VictorJuiz/thesis huggingface dataset:
Design of optical microsystems [electronic resource] : applications in optical communication and biomedical imaging
Keywords: Electrical Engineering

Optical MEMS (Micro-Electro-Mechanical Systems) is an enabling technology that realizes miniaturized optical systems with high functionality and excellent performance. In this dissertation, two novel miniaturized optical systems are presented for applications in optical communication and biomedical imaging. In Part I, a multi-functional MEMS tunable optical filter are described, which is a key element for dynamic wavelength provisioning in reconfigurable optical networks and communication systems. This filter is built based on MEMS platform technology that allows large vertical mirrors (311 [mu]m × 450 [mu]m × 40 [mu]m) to be micro-assembled on actuated platforms to enable compact tunable optical filters with large apertures and high-quality optical mirrors with very low scattering losses. Besides, electrostatic combdrive actuators connected to the MEMS platforms provide independent and continuous control of the center wavelength and the optical bandwidth. The filter has been tested by integrating it in a 10Gb/s communication system. The performance of the MEMS tunable filter is demonstrated for amplified spontaneous emission (ASE) rejection and wavelength selection. In PART II, a 3-D MEMS scanning system utilizing 2-D lateral and 1-D vertical MEMS scanners is introduced for use in miniature in vivo dual-axis confocal (DAC) microendoscopes, which is an emerging biomedical-imaging technology with high resolution, good tissue penetration, large field of view, and ability to provide both reflectance and fluorescence contrast images. Both MEMS scanners are fabricated exclusively by front-side processing to enable compact and robust structures that facilitate handling and packaging for miniaturized optical instrumentation. Co-operation of a 2-D lateral scanner and a 1-D vertical scanner enables fast 3-D microscopy over a volume that measures 340[mu]m by 236[mu]m by 286[mu]m. This part describes the principle of the all-MEMS-based 3-D scanning DAC microscopy that gives the functionality of OCT (real-time vertical cross-section imaging) to a confocal microscope.
Design of quiet UAV propellers [electronic resource]
Keywords: Aeronautics and Astronautics

Extensive recent development and deployment of electric-powered unmanned aerial vehicles (UAVs) has placed increased focus on the design of quiet propellers for these vehicles. To aid in this effort, a design methodology is developed to synthesize low-noise and efficient propellers for electric UAVs. This methodology employs a blade-element momentum theory performance analysis method, a compact-chord aeroacoustic analysis method based on Farassat's Formulation 1A of the Ffowcs Williams-Hawkings equation, and a beam analogy aeroelastic model. Blade geometries that minimize induced and profile losses are analytically determined, and other design parameters are chosen parametrically to balance noise reduction and aerodynamic performance. Various noise-reduction techniques and their impacts on propeller performance are analyzed, and reduced tip speeds and increased blade counts are selected as most promising for the chosen conditions. Two propellers of different blade counts designed using this methodology are manufactured to validate the methodology, and static test stands are developed to perform this validation. Comparisons between the predicted and experimental performance reveal a deficit in thrust; this is partially explained by the inaccurate wake geometry assumptions of the blade-element momentum theory at static conditions. The remainder of this discrepancy is likely attributable to a combination of experimental error and rotational and three-dimensional aerodynamic effects not analytically modeled. A significant noise reduction was experimentally demonstrated between propellers of low and high blade counts, validating trends identified analytically; this reduction, while large, was less than the predicted magnitude, likely due to manufacturing irregularities limiting destructive acoustic cancellation between propeller blades. Although the performance discrepancy precludes the use of this methodology for the design of production propellers, the methodology is valuable in easily identifying practical quiet propeller configurations for preliminary design studies.
The design of TRIP, a tendon-coupled, dynamic and static articulated bipedal robot [electronic resource]
Keywords: Mechanical Engineering

Over the last several years advances and miniaturization of technology have allowed legged robots to move from the research laboratory to the real world, as evidenced by the large number of flexible platforms only recently available. These platforms are successful, but fall short of capitalizing on the biggest advantage of legs: the ability for high speed locomotion over unstructured terrain. Additionally, no current articulated legged robot is able to perform both static and truly dynamic locomotion, as the actuation technology is not conducive to these two very different regimes of operation. Currently, machines that are dynamic are unable to walk or position themselves accurately, and machines that function very well with static motions are unable to move dynamically. The bipedal robot TRIP (Tendonized Running Inspired Platform) was built to study dynamic maneuvering and control of an articulated legged robot with high-inertia legs. Each leg includes an inelastic tendon which couples ankle joint rotations to knee joint rotations, resulting in simplified effective dynamics and control, and a high degree of passive stabilization with dynamic maneuvers. A hybrid pneumatic-electric actuator was built and tested while driving a knee joint; this actuator is capable of precise positioning, as well as highly energetic thrusting. It will be shown that static and dynamic maneuvers are attainable with a tendon-coupled ankle, and that a tendon allows simpler dynamic control through the concept of impulse compensation, and through the use of passive stabilization from the kinematics resulting from the tendon. Second, it will be shown that the hybrid actuator is superior in some respects to current available technology, both by specification, and by experimental results of static and dynamic maneuvers achieved through its use. And third, using a general heuristic-based control algorithm developed for various sized bipedal machines, dynamic maneuvers are possible with a high leg-to-body moment of inertia ratio, which would show that the control strategy takes into account the dynamics of the legs during high speed locomotion.
Designing and engineering [electronic resource] : ambidextrous mindsets for innovation
Keywords: Mechanical Engineering

Design transforms people and the stuff they make. How technical engineers learn and advance a human-centered design approach, and what catalysts and barriers for their learning exist, will be illustrated with research done with student mechanical engineering designers engaged in work practice. Ambidextrous Mindsets for Innovation is a framework for relating designerly ways of knowing-doing-acting and engineering ways of knowing-doing-acting. Empirical findings are based on evidence collected from nine engineering design teams in a graduate mechanical engineering course. The focus is on their prototyping habits over time, supported by observations of team meetings and review of team documentation reports.
Detecting low frequency earthquakes within deep tectonic tremor [electronic resource]
Keywords: Geophysics

Prior to analyzing any earthquake, the event must be detected. Although fundamental, earthquake detection remains a challenging task- particularly for small-magnitude events where the amplitudes of the signal of interest approach the noise level of the recording instrument. In particular, high precision event detection is important with regard to analyzing tectonic tremor, formerly referred to as non-volcanic tremor. Tectonic tremor is a non-impulsive, low-amplitude, semi-continuous seismic signal whose time series is similar to volcanic tremor. Despite the time series similarity to volcanic tremor, tectonic tremor is often recorded at plate boundaries- mostly convergent boundaries at subduction zones- away from volcanic centers. Unlike large earthquakes where seismic signals are impulsive with high amplitudes and a finite duration, tectonic tremor is emergent, semi-continuous and low amplitude causing tremor locations to have large errors. In southwest Japan tectonic tremor has been shown to consist of several repeating low frequency earthquakes indicating shear failure on the subduction plate interface that occur during several days, however these events are difficult to detect due to their low amplitude approaching the noise level. In this thesis I demonstrate two methods to identify periods of waveform similarity during the tremor and show that these similar events are low frequency earthquake swarms located on the deep extent of four plate boundaries: southwest Japan, Cascadia, Costa Rica, and the Alaska-Aleutian subduction zones. Although each subduction zone has a different incoming plate rate, age and thermal profile, these factors do not exhibit a strong influence on tremor occurrence indicating tremor may occur in almost- if not all subduction zones.
Detection of bumps on the intensity function of an inhomogeneous Poisson process [electronic resource]
Keywords: Statistics

The Scanning Statistics methodology has been applied to problems arising in many disciplines including epidemiology and genomics. In this thesis we propose a modification of the Scan Statistic for the Poisson model and we compare it with other tests based on this methodology. Poisson Processes have been widely used in credit risk to build models of credit defaults across time. Despite this, few papers are focused on building tools to test these models. In this thesis we use Scanning Statistics to test a particular family of credit risk models. We haven't seen an application of Scanning Statistics to credit risk. We consider the inhomogeneous Poisson Process with intensity p[mu](t) on an interval I and q[mu](t) on [0,1]\I. Suppose that the intensity [mu](t) is known while p, q and I are un- known. This problem can be transformed into detecting clusters on a sample of iid uniform random variables. We use scanning statistics to approach these problems and propose a scale penalty for the Scan (Maximum Likelihood Ratio Test). We establish detection conditions for this penalized test and also study the power of the Average Maximum Likelihood Ratio Test.
Development and application of a miniature, integrated fluorescence microscope for in vivo brain imaging [electronic resource]
Keywords: Applied Physics

A longstanding goal in neuroscience is to understand animal behavior in terms of underlying cellular activity. Traditionally, conventional microscopes have not been suitable for imaging studies in awake behaving animals due to their large size and physical constraints. To overcome these limitations, we have developed a miniature (< 2 g mass), integrated fluorescence microscope to enable high-speed cellular imaging (20-100 Hz) in the brain of freely behaving mice. By combining the integrated microscope with an established mouse preparation for in vivo fluorescence imaging, we monitored calcium dynamics in many neurons simultaneously in mice performing typical behaviors such as running, grooming, and exploring an environment. The use of a recently developed genetically encoded calcium indicator, GCaMP3, permitted repeated observations of calcium dynamics in the same individual neurons at multiple times over the period of weeks to months. When additionally combined with image registration routines that track cellular identity across different imaging sessions, our system enables cellular-level monitoring of changes in calcium dynamics in a population of neurons over many weeks.
Development of a nitrous oxide monopropellant hot gas generator for rocket propellant pressurization and spacecraft thruster applications [electronic resource]
Keywords: Mechanical Engineering

Interest in space flight is increasing toward levels not seen since the race to the moon in the late 1960s. Government funding for manned and unmanned space flight has been waning in the years following 2000. However, investment from the private sector has stepped in to fill this void, with the ultimate aims of privatizing manned space flight (i.e., for space tourism or even selling transportation services back to governments) and providing private launch services for commercial satellites. If the promise of commercialized space flight is to be delivered in a widespread fashion, critical obstacles must be overcome, such as cost, reliability, and environmental impact. The research demonstrated here addresses the above obstacles by proposing and investigating the catalytic decomposition of nitrous oxide (N2O) for several applications, including a monopropellant for propulsion, pressurization, and power generation. N2O has the potential to serve as a low-cost, safe, and environmentally friendly 'green' monopropellant. As a self-pressuring gas with self-sustaining decomposition, it could be used to pressurize liquid oxidizer propellant tanks, which would allow for the replacement of bulky, high pressure tanks with much smaller, lighter tanks. It could also be used as propulsion for small satellites, or as a reliable auto-ignition source for larger liquid or hybrid rocket engines. However, if the promise of N2O for propulsion and pressurization applications is to be realized, its performance (e.g., energy release efficiency and specific impulse under various bed loading conditions) must be experimentally determined and optimized. To experimentally investigate the capabilities of N2O, a test facility has been constructed, and a robust subscale test article has been fabricated. Three catalyst bed configurations have been hot fire tested in this facility: (1) a silicon carbide planar device, (2) a 1/2" axisymmetric metallic device, and (3) a 1" axisymmetric metallic device. Novel designs were successfully developed and tested in order to address the main challenges of this work (containing high temperature oxidizing core flow, lowering activation energy (thus minimizing input power), and ensuring safety). Also, the capability of rhodium and iridium as catalysts was investigated, and low-power startup was demonstrated. The experimental results demonstrated in this work are used to describe a path to scaling down the device to MEMS-fabricated micro-scale, as well as scaling up to power-generating gas generator.
Development of a resonance ionization spectroscopy ion-transport probe for the enriched xenon observatory [electronic resource]
Keywords: Chemistry

The Enriched Xenon Observatory (EXO) is a series of experiments seeking to measure the neutrino mass through observation of neutrinoless double beta decay (0nbb). The next generation of 0nbb experiments aims to probe Majorana neutrino masses at or below 10 meV. To reach this sensitivity, ton-scale detectors are needed with lower radioactive backgrounds than the best ones operating today. The EXO collaboration is developing a novel strategy for a virtually background-free search for the 0nbb of Xe-136, based around detecting individual Ba-136 ions resulting from such decays. This dissertation details the efforts to develop a barium tagging technique which uses resonance ionization spectroscopy (RIS) to selectively and efficiently ionize barium atoms for injection and detection in an ion trap. A simple radionuclide-driven single-ion source has been developed to push the technology to high efficiency with a small number of ions.
Development of manganese oxide based catalysts for the oxygen reduction and oxygen evolution reactions [electronic resource]
Keywords: Chemical Engineering

Development of active catalytic materials for the oxygen reduction reaction (ORR, O2 + 4H+ + 4e- → 2H2O) and the oxygen evolution reaction (OER, 2H2O → O2 + 4H+ + 4e-) is one of the major challenges in energy conversion and storage technologies such as fuel cells, metal-air batteries, electrolysis cells, and solar fuel synthesis. The majority of industrially relevant active materials are precious metals and metal oxides, whose high cost and scarcity limits the widespread adoption of these important technologies. Manganese oxides (MnOx) catalysts are an interesting alternative to the precious metal based electrodes under alkaline conditions due to their abundance, low cost, and a demonstrated ability to catalyze both the ORR and the OER at high pH values. To accelerate the development of MnOx catalysts with improved activities for the ORR and the OER, it is important to increase understanding of the surface properties at the ORR and the OER relevant potentials. Spectroscopy techniques, including x-ray photoelectron spectroscopy (XPS) and x-ray absorption spectroscopy (XAS), are among the possible methods to monitor surface chemistry of catalytic materials. Both XPS and XAS can offer information on the electronic structure of the surface, while XAS can also probe the short-range order of the catalysts. This dissertation focuses on development of MnOx catalysts for the ORR and the OER and uses ex-situ XPS, ex-situ Mn L-edge XAS, and in-situ Mn K-edge XAS to correlate measured catalytic activity to the surface oxidation state of Mn.
Differential expression identification and false discovery rate estimation in RNA-Seq data [electronic resource]
Keywords: Statistics

RNA-Seq is becoming the primary tool for measuring genome-wide transcript expression. We discuss the identification of features (genes, isoforms, exons, etc.) that are differentially expressed in samples in different biological conditions or under different disease statuses. Besides finding the right set of significant features, we emphasize on accurately estimating the corresponding false discovery rate (FDR). RNA-Seq data take the form of counts, so models based on Gaussian distribution are generally unsuitable. Also, different sequencing experiments have very different sequencing depths, which need to be estimated accurately and then used to normalize the data. Current methods model counts by Gaussian, Poisson or negative binomial distributions, and they apply the Benjamini-Hochberg procedure for FDR estimation. They have obvious limitations: (1) They are only applicable to two-class data, but not quantitative or survival data. (2) They are sensitive to the violations of distributional assumptions and often fail completely when outliers present in the data. (3) Their estimation of FDR can often be inaccurate. To overcome these difficulties, we propose two novel methods, a parametric one and a nonparametric one. Our parametric method uses a new permutation plug-in procedure for estimating FDR, and our nonparametric method utilizes a novel resampling strategy for normalizing the count data. Both methods can be applied to different types of RNA-Seq data. The parametric method is less sensitive to violations of its distributional assumptions, and the nonparametric method is very robust even to outliers. Both of them often give reliable estimate of FDRs in the cases where other methods cannot. Although we mainly discuss the identification of differentially expressed genes in RNA-Seq data, the two methods we develop should be equally applicable to data generated by other sequencing technologies, such as DNA-Seq, ChIP-Seq, and 3SEQ.
Digital filters with quantized coefficients [electronic resource] : optimization and overflow analysis using extreme value theory
Keywords: Electrical Engineering

Parks-McClellan filter designs optimally minimize the maximum magnitude ripple in specific frequency bands for a given filter order. There are simple tools available for estimating the minimum order of a Parks-McClellan filter to meet a specific ripple requirement. However, many real-world filter implementations must first quantize filter coefficients, which can drastically reduce the performance of a Parks-McClellan design by increasing the maximum ripple. By making changes in the filter design specification within a very small range, a very large number of filter realizations with quantized coefficients can be generated. The problem is to choose the best design in the spirit of the original specification. For a Parks-McClellan specification, the best choice minimizes the peak of the ripple. Finding the best design out of perhaps millions of possibilities can be aided by making use of a statistical theory of extreme values. Extreme Value Theory is the study of the statistics of the maximum value or extreme outliers in a random process. We apply the theorems of Extreme Value Theory to the magnitude response of quantized filter designs with the goal of modeling the PDF of the maximum ripple in a particular frequency band. We find that the Generalized Extreme Value distribution, a fundamental distribution of Extreme Value Theory, well models the maximum ripple in quantized filters. This model can be used to estimate the filter order required to achieve a specific performance level for quantized Parks-McClellan designs. In addition, we present algorithms for generating fixed-point Parks-McClellan filters with improved performance, and compare the new algorithm to existing MATLAB tools. Secondly, we introduce a model for the distribution of extreme outliers at an arbitrary point within a digital system. Accurate estimates can be made of the probability that the signal is above a specific threshold, without knowledge of the exact PDF of the signal at that point. This model is useful for overflow analysis, as it can estimate the rate that a fixed-point signal will exceed its maximum representable value. Algorithms are presented and compared that can efficiently use this model on large datasets for automated overflow analysis.
Directed learning [electronic resource]
Keywords: Electrical Engineering

In machine learning, it is common to treat estimation of model parameters separately from subsequent use of the model to guide decisions. In particular, the learning process typically aims to maximize ``goodness of fit'' without consideration of decision objectives. In this dissertation, we propose a new approach -- directed learning -- which factors decision objectives into the model fitting procedure in order to improve decision quality. We develop and analyze directed learning algorithms for three classes of problems. In the first case, we consider a problem where linear regression analysis is used to guide decision making. We propose directed regression, an efficient algorithm that takes into account the decision objective when computing regression coefficients. We demonstrate through a computational study that directed regression can generate significant performance gains, and establish a theoretical result that motivates it. This setting is then extended to a multi-stage decision problem as our second case, and we show that a variation of directed regression, directed time-series regression, improves performance in this context as well. Lastly, we consider a problem that involves estimating a covariance matrix and making a decision based on that estimate. Such problems arise in portfolio management among other areas, and a common approach is to employ principal component analysis (PCA) to estimate a parsimonious factor model. We propose directed PCA, an efficient algorithm that accounts for the decision objective in the selection of components, and demonstrate through experiments that it leads to significant improvement. We also establish through a theoretical result that the possible degree of improvement can be unbounded.