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- Artificial neural networks (3)
- DDS (2)
- Deep learning (2)
- Kerberos (2)
- Machine learning (2)
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- QoS (2)
- #afcec (1)
- #antcenter (1)
- 2ND-ORDER CYCLOSTATIONARITY (1)
- ALGORITHM (1)
- AUTOMATIC MODULATION (1)
- Aircraft availability (1)
- Asset management (1)
- Bayesian estimation (1)
- Blind modulation classification (1)
- Blind modulation classification; orthogonal frequency division multiplexing; higher-order cumulant and cyclic cumulant; maximum-likelihood; maximum a posteriori; deep learning; convolutional neural networks; probability of correct classification; testbed implementation; DEEP NEURAL-NETWORK; AUTOMATIC MODULATION; 2ND-ORDER CYCLOSTATIONARITY; IDENTIFICATION; ALGORITHM; IMPLEMENTATION; SYSTEMS; DESIGN; STREAM (1)
- C-130J (Aircraft) (1)
- CFD modeling (1)
- Charge compensation (1)
- Convolutional neural networks (1)
- Cyber Intrusion (1)
- Cyclone DDS (1)
- DEEP NEURAL-NETWORK (1)
- DESIGN (1)
- Degradation (1)
- Discrete energy levels (1)
- Factor graphs (1)
- FbEs (1)
- FoEs (1)
- Forecasting (1)
Articles 1 - 26 of 26
Full-Text Articles in Physical Sciences and Mathematics
A Statistical Analysis Of Sporadic-E Characteristics Associated With Gnss Radio Occultation Phase And Amplitude Scintillations, Daniel J. Emmons, Dong L. Wu, Nimalan Swarnalingam
A Statistical Analysis Of Sporadic-E Characteristics Associated With Gnss Radio Occultation Phase And Amplitude Scintillations, Daniel J. Emmons, Dong L. Wu, Nimalan Swarnalingam
Faculty Publications
Statistical GNSS-RO measurements of phase and amplitude scintillation are analyzed at the mid-latitudes in the local summer for a 100 km altitude. These conditions are known to contain frequent sporadic-E, and the S4-σϕ trends provide insight into the statistical distributions of the sporadic-E parameters. Joint two-dimensional S4-σϕ histograms are presented, showing roughly linear trends until the S4 saturates near 0.8. To interpret the measurements and understand the sporadic-E contributions, 10,000 simulations of RO signals perturbed by sporadic-E layers are performed using length, intensity, and vertical thickness distributions from previous studies, with the assumption that the sporadic-E layer acts …
Transition-Metal Ions In Β-Ga2O3 Crystals: Identification Of Ni Acceptors, Timothy D. Gustafson, Nancy C. Giles, Brian C. Holloway, J. Jesenovec, B. L. Dutton, M. D. Mccluskey, Larry E. Halliburton
Transition-Metal Ions In Β-Ga2O3 Crystals: Identification Of Ni Acceptors, Timothy D. Gustafson, Nancy C. Giles, Brian C. Holloway, J. Jesenovec, B. L. Dutton, M. D. Mccluskey, Larry E. Halliburton
Faculty Publications
Excerpt: Transition-metal ions (Ni, Cu, and Zn) in β-Ga2O3 crystals form deep acceptor levels in the lower half of the bandgap. In the present study, we characterize the Ni acceptors in a Czochralski-grown crystal and find that their (0/−) level is approximately 1.40 eV above the maximum of the valence band.
Long-Distance Propagation Of 162 Mhz Shipping Information Links Associated With Sporadic E, Alex T. Chartier, Thomas R. Hanley, Daniel J. Emmons
Long-Distance Propagation Of 162 Mhz Shipping Information Links Associated With Sporadic E, Alex T. Chartier, Thomas R. Hanley, Daniel J. Emmons
Faculty Publications
This is a study of anomalous long-distance (>1000 km) radio propagation that was identified in United States Coast Guard monitors of automatic identification system (AIS) shipping transmissions at 162 MHz. Our results indicate this long-distance propagation is caused by dense sporadic E layers in the daytime ionosphere, which were observed by nearby ionosondes at the same time. This finding is surprising because it indicates these sporadic E layers may be far more dense than previously thought.
Optimizing Switching Of Non-Linear Properties With Hyperbolic Metamaterials, James A. Ethridge, John G. Jones, Manuel R. Ferdinandus, Michael J. Havrilla, Michael A. Marciniak
Optimizing Switching Of Non-Linear Properties With Hyperbolic Metamaterials, James A. Ethridge, John G. Jones, Manuel R. Ferdinandus, Michael J. Havrilla, Michael A. Marciniak
Faculty Publications
Hyperbolic metamaterials have been demonstrated to have special potential in their linear response, but the extent of their non-linear response has not been extensively modeled or measured. In this work, novel non-linear behavior of an ITO/SiO2 layered hyperbolic metamaterial is modeled and experimentally confirmed, specifically a change in the sign of the non-linear absorption with intensity. This behavior is tunable and can be achieved with a simple one-dimensional layered design. Fabrication was performed with physical vapor deposition, and measurements were conducted using the Z-scan technique. Potential applications include tunable optical switches, optical limiters, and tunable components of laser sources.
Oxygen Vacancies In Lib3O5 Crystals And Their Role In Nonlinear Absorption, Brian C. Holloway, Christopher A. Lenyk, Timothy D. Gustafson, Nancy C. Giles
Oxygen Vacancies In Lib3O5 Crystals And Their Role In Nonlinear Absorption, Brian C. Holloway, Christopher A. Lenyk, Timothy D. Gustafson, Nancy C. Giles
Faculty Publications
LiB3O5 (LBO) crystals are used to generate the second, third, and fourth harmonics of near-infrared solid-state lasers. At high power levels, the material’s performance is adversely affected by nonlinear absorption. We show that as-grown crystals contain oxygen and lithium vacancies. Transient absorption bands are formed when these intrinsic defects serve as traps for “free” electrons and holes created by x rays or by three- and four-photon absorption processes. Trapped electrons introduce a band near 300 nm and trapped holes produce bands in the 500-600 nm region. Electron paramagnetic resonance (EPR) is used to identify and characterize the …
Clustering Behavior In Solar Flare Dynamics, Elmer C. Rivera, Jay R. Johnson, Jonathan Homan, Simon Wing
Clustering Behavior In Solar Flare Dynamics, Elmer C. Rivera, Jay R. Johnson, Jonathan Homan, Simon Wing
Faculty Publications
The solar magnetic activity cycle provides energy input that is released in intense bursts of radiation known as solar flares. As such, the dynamics of the activity cycle is embedded in the sequence of times between the flare events. Recent analysis shows that solar flares exhibit memory on different timescales. These previous studies showed that the time ordering of flare events is not random, but rather there is dependence between successive flares. In the present work, the clustering of flares is demonstrated through a straightforward nonparametric method where the cumulative distribution function of successive flares is compared with the cumulative …
Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Quantifying Dds-Cerberus Network Control Overhead, Andrew T. Park, Nathaniel R. Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Faculty Publications
Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of …
Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams, Andrew T. Park, Nathaniel Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Distribution Of Dds-Cerberus Authenticated Facial Recognition Streams, Andrew T. Park, Nathaniel Peck, Richard Dill, Douglas D. Hodson, Michael R. Grimaila, Wayne C. Henry
Faculty Publications
Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial …
Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt
Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt
Faculty Publications
Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) …
Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley
Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley
Faculty Publications
Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative …
A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela
A Monte Carlo Framework For Incremental Improvement Of Simulation Fidelity, Damian Lyons, James Finocchiaro, Misha Novitsky, Chris Korpela
Faculty Publications
Robot software developed in simulation often does not be- have as expected when deployed because the simulation does not sufficiently represent reality - this is sometimes called the `reality gap' problem. We propose a novel algorithm to address the reality gap by injecting real-world experience into the simulation. It is assumed that the robot program (control policy) is developed using simulation, but subsequently deployed on a real system, and that the program includes a performance objective monitor procedure with scalar output. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are used to generate …
Pilot Development: An Empirical Mixed-Method Analysis, Jonathan Slottje, Jason Anderson, John M. Dickens, Adam D. Reiman
Pilot Development: An Empirical Mixed-Method Analysis, Jonathan Slottje, Jason Anderson, John M. Dickens, Adam D. Reiman
Faculty Publications
Purpose — Pilot upgrade training is critical to aircraft and passenger safety. This study aims to identify variances in the US Air Force C-130J pilot upgrade training based on geographic location and provide a model to enhance policy that will impact future pilot training efforts that lower cost and increase operator quality and proficiency.
Design/methodology/approach — This research employed a mixed-method approach. First, the authors collected data and analyzed 90 C-130J pilots' aviation records and then contextualized this analysis with interviews of experts. Finally, the authors present a modified version of Six Sigma's define–measure–analyze–improve–control (DMAIC) that identifies and reduces the …
Transportation Service Level Impact On Aircraft Availability, Vincent Mclean, Adam D. Reiman
Transportation Service Level Impact On Aircraft Availability, Vincent Mclean, Adam D. Reiman
Faculty Publications
Purpose — Aircraft fail to meet mission capable rate goals due to a lack of supply of aircraft parts in inventory where the aircraft breaks. This triggers an order at the repair location. To maximize mission capable rate, the time from order to delivery needs to be minimized. The purpose of this research is to examine the case of three airfields for the order to delivery time of mission critical aircraft parts for a specific aircraft type. Design/methodology/approach — This research captured data from three information systems to assess the order fulfillment process. The data were analyzed to determine the …
A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz
A Unified View Of A Human Digital Twin, Michael Miller, Emily Spatz
Faculty Publications
The term human digital twin has recently been applied in many domains, including medical and manufacturing. This term extends the digital twin concept, which has been illustrated to provide enhanced system performance as it combines system models and analyses with real-time measurements for an individual system to improve system maintenance. Human digital twins have the potential to change the practice of human system integration as these systems employ real-time sensing and feedback to tightly couple measurements of human performance, behavior, and environmental influences throughout a product’s life cycle to human models to improve system design and performance. However, as this …
Scalable Deeper Graph Neural Networks For High-Performance Materials Property Prediction, Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu
Scalable Deeper Graph Neural Networks For High-Performance Materials Property Prediction, Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu
Faculty Publications
Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-level features from crystal structures. However, existing GNN models suffer from their lack of scalability, high hyperparameter tuning complexity, and constrained performance due to over-smoothing. We propose a scalable global graph attention neural network model DeeperGATGNN with differentiable group normalization (DGN) and skip connections for high-performance materials property prediction. Our systematic benchmark studies show that our model achieves the state-of-the-art prediction results on five out of six …
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger
Faculty Publications
Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used …
Aerial Radiation Detection Identification And Measurement System Detector Material Comparison Study, Benjamin C. Troxell, Kacey D. Mcgee, Christina L. Dugan
Aerial Radiation Detection Identification And Measurement System Detector Material Comparison Study, Benjamin C. Troxell, Kacey D. Mcgee, Christina L. Dugan
Faculty Publications
The 20th Chemical Biological Radiological Nuclear and Explosives Command (CBRNE) currently utilizes an airborne sodium iodide gamma and beta detection system to map radiation fields over large areas of interest. The 20th CBRNE explored emergent detector technologies utilizing two detection materials; thallium-activated cesium iodide and high purity germanium (HPGe). These detectors were simulated at various altitudes and compared to background measurements. The sodium iodide detector failed to provide isotopic discrimination at distance. The thallium-activated cesium iodide CsI(Tl) detector provided sufficient absolute efficiency and energy resolution to identify isotopics at distance. The HPGe detector provided the best energy resolution. However, current …
Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley
Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley
Faculty Publications
Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional …
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Faculty Publications
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from …
Thermo-Fluidic Transport Process In A Novel M-Shaped Cavity Packed With Non-Darcian Porous Medium And Hybrid Nanofluid: Application Of Artificial Neural Network (Ann), Dipak Kumar Mandal, Nirmalendu Biswas, Nirmal K. Manna, Dilip Kumar Gayen, Rama S. R. Gorla, Ali J. Chamkha
Thermo-Fluidic Transport Process In A Novel M-Shaped Cavity Packed With Non-Darcian Porous Medium And Hybrid Nanofluid: Application Of Artificial Neural Network (Ann), Dipak Kumar Mandal, Nirmalendu Biswas, Nirmal K. Manna, Dilip Kumar Gayen, Rama S. R. Gorla, Ali J. Chamkha
Faculty Publications
In this work, an attempt has been made to explore numerically the thermo-fluidic transport process in a novel M-shaped enclosure filled with permeable material along with Al2O3-Cu hybrid nanoparticles suspended in water under the influence of a horizontal magnetizing field. To exercise the influence of geometric parameters, a classical trapezoidal cavity is modified with an inverted triangle at the top to construct an M-shaped cavity. The cavity is heated isothermally from the bottom and cooled from the top, whereas the inclined sidewalls are insulated. The role of geometric parameters on the thermal performance is scrutinized thoroughly …
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
Faculty Publications
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind …
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
Faculty Publications
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind …
A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons
A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons
Faculty Publications
Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Faculty Publications
This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the nonlinear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement …
Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …