this dissertation identifies distinct factors affecting noise in the machine, which are affected by electromagnetic design and power electronic control. From the electromagnetic perspective, geometrical optimizations in the stator and rotor structures are evaluated to understand the impact on NVH (noise, vibration and harshness) performance. This background is used to develop a fast geometry-sensitive analytical approach to reduce acoustic noise in the machine. While optimizing the geometry for a silent machine design, different design of experiments (DoE) methods and response surface (RS) optimization methods are also compared and presented. Furthermore, material analysis is included in structural design, where high flux material effect on vibration and acoustic noise is observed. The second topic of the NVH analysis is power electronic and switching solutions. In this study, multiple basic and advanced switching techniques have been considered and optimized to reduce acoustic noise under a preset efficiency constraint. Further, a time efficient model of SRM is introduced with vibro-acoustic noise perspective by developing a computationally cost effective SRM modeling. By using this analytical time-efficient NVH model, a current shape optimization is implemented, and results are discussed. Finally, experimental validations are provided for NVH and psychoacoustics analysis for different operating conditions and current control methods.
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Title: THE K+ ACTIVATION MECHANISM OF V. CHOLERAE APBE
Creator: Yang, Jun
Date: 2019
Description: ABSTRACTNa+-translocating NADH: quinone oxidoreductase (NQR) is a protein complex that exists in the respiratory chain of Vibrio cholerae....
Show moreABSTRACTNa+-translocating NADH: quinone oxidoreductase (NQR) is a protein complex that exists in the respiratory chain of Vibrio cholerae. This complex can transport sodium ions to the outside of the plasma membrane. NQR has important influences on the survival and pathogenesis of V. cholerae. Two of the subunits of NQR, NqrC and NqrB, has a covalently bound FMN coenzyme. This FMN is necessary for the activity of NQR complex. A protein, alternative pyrimidine biosynthesis protein (ApbE) can transfer the FMN molecule to NqrB and NqrC covalently. And ApbE is also important to some other flavoproteins like the NOS and RNF. The ApbE protein use the FAD as the substrate to transfer the FMN group to the NqrC and NqrB apo-enzyme. Mg2+ is necessary for the activity of ApbE protein. Sodium and potassium ion are not necessary, but potassium ion can increase the activity of the ApbE by about ten times. In order to understand the mechanism of potassium activation of ApbE, several potassium binding sites were identified by molecular docking in this study. Point mutations of the amino acid residues constituting these sites were performed. The FMN transfer activity and affinity to potassium ions of these mutants were measured. The results suggest that when G125 was mutated, the binding of potassium ions was affected. Therefore, the structure composed of P126 and G125 may play a significant role in the activation of ApbE potassium ions.
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Title: A BOUNDARY INTEGRAL METHOD FOR COMPUTING THE FORCES OF MOVING BEADS IN A THREE-DIMENSIONAL LINEAR VISCOELASTIC FLOW
Creator: Hernandez, Francisco
Date: 2019
Description: Computing the forces acting on particles in fluids is fundamental to understanding particle dynamics and interactions. In this thesis, we...
Show moreComputing the forces acting on particles in fluids is fundamental to understanding particle dynamics and interactions. In this thesis, we study the dynamics of a two-particle system in a three-dimensional linear viscoelastic flow. Using a correspondence principle between unsteady Stokes flow and viscoelastic flow, we reformulate the problem and derive a boundary integral formulation that solves the Brinkman’s equation in the Fourier domain. We show that computational costs can be reduced by carefully eliminating the double-layer potential, and that a unique solution can be obtained by desingularizing the equation. We develop a highly accurate numerical integration scheme to evaluate the resulting boundary integrals. We solve the backward problem by making use of our numerical integration scheme, variable transformations, generalized minimum residual (GMRES) method, and spherical harmonic interpolations. In particular, spherical harmonic interpolations ensure that this numerical scheme is of high accuracy. Our method also has the advantage of working for both unsteady Stokes and linear viscoelastic flow by appropriately adjusting the oscillation frequency. Our numerical results are in agreement with the exact solution for a single-particle system, as well as the asymptotic solution for large particle separation in the two-particle system. Last, we analyze the numerical results for high oscillation frequencies and small particle separation. Our numerical method is shown to only depend on the frequency parameter and the distance between the particles. We find that for high frequencies, the forces on the particles behave differently for unsteady Stokes and linear viscoelastic flows.
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Title: NON-INTRUSIVE LOAD MONITORING IN RESIDENTIAL BUILDING
Creator: Lu, Mengqi
Date: 2019
Description: Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to...
Show moreNon-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. With this information, utilities can do many tasks such as energy conservation, planning generation more wisely, and demand response (DR) study. For house owners, they can understand their bill more clearly and make monthly budget plan. For researchers, NILM system is a good way to do the energy management in buildings and help to provide power information for smart homes design. Thus, an increasing number of new algorithms have been developed in recent years. In these algorithms, researchers either use existing public datasets or collect their own data which causes such problems as insufficiency of electrical parameters, missing of ground-truth data, absence of many appliances, and lack of appliance information. To solve these problems, this dissertation presents a model-based platform for NILM system development, namely Functional Intrusive Load Monitor (FILM). By using this platform, the state transitions and activities of all the involved appliances can be preset by researchers, and multiple electrical parameters such as harmonics and power factor can be monitored or calculated. This platform will help researchers save the time of collecting experimental data, utilize precise control of individual appliance activities, and develop load signatures of devices. Moreover, event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This dissertation also presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets.Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.
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Title: Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems
Creator: Sui, Wenbo
Date: 2018
Description: High combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control...
Show moreHigh combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control systems. In this work, different control strategies are investigated to improve the combustion efficiency of engines and to reduce the nitrogen oxide (NOx) emissions of vehicles.There are three main contributions of this work. First, to address emissions concerns, neural network based control algorithms were applied to selective catalyst reduction (SCR) systems. Compared with conventional model-based control, the control strategy based on neural networks can reduce the amount of time and cost required for model identification for these complex systems. The neural network controllers are developed and tested in simulations at different operating conditions for the Fe-zeolite SCR system first. In addition, methods for Jacobian information prediction are also discussed. According to the simulation results, the control strategy based on neural networks can track the desired reference and have reasonable NOx reduction efficiencies in most operating conditions. However, the NOx reduction efficiencies are poor at the low temperature situations in Fe-zeolite SCR systems. To improve this issue, the neural network control strategy was applied to a Cu-zeolite SCR and an improvement in the NOx reduction efficiencies was observed with reductions over 98% at different operating conditions. Second, to address efficiency concerns, a nonlinear model-based combustion control approach was investigated. This control approach aims to track a desired optimal combustion timing and leverages a combustion phasing model for a diesel engine that was developed and validated as part of this work. An intake gas properties model is also developed to capture the cylinder-to-cylinder difference of the temperature and pressure at intake valve closing (IVC). An adaptive controller and model-based controller were then designed for the diesel engine. These control strategies are evaluated in simulations and results show that the combustion phasing control system can track the optimal CA50 (crank angle at 50% mass of fuel burned). The combustion phasing control strategies were also expanded for use on dual-fuel compression ignition engines. The dual-fuel compression ignition engine is being considered as one of the candidates for the next generation of the modern diesel engines due to its ability to achieve high combustion efficiency and low emissions. To track the optimal combustion phasing in a dual-fuel engine, a non-linear combustion phasing model for this application was also developed and calibrated based on simulations. With the control-oriented model, controllers based on an adaptive control strategy and a feedforward control strategy are designed. The controllers are evaluated and shown to track the reference CA50s at varied operating conditions.
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Title: ESTIMATES OF FINE AND ULTRAFINE PARTICLE REMOVAL EFFICIENCY FOR RESIDENTIAL HVAC FILTERS USING IN-SITU SIZE-RESOLVED EFFICIENCY MEASUREMENTS
Creator: Zeng, Yicheng
Date: 2018
Description: Central heating, ventilating, and air-conditioning (HVAC) filters are commonly evaluated for their size-resolved particle removal efficiency ...
Show moreCentral heating, ventilating, and air-conditioning (HVAC) filters are commonly evaluated for their size-resolved particle removal efficiency (for particles 0.3 to 10 µm in diameter) by challenging them with a test aerosol in a laboratory setting. However, aerosol measurement and reporting classifications that are most commonly used in regulatory monitoring and building measurements include integral measures of mass-based concentrations (e.g., PM2.5, or the mass concentration of particles smaller than 2.5 µm) or total number concentrations (e.g., total UFPs, or ultrafine particles smaller than 100 nm). Because filter test standards have not traditionally considered these measures, building owners, occupants, and other key personnel cannot make informed decisions on HVAC filtration for these classifications. Moreover, because the removal efficiency for integral measures of total mass and number concentrations are also a function of the underlying particle size distributions that challenge the filter, one must consider the varied sources and size distributions of aerosols that filters encounter in real building applications. This work has two objectives: (1) to measure the in-situ size-resolved particle removal efficiency of a large number of commercially available residential HVAC filters, and (2) to use those size-resolved efficiency data to estimate integral measures of PM2.5 and total UFP removal efficiency for the same filters for typical residential indoor settings based on a literature survey of measured indoor particle size distributions. Particle concentration measurements were made upstream and downstream of a wide range of commercially available filters installed in a central air handling unit in an unoccupied residential apartment unit. A literature review was conducted to gather a variety of indoor particle size distributions (PSDs) from across the world and tri-modal lognormal distributions were fit to each of them. Finally, the particle removal efficiency for each filter for integral measures of indoor UFPs and PM2.5 were calculated for each indoor PSD. In-situ size-resolved measurements indicate that filters with similar rating values but from different manufacturers can have very different removal efficiencies for integral measures of PM2.5 and total UFPs, and that the assumption for indoor PSDs can greatly impact estimates of removal efficiency.
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Title: THE RELATION BETWEEN DEPRESSION AND TRAIT ANXIETY SYMPTOMS AND MATERNAL UTTERANCES DURING SONOGRAM PROCEDURES
Creator: Hamilton, Catharine Elizabeth
Date: 2018
Description: The present study examines the relation between depression and trait anxiety symptoms and women’s utterances during a routine ultrasound...
Show moreThe present study examines the relation between depression and trait anxiety symptoms and women’s utterances during a routine ultrasound procedure in the second trimester of pregnancy. Participants included a diverse group of 70 women seeking prenatal care at an academic medical center in the Midwestern United States. The Depression Anxiety Stress Scales (DASS-21) depression subscale and the State Trait Anxiety Inventory (STAI), trait form were used to assess symptoms of depression and trait anxiety, respectively. Audio and video of participants’ faces during the ultrasound examination were used to assess the content, sentiment, and number of utterances. Results of regression analyses indicated that higher levels of depression symptoms were significantly related to a lower proportion of fetus-related utterances to total utterances. Higher levels of depression symptoms and trait anxiety were significantly related to a lower proportion of positive fetus-related utterances to total fetus-related utterances, after controlling for gestational age. Higher levels of depression symptoms were significantly related to a higher proportion of negative-fetus-related utterances to total fetus-related utterances, after controlling for education. These findings suggest that pregnant women who are experiencing symptoms of depression and anxiety may exhibit certain types and patterns of utterances during routine prenatal sonogram procedures. Thus, observation of pregnant women’s naturalistic speech may provide helpful supplemental information to the traditional self-report measure in screening for symptoms of depression and anxiety.
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Title: Maternal-Fetal Attachment: Does it predict parenting outcomes?
Creator: Desai, Shivani S.
Date: 2018
Description: Maternal-fetal attachment (MFA) predicts critical aspects of the caregiver-child relationship, including parental sensitivity and engagement....
Show moreMaternal-fetal attachment (MFA) predicts critical aspects of the caregiver-child relationship, including parental sensitivity and engagement. However, little is known about the relation between MFA and specific parenting beliefs and attitudes that contribute to these positive parenting behaviors, such as parenting sense of competence and parenting stress. The aim of this longitudinal study was to examine if MFA predicts specific domains of parenting sense of competence and parenting stress when children are two years of age. Participants included 53 mainly Caucasian women with a mean age of 33.9 years. MFA was assessed during pregnancy (mean gestational age = 27.02 weeks) using the Maternal Fetal Attachment Questionnaire. Parenting sense of competence and stress were assessed when the children were 2 years of age with the Parenting Sense of Competence questionnaire and the Parenting Stress Index questionnaire. Results of regression analyses indicated that higher levels of MFA significantly predicted higher levels of parenting satisfaction, a domain of parenting sense of competence. They also indicated that higher levels of MFA predicted lower levels of two domains of parenting stress, including stress associated with attachment and role restriction. These findings suggest that prenatal attachment is important to assess during pregnancy, as it may predict future parenting beliefs and attitudes, including sense of competence and stress.
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Title: SPIN TRANSPORT AND SPIN-ORBIT TORQUES IN ANTIFERROMAGNETS
Creator: Saglam, Hilal
Date: 2019
Description: The electron has two fundamental degrees of freedom, i.e., charge and spin. Existing semiconductor electronics utilizes the charge degree of...
Show moreThe electron has two fundamental degrees of freedom, i.e., charge and spin. Existing semiconductor electronics utilizes the charge degree of freedom in its functionalities. Spintronics seeks, in addition, to exploit the spin degree of freedom, which can suggest promising pathways for low-power and faster operations. In conventional spintronics devices, ferromagnetic materials (FMs) have been employed as active components. However, it has recently been recognized that antiferromagnetic materials (AFMs) can also play an active role in spintronic devices. Antiferromagnets have several advantages over ferromagnets; for instance, they have net zero magnetization so that they are invisible to external magnetic fields. Also, they show resonances in the terahertz frequency range. Towards this end, this thesis focuses on spin transport and spin-orbit torques in various antiferromagnetic materials. With respect to the former, I demonstrated that spin currents can be transmitted efficiently through a metallic antiferromagnet FeMn. I detect two distinctly different spin transport regimes, which can be associated with electronic and magnonic spin currents. With respect to the latter, I investigated a possible correlation between two important spintronics concepts, i.e., spin-orbit torques and exchange bias since the ferromagnetic/antiferromagnetic interface is crucial for both phenomena. The measured spin Hall angles suggest that these two effects are independent of each other, although it is worthy to mention that there are still strong spin-orbit torques even when the antiferromagnet is directly exchange coupled to the ferromagnet. Furthermore, I discuss anomalous Hall effect (AHE) and anomalous Nernst effect (ANE) in another metallic antiferromagnet, FeRh, which undergoes a temperature driven antiferromagnetic-to-ferromagnetic phase transition. The temperature dependent results show a drastic suppression of both AHE and ANE signals in the antiferromagnetic phase. Interestingly, these non-vanishing signals are opposite in sign compared to their ferromagnetic counterparts, which can suggest changes of inherent symmetries in the electronic structure of FeRh across its magnetic phase transition.
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Title: DIAGNOSING AND TREATING ADHD: CLINICIAN CHARACTERISTICS, METHODS OF DIAGNOSIS, DIAGNOSTIC RATES, AND TREATMENT RECOMMENDATIONS
Creator: Haak, Christopher Luke
Date: 2019
Description: Attention-deficit/hyperactivity disorder (ADHD) is one of the top five most common referrals among all neuropsychologists (Sweet et al. 2015)...
Show moreAttention-deficit/hyperactivity disorder (ADHD) is one of the top five most common referrals among all neuropsychologists (Sweet et al. 2015) and continues to elicit public and professional concern about over-diagnosis in children (Sciutto & Eisenberg, 2007) and under-diagnosis in adults (Asheron et al., 2012; Kooji et al., 2010). In recent years, the prevalence of ADHD has increased (Polanczyk et al., 2007 & 2014, Thomas et al., 2015). It is unclear what is driving these changes though changes in criteria may be playing a role (van de Voort et al., 2014). Further, there has been little research on whether professional training, beliefs, and practice factors can influence the likelihood to diagnose ADHD. The purpose of this study was to examine the extent to which neuropsychologists’ professional characteristics, training, and beliefs about ADHD diagnosis and treatment influence their likelihood to diagnose ADHD. The study also evaluated whether there are differences in assessing and treating ADHD based upon the client population focus (child, lifespan, or adult) of neuropsychologists. Participants in this study were 106 neuropsychologists from across the United States and Canada who were recruited through neuropsychology listservs to participate in an online survey. Results indicated that population focus was associated with significant differences in approach to diagnosing and treating ADHD, with child- and lifespan-focused neuropsychologists reporting higher rates of ADHD diagnosis. Additionally, having a higher percent of clinical cases in which ADHD is a referral question and greater self-reported adherence to following full diagnostic criteria for making a diagnosis were associated with higher ADHD diagnostic rates, controlling for age, gender, ethnicity, and other professional characteristics. This study is among the first to examine specific clinician factors impacting diagnostic rates and its findings have several implications for practice and research.
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Title: A Novel Remote Sensing System Using Reflected GNSS Signals
Creator: Parvizi, Roohollah
Date: 2020
Description: This dissertation presents a method to remotely sense freshwater surface ice and water using reflected signals from Global Navigation...
Show moreThis dissertation presents a method to remotely sense freshwater surface ice and water using reflected signals from Global Navigation Satellite Systems (GNSS). A portable ground-based sensor system is designed and built for collecting both scattered Global Positioning System (GPS) signals and independent validation data (lidar and camera) from the surface. GPS front-end signals are collected from both a direct receiving antenna facing upward and from a reflection-receiving antenna facing downward. Multiple data campaigns are conducted on the Lake Michigan waterfront in Chicago. A customized software receiver tests a new signal processing method to detect and acquire Global Navigation Satellite System (GNSS) signals reflected from the lake surface ice and collected by a downward-facing antenna. The method, modified differential coherent integration, multiplies time-shifted auto-correlation samples. The new method is evaluated against three conventional integration methods (coherent, incoherent, and differential integration) with signals from the direct antenna. With front-end samples from the reflection antenna, the new method is the only one of the four methods compared that acquires satellites in the reflected GPS signals, with three acquired using 10 ms of integration.The lidar surface scans are mapped with camera images and estimated reflection points to indicate the surface reflection type and to provide surface height relative to the sensors. For one satellite whose specular point is estimated to be on the ice surface, a Delay Doppler Map (DDM), signal-to-noise (SNR) ratio, and surface reflectivity (SR) are computed with the modified differential coherent integration method using the GPS. The DDM shows that, with modified differential integration, the satellite can be acquired in the reflected signal. For two satellites whose reflection points scan across ice and water over time the SNR and SR are computed over time. The SR is shown to be lower for liquid water than lake ice. This system concept may be used in the future for more complete mapping of phase changes in the cryosphere.
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Title: Economic and Computational Methods for the Control of Uncertain Systems
Creator: Zhang, Jin
Date: 2019
Description: The Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional...
Show moreThe Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional optimization and control hierarchy. This dissertation investigates the computational procedures used to obtain a global solution to the ELOC problem. The proposed method employs the Generalized Benders Decomposition (GBD) algorithm. Compared to the previous branch and bound approach, the application of GBD to the ELOC problem will greatly improve computational performance. A technological benefit of decomposing the problem into steady-state and dynamic parts is the ability to utilize nonlinear steady-state models, since the relaxed master problem is free of SDP type constraints and can be solved using any global nonlinear programming algorithm.In order to address the issue of model/plant mismatch, the dissertation will also investigate how to handle box-type uncertainties in ELOC. We consider two methods, a robust formulation for when the uncertainty is completely unknown and a Linear Parameter Varying formulation for when uncertainty can be measured in real time. In both cases, the infinite number of conditions that need to be satisfied are reduced to a finite set of constraints. The resulting problem formulations have a similar structure to the ELOC and can be solved globally by employing the generalized Benders decomposition.Despite a high-quality control law, the ultimate performance of closed-loop systems will be dictated by the quality and limitation of hardware element. Thus, hardware selection is also investigated in the dissertation. The cost-optimal hardware selection problem has been shown to be of the Mixed Integer Convex Programming (MICP) class. While such a formulation provides a route to global optimality, use of the branch and bound search procedure has limited application to fairly small systems. In this dissertation, we illustrate that a simple reformulation of the MICP and subsequent application of the GBD algorithm will result in massive reductions in computational effort.Finally, the problems of value-optimal sensor network design (SND) for steady-state and closed-loop systems are investigated. The value-optimal SND problem has been shown to be of the nonconvex mixed integer programming class. In the dissertation, it is demonstrated after transforming into an equivalent reformation, the application of GBD algorithm will significantly reduce the computational effort.
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Title: A Novel CNFET SRAM-Based Computing-In-Memory Design and Low Power Techniques for AI Accelerator
Creator: Kim, Young Bae
Date: 2023
Description: Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI...
Show morePower consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). In addition, according to the 2020 International Technology Road map for Semiconductors (ITRS), the high power consumption trend of AI chips far exceeds the power requirements. As a result, power optimization techniques are highly regarded in nowadays AI chip designs. There are various low-power methodologies from the system level to the layout level, and we are focusing on transistor level and register transfer level (RTL) through this thesis. In this thesis, we propose a novel ultra-low power voltage-based computing-in- memory (CIM) design with a new SRAM bit cell structure for AI Accelerator. The basic working principle of CIM (Computing-in-memory) is to use the existing internal embedded memory array (e.g. SRAM) instead of external memory, and it reduces unnecessary access to external memory by calculating with internal embedded mem- ory. Since the proposed our SRAM bit cell uses a single bitline for CIM calculation with decoupled read and write operations, it supports much higher energy eciency. In addition, to separate read and write operations, the stack structure of the read unit minimizes leakage power consumption. Moreover, the proposed bit cell structure provides better read and write stability due to the isolated read path, write path and greater pull-up ratio. Compared to the state-of-the-art SRAM-CIM, our proposed SRAM-CIM does not require extra transistors for CIM vector-matrix multiplication. We implemented a 16k (128⇥128) bit cell array for the computation of 128x neurons, and used 64x binary inputs (0 or 1) and 64⇥128 binary weights (-1 or +1) values for the binary neural networks (BNNs). Each row of the bit cell array corresponding to a single neuron consists of a total of 128 cells, 64x cells for dot-product and 64x replicas cells for ADC reference. And 64x replicas cells consist of 32x cells for ADC reference and 32x cells for o↵set calibration. We used a row-by-row ADC for the quantized outputs of each neuron, which supports 1-7 bits of output for each neuron. The ADC uses the sweeping method using 32x duplicate bit cells, and the sweep cycle is set to 2N1 +1, where N is the number of output bits. The simulation is performed at room temperature (27C) using 32nm CNFET and 20nm FinFET technology via Synopsys Hspice, and all transistors in bitcells use the minimum size considering the area, power, and speed. The proposed SRAM-CIM has reduced power consumption for vector-matrix multiplication by 99.96% compared to the existing state-of-the-art SRAM-CIM. Moreover, because of the separated reading unit from an internal node of latch, there is no feedback from the read access circuit, which makes it read static noise margin (SNM) free. Furthermore, for the low power AI accelerator design, we propose a new AI accelerator design method that applies low power techniques such as bus specific clock gating (BSCG) and local explicit clock gating (LECG) at the register-transfer- level (RT-level). And evaluates them on the Xilinx ZCU-102 FPGA SoC hardware platform and 45nm technology for ASIC, respectively. It measures dynamic power using a commercial EDA tool, and chooses only a subset of FFs to be gated selectively based on their switching activities. We achieve up to a 53.21% power reduction in the ASIC implementation and saved 32.72% of the dynamic power dissipation in the FPGA implementation. This shows that our RTL low power schemes have a powerful possibility of dynamic power reduction when applied to the FPGA design flow and ASIC design flow for the implementation of the AI system.
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