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Air Force Institute of Technology

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Articles 301 - 330 of 2678

Full-Text Articles in Physical Sciences and Mathematics

Multi-Gaussian Random Variables For Modeling Optical Phenomena, Olga Korotkova, Milo W. Hyde Iv Aug 2021

Multi-Gaussian Random Variables For Modeling Optical Phenomena, Olga Korotkova, Milo W. Hyde Iv

Faculty Publications

A generalization of the classic Gaussian random variable to the family of multi-Gaussian (MG) random variables characterized by shape parameter M > 0, in addition to the mean and the standard deviation, is introduced. The probability density function (PDF) of the MG family members is an alternating series of Gaussian functions with suitably chosen heights and widths. In particular, for integer values of M, the series has a finite number of terms and leads to flattened profiles, while reducing to the classic Gaussian PDF for M = 1. For non-integer, positive values of M, a convergent infinite series of …

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Node Generation For Rbf-Fd Methods By Qr Factorization, Tony Liu, Rodrigo B. Platte Aug 2021

Node Generation For Rbf-Fd Methods By Qr Factorization, Tony Liu, Rodrigo B. Platte

Faculty Publications

Polyharmonic spline (PHS) radial basis functions (RBFs) have been used in conjunction with polynomials to create RBF finite-difference (RBF-FD) methods. In 2D, these methods are usually implemented with Cartesian nodes, hexagonal nodes, or most commonly, quasi-uniformly distributed nodes generated through fast algorithms. We explore novel strategies for computing the placement of sampling points for RBF-FD methods in both 1D and 2D while investigating the benefits of using these points. The optimality of sampling points is determined by a novel piecewise-defined Lebesgue constant. Points are then sampled by modifying a simple, robust, column-pivoting QR algorithm previously implemented to find sets of …

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Zernike Integrated Partial Phase Error Reduction Algorithm, Stephen C. Cain Aug 2021

Zernike Integrated Partial Phase Error Reduction Algorithm, Stephen C. Cain

Faculty Publications

A modification to the error reduction algorithm is reported in this paper for determining the prescription of an imaging system in terms of Zernike polynomials. The technique estimates the Zernike coefficients of the optical prescription as part of a modified Gerchberg-Saxton iteration combined with a new gradient-based phase unwrapping algorithm. Zernike coefficients are updated gradually as the error reduction algorithm converges by recovering the partial pupil phase that differed from the last known pupil phase estimate. In this way the wrapped phase emerging during each iteration of the error reduction algorithm does not represent the entire wrapped phase of the …

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Beam Formation And Vernier Steering Off Of A Rough Surface, Eric K. Nagamine, Kenneth W. Burgi, Samuel D. Butler Aug 2021

Beam Formation And Vernier Steering Off Of A Rough Surface, Eric K. Nagamine, Kenneth W. Burgi, Samuel D. Butler

Faculty Publications

Wavefront shaping can refocus light after it reflects from an optically rough surface. One proposed use case of this effect is in indirect imaging; if any rough surface could be turned into an illumination source, objects out of the direct line of sight could be illuminated. In this paper, we demonstrate the superior performance of a genetic algorithm compared to other iterative feedback-based wavefront shaping algorithms in achieving reflective inverse diffusion for a focal plane system. Next, the ability to control the pointing direction of the refocused beam with high precision over a narrow angular range is demonstrated, though the …

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Investigation And Statistical Modeling Of The Mechanical Properties Of Additively Manufactured Lattices, Derek G. Spear, Anthony N. Palazotto Jul 2021

Investigation And Statistical Modeling Of The Mechanical Properties Of Additively Manufactured Lattices, Derek G. Spear, Anthony N. Palazotto

Faculty Publications

This paper describes the background, test methodology, and experimental results associated with the testing and analysis of quasi-static compression testing of additively manufactured open-cell lattice structures. The study aims to examine the effect of lattice topology, cell size, cell density, and surface thickness on the mechanical properties of lattice structures. Three lattice designs were chosen, the Diamond, I-WP, and Primitive Triply Periodic Minimal Surfaces (TPMSs). Uniaxial compression tests were conducted for every combination of the three lattice designs, three cell sizes, three cell densities, and three surface thicknesses. In order to perform an efficient experiment and gain the most information …

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Efficient, Dual-Particle Directional Detection System Using A Rotating Scatter Mask, Robert Olesen, Bryan V. Egner, Darren E. Holland, Valerie Martin, James E. Bevins, Larry W. Burggraf, Buckley E. O'Day Iii Jul 2021

Efficient, Dual-Particle Directional Detection System Using A Rotating Scatter Mask, Robert Olesen, Bryan V. Egner, Darren E. Holland, Valerie Martin, James E. Bevins, Larry W. Burggraf, Buckley E. O'Day Iii

AFIT Patents

A directional radiation detection system and an omnidirectional radiation detector. The omnidirectional radiation detector detects radiation comprising at least one of: (i) gamma rays; and (ii) neutron particles. A radiation scatter mask (RSM) of the radiation detection system includes a rotating sleeve received over the omnidirectional radiation detector and rotating about a longitudinal axis. The RSM further includes: (i) a fin extending longitudinally from one side of the rotating sleeve; and (ii) a wall extending from the rotating sleeve and spaced apart from the fin having an upper end distally positioned on the rotating sleeve spaced apart or next to …

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Estimating Turbulence Distribution Over A Heterogeneous Path Using Time‐Lapse Imagery From Dual Cameras, Benjamin Wilson, Santasri Bose-Pillai, Jack E. Mccrae, Kevin J. Keefer, Steven T. Fiorino Jul 2021

Estimating Turbulence Distribution Over A Heterogeneous Path Using Time‐Lapse Imagery From Dual Cameras, Benjamin Wilson, Santasri Bose-Pillai, Jack E. Mccrae, Kevin J. Keefer, Steven T. Fiorino

Faculty Publications

Knowledge of turbulence distribution along an experimental path can help in effective turbulence compensation and mitigation. Although scintillometers are traditionally used to measure the strength of turbulence, they provide a path-integrated measurement and have limited operational ranges. A technique to profile turbulence using time-lapse imagery of a distant target from spatially separated cameras is presented here. The method uses the turbulence induced differential motion between pairs of point features on a target, sensed at a single camera and between cameras to extract turbulence distribution along the path. The method is successfully demonstrated on a 511 m almost horizontal path going …

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Strengthening Criteria Independence Through Optimization Of Alternative Value Ratio Comparisons, Joseph P. Kristbaum, Frank W. Ciarallo Jun 2021

Strengthening Criteria Independence Through Optimization Of Alternative Value Ratio Comparisons, Joseph P. Kristbaum, Frank W. Ciarallo

Faculty Publications

Every decision maker’s internal scale is different based on a myriad of possible factors unique to that decision maker. Conflicting criteria within and between alternatives in multicriteria decision making can create negative effects within the weighting schemes and amplify preference biases and scale disparities between decision makers in a group decision context. Additionally, the weighting of group decision-making frameworks can intensify the already skewed criteria values. When making judgments against requirements, it may be preferable to reduce scale trend distortions between decision makers as much as possible. Previous research supports that certain information presentation modes can significantly reduce preference bias …

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Rotating Scatter Mask For Directional Radiation Detection And Imaging, Darren Holland, Robert Olesen, Larry Burggraf, Buckley O'Day, James E. Bevins Jun 2021

Rotating Scatter Mask For Directional Radiation Detection And Imaging, Darren Holland, Robert Olesen, Larry Burggraf, Buckley O'Day, James E. Bevins

AFIT Patents

A radiation imaging system images a distributed source of radiation from an unknown direction by rotating a scatter mask around a central axis. The scatter mask has a pixelated outer surface of tangentially oriented, flat geometric surfaces that are spherically varying in radial dimension that corresponds to a discrete amount of attenuation. Rotation position of the scatter mask is tracked as a function of time. Radiation counts from gamma and/or neutron radiation are received from at least one radiation detector that is positioned at or near the central axis. A rotation-angle dependent detector response curve (DRC) is generated based on …

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One Dimensional Study Of Magnetoplasmadynamic Thrusters For A Potential New Class Of Heavy Ion Drivers For Plasma Jet Driven Magnetoinertial Fusion, Patrick M. Brown Jun 2021

One Dimensional Study Of Magnetoplasmadynamic Thrusters For A Potential New Class Of Heavy Ion Drivers For Plasma Jet Driven Magnetoinertial Fusion, Patrick M. Brown

Theses and Dissertations

Plasma Jet Driven Magnetoinertial Fusion (PJMIF) requires high velocity heavy ion drivers in order to compress a magnetized target to fusion conditions. Previous work with heavy ion drivers has revealed sub-par accelerations due to plasma instabilities; thus, it is necessary to investigate new methods of heavy ion plasma acceleration. One such method is Magnetoplasmadynamic (MPD) thrusters. Past studies of these thrusters have been conducted at an initial temperature at or below the energy of full ionization. Here MPD thrusters are investigated using a Godunov type MHD solver with a Harten-Lax van Leer-D (HLLD) flux solving scheme assuming the plasma is …

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Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals Jun 2021

Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco [*], Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …

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Statistically Defensible Wind Tunnel Models, Timothy A. Roche Jun 2021

Statistically Defensible Wind Tunnel Models, Timothy A. Roche

Theses and Dissertations

Wind tunnels are used to test scale-model air frames in order to collect aerodynamic data. The Subsonic Aerodynamic Research Laboratory (SARL) Wind Tunnel is a low speed wind tunnel located at Wright-Patterson Air Force Base. The SARL Wind Tunnel team approached AFIT for assistance in creating statistically defensible models for the conditions inside the wind tunnel. During a wind tunnel test, pressure sensors cannot be placed at the test model. Instead, pressure is measured by a pitot probe permanently mounted in the corner of the test chamber. The pressure at the model location is predicted from the measurements taken by …

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Evolutionary Generation Of Diversity In Embedded Binary Executables For Cyber Resiliency, Mitchell D. I. Hirschfeld Jun 2021

Evolutionary Generation Of Diversity In Embedded Binary Executables For Cyber Resiliency, Mitchell D. I. Hirschfeld

Theses and Dissertations

Hardening avionics systems against cyber attack is difficult and expensive. Attackers benefit from a "break one, break all" advantage due to the dominant mono-culture of automated systems. Also, undecidability of behavioral equivalence for arbitrary algorithms prevents the provable absence of undesired behaviors within the original specification. This research presents results of computational experiments using bio-inspired genetic programming to generate diverse implementations of executable software and thereby disrupt the mono-culture. Diversity is measured using the SSDeep context triggered piecewise hashing algorithm. Experiments are divided into two phases. Phase I explores the use of semantically-equivalent alterations that retain the specified behavior of …

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Neutron Pulse-Time Extension Through Conversion To Positronium, Shawn T. Mctaggart Jun 2021

Neutron Pulse-Time Extension Through Conversion To Positronium, Shawn T. Mctaggart

Theses and Dissertations

Laser-Plasma interactions have strong potential as future neutron sources. Measuring the neutron rate is difficult due to several issues: the very short duration of the laser pulse and subsequent fusion events (on the order of a few picoseconds), the corresponding short duration of the neutron pulse, and the simultaneous emission of other ionizing particles such as protons and electrons. A system was designed to measure neutron emission by imposing a delay from the emission of other radiation by conversion of the neutrons into ortho-positronium (o-Ps), the triplet state of positronium. This lifetime extension enables more sensitive and selective detection of …

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A Quantitative Argument For Autonomous Aerial Defense Overembedded Missile Systems To Thwart Cruise Threats, Andrew R. Davis Jun 2021

A Quantitative Argument For Autonomous Aerial Defense Overembedded Missile Systems To Thwart Cruise Threats, Andrew R. Davis

Theses and Dissertations

Given the high cost of missile defense systems, their ability to be overwhelmed, and rising tensions between the U.S. and adversaries in the Indo-Pacific region, a new modeled is proposed to investigate a new approach to missile defense. The Autonomous Aerial Defense Against Missiles (AADAM) system leverages reusable, small-scale UAVs to propose a cheaper, more effective system in defending against cruise missile threats. The aim of this system is to provide and additional layer in current missile defense strategies at lower-cost. This modeled system is found to outperform a modeled Patriot system in close-range interception of designated assets, with no …

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Synthetic Aperture Radar Image Recognition Of Armored Vehicles, Christopher Szul [*], Torrey J. Wagner, Brent T. Langhals Jun 2021

Synthetic Aperture Radar Image Recognition Of Armored Vehicles, Christopher Szul [*], Torrey J. Wagner, Brent T. Langhals

Faculty Publications

Synthetic Aperture Radar (SAR) imagery is not affected by weather and allows for day-and-night observations, however it can be difficult to interpret. This work applies classical and neural network machine learning techniques to perform image classification of SAR imagery. The Moving and Stationary Target Acquisition and Recognition dataset from the Air Force Research Laboratory was used, which contained 2,987 total observations of the BMP-2, BTR-70, and T-72 vehicles. Using a 75%/25% train/test split, the classical model achieved an average multi-class image recognition accuracy of 70%, while a convolutional neural network was able to achieve a 97% accuracy with lower model …

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Correlated Positron-Electron Orbital (Cpeo): A Novel Method That Models Positron-Electron Correlation In Virtual Ps At The Mean-Field Level, Kevin E. Blaine Jun 2021

Correlated Positron-Electron Orbital (Cpeo): A Novel Method That Models Positron-Electron Correlation In Virtual Ps At The Mean-Field Level, Kevin E. Blaine

Theses and Dissertations

The Correlated Positronic-Electronic Orbital (CPEO) method was developed and implemented to capture correlation effects at between the positron and electron in the modeling of systems that involve a bound positron. Methods that effectively model these systems require many hundred basis functions and use a mean field approach as the beginning step. CPEO builds an orbital for virtual Positronium (Ps) that contains a positron in a bound state along with an accompanying electron to the larger system. Assigning the virtual Ps orbital allows for the two particle variational optimization in conjunction with the other particles that compose the whole system. This …

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Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals May 2021

Year-Independent Prediction Of Food Insecurity Using Classical & Neural Network Machine Learning Methods, Caleb Christiansen, Torrey J. Wagner, Brent Langhals

Faculty Publications

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

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Model For Quantifying The Quality Of Secure Service, Paul M. Simon, Scott R. Graham, Christopher Talbot, Micah J. Hayden May 2021

Model For Quantifying The Quality Of Secure Service, Paul M. Simon, Scott R. Graham, Christopher Talbot, Micah J. Hayden

Faculty Publications

Although not common today, communications networks could adjust security postures based on changing mission security requirements, environmental conditions, or adversarial capability, through the coordinated use of multiple channels. This will require the ability to measure the security of communications networks in a meaningful way. To address this need, in this paper, we introduce the Quality of Secure Service (QoSS) model, a methodology to evaluate how well a system meets its security requirements. This construct enables a repeatable and quantifiable measure of security in a single- or multi-channel network under static configurations. In this approach, the quantification of security is based …

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The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models, Alexander J. Kamrud [*], Brett J. Borghetti, Christine M. Schubert Kabban May 2021

The Effects Of Individual Differences, Non‐Stationarity, And The Importance Of Data Partitioning Decisions For Training And Testing Of Eeg Cross‐Participant Models, Alexander J. Kamrud [*], Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper …

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Single-Shot Positron Annihilation Lifetime Spectroscopy Using A Liquid Scintillator, Joshua R. Machacek, Shawn Mctaggart, Larry W. Burggraf May 2021

Single-Shot Positron Annihilation Lifetime Spectroscopy Using A Liquid Scintillator, Joshua R. Machacek, Shawn Mctaggart, Larry W. Burggraf

Faculty Publications

Liquid scintillators provide a fast, single component response. However, they traditionally have a low flashpoint and high vapor pressure. We demonstrate the use of an EJ-309 scintillator (high flashpoint and low vapor pressure variant) to acquire single-shot positron annihilation lifetime spectroscopy spectra using a trap-based positron beam.

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Defect Detection In Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning, Philip Cho, Aihua W. Wood, Krishnamurthy Mahalingam, Kurt Eyink May 2021

Defect Detection In Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning, Philip Cho, Aihua W. Wood, Krishnamurthy Mahalingam, Kurt Eyink

Faculty Publications

Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via …

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Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah Bill, Lance E. Champagne, Bruce Cox, Trevor J. Bihl Apr 2021

Meta-Heuristic Optimization Methods For Quaternion-Valued Neural Networks, Jeremiah Bill, Lance E. Champagne, Bruce Cox, Trevor J. Bihl

Faculty Publications

In recent years, real-valued neural networks have demonstrated promising, and often striking, results across a broad range of domains. This has driven a surge of applications utilizing high-dimensional datasets. While many techniques exist to alleviate issues of high-dimensionality, they all induce a cost in terms of network size or computational runtime. This work examines the use of quaternions, a form of hypercomplex numbers, in neural networks. The constructed networks demonstrate the ability of quaternions to encode high-dimensional data in an efficient neural network structure, showing that hypercomplex neural networks reduce the number of total trainable parameters compared to their real-valued …

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Impact Of Hurricane Michael (2018) On Local Vertical Total Electron Content, Joanna E.S. Williams, Robert C. Tournay, H. Rose Tseng, Daniel J. Emmons Ii, Omar A. Nava Apr 2021

Impact Of Hurricane Michael (2018) On Local Vertical Total Electron Content, Joanna E.S. Williams, Robert C. Tournay, H. Rose Tseng, Daniel J. Emmons Ii, Omar A. Nava

Faculty Publications

An analysis of vertical total electron content (TEC) estimates from the MIT Madrigal database is performed for the regions surrounding the eye of Hurricane Michael (2018). Absolute and detrended TEC values show a noticeable increase during the tropical cyclone (TC) relative to fluctuations at the same locations prior to the storm. Direct comparisons of TEC perturbation magnitudes to the number of lightning flashes in latitude-longitude boxes surrounding the eye of Hurricane Michael for each 5 min period of 10 October 2018 showed no visible trends. A similar comparison of the vertical TEC fluctuations with respect to the rainfall rates showed …

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Zn Acceptors In Β-Ga2O3 Crystals, Timothy D. Gustafson, J. Jesenovec, Christopher A. Lenyk, Nancy C. Giles, J. S. Mccloy, M. Mccluskey, Larry E. Halliburton Apr 2021

Zn Acceptors In Β-Ga2O3 Crystals, Timothy D. Gustafson, J. Jesenovec, Christopher A. Lenyk, Nancy C. Giles, J. S. Mccloy, M. Mccluskey, Larry E. Halliburton

Faculty Publications

Electron paramagnetic resonance (EPR) is used to identify and characterize neutral zinc acceptors in Zn-doped β-Ga2O3 crystals. Two EPR spectra are observed at low temperatures, one from Zn ions at tetrahedral Ga(1) sites (the Zn0Ga1 acceptor) and one from Zn ions at octahedral Ga(2) sites (the Zn0Ga2 acceptor). These Zn acceptors are small polarons, with the unpaired spin localized in each case on a threefold coordinated oxygen O(I) ion adjacent to the Zn ion. Resolved hyperfine interactions with neighboring 69Ga and 71Ga nuclei allow the EPR spectra from the two acceptors …

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Twisted Spatiotemporal Optical Vortex Random Fields, Milo W. Hyde Iv Apr 2021

Twisted Spatiotemporal Optical Vortex Random Fields, Milo W. Hyde Iv

Faculty Publications

We present twisted spatiotemporal optical vortex (STOV) beams, which are partially coherent light sources that possess a coherent optical vortex and a random twist coupling their space and time dimensions. These beams have controllable partial coherence and transverse orbital angular momentum (OAM), which distinguishes them from the more common spatial vortex and twisted beams (known to carry longitudinal OAM) in the literature and should ultimately make them useful in applications such as optical communications and optical tweezing. We present the mathematical analysis of twisted STOV beams, deriving the mutual coherence function and linear and angular momentum densities. We simulate the …

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Achieving The Shot-Noise Limit Using Experimental Multi-Shot Digital Holography Data, Douglas E. Thornton, Cameron J. Radosevich, Samuel Horst, Mark F. Spencer Mar 2021

Achieving The Shot-Noise Limit Using Experimental Multi-Shot Digital Holography Data, Douglas E. Thornton, Cameron J. Radosevich, Samuel Horst, Mark F. Spencer

Faculty Publications

In this paper, we achieve the shot-noise limit using straightforward image-post-processing techniques with experimental multi-shot digital holography data (i.e., off-axis data composed of multiple noise and speckle realizations). First, we quantify the effects of frame subtraction (of the mean reference-only frame and the mean signal-only frame from the digital-hologram frames), which boosts the signal-to-noise ratio (SNR) of the baseline dataset with a gain of 2.4 dB. Next, we quantify the effects of frame averaging, both with and without the frame subtraction. We show that even though the frame averaging boosts the SNR by itself, the frame subtraction and the stability …

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Temperature-Immune Self-Referencing Fabry–Pérot Cavity Sensors, Hengky Chandrahalim, Jonathan W. Smith Mar 2021

Temperature-Immune Self-Referencing Fabry–Pérot Cavity Sensors, Hengky Chandrahalim, Jonathan W. Smith

AFIT Patents

A passive microscopic Fabry-Pérot Interferometer (FPI) sensor an optical fiber a three-dimensional microscopic optical structure formed on a cleaved tip of an optical fighter that reflects a light signal back through the optical fiber. The reflected light is altered by refractive index changes in the three-dimensional structure that is subject to at least one of: (i) thermal radiation; and (ii) volatile organic compounds.

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A Comparison Of Sporadic-E Occurrence Rates Using Ionosondes And Gps Radio Occultation Measurements, Rodney A. Carmona Jr. Mar 2021

A Comparison Of Sporadic-E Occurrence Rates Using Ionosondes And Gps Radio Occultation Measurements, Rodney A. Carmona Jr.

Theses and Dissertations

Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by nearly an order of magnitude between studies, motivating a comparison with ground-based measurements. In an attempt to find an accurate GPS-RO technique for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010-2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. Each technique is compared individually for each ionosonde site and then combined to determine …

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Solving The Quantum Layout Problem For Nisq-Era Quantum Computers Via Metaheuristic Algorithms, Brian D. Curran Jr. Mar 2021

Solving The Quantum Layout Problem For Nisq-Era Quantum Computers Via Metaheuristic Algorithms, Brian D. Curran Jr.

Theses and Dissertations

In the noisy intermediate-scale quantum (NISQ)-era, quantum computers (QC) are highly prone to noise-related errors and suffer from limited connectivity between their physical qubits. Circuit transformations must be made to abstract circuits to address the noise and hardware constraints of NISQ-era devices. Such transformations introduce additional gates to the original circuit, thereby reducing the circuit's overall fidelity. To address the aforementioned constraints of NISQ-era QCs, dynamic remapping procedures permute logical qubits about physical qubits of the device to increase the fidelity of operations and make operations hardware-compliant. The quantum layout problem (QLP) is the problem of mapping logical qubits of …

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