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Air Force Institute of Technology

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Articles 661 - 690 of 3057

Full-Text Articles in Physical Sciences and Mathematics

Agile Software Development: Creating A Cost Of Delay Framework For Air Force Software Factories, J. Goljan, Jonathan D. Ritschel, Scott Drylie, Edward D. White Jan 2021

Agile Software Development: Creating A Cost Of Delay Framework For Air Force Software Factories, J. Goljan, Jonathan D. Ritschel, Scott Drylie, Edward D. White

Faculty Publications

The Air Force software development environment is experiencing a paradigm shift. The 2019 Defense Innovation Board concluded that speed and cycle time must become the most important software metrics if the US military is to maintain its advantage over adversaries.1 This article proposes utilizing a cost-o­f-d­elay (CoD) framework to prioritize projects toward optimizing readiness. Cost-­of-d­elay is defined as the economic impact resulting from a delaying product delivery or, said another way, opportunity cost. In principle, CoD assesses the negative impacts resulting from changes to the priority of a project.

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Acceleration Of Boltzmann Collision Integral Calculation Using Machine Learning, Ian Holloway, Aihua W. Wood, Alexander Alekseenko Jan 2021

Acceleration Of Boltzmann Collision Integral Calculation Using Machine Learning, Ian Holloway, Aihua W. Wood, Alexander Alekseenko

Faculty Publications

The Boltzmann equation is essential to the accurate modeling of rarefied gases. Unfortunately, traditional numerical solvers for this equation are too computationally expensive for many practical applications. With modern interest in hypersonic flight and plasma flows, to which the Boltzmann equation is relevant, there would be immediate value in an efficient simulation method. The collision integral component of the equation is the main contributor of the large complexity. A plethora of new mathematical and numerical approaches have been proposed in an effort to reduce the computational cost of solving the Boltzmann collision integral, yet it still remains prohibitively expensive for …

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A Learning Curve Model Accounting For The Flattening Effect In Production Cycles, Evan R. Boone, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru Jan 2021

A Learning Curve Model Accounting For The Flattening Effect In Production Cycles, Evan R. Boone, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru

Faculty Publications

We investigate production cost estimates to identify and model modifications to a prescribed learning curve. Our new model examines the learning rate as a decreasing function over time as opposed to a constant rate that is frequently used. The purpose of this research is to determine whether a new learning curve model could be implemented to reduce the error in cost estimates for production processes. A new model was created that mathematically allows for a “flattening effect,” which typically occurs later in the production process. This model was then compared to Wright’s learning curve, which is a popular method used …

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Extending Critical Infrastructure Element Longevity Using Constellation-Based Id Verification, Christopher M. Rondeau, Michael A. Temple, J. Addison Betances, Christine M. Schubert Kabban Jan 2021

Extending Critical Infrastructure Element Longevity Using Constellation-Based Id Verification, Christopher M. Rondeau, Michael A. Temple, J. Addison Betances, Christine M. Schubert Kabban

Faculty Publications

This work supports a technical cradle-to-grave protection strategy aimed at extending the useful lifespan of Critical Infrastructure (CI) elements. This is done by improving mid-life operational protection measures through integration of reliable physical (PHY) layer security mechanisms. The goal is to improve existing protection that is heavily reliant on higher-layer mechanisms that are commonly targeted by cyberattack. Relative to prior device ID discrimination works, results herein reinforce the exploitability of constellation-based PHY layer features and the ability for those features to be practically implemented to enhance CI security. Prior work is extended by formalizing a device ID verification process that …

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The Traded Water Footprint Of Global Energy From 2010 To 2018, Christopher M. Chini, Rebecca A. M. Peer Jan 2021

The Traded Water Footprint Of Global Energy From 2010 To 2018, Christopher M. Chini, Rebecca A. M. Peer

Faculty Publications

The energy-water nexus describes the requirement of water-for-energy and energy-for-water. The consumption of water in the production and generation of energy resources is also deemed virtual water. Pairing the virtual water estimates for energy with international trade data creates a virtual water trade network, facilitating analysis of global water resources management. In this database, we identify the virtual water footprints for the trade of eleven different energy commodities including fossil fuels, biomass, and electricity. Additionally, we provide the necessary scripts for downloading and pairing trade data with the virtual water footprints to create a virtual water trade network. The resulting …

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Modeling And Simulation Techniques Used In High Strain Rate Projectile Impact, Derek G. Spear, Anthony N. Palazotto, Ryan A. Kemnitz Jan 2021

Modeling And Simulation Techniques Used In High Strain Rate Projectile Impact, Derek G. Spear, Anthony N. Palazotto, Ryan A. Kemnitz

Faculty Publications

A series of computational models and simulations were conducted for determining the dynamic responses of a solid metal projectile impacting a target under a prescribed high strain rate loading scenario in three-dimensional space. The focus of this study was placed on two different modeling techniques within finite element analysis available in the Abaqus software suite. The first analysis technique relied heavily on more traditional Lagrangian analysis methods utilizing a fixed mesh, while still taking advantage of the finite difference integration present under the explicit analysis approach. A symmetry reduced model using the Lagrangian coordinate system was also developed for comparison …

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Arctic Observations And Numerical Simulations Of Surface Wind Effects On Multi-Angle Snowflake Camera Measurements, Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, Timothy Garrett Jan 2021

Arctic Observations And Numerical Simulations Of Surface Wind Effects On Multi-Angle Snowflake Camera Measurements, Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, Timothy Garrett

Faculty Publications

Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple midlatitude and polar locations both with and without wind shielding. Here, we present an analysis of Arctic field observations – with and without a Belfort double Alter shield – and compare the results to computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC. MASC-measured fall speeds compare well with …

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A Radial Basis Function Finite Difference Scheme For The Benjamin–Ono Equation, Benjamin F. Akers, Tony Liu, Jonah A. Reeger Dec 2020

A Radial Basis Function Finite Difference Scheme For The Benjamin–Ono Equation, Benjamin F. Akers, Tony Liu, Jonah A. Reeger

Faculty Publications

A radial basis function-finite differencing (RBF-FD) scheme was applied to the initial value problem of the Benjamin–Ono equation. The Benjamin–Ono equation has traveling wave solutions with algebraic decay and a nonlocal pseudo-differential operator, the Hilbert transform. When posed on ℝ, the former makes Fourier collocation a poor discretization choice; the latter is challenging for any local method. We develop an RBF-FD approximation of the Hilbert transform, and discuss the challenges of implementing this and other pseudo-differential operators on unstructured grids. Numerical examples, simulation costs, convergence rates, and generalizations of this method are all discussed.

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Impact Of The Liquid Crystal Director Twisting On Two-Beam Energy Exchange In A Hybrid Photorefractive Inorganic-Liquid Crystal Cell, Victor Y. Reshetnyak, Igor P. Pinkevych, Michael E. Mcconney, Jonathan E. Slagle, Dean R. Evans Dec 2020

Impact Of The Liquid Crystal Director Twisting On Two-Beam Energy Exchange In A Hybrid Photorefractive Inorganic-Liquid Crystal Cell, Victor Y. Reshetnyak, Igor P. Pinkevych, Michael E. Mcconney, Jonathan E. Slagle, Dean R. Evans

Student Publications

We studied the energy transfer between light beams on the director grating in a hybrid photorefractive liquid crystal (LC) cell assuming the propagation of light waves in the cell to be in the Mauguin regime. This approach makes it possible to trace the change of the gain coefficient dependence on the director grating spacing with the change of the LC director twist. Conditions for the LC flexoelectric parameters and the director helix pitch necessary for transformation the gain coefficient dependence from the nematic to cholesteric type are obtained. The influence of the director splay and bend deformations on the gain …

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Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm, Yasin Tamer Dec 2020

Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm, Yasin Tamer

Theses and Dissertations

Satellite constellation design is a complex, highly constrained, and multidisciplinary problem. Unless optimization tools are used, tradeoffs must be conducted at the subsystem level resulting in feasible, but not necessarily optimal, system designs. As satellite technology advances, new methods to optimize the system objectives are developed. This study is based on the development of a representative regional remote sensing constellation design. This thesis analyses the design process of an electrooptic satellite constellation with regional coverage considerations using system-level optimization tools. A multi objective genetic algorithm method is used to optimize the constellation design by utilizing MATLAB and STK integration. Cost, …

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Spectroscopic Diagnostics For Supersonic Air Microwave Discharges, James E. Caplinger Dec 2020

Spectroscopic Diagnostics For Supersonic Air Microwave Discharges, James E. Caplinger

Theses and Dissertations

Optical Emission Spectroscopy (OES) is an increasingly relevant technique in plasma diagnostics due to its inherent non-invasive nature and simple application relative to other popular techniques. In this work, common OES techniques are combined with novel methods, developed here, in an effort to provide comprehensive OES techniques for stationary and supersonic air microwave discharges. To this end, a detailed collisional-radiative model for strong atomic oxygen lines has been developed and used to identify the importance of often overlooked mechanisms including cascade emission and metastable excitation. Using these results, a combined argon actinometry technique was developed which makes use of the …

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Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell Dec 2020

Deep Learning-Based, Passive Fault Tolerant Control Facilitated By A Taxonomy Of Cyber-Attack Effects, Dean C. Wardell

Theses and Dissertations

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control …

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Engaging Empirical Dynamic Modeling To Detect Intrusions In Cyber-Physical Systems, David R. Crow, Scott R. Graham, Brett J. Borghetti, Patrick J. Sweeney Dec 2020

Engaging Empirical Dynamic Modeling To Detect Intrusions In Cyber-Physical Systems, David R. Crow, Scott R. Graham, Brett J. Borghetti, Patrick J. Sweeney

Faculty Publications

Modern cyber-physical systems require effective intrusion detection systems to ensure adequate critical infrastructure protection. Developing an intrusion detection capability requires an understanding of the behavior of a cyber-physical system and causality of its components. Such an understanding enables the characterization of normal behavior and the identification and reporting of anomalous behavior. This chapter explores a relatively new time series analysis technique, empirical dynamic modeling, that can contribute to system understanding. Specifically, it examines if the technique can adequately describe causality in cyber-physical systems and provides insights into it serving as a foundation for intrusion detection.

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2 × 2 Anisotropic Transfer Matrix Approach For Optical Propagation In Uniaxial Transmission Filter Structures, Hammid Al-Ghezi, Rudra Gnawali, Partha P. Banerjee, Lirong Sun, Jonathan E. Slagle, Dean Evans Nov 2020

2 × 2 Anisotropic Transfer Matrix Approach For Optical Propagation In Uniaxial Transmission Filter Structures, Hammid Al-Ghezi, Rudra Gnawali, Partha P. Banerjee, Lirong Sun, Jonathan E. Slagle, Dean Evans

Student Publications

Multi-layered metamaterial structures show promise in a wide variety of optical applications such as superlenses, electromagnetic cloaking, tunable filters, sensors, and spatial light modulators. Optical transmission analysis of multilayer metallo-dielectric stacks with overall thickness less than the wavelength of light can be modeled using effective medium theory and the Berreman matrix method. For multilayer anisotropic stacks of arbitrary thickness, a rigorous 4 × 4 transfer matrix embodiment is typically used. In this work, a 2 × 2 anisotropic transfer matrix method is developed to analyze optical propagation through multilayer uniaxial stacks of arbitrary thicknesses. Optical transmission of a multilayer silver–zinc …

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Legendre G-Array Pairs And The Theoretical Unification Of Several G-Array Families, K. T. Arasu, Dursun A. Bulutoglu, J. R. Hollon Nov 2020

Legendre G-Array Pairs And The Theoretical Unification Of Several G-Array Families, K. T. Arasu, Dursun A. Bulutoglu, J. R. Hollon

Faculty Publications

We investigate how Legendre G-array pairs are related to several different perfect binary G-array families. In particular we study the relations between Legendre G-array pairs, Sidelnikov-Lempel-Cohn-Eastman ℤq−1-arrays, Yamada-Pott G-array pairs, Ding-Helleseth-Martinsen ℤ2×ℤmp-arrays, Yamada ℤ(q−1)/2-arrays, Szekeres ℤmp-array pairs, Paley ℤmp-array pairs, and Baumert ℤm1p1×ℤm2p2-array pairs. Our work also solves one of the two open problems posed in Ding~[J. Combin. Des. 16 (2008), 164-171]. Moreover, we provide several computer search based existence and non-existence results regarding Legendre ℤn-array pairs. …

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Generation Of Vector Partially Coherent Optical Sources Using Phase-Only Spatial Light Modulators, Milo W. Hyde Iv, Santasri R. Bose-Pillai Oct 2020

Generation Of Vector Partially Coherent Optical Sources Using Phase-Only Spatial Light Modulators, Milo W. Hyde Iv, Santasri R. Bose-Pillai

AFIT Patents

A vector partially coherent source (VPCS) generator includes a laser that emits coherent light; an interferometer consisting of polarizing beam splitters (PBSs) to split the laser light into its vertical and horizontal polarization components;] first and second spatial light modulators (SLMs) that respectively control the vertical and horizontal polarization components; and a control system communicatively coupled to the first and second SLMs to adjust beam shape and coherence without physically moving or removing optical elements in the interferometer.

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Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine Oct 2020

Cost Estimating Using A New Learning Curve Theory For Non-Constant Production Rates, Dakotah Hogan, John J. Elshaw, Clay M. Koschnick, Jonathan D. Ritschel, Adedeji B. Badiru, Shawn M. Valentine

Faculty Publications

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in …

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Superconducting Phase Transition In Inhomogeneous Chains Of Superconducting Islands, Eduard Ilin, Irina Burkova, Xiangyu Song, Michael V. Pak, Dmitri S. Golubev, Alexey Bezryadin Oct 2020

Superconducting Phase Transition In Inhomogeneous Chains Of Superconducting Islands, Eduard Ilin, Irina Burkova, Xiangyu Song, Michael V. Pak, Dmitri S. Golubev, Alexey Bezryadin

Faculty Publications

We study one-dimensional chains of superconducting islands with a particular emphasis on the regime in which every second island is switched into its normal state, thus forming a superconductor-insulator-normal metal (S-I-N) repetition pattern. As is known since Giaever tunneling experiments, tunneling charge transport between a superconductor and a normal metal becomes exponentially suppressed, and zero-bias resistance diverges, as the temperature is reduced and the energy gap of the superconductor grows larger than the thermal energy. Here we demonstrate that this physical phenomenon strongly impacts transport properties of inhomogeneous superconductors made of weakly coupled islands with fluctuating values of the critical …

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Impacts Of Laboratory Vibrations And Laser Flicker Noise On Digital Holography, Douglas E. Thornton, Mark F. Spencer, Christopher A. Rice, Glen P. Perram Oct 2020

Impacts Of Laboratory Vibrations And Laser Flicker Noise On Digital Holography, Douglas E. Thornton, Mark F. Spencer, Christopher A. Rice, Glen P. Perram

Faculty Publications

In this paper, we experimentally demonstrate the impacts of laboratory vibrations and laser flicker noise on digital holography (DH). Specifically, we measure both the vibration efficiency and the coherence efficiency of our DH system at various focal-plane array integration times and path-length differences between the signal and reference. These efficiencies, in practice, contribute to the overall mixing efficiency, which is a measure for how well the detected signal and reference interfere. The results show that when the integration time is ≤1ms, the laboratory vibrations are negligible with a vibration efficiency of 100%; however, when the integration time equals 100 ms, …

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A Fully Quantum Calculation Of Broadening And Shifting Coefficients Of The D1 And D2 Spectral Lines Of Alkali-Metal Atoms Colliding With Noble-Gas Atoms, Robert D. Loper, David E. Weeks Oct 2020

A Fully Quantum Calculation Of Broadening And Shifting Coefficients Of The D1 And D2 Spectral Lines Of Alkali-Metal Atoms Colliding With Noble-Gas Atoms, Robert D. Loper, David E. Weeks

Faculty Publications

We use the Baranger model to compute collisional broadening and shift rates for the D1 and D2 spectral lines of M + Ng, where M = K, Rb, Cs and Ng = He, Ne, Ar. Scattering matrix elements are calculated using the channel packet method, and non-adiabatic wavepacket dynamics are determined using the split-operator method together with a unitary transformation between adiabatic and diabatic representations. Scattering phase shift differences are weighted thermally and are integrated over temperatures ranging from 100 K to 800 K. We find that predicted broadening rates compare well with experiment, but shift rates are …

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Analytic Provenance For Software Reverse Engineers, Wayne C. Henry Sep 2020

Analytic Provenance For Software Reverse Engineers, Wayne C. Henry

Theses and Dissertations

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. However, they have not been explored in the reverse engineering domain. This dissertation presents SensorRE, the first analytic provenance tool designed to support software reverse engineers. A …

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Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri Sep 2020

Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri

Theses and Dissertations

This research provides a heuristic algorithm for the detectives, who try to collectively capture a criminal known as Mr. X, in the Scotland Yard pursuer-evasion game. In Scotland Yard, a team of detectives attempts to converge on and capture a criminal known as Mr. X. The heuristic algorithm developed in this thesis is designed to emulate human strategies when playing the game. The algorithm uses the current state of the board at each time step, including the current positions of the detectives as well as the last known position of Mr. X. The heuristic algorithm then analyses all of the …

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Low-Information Radiation Imaging Using Rotating Scatter Mask Systems And Neural Network Algorithms, Robert J. Olesen Sep 2020

Low-Information Radiation Imaging Using Rotating Scatter Mask Systems And Neural Network Algorithms, Robert J. Olesen

Theses and Dissertations

While recent studies have demonstrated the directional capabilities of the single-detector rotating scatter mask (RSM) system for discrete, dual-particle environments, there has been little progress towards adapting it as a true imaging device. In this research, two algorithms were developed and tested using an RSM mask design previously optimized for directional detection and simulated 137Cs signals from a variety of source distributions. The first, maximum-likelihood expectation-maximization (ML-EM), was shown to generate noisy images, with relatively low accuracy (145% average relative error) and signal-to-noise ratio (0.27) for most source distributions simulated. The second, a novel regenerative neural network (ReGeNN), performed exceptionally …

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A Methodology To Identify Alternative Suitable Nosql Data Models Via Observation Of Relational Database Interactions, Paul M. Beach Sep 2020

A Methodology To Identify Alternative Suitable Nosql Data Models Via Observation Of Relational Database Interactions, Paul M. Beach

Theses and Dissertations

The effectiveness and performance of data-intensive applications are influenced by the suitability of the data models upon which they are built. The relational data model has been the de facto data model underlying most database systems since the 1970’s. However, the recent emergence of NoSQL data models have provided users with alternative ways of storing and manipulating data. Previous research has demonstrated the potential value in applying NoSQL data models in non-distributed environments. However, knowing when to apply these data models has generally required inputs from system subject matter experts to make this determination. This research, sponsored by the Air …

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Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung Sep 2020

Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung

Theses and Dissertations

This dissertation focuses on improving the ability to detect dim stellar objects that are in close proximity to a bright one, through statistical image processing using short exposure images. The goal is to improve the space domain awareness capabilities with the existing infrastructure. Two new algorithms are developed. The first one is through the Neighborhood System Blind Deconvolution where the data functions are separated into the bright object, the neighborhood system, and the background functions. The second one is through the Dimension Reduction Blind Deconvolution, where the object function is represented by the product of two matrices. Both are designed …

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Chronos Spacecraft With Chiron Probe: Exploration Of The Hydrosphere, Principle Satellites, Atmosphere, And Rings Of Uranus, Payton E. Pearson Sep 2020

Chronos Spacecraft With Chiron Probe: Exploration Of The Hydrosphere, Principle Satellites, Atmosphere, And Rings Of Uranus, Payton E. Pearson

Theses and Dissertations

A design reference mission using more modern technological innovations has been developed for exploration of the outer reaches of our Solar System, specifically Uranus and its system of satellites. This mission will utilize theoretical technologies mostly without regard to their current technological readiness level (TRL), though most systems have a TRL of at least 5. The primary innovations explored in this thesis are the new launch systems that provide far greater payload capacity potentially sent to anywhere in the Solar System, new Stirling-engine radioisotope thermoelectric generators (SRTGs), vastly improved data storage technologies, optimized satellite antenna relay of data using much …

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Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel Sep 2020

Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel

Theses and Dissertations

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O'Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these …

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Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios, Nicholas M. Westing Sep 2020

Physics-Constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios, Nicholas M. Westing

Theses and Dissertations

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using …

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Direct Digital Synthesis: A Flexible Architecture For Advanced Signals Research For Future Satellite Navigation Payloads, Pranav R. Patel Sep 2020

Direct Digital Synthesis: A Flexible Architecture For Advanced Signals Research For Future Satellite Navigation Payloads, Pranav R. Patel

Theses and Dissertations

In legacy Global Positioning System (GPS) Satellite Navigation (SatNav) payloads, the architecture does not provide the flexibility to adapt to changing circumstances and environments. GPS SatNav payloads have largely remained unchanged since the system became fully operational in April 1995. Since then, the use of GPS has become ubiquitous in our day-to-day lives. GPS availability is now a basic assumption for distributed infrastructure; it has become inextricably tied to our national power grids, cellular networks, and global financial systems. Emerging advancements of easy to use radio technologies, such as software-defined radios (SDRs), have greatly lowered the difficulty of discovery and …

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Artificial Neural Network Discovery Of A Switchable Metasurface Reflector, J. R. Thompson, J. A. Burrow, P. J. Shah, Jonathan E. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, M. S. Mills Aug 2020

Artificial Neural Network Discovery Of A Switchable Metasurface Reflector, J. R. Thompson, J. A. Burrow, P. J. Shah, Jonathan E. Slagle, E. S. Harper, A. Van Rynbach, I. Agha, M. S. Mills

Student Publications

Optical materials engineered to dynamically and selectively manipulate electromagnetic waves are essential to the future of modern optical systems. In this paper, we simulate various metasurface configurations consisting of periodic 1D bars or 2D pillars made of the ternary phase change material Ge2Sb2Te5 (GST). Dynamic switching behavior in reflectance is exploited due to a drastic refractive index change between the crystalline and amorphous states of GST. Selectivity in the reflection and transmission spectra is manipulated by tailoring the geometrical parameters of the metasurface. Due to the immense number of possible metasurface configurations, we train deep neural networks capable of exploring …

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