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Air Force Institute of Technology

Faculty Publications

2020

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Articles 1 - 30 of 37

Full-Text Articles in Physical Sciences and Mathematics

3-D Fabry–Pérot Cavities Sculpted On Fiber Tips Using A Multiphoton Polymerization Process, Jonathan W. Smith, Jeremiah C. Williams, Joseph S. Suelzer, Nicholas G. Usechak, Hengky Chandrahalim Dec 2020

3-D Fabry–Pérot Cavities Sculpted On Fiber Tips Using A Multiphoton Polymerization Process, Jonathan W. Smith, Jeremiah C. Williams, Joseph S. Suelzer, Nicholas G. Usechak, Hengky Chandrahalim

Faculty Publications

This paper presents 3-D Fabry–Pérot (FP) cavities fabricated directly onto cleaved ends of low-loss optical fibers by a two-photon polymerization (2PP) process. This fabrication technique is quick, simple, and inexpensive compared to planar microfabrication processes, which enables rapid prototyping and the ability to adapt to new requirements. These devices also utilize true 3-D design freedom, facilitating the realization of microscale optical elements with challenging geometries. Three different device types were fabricated and evaluated: an unreleased single-cavity device, a released dual-cavity device, and a released hemispherical mirror dual-cavity device. Each iteration improved the quality of the FP cavity's reflection spectrum. The …

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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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An Integrated Assessment Of The Global Virtual Water Trade Network Of Energy, Rebecca A. M. Peer, Christopher M. Chini Nov 2020

An Integrated Assessment Of The Global Virtual Water Trade Network Of Energy, Rebecca A. M. Peer, Christopher M. Chini

Faculty Publications

The global trade of energy allows for the distribution of the world's collective energy resources and, therefore, an increase in energy access. However, this network of trade also generates a network of virtually traded resources that have been used to produce energy commodities. An integrated database of energy trade water footprints is necessary to capture interrelated energy and water concerns of a globalized economy,and is also motivated by current climate and population trends. Here, we quantify and present the virtual water embedded in energy trade across the globe from 2012 to 2018, building on previous water footprinting and energy virtual …

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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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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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Detection Of Reconnection Signatures In Solar Flares, Taylor R. Whitney Aegerter, Daniel J. Emmons Ii, Robert D. Loper Oct 2020

Detection Of Reconnection Signatures In Solar Flares, Taylor R. Whitney Aegerter, Daniel J. Emmons Ii, Robert D. Loper

Faculty Publications

Solar flare forecasting is limited by the current understanding of mechanisms that govern magnetic reconnection, the main physical phenomenon associated with these events. As a result, forecasting relies mainly on climatological correlations to historical events rather than the underlying physics principles. Solar physics models place the neutral point of the reconnection event in the solar corona. Correspondingly, studies of photospheric magnetic fields indicate changes during solar flares—particularly in relation to the field helicity—on the solar surface as a result of the associated magnetic reconnection. This study utilizes data from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI) and …

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Through-The-Wall Radar Detection Using Machine Learning, Aihua W. Wood, Ryan Wood, Matthew Charnley Aug 2020

Through-The-Wall Radar Detection Using Machine Learning, Aihua W. Wood, Ryan Wood, Matthew Charnley

Faculty Publications

This paper explores the through-the-wall inverse scattering problem via machine learning. The reconstruction method seeks to discover the shape, location, and type of hidden objects behind walls, as well as identifying certain material properties of the targets. We simulate RF sources and receivers placed outside the room to generate observation data with objects randomly placed inside the room. We experiment with two types of neural networks and use an 80-20 train-test split for reconstruction and classification.

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Applications Of Portable Libs For Actinide Analysis, Ashwin P. Rao, John D. Auxier Ii, Dung Vu, Michael B. Shattan Jul 2020

Applications Of Portable Libs For Actinide Analysis, Ashwin P. Rao, John D. Auxier Ii, Dung Vu, Michael B. Shattan

Faculty Publications

A portable LIBS device was used for rapid elemental impurity analysis of plutonium alloys. This device demonstrates the potential for fast, accurate in-situ chemical analysis and could significantly reduce the fabrication time of plutonium alloys.

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Turbulence Profiling Using Pupil Plane Wavefront Data Derived Fried Parameter Values For A Dynamically Ranged Rayleigh Beacon, Steven M. Zuraski, Elizabeth Beecher, Jack E. Mccrae, Steven T. Fiorino Jul 2020

Turbulence Profiling Using Pupil Plane Wavefront Data Derived Fried Parameter Values For A Dynamically Ranged Rayleigh Beacon, Steven M. Zuraski, Elizabeth Beecher, Jack E. Mccrae, Steven T. Fiorino

Faculty Publications

Long-range optical imaging applications are typically hindered by atmospheric turbulence. The effect of turbulence on an imaging system can manifest itself as an image blur effect usually quantified by the phase distortions present in the system. The blurring effect can be understood on the basis of the measured strength of atmospheric optical turbulence along the propagation path and its impacts on phase perturbation statistics within the imaging system. One method for obtaining these measurements is by the use of a dynamically ranged Rayleigh beacon system that exploits strategically varied beacon ranges along the propagation path, effectively obtaining estimates of the …

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Implications Of Four-Dimensional Weather Cubes For Improved Cloud-Free Line-Of-Sight Assessments Of Free-Space Optical Communications Link Performance, Steven T. Fiorino, Santasri Bose-Pillai, Jaclyn Schmidt, Brannon Elmore, Kevin J. Keefer Jul 2020

Implications Of Four-Dimensional Weather Cubes For Improved Cloud-Free Line-Of-Sight Assessments Of Free-Space Optical Communications Link Performance, Steven T. Fiorino, Santasri Bose-Pillai, Jaclyn Schmidt, Brannon Elmore, Kevin J. Keefer

Faculty Publications

We advance the benefits of previously reported four-dimensional (4-D) weather cubes toward the creation of high-fidelity cloud-free line-of-sight (CFLOS) beam propagation for realistic assessment of autotracked/dynamically routed free-space optical (FSO) communication datalink concepts. The weather cubes accrue parameterization of optical effects and custom atmospheric resolution through implementation of numerical weather prediction data in the Laser Environmental Effects Definition and Reference atmospheric characterization and radiative transfer code. 4-D weather cube analyses have recently been expanded to accurately assess system performance (probabilistic climatologies and performance forecasts) at any wavelength/frequency or spectral band in the absence of field tests and employment data. The …

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Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: I. Using Steady-State Simulations, Mark F. Spencer Jul 2020

Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: I. Using Steady-State Simulations, Mark F. Spencer

Faculty Publications

Part I of this two-part paper uses wave-optics simulations to look at the Monte Carlo averages associated with turbulence and steady-state thermal blooming (SSTB). The goal is to investigate turbulence thermal blooming interaction (TTBI). At wavelengths near 1 μm, TTBI increases the amount of constructive and destructive interference (i.e., scintillation) that results from high-power laser beam propagation through distributed-volume atmospheric aberrations. As a result, we use the spherical-wave Rytov number and the distortion number to gauge the strength of the simulated turbulence and SSTB. These parameters simplify greatly given propagation paths with constant atmospheric conditions. In addition, we use the …

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Measurements Of Optical Turbulence Over 149-Km Path, Jack E. Mccrae, Santasri Bose-Pillai, Steven T. Fiorino, Aaron J. Archibald, Joel Meoak, Brannon Elmore, Thomas Kesler, Christopher A. Rice Jul 2020

Measurements Of Optical Turbulence Over 149-Km Path, Jack E. Mccrae, Santasri Bose-Pillai, Steven T. Fiorino, Aaron J. Archibald, Joel Meoak, Brannon Elmore, Thomas Kesler, Christopher A. Rice

Faculty Publications

An experiment was conducted to study turbulence along a 149-km path between the Mauna Loa and Haleakala mountain tops using digital cameras and light-emitting diode (LED) beacons. Much of the path is over the ocean, and a large portion of the path is 3 km above sea level. On the Mauna Loa side, six LED beacons were placed in a roughly linear array with pair spacings from 7 to 62 m. From the Haleakala side, a pair of cameras separated by 83.8 cm observed these beacons. Turbulence along the path induces tilts on the wavefronts, which results in displacements of …

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Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: Ii. Using Time-Dependent Simulations, Mark F. Spencer Jul 2020

Wave-Optics Investigation Of Turbulence Thermal Blooming Interaction: Ii. Using Time-Dependent Simulations, Mark F. Spencer

Faculty Publications

Part II of this two-part paper uses wave-optics simulations to look at the Monte Carlo averages associated with turbulence and time-dependent thermal blooming (TDTB). The goal is to investigate turbulence thermal blooming interaction (TTBI). At wavelengths near 1 μm, TTBI increases the amount of constructive and destructive interference (i.e., scintillation) that results from high-power laser beam propagation through distributed-volume atmospheric aberrations. As a result, we use the spherical-wave Rytov number, the number of wind-clearing periods, and the distortion number to gauge the strength of the simulated turbulence and TDTB. These parameters simply greatly given propagation paths with constant atmospheric conditions. …

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Cyberspace Odyssey: A Competitive Team-Oriented Serious Game In Computer Networking, Kendra Graham [I], James Anderson [I], Conrad Rife [I], Bryce Heitmeyer [I], Pranav R. Patel [*], Scott L. Nykl, Alan C. Lin, Laurence D. Merkle Jul 2020

Cyberspace Odyssey: A Competitive Team-Oriented Serious Game In Computer Networking, Kendra Graham [I], James Anderson [I], Conrad Rife [I], Bryce Heitmeyer [I], Pranav R. Patel [*], Scott L. Nykl, Alan C. Lin, Laurence D. Merkle

Faculty Publications

Cyber Space Odyssey (CSO) is a novel serious game supporting computer networking education by engaging students in a race to successfully perform various cybersecurity tasks in order to collect clues and solve a puzzle in virtual near-Earth 3D space. Each team interacts with the game server through a dedicated client presenting a multimodal interface, using a game controller for navigation and various desktop computer networking tools of the trade for cybersecurity tasks on the game's physical network. Specifically, teams connect to wireless access points, use packet monitors to intercept network traffic, decrypt and reverse engineer that traffic, craft well-formed and …

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Effects Of Temperature And Antioxidants On The Oxidation Of Biodiesel Derived From Waste Vegetable Oil, Randy L. Maglinao, Torrey J. Wagner, Keegan Duff Jun 2020

Effects Of Temperature And Antioxidants On The Oxidation Of Biodiesel Derived From Waste Vegetable Oil, Randy L. Maglinao, Torrey J. Wagner, Keegan Duff

Faculty Publications

Biodiesel offers several environmental benefits and improvements to some fuel performance properties, but its poor oxidative stability has been a major concern. Currently, the accepted practice to improve biodiesel oxidative stability is the addition of antioxidants; numerous antioxidants have been studied but their effectiveness in inhibiting biodiesel oxidation is difficult to predict due to variation with resonance stability, solubility, reactivity, and volatility. To improve prediction efforts, this study explored the Rapid Small-Scale Oxidation Test (RSSOT) as a means to investigate how biodiesel oxidation is affected by antioxidant concentration and temperature, and compared its results with the oxidative stability index test. …

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Fourier Propagation Tool For Aberration Analysis And A Point Spread Function Calculation Of Systems With Curved Focal Planes, Stephen C. Cain Jun 2020

Fourier Propagation Tool For Aberration Analysis And A Point Spread Function Calculation Of Systems With Curved Focal Planes, Stephen C. Cain

Faculty Publications

This paper describes a new Fourier propagator for computing the impulse response of an optical system with a curved focal plane array, while including terms ignored in Fresnel and Fraunhofer calculations. The propagator includes a Rayleigh-Sommerfeld diffraction formula calculation from a distant point through the optical system to its image point predicted by geometric optics on a spherical surface. The propagator then approximates the neighboring field points via the traditional binomial approximation of the Taylor series expansion around that field point. This technique results in a propagator that combines the speed of a Fourier transform operation with the accuracy of …

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Securing Photovoltaic (Pv) System Deployments With Data Diodes, Robert D. Larkin, Torrey J. Wagner, Barry E. Mullins Jun 2020

Securing Photovoltaic (Pv) System Deployments With Data Diodes, Robert D. Larkin, Torrey J. Wagner, Barry E. Mullins

Faculty Publications

A survey of a typical photovoltaic (PV) system with and without the cybersecurity protections of a data diode is explored. This survey includes a brief overview of Industrial Control Systems (ICS) and their relationship to the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Industry 4.0 terminology. The cybersecurity features of eight data diodes are compared, and the cyber attack surface, attack scenarios, and mitigations of a typical PV system are discussed. After assessing cybersecurity, the economic considerations to purchase a data diode are considered. At 13.19 cents/kWh, the sale of 227,445 kWh is needed to fund one …

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Battlespace Next™: Developing A Serious Game To Explore Multi-Domain Operations, Nathaniel Flack, Alan C. Lin, Gilbert L. Peterson, Mark G. Reith Jun 2020

Battlespace Next™: Developing A Serious Game To Explore Multi-Domain Operations, Nathaniel Flack, Alan C. Lin, Gilbert L. Peterson, Mark G. Reith

Faculty Publications

Changes in the geopolitical landscape and increasing technological complexity have prompted the U.S. Military to coin the terms Multi-Domain Operations (MDO) and Joint All-Domain Command and Control (JADC2) as over-arching strategy to frame the complexity of warfare across both traditional and emerging warfighting domains. Teaching new concepts associated with these terms requires both innovation as well as distinct education and training tools in order to realize the cultural change advocated by senior military leaders. Battlespace Next™ (BSN) is a serious game designed to teach concepts integral to MDO and initiate discussion on military strategy while conserving time, money, and manpower. …

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A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin May 2020

A Physics-Based Machine Learning Study Of The Behavior Of Interstitial Helium In Single Crystal W–Mo Binary Alloys, Adib J. Samin

Faculty Publications

In this work, the behavior of dilute interstitial helium in W–Mo binary alloys was explored through the application of a first principles-informed neural network (NN) in order to study the early stages of helium-induced damage and inform the design of next generation materials for fusion reactors. The neural network (NN) was trained using a database of 120 density functional theory (DFT) calculations on the alloy. The DFT database of computed solution energies showed a linear dependence on the composition of the first nearest neighbor metallic shell. This NN was then employed in a kinetic Monte Carlo simulation, which took into …

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Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey J. Wagner, Clay Koschnick, Steven J. Schuldt, Jada B. Williams, Kevin Hallinan May 2020

Machine Learning Modeling Of Horizontal Photovoltaics Using Weather And Location Data, Christil Pasion, Torrey J. Wagner, Clay Koschnick, Steven J. Schuldt, Jada B. Williams, Kevin Hallinan

Faculty Publications

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined …

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Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola Apr 2020

Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing [*], Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola

Faculty Publications

Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed …

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Single-Pulse, Kerr-Effect Mueller Matrix Lidar Polarimeter, Keyser, Christian K., Richard K. Martin, Helena Lopez-Aviles, Khanh Nguyen, Arielle M. Adams, Demetrios Christodoulides Apr 2020

Single-Pulse, Kerr-Effect Mueller Matrix Lidar Polarimeter, Keyser, Christian K., Richard K. Martin, Helena Lopez-Aviles, Khanh Nguyen, Arielle M. Adams, Demetrios Christodoulides

Faculty Publications

We present a novel light detection and ranging (LiDAR) polarimeter that enables measurement of 12 of 16 sample Mueller matrix elements in a single, 10 ns pulse. The new polarization state generator (PSG) leverages Kerr phase modulation in a birefringent optical fiber, creating a probe pulse characterized by temporally varying polarization. Theoretical expressions for the Polarization State Generator (PSG) Stokes vector are derived for birefringent walk-off and no walk-off and incorporated into a time-dependent polarimeter signal model employing multiple polarization state analyzers (PSA). Polarimeter modeling compares the Kerr effect and electro-optic phase modulator–based PSG using a single Polarization State Analyzer …

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Experimental Determination Of The (0/−) Level For Mg Acceptors In Β-Ga2O3 Crystals, Christopher A. Lenyk, Trevor A . Gustafson, Sergey A. Basun, Larry E. Halliburton, Nancy C. Giles Apr 2020

Experimental Determination Of The (0/−) Level For Mg Acceptors In Β-Ga2O3 Crystals, Christopher A. Lenyk, Trevor A . Gustafson, Sergey A. Basun, Larry E. Halliburton, Nancy C. Giles

Faculty Publications

Electron paramagnetic resonance (EPR) is used to experimentally determine the (0/−) level of the Mg acceptor in an Mg-doped β-Ga2O3 crystal. Our results place this level 0.65 eV (±0.05 eV) above the valence band, a position closer to the valence band than the predictions of several recent computational studies. The crystal used in this investigation was grown by the Czochralski method and contains large concentrations of Mg acceptors and Ir donors, as well as a small concentration of Fe ions and an even smaller concentration of Cr ions. Below room temperature, illumination with 325 nm laser light …

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Non-Linear Statistical Photocalibration Of Photodetectors Without Calibrated Light Sources, Stephen C. Cain Mar 2020

Non-Linear Statistical Photocalibration Of Photodetectors Without Calibrated Light Sources, Stephen C. Cain

Faculty Publications

Calibration of CCD arrays is commonly conducted using dark frames. Non-absolute calibration techniques only measure the relative response of the detectors. For absolute calibration to be achieved, a second calibration is sometimes utilized by looking at sources with known radiances. A process like this can be used to calibrate photodetectors if a calibration source is available and sensor time can be spared to perform the operation. A previous attempt at creating a procedure for calibrating a photodetector using the underlying Poisson nature of the photodetection statistics relied on a linear model. This effort produced the statistically applied non-uniformity calibration algorithm, …

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Optimizing The Environmental And Economic Sustainability Of Remote Community Infrastructure, Jamie E. Filer, Justin D. Delorit, Andrew J. Hoisington, Steven J. Schuldt Mar 2020

Optimizing The Environmental And Economic Sustainability Of Remote Community Infrastructure, Jamie E. Filer, Justin D. Delorit, Andrew J. Hoisington, Steven J. Schuldt

Faculty Publications

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, …

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Synthesizing General Electromagnetic Partially Coherent Sources From Random, Correlated Complex Screens, Milo W. Hyde Iv Mar 2020

Synthesizing General Electromagnetic Partially Coherent Sources From Random, Correlated Complex Screens, Milo W. Hyde Iv

Faculty Publications

We present a method to generate any genuine electromagnetic partially coherent source (PCS) from correlated, stochastic complex screens. The method described here can be directly implemented on existing spatial-light-modulator-based vector beam generators and can be used in any application which utilizes electromagnetic PCSs. Our method is based on the genuine cross-spectral density matrix criterion. Applying that criterion, we show that stochastic vector field realizations (corresponding to a desired electromagnetic PCS) can be generated by passing correlated Gaussian random numbers through “filters” with space-variant transfer functions. We include step-by-step instructions on how to generate the electromagnetic PCS field realizations. As an …

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Near-Infrared-Sensitive Photorefractive Sn2P2S6 Crystals Grown By The Bridgman Method, O. M. Shumelyuk, A. Y. Volkov, Y. Skrypka, Larry E. Halliburton, Nancy C. Giles, Christopher A. Lenyk, Sergey A. Basun, A. A. Grabar, Y. M. Vysochansky, S. G. Odoulov, D. R. Evans Mar 2020

Near-Infrared-Sensitive Photorefractive Sn2P2S6 Crystals Grown By The Bridgman Method, O. M. Shumelyuk, A. Y. Volkov, Y. Skrypka, Larry E. Halliburton, Nancy C. Giles, Christopher A. Lenyk, Sergey A. Basun, A. A. Grabar, Y. M. Vysochansky, S. G. Odoulov, D. R. Evans

Faculty Publications

Ferroelectric tin hypothiodiphosphate (Sn2P2S6) crystals are well-known for their significant piezoelectric, electro-optic, and nonlinear optical properties. These crystals have usually been grown by a vapor transport technique. We report in this paper on the first study of photorefractive nonlinearity in Sn2P2S6 crystals grown by the Bridgman method. Pronounced photorefraction is observed in the near-infrared region of the spectrum even with no preliminary optical sensitizing.

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Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple Feb 2020

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …

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A Sequential Partial Information Bomber‐Defender Shooting Problem, Krishna Kalyanam, David W. Casbeer, Meir Pachter Feb 2020

A Sequential Partial Information Bomber‐Defender Shooting Problem, Krishna Kalyanam, David W. Casbeer, Meir Pachter

Faculty Publications

No abstract provided.

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An Ultra-Sparse Approximation Of Kinetic Solutions To Spatially Homogeneous Flows Of Non-Continuum Gas, Alexander Alekseenko, Amy Grandilli, Aihua W. Wood Feb 2020

An Ultra-Sparse Approximation Of Kinetic Solutions To Spatially Homogeneous Flows Of Non-Continuum Gas, Alexander Alekseenko, Amy Grandilli, Aihua W. Wood

Faculty Publications

We consider a compact approximation of the kinetic velocity distribution function by a sum of isotropic Gaussian densities in the problem of spatially homogeneous relaxation. Derivatives of the macroscopic parameters of the approximating Gaussians are obtained as solutions to a linear least squares problem derived from the Boltzmann equation with full collision integral. Our model performs well for flows obtained by mixing upstream and downstream conditions of normal shock wave with Mach number 3. The model was applied to explore the process of approaching equilibrium in a spatially homogeneous flow of gas. Convergence of solutions with respect to the model …

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