Dr. Steven R. Price Profile

Dr. Steven R. Price

Assistant Professor at USACE - ERDC

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Author

Publications (19)

Proceedings Article | 7 June 2024 Presentation + Paper

Time-series neural networks to predict electromagnetic wave propagation

Andy Varner, Scott Kovaleski, Charlie Veal, Marshall Lindsay, Junyoung Shin, Derek Anderson, Stanton Price, Steven Price

Proceedings Volume 13042, 1304206 (2024) https://doi.org/10.1117/12.3013488

KEYWORDS: Wave propagation, Machine learning, Neural networks, Simulations, Near field optics, Electric fields

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Proceedings Article | 7 June 2024 Presentation + Paper

Addressing multi-scale challenges in metasurface design with differentiable far-field propagation

Marshall Lindsay, Scott Kovaleski, Andy Varner, Charlie Veal, Derek Anderson, Stanton Price, Steven Price

Proceedings Volume 13042, 1304205 (2024) https://doi.org/10.1117/12.3013490

KEYWORDS: Wave propagation, Holograms, Wavefronts, Design, Imaging systems, Fourier optics, Aliasing

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Proceedings Article | 7 June 2024 Presentation + Paper

Cooperative optimization of neural-network-based diffraction simulations and benchtop spatial light modulators

Marshall Lindsay, Scott Kovaleski, Andy Varner, Charlie Veal, Derek Anderson, Stanton Price, Steven Price

Proceedings Volume 13042, 1304207 (2024) https://doi.org/10.1117/12.3013482

KEYWORDS: Simulations, Holograms, Spatial light modulators, Wavefronts, Modulation, Device simulation

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Proceedings Article | 7 June 2024 Presentation + Paper

Generalized comparison assessment of unstructured data using foundation model features

Charlie Veal, Marshall Lindsay, Andy Varner, Scott Kovaleski, Derek Anderson, Stanton Price, Steven Price

Proceedings Volume 13040, 1304008 (2024) https://doi.org/10.1117/12.3013836

KEYWORDS: Computer vision technology, Object detection

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Proceedings Article | 7 June 2024 Presentation + Paper

Developing robust autonomous vehicles with ROS

Dylan Kangas, Mohamed Salem, Kevin Li, Tyler Ryynanen, Steven Senczyszyn, Anthony Pinar, Steven Price, Stanton Price, Stephen Taylor, Timothy Murphy, Timothy Havens

Proceedings Volume 13052, 130520A (2024) https://doi.org/10.1117/12.3013941

KEYWORDS: Point clouds, Sensors, LIDAR, Unmanned vehicles, Systems modeling, Global Positioning System, Environmental sensing

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Proceedings Article | 7 June 2024 Poster + Paper

Investigation into deep learning methods for in-the-wild applications

Haley Land, Stanton Price, Samantha Carley, Samantha Butler, Steven Price

Proceedings Volume 13052, 130520K (2024) https://doi.org/10.1117/12.3021631

KEYWORDS: Object detection, Deep learning, Unmanned vehicles

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Proceedings Article | 7 June 2024 Poster + Paper

Evaluation of hyperspectral data for deep learning model performance

Samantha Butler, Stanton Price, Samantha Carley, Haley Land, Steven Price

Proceedings Volume 13031, 130310R (2024) https://doi.org/10.1117/12.3014030

KEYWORDS: Data modeling, Short wave infrared radiation, Image classification, Education and training, Hyperspectral imaging, Machine learning, Performance modeling, Deep learning, Image sensors, Sensors

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Proceedings Article | 14 June 2023 Presentation + Paper

Initial investigation of UAV swarm behaviors in a search-and-rescue scenario using reinforcement learning

Samantha Carley, Stanton Price, Xian Mae Hadia, Steven Price, Samantha Butler

Proceedings Volume 12549, 125490F (2023) https://doi.org/10.1117/12.2663629

KEYWORDS: Machine learning, Unmanned aerial vehicles, Simulations, Deep learning, Artificial neural networks, Collision avoidance

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Proceedings Article | 14 June 2023 Presentation + Paper

Impact of data variety on physics-informed neural network lens design

Marshall Lindsay, Scott Kovaleski, Andy Varner, Charlie Veal, Justice Weber, Derek Anderson, Stanton Price, Steven Price

Proceedings Volume 12530, 125300B (2023) https://doi.org/10.1117/12.2661856

KEYWORDS: Neural networks, Wave propagation, Data modeling, Wavefronts, Modulation, Overfitting, Mathematical optimization, Lens design

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The adoption of neural networks for optical component design has increased rapidly in recent years. In this design framework, the numerical simulation of optical wave propagation and material wave modulation are encoded directly as layers of a neural network. This direct encoding enables the optimization of physical quantities (e.g., the transmissivity values of the diffractive optical elements) by gradient descent and the backpropagation algorithm. For the body of work which uses these networks for simulation and optimization, there is a tendency to treat the training process as identical to traditional deep neural networks. However, to the best of our knowledge, there is yet an explicit evaluation of training parameters to support this intuition. This work aims to help fill this gap by providing an exploration and evaluation of data variety to help accelerate those in the community who wish to use this emerging design framework.The application of neural networks in optical component design has witnessed rapid growth in recent years. This design framework encodes the numerical simulation of optical wave propagation and material wave modulation directly within neural network layers, enabling the optimization of physical quantities, such as transmissivity values of diffractive optical elements, through gradient descent and backpropagation algorithms. Physics-informed neural networks have been employed in designing diffractive deep neural networks, optimizing holograms for near-eye displays and creating multi-objective traditional optics. However, there remains a lack of evaluation for training parameters, and discrete sampling considerations are often overlooked. To address these gaps, this study examines the impact of dataset variety on physics-informed neural networks in optimizing lenses that either satisfy or violate the Nyquist sampling criteria. Results show that increased data variety enhances optimized lens performance across all cases. Optimized lenses demonstrate improved imaging performance by reducing diffraction orders present in aliased analytical lenses. Moreover, we reveal that low data variety leads to overfit lenses that function as selective imagers, providing valuable insights for future lens design and optimization.

Proceedings Article | 14 June 2023 Presentation + Paper

Mapping meta-atoms to near field response for inverse design of optical metasurfaces

Andy Varner, Marshall Lindsay, Charlie Veal, Scott Kovaleski, Derek Anderson, Stanton Price, Steven Price

Proceedings Volume 12530, 125300J (2023) https://doi.org/10.1117/12.2661887

KEYWORDS: Electric fields, Machine learning, Design and modelling, Image restoration, Simulations, Near field, Near field optics

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Proceedings Article | 12 June 2023 Poster + Paper

Exploring new strategies for comparing deep-learning models

Samantha Butler, Stanton Price, Xian Mae Hadia, Steven Price, Samantha Carley

Proceedings Volume 12538, 125381K (2023) https://doi.org/10.1117/12.2663401

KEYWORDS: Education and training, Convolution, Data modeling, Deep learning, Performance modeling, Object detection, Neural networks, Visual process modeling, Genetic algorithms, Army

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Proceedings Article | 12 April 2021 Presentation + Paper

Machine learning for longwave infrared image enhancement to improve maneuverability hazard detection

Charley Rhea, Stanton Price, Stephanie Price, Joshua Fairley, Steven Price

Proceedings Volume 11746, 117461C (2021) https://doi.org/10.1117/12.2585786

KEYWORDS: Infrared imaging, Image enhancement, Image quality, Long wavelength infrared, Infrared radiation, Infrared detectors, Machine learning, Sensors, Image contrast enhancement, Genetic algorithms

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Proceedings Article | 19 May 2020 Paper

Investigation of search strategies to identify optimized and efficient templates for automatic target recognition in remotely sensed imagery

Samantha Carley, Stanton Price, Samantha Tidrick, Steven Price

Proceedings Volume 11394, 1139405 (2020) https://doi.org/10.1117/12.2556816

KEYWORDS: Particle swarm optimization, Detection and tracking algorithms, Genetic algorithms, Machine learning, Visualization, Automatic target recognition, Machine vision

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Proceedings Article | 15 May 2020 Presentation + Paper

Object detection on aerial imagery to improve situational awareness for ground vehicles

Xian Hadia, Stanton Price, Steven Price, Stephanie Price, Joshua Fairley

Proceedings Volume 11413, 114131J (2020) https://doi.org/10.1117/12.2556812

KEYWORDS: Sensors, Unmanned aerial vehicles, Airborne remote sensing, Machine learning, Feature extraction, Situational awareness sensors, Machine vision, Computer vision technology

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Proceedings Article | 14 May 2019 Paper

iECO learned matched filters for automatic target recognition in synthetic midwave infrared imagery

Stanton Price, Steven Price

Proceedings Volume 10988, 1098816 (2019) https://doi.org/10.1117/12.2518741

KEYWORDS: Filtering (signal processing), Automatic target recognition, Image filtering, Detection and tracking algorithms, Machine vision, Computer vision technology, Machine learning, Mid-IR, Algorithm development, Image processing

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Proceedings Article | 3 May 2019 Paper

Further investigation of the application of deep learning for electromagnetic simulation prediction

Steven Price, Stanton Price, Carey Price, Clay Blount

Proceedings Volume 11003, 110030X (2019) https://doi.org/10.1117/12.2518975

KEYWORDS: Finite-difference time-domain method, Neural networks, Computer simulations, Feature extraction, Radar, Electromagnetic simulation

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Proceedings Article | 4 May 2018 Paper

Investigating the application of deep learning for electromagnetic simulation prediction

Steven Price, Stanton Price, Carey Price, Clay Blount

Proceedings Volume 10633, 106331C (2018) https://doi.org/10.1117/12.2305030

KEYWORDS: Finite-difference time-domain method, Neural networks, Radar, Target detection, Computer simulations, Electromagnetic simulation, Electromagnetic radiation, Scattering

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Proceedings Article | 30 April 2018 Paper

Pre-screener for automatic detection of road damage in SAR imagery via advanced image processing techniques

Stanton Price, Steven Price, Carey Price, Clay Blount

Proceedings Volume 10649, 1064913 (2018) https://doi.org/10.1117/12.2305052

KEYWORDS: Landslides, Roads, Synthetic aperture radar, Image processing, Image filtering, Satellite imaging, Earth observing sensors, Image classification, Convolution

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Proceedings Article | 3 May 2017 Paper

Matched illumination waveform design for enhanced tunnel detection

Steven Price, Carey Price, Clay Blount

Proceedings Volume 10182, 101821I (2017) https://doi.org/10.1117/12.2262707

KEYWORDS: Soil science, Target detection, Radar, Signal to noise ratio, Receivers, Synthetic aperture radar, Interference (communication), Imaging systems, Scattering, Numerical simulations

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Showing 5 of 19 publications

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