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Your search keyword '"Cottrell, Garrison W."' showing total 21 results
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21 results on '"Cottrell, Garrison W."'

1. Multimodal Wildland Fire Smoke Detection

Author

Baldota, Siddhant, Ramaprasad, Shreyas Anantha, Bhamra, Jaspreet Kaur, Luna, Shane, Ramachandra, Ravi, Zen, Eugene, Kim, Harrison, Crawl, Daniel, Perez, Ismael, Altintas, Ilkay, Cottrell, Garrison W., and Nguyen, Mai H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Published
2022

3. FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

Author

Dewangan, Anshuman, Pande, Yash, Braun, Hans-Werner, Vernon, Frank, Perez, Ismael, Altintas, Ilkay, Cottrell, Garrison W., and Nguyen, Mai H.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response.

Published
2021
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5. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

Author

Kim, Hyun Woo, Wang, Mingxun, Leber, Christopher A, Nothias, Louis-Félix, Reher, Raphael, Bin Kang, Kyo, van der Hooft, Justin JJ, Dorrestein, Pieter C, Gerwick, William H, and Cottrell, Garrison W

Subjects
Biological Products, Biosynthetic Pathways, Neural Networks, Computer, Chemical Sciences, Biological Sciences, Medical and Health Sciences, Medicinal & Biomolecular Chemistry
Abstract

Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.

Published
2021

12. Multimodal Wildland Fire Smoke Detection

Author

Bhamra, Jaspreet Kaur, primary, Anantha Ramaprasad, Shreyas Anantha, additional, Baldota, Siddhant, additional, Luna, Shane, additional, Zen, Eugene, additional, Ramachandra, Ravi, additional, Kim, Harrison, additional, Schmidt, Chris, additional, Arends, Chris, additional, Block, Jessica, additional, Perez, Ismael, additional, Crawl, Daniel, additional, Altintas, Ilkay, additional, Cottrell, Garrison W., additional, and Nguyen, Mai H., additional

Published
2023
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13. The Model 2.0 and Friends: An Interim Report

Author

Cottrell, Garrison W, Gahl, Martha, Kulkarni, Shubham, Venkatramani, Shashank, Shah, Yash, Long, Keyu, Zhi, Xuzhe, Agarwal, Shivaank, Li, Cody, He, Jingyuan, and Fischer, Thomas

Subjects
Biologically-inspired eep learning, Artificial Intelligence and Robotics, Cognitive Science, Face recognition, FFA
Abstract

Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation! We look forward to the group’s suggestions for these projects.

Published
2023

14. Visual Expertise in an Anatomically-inspired Model of the Visual System

Author

Cottrell, Garrison W, Gahl, Martha, and Kulkarni, Shubham

Subjects
Computational Neuroscience, Deep Learning, Cognition and Perception, Cognitive Neuroscience, Cognitive Science, Visual Expertise, Neurocomputational models of vision
Abstract

We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. We find that the more faces the network knows, the more the network shows the face inversion effect. In contrast, a standard convolutional network’s inversion performance drops to nearly 0 in the same situation.

Published
2022

20. SMART‐Miner: A convolutional neural network‐based metabolite identification from 1H‐13C HSQC spectra.

Author

Kim, Hyun Woo, Zhang, Chen, Cottrell, Garrison W., and Gerwick, William H.

Subjects
CONVOLUTIONAL neural networks, NUCLEAR magnetic resonance, TISSUE extracts
Abstract

The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to 1H‐13C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR‐based metabolomic tools. [ABSTRACT FROM AUTHOR]

Published
2022
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21. SMART-Miner: A convolutional neural network-based metabolite identification from 1 H- 13 C HSQC spectra.

Author

Kim HW, Zhang C, Cottrell GW, and Gerwick WH

Subjects
Complex Mixtures, Magnetic Resonance Spectroscopy methods, Neural Networks, Computer, Metabolome, Metabolomics methods
Abstract

The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to 1 H- 13 C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools., (© 2021 John Wiley & Sons, Ltd.)

Published
2022
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