Published in HORTSCIENCE, 2026
We integrate multispectral imaging with low-field MRI for comprehensive plant phenotyping.
Recommended citation: A. McFarland, L. Rossi, F. Nia, J. Peeples, A. Svyanek. (2025). "Integrating Multispectral Imaging and Low-Field Magnetic Resonance Imaging for Comprehensive Phenotyping of Horticultural Crops." HORTSCIENCE.
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Published in HORTSCIENCE, 2025
We integrate multispectral imaging with low-field MRI for comprehensive plant phenotyping.
Recommended citation: A. McFarland, L. Rossi, F. Nia, J. Peeples, A. Svyanek. (2025). "Integrating Multispectral Imaging and Low-Field Magnetic Resonance Imaging for Comprehensive Phenotyping of Horticultural Crops." HORTSCIENCE. 60(7).
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Published in American Control Conference (ACC) 2025, 2026
We introduce a traffic-aware graph neural network architecture (TA-STGCN) for predicting pedestrian intentions in dynamic traffic environments.
Recommended citation: Fahimeh Orvati Nia, Hai Lin. (2025). "Traffic-Aware Pedestrian Intention Prediction." American Control Conference (ACC) 2025.
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Published in bioRxiv (Under revision to Plant Phenome Journal), 2026
An integrated multispectral phenotyping framework for high-throughput plant monitoring and analysis. Under revision to the Plant Phenome Journal.
Recommended citation: Fahimeh Orvati Nia, Joshua Peeples, Seth C Murray, Andrew McFarland, and colleagues. (2026). "A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments." bioRxiv. 2026.02.25.707797.
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Published in TPEC 2026, 2026
We evaluate GAN-LSTM models for detecting anomalies in smart meter data.
Recommended citation: Fahimeh Orvati Nia, S. Salehi, J. Peeples. (2026). "Evaluating GAN-LSTM for Smart Meter Anomaly Detection in Power Systems." TPEC 2026.
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Published in WACV 2026 Workshop on Earth Observation, 2025
We propose a neighborhood feature pooling method for remote sensing image classification.
Recommended citation: Fahimeh Orvati Nia, Joshua Peeples, et al. (2025). "Neighborhood Feature Pooling for Remote Sensing Image Classification." WACV 2026 Workshop on Earth Observation.
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Published in American Control Conference (ACC) 2025, 2025
We introduce a traffic-aware graph neural network architecture (TA-STGCN) for predicting pedestrian intentions in dynamic traffic environments.
Recommended citation: Fahimeh Orvati Nia, Hai Lin. (2025). "Traffic-Aware Pedestrian Intention Prediction." American Control Conference (ACC) 2025.
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