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Full-Text Articles in Entire DC Network
An Agent-Based Modeling Framework For Evaluating The Linkage Between Disaster Facility Damage And Mental Health, Emily S. Reeves
An Agent-Based Modeling Framework For Evaluating The Linkage Between Disaster Facility Damage And Mental Health, Emily S. Reeves
Theses and Dissertations
This research establishes a novel agent-based modeling framework to establish the linkage between disaster-induced facility damage and mental health outcomes and the evaluation of treatment methods within the civilian and USAF mental health spheres. The study models the degradation and recovery of agent mental health using simulated data and evaluates the efficacy of three distinct treatment approaches through statistical methods. The methodology integrate agent-based modeling with resilient engineering concepts to simulate mental health resilience curves based on vulnerability, exposure, and facility damage. Agents’ mental health indices were tracked through phases of degradation, stagnation, and recovery based on the three treatments …
Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff
Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff
Theses and Dissertations
This research develops a digital twin of the global maritime shipping system to model disruptions in major shipping lanes like the Suez and Panama Canals. By incorporating live ship-tracking data, the model simulates closures, forecasts queue lengths, and determines the best rerouting options. Findings show that canal closures cause large traffic backlogs and increased congestion at alternative chokepoints, while rerouted ships may face higher piracy risks in regions like the Gulf of Guinea and the Strait of Malacca. This tool helps decision-makers respond effectively to maritime disruptions.
Design, Simulation, And Characterization Of A Deuterated Plastic Scintillator For A Portable Neutron Spectrometer, Anders M. Kinney Ii
Design, Simulation, And Characterization Of A Deuterated Plastic Scintillator For A Portable Neutron Spectrometer, Anders M. Kinney Ii
Theses and Dissertations
This thesis examines the design, simulation, and experimental characterization of a novel deuterated plastic scintillator for portable neutron spectroscopy applications related to homeland security and nuclear non-proliferation. Using Monte Carlo N-Particle (MCNP) simulations and experimental tests with gamma-ray (137Cs) and neutron sources (AmBe, 252Cf), this research shows that deuterium substitution in scintillators significantly enhances pulse shape discrimination (PSD) and neutron energy spectrum unfolding capabilities. Although deuterated scintillators demonstrate reduced overall light output, their higher stopping power and increased ionization quenching lead to superior neutron event differentiation, particularly between (α, n) and spontaneous fission neutrons critical for detecting …
Radiation Assessment And Distribution In Changing Atmospheric Structures And Turbulence (Radcast), Steven B. Hernandez
Radiation Assessment And Distribution In Changing Atmospheric Structures And Turbulence (Radcast), Steven B. Hernandez
Theses and Dissertations
The accessibility of materials and online information has made radiological dispersal devices (RDDs) a more probable threat than conventional nuclear weapons. This study analyzed five datasets from three test periods, using a correlation analysis between optical turbulence, aerosol concentration, and environmental parameters, and geometric mean diameter (GMD) calculations. The correlation analysis failed to yield meaningful insights, prompted a transition to the GMD analysis, which produced clearer results. Findings show GMD ranged from 38–41 nm, increasing at night and during neutral events as reduced vertical motion and boundary layer contraction correlated with optical turbulence. An alternative GMD dataset, calculated by the …
Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling
Investigation Of Neutron Inelastic Scatter Cross Sections On 16O, Molly A. Wakeling
Theses and Dissertations
Nuclear data underpin a number of applications across nuclear reactor design, medicine, astrophysics, nonproliferation, national security, and other related fields. However, data are conflicting or missing across the spectrum of isotopes and reactions, and theoretical calculations can only go so far to predict nuclear properties. A key reaction with incomplete data is neutron inelastic scatter on 16O, which reduces neutron energies and produces high-energy gamma rays that are often not taken into account in simulations of nuclear reactors, nuclear weapon detonations, nuclear fusion, and other areas. To fill these gaps, the Gamma-Energy Neutron-Energy Spectrometer for Inelastic Scattering, or GENESIS, …
Deep Reinforcement Learning For Leo Satellite Grouping, James E. Minteer
Deep Reinforcement Learning For Leo Satellite Grouping, James E. Minteer
Theses and Dissertations
This research investigates jamming evasion using DRL to provide an autonomous solution that repositions a geostationary satellite experiencing directed, terrestrial based jamming. Second, this thesis applies DRL to an area of research for LEO satellite constellations, user grouping, using a portion of an Air Force Research Lab reinforcement learning framework. As LEO satellites orbit the earth, they must constantly re-evaluate not only which users they are able to connect to, but on which of its multiple beams. This model succeeds in finding a balance between maximizing signal strength and minimizing overhead from switching user assignments, all while requiring fewer costly …
Deployable Origami Structure, David J. Garcia, Robert Bettinger
Deployable Origami Structure, David J. Garcia, Robert Bettinger
AFIT Patents
The present invention relates to deployable origami structures and methods of making and using same. Such deployable origami structures can be folded into a compact state utilizing origami, yet easily deployed as the structures comprise pseudo-elastic connectors. Additive manufacturing techniques can be used to make such deployable origami structures as such techniques can lower cost and structure weight.
Accelerated Multiobjective Calibration Of Fused Deposition Modeling 3d Printers Using Multitask Bayesian Optimization And Computer Vision, Craig S. Ganitano, Benji Maruyama, Gilbert L. Peterson
Accelerated Multiobjective Calibration Of Fused Deposition Modeling 3d Printers Using Multitask Bayesian Optimization And Computer Vision, Craig S. Ganitano, Benji Maruyama, Gilbert L. Peterson
Faculty Publications
Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three-dimensional (3D) printing, but is time-consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera-based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct …
Construction Change Order Data As Inputs For A Programmatic Risk Model, Eric C. Klein
Construction Change Order Data As Inputs For A Programmatic Risk Model, Eric C. Klein
Theses and Dissertations
Hurricane Michael struck Tyndall Air Force Base as a Category 5 storm with 160 mph winds, damaging or destroying 90% of its infrastructure (484 buildings). This devastation prompted the Department of Defense to rebuild Tyndall as the “Installation of the Future,” encompassing 44 Military Construction (MILCON) projects and 260 Facility Restoration, Sustainment, and Modernization (FRSM) projects. Since reconstruction began, over 630 Change Request Forms (CRFs) have been closed, adding $67.8 million in costs and 4,880 construction days. The Air Force Civil Engineer Center's Natural Disaster Recovery (NDR) Division developed a programmatic risk model to predict future costs and delays. However, …
The Space Rescue Professional: Operationalizing Guardians For The Future, Benjamin Johnis, Robert Bettinger, James Dean
The Space Rescue Professional: Operationalizing Guardians For The Future, Benjamin Johnis, Robert Bettinger, James Dean
Faculty Publications
The escalating competition in space exploration demands a dedicated space rescue capability. This article argues for the establishment of a space rescue professional (SRP) career field within the Department of Defense and the US Space Force. By harnessing existing US Air Force combat rescue expertise and partnering with the National Aeronautics and Space Administration, the Space Force can rapidly develop a robust SRP program with the warrant officer corps as its backbone. This article addresses a critical gap in national space policy to outline a cost-effective dual-service training pipeline and SRP operational roles. Such an investment safeguards astronauts, bolsters US …
Finite Element Simulation Of Interstitial–Lymphatic Fluid Flow And Nanodrug Transport In A Solid Tumor: An Intratumoral Injection Approach, Gobinda Debnath, Buddakkagari Vasu, Rama Subba Reddy Gorla
Finite Element Simulation Of Interstitial–Lymphatic Fluid Flow And Nanodrug Transport In A Solid Tumor: An Intratumoral Injection Approach, Gobinda Debnath, Buddakkagari Vasu, Rama Subba Reddy Gorla
Faculty Publications
Objective: This study presents a mathematical model and finite element simulations to investigate interstitial fluid flow and nanodrug transport in a solid tumor, incorporating transvascular exchange, convection–diffusion–reaction dynamics, and intratumoral injection mechanisms. Impact Statement: Optimizing nanodrug distribution remains a critical challenge in cancer therapy. The proposed model advances nanomedicine by enhancing the mechanistic understanding of nanodrug transport in a solid tumor. Introduction: Cancer, a global threat, often manifests as solid tumors driven by uncontrolled cell growth. The heterogeneous microenvironment, lymphatic drainage, nano-bio interactions, and elevated interstitial fluid pressure (IFP) hinder effective nanodrug delivery. Nanoparticle (NP)-based drug delivery systems offer a …
High-Speed Aircraft Stability And Control Metrics, Timothy T. Takahashi, Jack A. Griffin, Ramana Grandhi
High-Speed Aircraft Stability And Control Metrics, Timothy T. Takahashi, Jack A. Griffin, Ramana Grandhi
Faculty Publications
This review paper identifies key stability and control screening parameters needed to design low-risk, general-purpose high-speed aircraft. These derive from MIL-STD-8785C, MIL-STD-1797, and older AGARD reports, and are suitable for assessing conceptual high-speed vehicles. We demonstrate their applicability using published ground test, computation, and flight test data from the Bell X-2, North American X-15, Martin X-24A, Northrop HL-10, Lockheed Blackbird (YF-12/SR-71), and North American XB-70 as well as the Rockwell Space Shuttle Orbiter. The relative success of the X-15 and Blackbird and the performance limitations of the others indicate the need to scrutinize lateral-directional stability at the preliminary design phase. …
Navigating Together: The Conav Testbed And Framework For Benchmarking Cooperative Localization, Rohith Boyinine, Jayanth Ammapalli, Anusna Chakraborty, Rajnikant Sharma, Kevin Brink, Clark N. Taylor
Navigating Together: The Conav Testbed And Framework For Benchmarking Cooperative Localization, Rohith Boyinine, Jayanth Ammapalli, Anusna Chakraborty, Rajnikant Sharma, Kevin Brink, Clark N. Taylor
Faculty Publications
This paper presents CoNaV, a comprehensive framework for creating a multi-vehicle cooperative localization (CL) testbed designed to support the benchmarking, development, and deployment of cooperative navigation algorithms. Given the essential role of CL in improving localization accuracy for both defense and civilian applications, CoNaV provides a robust environment for rigorously validating algorithms under real-world conditions. By establishing a benchmark for CL algorithms, CoNaV lays a foundation for advancing research into more sophisticated and distributed CL solutions. This framework highlights the potential of cooperative navigation to enhance multi-vehicle operations and offers a scalable, practical approach for future developments in CL technology.
Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
AFIT Documents
AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …
Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer
AFIT Documents
The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.
Celebrating 25 Years Of Collaboration: A Bibliography Of Afit-Afrl’S Top 50 Most-Cited Works, Wheeler Hall, Michelle Burwinkel
Celebrating 25 Years Of Collaboration: A Bibliography Of Afit-Afrl’S Top 50 Most-Cited Works, Wheeler Hall, Michelle Burwinkel
AFIT Library Knowledge Base
This bibliography compiles the 50 most-cited collaborative publications between the Air Force Institute of Technology (AFIT) and the Air Force Research Laboratory (AFRL) from 2000 to early 2025, showcasing their interdisciplinary impact across advanced science and engineering. Spanning materials science, remote sensing, terahertz spectroscopy, nanomaterials, aerospace, fatigue mechanics, biomedical applications, communication systems, optics, machine learning, and hypersonic engineering, the works highlight innovative methodologies and transformative applications. This collection underscores AFIT-AFRL’s contributions to cutting-edge research, offering valuable insights for researchers, engineers, and stakeholders in defense and technology development.
Editorial: Artificial Intelligence For Smart Health: Learning, Simulation, And Optimization, Bing Yao, Nathan Gaw, Hyo Kyung Lee
Editorial: Artificial Intelligence For Smart Health: Learning, Simulation, And Optimization, Bing Yao, Nathan Gaw, Hyo Kyung Lee
Faculty Publications
With rapid developments in medical sensing and imaging, we now live in an era of data explosion in which large amounts of data are readily available in clinical environments. The fast-growing biomedical and healthcare data provide unprecedented opportunities for data-driven scientific knowledge discovery and clinical decision support. Our Research Topic aims to catalyze synergies among biomedical informatics, machine learning, computer simulation, operations research, systems engineering, and other related fields with three specific goals: (1) develop cutting-edge data-driven models to accelerate scientific knowledge discovery in biomedicine using healthcare data collected from laboratory systems, imaging systems, and medical and sensing devices; (2) …
Dynamic Analysis Of Additively Manufactured Tensegrity Structures, Keivan Davami, Russell A. Rowe, Ben Gulledge, Jesse Park, Ali Beheshti, Anthony N. Palazotto, Fariborz Tavangarian, Sadie Beck
Dynamic Analysis Of Additively Manufactured Tensegrity Structures, Keivan Davami, Russell A. Rowe, Ben Gulledge, Jesse Park, Ali Beheshti, Anthony N. Palazotto, Fariborz Tavangarian, Sadie Beck
Faculty Publications
Herein, we present an analysis, design, and experimental testing of modular prestressed pin-jointed structures constructed from bistable units and inspired by the classical triangular tensegrity prism. Tensegrity structures, characterized by a combination of tension members (cables) and compression members (bars) in a self-equilibrated state, have gained significant attention in engineering over the past two decades due to their unique nonlinear mechanical behavior. The discontinuity of the compression members in tensegrity structures leads to a slightly different failure behavior compared to their lattice structure counterparts, with unprecedented applications. However, traditional fabrication and assembly methods have posed challenges for their widespread adoption. …
The Boris Experience: Evaluating Omnichannel Returns And Repurchase Intention, Jianliang Hao, Robert G. Richey Jr., Tyler R. Morgan, Ian M. Slazinik
The Boris Experience: Evaluating Omnichannel Returns And Repurchase Intention, Jianliang Hao, Robert G. Richey Jr., Tyler R. Morgan, Ian M. Slazinik
Faculty Publications
Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.
Ultraviolet And Blue Stimulated Emission From Cs Alkali Vapor Pumped Using Two-Photon Absorption And Four-Wave Mixing, Ricardo C. Davila, Christopher A. Rice, Nathan B. Terry, Glen P. Perram
Ultraviolet And Blue Stimulated Emission From Cs Alkali Vapor Pumped Using Two-Photon Absorption And Four-Wave Mixing, Ricardo C. Davila, Christopher A. Rice, Nathan B. Terry, Glen P. Perram
Faculty Publications
Stimulated emission on the ultraviolet and blue transitions in Cs has been achieved by pumping via two-photon absorption and four-wave mixing for the pump transition 62S½ → 82D3/2,5/2. The emission performance of the optically pumped cesium vapor laser operating in ultraviolet and blue has been extended to 650 nJ/pulse for 387 nm, 1 to 3 μ J / pulse for 388 nm, 200 nJ/pulse for 455 nm, and 500 nJ/pulse for 459 nm. Emission performance improves dramatically as the cesium vapor density is increased, and no scaling limitations associated with energy pooling or …
Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor
Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor
Faculty Publications
This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise docking operations. Our study focuses on autonomous aerial refueling as a use case, demonstrating significant advancements in relative navigation and object detection. We introduce a novel method for aligning digital twins using fiducial targets and motion capture data, which facilitates accurate pose estimation from real-world imagery. Additionally, we develop cost-efficient annotation automation techniques for generating high-quality You Only Look Once training data. Experimental results indicate that …
Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill
Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill
Theses and Dissertations
This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …
Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira
Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira
Theses and Dissertations
Embedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial …
Training-Informed Air Traffic Control Workforce Scheduling, Eli Winett
Training-Informed Air Traffic Control Workforce Scheduling, Eli Winett
Theses and Dissertations
The Radar Control Facility (RCF) at Eglin Air Force Base creates monthly workforce schedules. Adherence to regulations must be verified manually and no evaluation criteria exist to compare schedules. A mixed-integer program model for air traffic controllers allocates workers across 19 positions for a user-defined number of weeks of 24-hour operations. Across eight consecutive months, this model produced schedules to nearly eliminate all shifts with critical shortages from the reference schedules. Without civilian overtime work, gains in positional coverage require a 35% increase in overtime work for military workers and 35% reduction in monthly training instances. We recommend users adopt …
Extracting Permittivity And Permeability Using The Position-Insensitive And Calibration-Independent Method On A Rectangular Waveguide, James Conrad Denemark
Extracting Permittivity And Permeability Using The Position-Insensitive And Calibration-Independent Method On A Rectangular Waveguide, James Conrad Denemark
Theses and Dissertations
Classic methods for extracting material characteristics require known measurements to accurately calibrate the network analyzer. Previous work demonstrated a position-insensitive and calibration-independent (PiCi) transmission/reflection method to extract a material’s permittivity. This thesis proposes a method with the same function, manipulated to use one empty measurement and then two samples of different thicknesses. The PiCi method is first adopted for rectangular waveguide which resulted in inaccurate permittivity data when compared to the calibrated solution. Once detector mismatch corrections were applied, the PiCi method produced accurate results. Using a 2-D numerical root search, permittivity and permeability material characteristics are now successfully extracted …
Applying Machine‐Learning Methods To Laser Acceleration Of Protons: Lessons Learned From Synthetic Data, Ronak Desai, Thomas Zhang, J. J. Felice, Ricky Oropeza, Joseph R. Smith, Alona Kryshchenko, Chris Orban, Michael L. Dexter, Anil K. Patnaik
Applying Machine‐Learning Methods To Laser Acceleration Of Protons: Lessons Learned From Synthetic Data, Ronak Desai, Thomas Zhang, J. J. Felice, Ricky Oropeza, Joseph R. Smith, Alona Kryshchenko, Chris Orban, Michael L. Dexter, Anil K. Patnaik
Faculty Publications
In this study, we consider three different machine-learning methods—a three-hidden-layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus …
Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl
Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl
Faculty Publications
Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.
Time Domain Spectral Lidar Enabled By Cascaded Raman In A Hydrogen-Filled Transmitter, Richard K. Martin, Trevor L. Courtney, Arielle M. Adams, Daniel E. Leaird, Luke Ausley, Christian K. Keyser
Time Domain Spectral Lidar Enabled By Cascaded Raman In A Hydrogen-Filled Transmitter, Richard K. Martin, Trevor L. Courtney, Arielle M. Adams, Daniel E. Leaird, Luke Ausley, Christian K. Keyser
Faculty Publications
We introduce what we believe to be novel spectral light detection and ranging (LiDAR) architectures that enable ultra-compact systems by a transition from spectral signal processing in space (gratings) to processing in time. The architectures leverage temporal dispersion and the unique spectro-temporal waveforms produced from the cascaded Raman scattering generated in the (H2) filled hollow core fiber. The characterized Raman source yields as many as six Raman orders from 1.06-1.70 μm; their unique spectro-temporal waveforms are measured. System performance simulations based on measured Raman waveforms show that high accuracy measurement of range and reflectivity are possible with proper …
Limitations Of Beam-Control Compensation, Matthew Kalensky, Darren Getts, Matthias T. Banet, Derek J. Burrell, Milo W. Hyde, Mark F. Spencer
Limitations Of Beam-Control Compensation, Matthew Kalensky, Darren Getts, Matthias T. Banet, Derek J. Burrell, Milo W. Hyde, Mark F. Spencer
Faculty Publications
In this paper, we use wave-optics simulations to explore the limitations of beam-control compensation. We evaluate performance in terms of the normalized power in a diffraction-limited bucket for the cases of no beam-control compensation, perfect phase compensation, and perfect full-field compensation. From these results, we are able to arrive at the following conclusions: (1) without any form of beam-control compensation, performance begins to degrade when D/r0 > 1; (2) with perfect phase compensation, performance begins to degrade when D/r0 > 1 and (λ/r0)/θ0 > 1; and (3) with perfect full-field compensation, performance begins to degrade when D/r0 …
Inferring Tlb Configuration With Performance Tools, Cristian Agredo, Tor J. Langehaug, Scott R. Graham
Inferring Tlb Configuration With Performance Tools, Cristian Agredo, Tor J. Langehaug, Scott R. Graham
Faculty Publications
Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish …