Dr. Matthew W. Farthing Profile

Dr. Matthew W. Farthing

at US Army Engineer Research and Development Ctr

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Author

Publications (4)

Proceedings Article | 14 June 2023 Presentation + Paper

Thermal modeling for autonomous vehicle simulations

Orie Cecil, Justin Carrillo, Matthew Bray, Andrew Trautz, Brian Brady, John Monroe, Matthew Farthing, Josh Fairley

Proceedings Volume 12549, 125490C (2023) https://doi.org/10.1117/12.2663842

KEYWORDS: Thermal modeling, Infrared sensors, Simulations, Sensors, Atmospheric modeling, Unmanned vehicles, Temperature metrology, Physics

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Proceedings Article | 6 June 2022 Presentation + Paper

Advanced high-fidelity autonomy systems simulation

Justin Carrillo, Orie Cecil, John Monroe, Andrew Trautz, Matthew Farthing, Matthew Bray

Proceedings Volume 12115, 121150D (2022) https://doi.org/10.1117/12.2618011

KEYWORDS: Sensors, Computer simulations, Atmospheric modeling, RGB color model, Virtual reality, Environmental sensing, Vegetation, Unmanned vehicles, Device simulation, Algorithm development

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Proceedings Article | 22 April 2020 Presentation + Paper

Augmenting wave-kinematics algorithms with machine learning to enable rapid littoral mapping and surf-zone state characterization from imagery

Katherine Brodie, Adam Collins, Tyler Hesser, Matthew Farthing, A. Spicer Bak, Jonghyun Lee

Proceedings Volume 11413, 1141313 (2020) https://doi.org/10.1117/12.2558686

KEYWORDS: Error analysis, Data modeling, Visualization, Statistical analysis, RGB color model, Coastal modeling, Image processing, Machine learning, Visual process modeling

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Proceedings Article | 17 October 2019 Paper

Exploitable synthetic sensor imagery from high-fidelity, physics-based target and background modeling

Stacy Howington, Jerrell Ballard, Nagaraju Kala, Andrew Trautz, Matthew Bray, Matthew Farthing, Amanda Hines

Proceedings Volume 11158, 1115809 (2019) https://doi.org/10.1117/12.2532923

KEYWORDS: Soil science, Sensors, Image processing, Image sensors, Vegetation, Data modeling, Process modeling, Computer simulations

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It is well established that object recognition by human perception and by detection/identification algorithms is confounded by false alarms (e.g., [1]). These false alarms often are caused by static or transient features of the background. Machine learning can help discriminate between real targets and false alarms, but requires large and diverse image sets for training. The potential number of scenarios, environmental processes, material properties and states to be assessed is overwhelming and cannot practically be explored by field/lab collections alone. High-fidelity, physics-based simulation can now augment training sets with accurate synthetic sensor imagery, but at a high computational cost. To make synthetic image generation practical, it should include the fewest processes and coarsest spatiotemporal resolution needed to capture the system physics/state and accomplish the training.

Among the features known or expected to generate false alarms are: (1.) soil/material variability (spatial heterogeneity in density, mineral composition, reflectance), (2.) non-threat objects (rocks, trash), (3.) soil disturbance (physical and spectral effects), (4.) soil processes (moisture migration, evaporation), (5.) surface hydrology (rainfall runoff and surface ponding), (6.) vegetation processes (transpiration, rainfall interception and evaporation, non-saturating rain events, multi-layer canopy, (including thatch), discrete versus parameterized vegetation), and (7.) energy reflected or emitted by other scene components. This paper presents a suite of computational tools that will allow the community to begin to explore the relative importance of these features and determine when and how individual processes must be included explicitly or through simplifying assumptions/parameterizations. The justification for this decision to simplify is driven ultimately by the performance of a detection algorithm with the generated synthetic imagery. Knowing the required level of modeling detail is critical for designing test matrices for building image sets capable of training improved algorithms.

A related consideration in the creation of synthetic sensor imagery is validation of these complex, coupled modeling tools. Very few analytical solutions or laboratory experiments include enough complexity to thoroughly test model formulations. Conversely, field data collection cannot normally be characterized and measured with sufficient spatial and temporal detail to support true validation. Intermediate-scale physical exploration of near surface soil and atmospheric processes (e.g., Trautz et al., [2]) offers an alternative that is intermediary to the laboratory column and field scales. This allows many field-scale-dependent processes and effects to be reproduced, manipulated, isolated, and measured within a well characterized and controlled test environment at requisite spatiotemporal resolutions in both the air and soil.

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