Dr. Austin R. J. Downey Profile

Dr. Austin R. J. Downey

Associate Professor at Univ of South Carolina

SPIE Involvement:

Author

Publications (30)

Proceedings Article | 10 June 2024 Presentation + Paper

Highway slope monitoring using 3D laser scanning at different seasons

AQM Zohuruzzaman, David Wamai, Weicong Feng, Sadik Khan, Austin R. Downey, Jie Wei, Erik Blasch, Paul Schrader

Proceedings Volume 13037, 1303706 (2024) https://doi.org/10.1117/12.3016172

KEYWORDS: LIDAR, Point clouds, Soil moisture, Roads, Soil science, Principal component analysis, Deformation, 3D scanning, Rain, Image segmentation

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Proceedings Article | 7 June 2024 Presentation + Paper

Edge processing for frequency identification on drone-deployed structural health monitoring sensor nodes

Ryan Yount, Joud Satme, David Wamai, Austin R. Downey

Proceedings Volume 13055, 130550L (2024) https://doi.org/10.1117/12.3013712

KEYWORDS: Sensors, Structural health monitoring, Vibration, Data processing, Detection and tracking algorithms, Computer hardware, Unmanned aerial vehicles, Bridges, Structural monitoring, Profiling

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For rapid civil infrastructure assessment following natural and man-made emergencies, the utilization of minimally invasive and cost-effective drone deployable sensor packages has the potential to become a valuable tool. Although compact sensors with wireless data transfer capabilities have proven effective in monitoring the structural dynamics of infrastructure, these systems require data processing to occur externally, frequently off-site. These extra steps impede the high-speed assessment of a structure’s state. Difficulties can arise when the transmission is unfeasible due to degraded communication links during natural or man-made emergencies. Additionally, off-site data processing can add unneeded interruptions to actions that can be taken by emergency personnel after infrastructure damage. To enhance the effectiveness of sensor packages in expediting infrastructure assessment, incorporating real-time data analysis through embedded edge computing techniques emerges as a promising solution. The objective of this work is to demonstrate on-device data processing for frequency-based structural health monitoring techniques using drone-deployable sensors. This approach advances the effectiveness of drone-deployable sensors in rapid infrastructure assessment by mitigating their susceptibility to errors or delays in data communications. The proposed approach computes the frequency components of vibration measurements taken from a structure of interest, for example, the monitoring of a bridge immediately following a damaging event such as a flood. This work presents contributions in terms of outlining a methodology that emphasizes the hardware-based implementation of edge computing algorithms and examines the required on-device performance and resource utilization for structural health monitoring at the edge. The execution time for the sensor’s edge computing functions was profiled, resulting in an additional 9.77 seconds per test, an advancement over traditional transmit and analyze methods.

Proceedings Article | 6 June 2024 Presentation + Paper

Assessing magnetic particle content in algae using compact time domain nuclear magnetic resonance

Parker Huggins, Win Janvrin, Jake Martin, Ashley Womer, Austin R. Downey, John Ferry, Mohammed Baalousha, Jin Yan

Proceedings Volume 13061, 1306102 (2024) https://doi.org/10.1117/12.3013987

KEYWORDS: Sampling rates, Magnetism, Pulse signals, Particles, Sensors, Environmental monitoring, Statistical analysis, Ecosystems, Water, Pulse generators

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Proceedings Article | 9 May 2024 Presentation + Paper

Performance evaluation of flexible capacitive sensors on non-uniform surfaces

Emmanuel Ogunniyi, Han Liu, John White, Austin R. Downey, Simon Laflamme, Jian Li, Caroline Bennett, William Collins, Hongki Jo, Paul Ziehl

Proceedings Volume 12949, 1294916 (2024) https://doi.org/10.1117/12.3010959

KEYWORDS: Sensors, Surface finishing, Signal to noise ratio, Transducers, Capacitors, Structural health monitoring, Structural monitoring, Capacitance

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The use of strain gauges is foundational to structural health monitoring, allowing infrastructure to continuously observe strain, infer stress, and potentially detect fatigue/fracture cracks. However, traditional strain gauges have drawbacks. In addition to being costly, a single-element strain gauge will only detect strain in a single direction and must be mounted on smooth surfaces to ensure good adhesion. Soft Elastomeric Capacitors (SECs) have been proposed as a low-cost alternative to traditional strain gauges while allowing for a broader range of applications. They are flexible and can be modeled with different dimensions based on the monitored structure. Each SEC consists of three layers; the two outer layers act as electrodes and are made of a styrene-ethylene-butylene-styrene polymer in a matrix with carbon black. The inner (dielectric) layer comprises titanium oxide in a matrix with SEBS. The use of the SECs is not limited by the geometry of the surface being monitored, and it can, therefore, be adhered to a variety of surfaces as its flexibility allows it to conform to the irregularity and complexity of the monitored structure. The change experienced by a structure will correlate directly to the change in capacitance observed across the sensor, which can be used to predict the monitored structure’s state. While SECs have been studied for applications on various materials, experiments have been limited to adhering the sensor to smooth surfaces. However, concrete structures have various surface finishes that are not uniform, often deriving from an architect’s aesthetic desire. This work tests a corrugated SEC through compression tests on concrete samples with different surface finishing to investigate the effect of surface finishing on the SEC-measured strain. Each concrete sample is subjected to loading by a dynamic testing system, and the data collected from the SEC are compared to off-the-shelf resistive strain gauges. The results show that the performance of the cSEC on the different surfaces is not hindered by different concrete finishes, where a high signal-to-noise ratio of 21 dB and low mean absolute error of 22 μϵ is seen on the concrete specimen with a rough concrete surface. The strain metrics and surface effect on SEC performance are discussed.

Proceedings Article | 9 May 2024 Presentation + Paper

Online model-based structural damage detection in electronic assemblies

Emmanuel Ogunniyi, Joud Satme, Austin R. Downey

Proceedings Volume 12946, 129461G (2024) https://doi.org/10.1117/12.3010987

KEYWORDS: Finite element methods, Vibration, Model based design, Damage detection, Performance modeling, Matrices, Printing, Modal analysis, Error analysis, Aerospace engineering

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Proceedings Article | 9 May 2024 Presentation + Paper

Exploratory investigation of early detection for high-C discharge-induced failure in 18650 lithium-ion batteries

Goerge Anthony, Connor Madden, Emmanuel Ogunniyi, Austin R. Downey, Ryan Limbaugh, Jarret Peskar, Jingjing Bao, Xinyu Huang

Proceedings Volume 12951, 129510Q (2024) https://doi.org/10.1117/12.3011097

KEYWORDS: Batteries, Safety, Digital image correlation, Failure analysis, Amplifiers, Deformation, Temperature metrology, Reliability, Nondestructive evaluation, Nickel

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Proceedings Article | 28 April 2023 Presentation + Paper

Hardware implementation of nonstationary structural dynamics forecasting

Puja Chowdhury, Austin R. Downey, Jason Bakos, Simon Laflamme, Chao Hu

Proceedings Volume 12483, 1248316 (2023) https://doi.org/10.1117/12.2658036

KEYWORDS: Field programmable gate arrays, Sampling rates, Vibration, Signal to noise ratio, Structural dynamics, Data acquisition, Windows, Manufacturing, Control systems, Computation time

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High-rate time series forecasting has applications in the domain of high-rate structural health monitoring and control. Hypersonic vehicles and space infrastructure are examples of structural systems that would benefit from time series forecasting on temporal data, including oscillations of control surfaces or structural response to an impact. This paper reports on the development of a software-hardware methodology for the deterministic and low-latency time series forecasting of structural vibrations. The proposed methodology is a software-hardware co-design of a fast Fourier transform (FFT) approach to time series forecasting. The FFT-based technique is implemented in a variable-length sequence configuration. The data is first de-trended, after which the time series data is translated to the frequency domain, and frequency, amplitude, and phase measurements are acquired. Next, a subset of frequency components is collected, translated back to the time domain, recombined, and the data's trend is recovered. Finally, the recombined signals are propagated into the future to the chosen forecasting horizon. The developed methodology achieves fully deterministic timing by being implemented on a Field Programmable Gate Array (FPGA). The developed methodology is experimentally validated on a Kintex-7 70T FPGA using structural vibration data obtained from a test structure with varying levels of nonstationarities. Results demonstrate that the system is capable of forecasting time series data 1 millisecond into the future. Four data acquisition sampling rates from 128 to 25600 S/s are investigated and compared. Results show that for the current hardware (Kintex-7 70T), only data sampled at 512 S/s is viable for real-time time series forecasting with a total system latency of 39.05 μs in restoring signal. In totality, this research showed that for the considered FFT-based time series algorithm the fine-tuning of hyperparameters for a specific sampling rate means that the usefulness of the algorithm is limited to a signal that does not shift considerably from the frequency information of the original signal. FPGA resource utilization, timing constraints of various aspects of the methodology, and the algorithm accuracy and limitations concerning different data are discussed.

Proceedings Article | 25 April 2023 Poster + Paper

Real-time splatter tracking in laser powder bed fusion additive manufacturing

Yanzhou Fu, Braden Priddy, Austin R. J. Downey, Lang Yuan

Proceedings Volume 12489, 124890N (2023) https://doi.org/10.1117/12.2658544

KEYWORDS: Image processing, Image filtering, Printing, Gaussian filters, Computation time, Optical filters, Feature extraction, Defect detection

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Proceedings Article | 18 April 2023 Presentation + Paper

Stand-alone geophone monitoring system for earthen levees

Puja Chowdhury, Joud Satme, Malichi Flemming, Austin R. Downey, Mohamed Elkholy, Jasim Imran, Mohammad Sadik Khan

Proceedings Volume 12486, 124860T (2023) https://doi.org/10.1117/12.2658552

KEYWORDS: Sensors, Moisture, Humidity, Environmental sensing, Environmental monitoring, Data storage, Batteries, Vibration, Equipment, Air temperature

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Proceedings Article | 18 April 2023 Paper

Soft elastomeric capacitors with an extended polymer matrix for strain sensing on concrete

Emmanuel Ogunniyi, Han Liu, Austin R. Downey, Simon Laflamme, Jian Li, Caroline Bennett, William Collins, Hongki Jo, Paul Ziehl

Proceedings Volume 12486, 1248618 (2023) https://doi.org/10.1117/12.2658568

KEYWORDS: Capacitance, Sensors, Capacitors, Polymers, Digital image correlation, Transducers, Electrodes, Dielectrics, Structural sensing, Structural health monitoring, Strain analysis

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Surface strain sensors, such as linear variable differential transformers, fiber Bragg gratings, and resistive strain gauges, have seen significant use for monitoring concrete infrastructure. However, spatial monitoring of concrete structures using these sensor systems is limited by challenges in the surface coverage provided by a specific sensor or issues related to mounting and maintaining numerous mechanical sensors on the structure. A potential solution to this challenge is the deployment of large-area electronics in the form of a sensing skin to provide complete coverage of a monitored area while being simple to apply and maintain. Along this line of effort, networks constituted of soft elastomeric capacitors have been deployed to monitor strain on steel and composite structures. However, using soft elastomeric capacitors on concrete surfaces has been challenging due to the electrical coupling between the sensors and concrete, which amplifies transduced strain signals obtained from the soft elastomeric capacitors. In this work, the authors investigate the isolation of the soft elastomeric capacitors from the concrete by extending the styrene-block-ethylene-co-butylene-block-styrene matrix of the soft elastomeric capacitors to include a decoupling layer between the electrode and the concrete. Experimental investigations are carried out on concrete specimens for which the soft elastomeric capacitor is adhered to with a thin layer of off-the-shelf epoxy and then loaded on the dynamic testing system to monitor strain provoked on the concrete samples. The results presented here demonstrate the viability of the electrically isolated soft elastomeric capacitors for monitoring strain on concrete structures. Initial comparisons between un-isolated and electrically isolated soft elastomeric capacitors showed that the nominal capacitance of the soft elastomeric capacitor is significantly lowered by adding an isolation layer of SEBS. Furthermore, strain results for the soft elastomeric capacitors are compared to ones from a resistive strain gauge and digital image correlation. The data obtained is significant for modifying soft elastomeric capacitors with the anticipation for future use on concrete structures.

Proceedings Article | 18 April 2023 Presentation + Paper

Non-linear vibration signal compensation technique for UAV-deployable sensor packages with edge computing

Joud Satme, Daniel Coble, Hung-Tien Huang, Austin R. Downey, Jason Bakos

Proceedings Volume 12486, 124861M (2023) https://doi.org/10.1117/12.2658563

KEYWORDS: Sensors, Education and training, Data modeling, Vibration, Structural health monitoring, Performance modeling, Accelerometers, Systems modeling, Signal to noise ratio, Signal attenuation

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Proceedings Article | 20 April 2022 Poster + Paper

Deterministic and low-latency time-series forecasting of nonstationary signals

Puja Chowdhury, Vahid Barzegar, Joud Satme, Austin R. Downey, Simon Laflamme, Jason Bakos, Chao Hu

Proceedings Volume 12043, 120431D (2022) https://doi.org/10.1117/12.2629025

KEYWORDS: Field programmable gate arrays, Clocks, Neurons, Signal to noise ratio, Structural health monitoring, Sensors, Detection and tracking algorithms, Data acquisition, Damage detection, Algorithm development

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