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Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning

Sensors (Basel, Switzerland), 2021-03, Vol.21 (6), p.2240

2021

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Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales

Sensors (Basel, Switzerland), 2022-06, Vol.22 (11), p.4240

2022

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Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification

Remote sensing (Basel, Switzerland), 2021-08, Vol.13 (16), p.3289

2021

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Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches

Remote sensing (Basel, Switzerland), 2024-07, Vol.16 (13), p.2454

2024

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Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping

Remote sensing (Basel, Switzerland), 2024-07, Vol.16 (13), p.2430

2024

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Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean

Applied sciences, 2018-12, Vol.8 (12), p.2687-2687

2018

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Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

Remote sensing (Basel, Switzerland), 2024-03, Vol.16 (6), p.996

2024

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Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

Environmental health insights, 2015-01, Vol.2015 (Suppl. 1), p.41-52

2015

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Janus Kinase Inhibitors and Risk of Venous Thromboembolism: A Systematic Review and Meta-analysis

Mayo Clinic proceedings, 2021-07, Vol.96 (7), p.1861-1873

2021

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Data-Driven EEG Band Discovery with Decision Trees

Sensors (Basel, Switzerland), 2022-04, Vol.22 (8), p.3048

2022

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Machine learning in geosciences and remote sensing

Di xue qian yuan., 2016-01, Vol.7 (1), p.3-10

2016

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Using Machine Learning for the Calibration of Airborne Particulate Sensors

Sensors (Basel, Switzerland), 2019-12, Vol.20 (1), p.99

2019

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PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

Remote sensing (Basel, Switzerland), 2021-12, Vol.13 (23), p.4788

2021

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Machine Learning for Light Sensor Calibration

Sensors (Basel, Switzerland), 2021-09, Vol.21 (18), p.6259

2021

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High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source

Remote sensing (Basel, Switzerland), 2022-01, Vol.14 (3), p.495

2022

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Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles

Remote sensing (Basel, Switzerland), 2012-05, Vol.4 (5), p.1355-1368

2012

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Estimating the global abundance of ground level presence of particulate matter (PM2.5)

Geospatial health, 2014-12, Vol.8 (3), p.S611-S630

2014

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Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

International journal of remote sensing, 2008-12, Vol.29 (24), p.7141-7158

2008

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Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning

BioMedInformatics, 2024-04, Vol.4 (2), p.1019-1046

2024

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Space-based measurements of HCl: Intercomparison and historical context

Journal of Geophysical Research - Atmospheres, 2008-08, Vol.113 (D15), p.D15S04-n/a

2008

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Using machine learning to examine the relationship between asthma and absenteeism

Environmental monitoring and assessment, 2019-06, Vol.191 (Suppl 2), p.332-9, Article 332

2019

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Catalytic destruction of stratospheric ozone

Journal of Geophysical Research, Washington, DC, 1997-09, Vol.102 (D17), p.21515-21526

Lary, D. J.

1997

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Specifying the equatorial ionosphere using CINDI on C/NOFS, COSMIC, and data interpolating empirical orthogonal functions

Journal of geophysical research. Space physics, 2013-10, Vol.118 (10), p.6706-6722

2013

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Combining domain filling with a self-organizing map to analyze multi-species hydrocarbon signatures on a regional scale

Environmental monitoring and assessment, 2019-06, Vol.191 (Suppl 2), p.337-17, Article 337

2019

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Using machine learning to understand the temporal morphology of the PM2.5 annual cycle in East Asia

Environmental monitoring and assessment, 2019-06, Vol.191 (Suppl 2), p.1-14, Article 272

2019

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Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning

Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales

Cloud Detection Using an Ensemble of Pixel-Based Machine Learning Models Incorporating Unsupervised Classification

Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches

Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping

Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean

Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction

Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

Janus Kinase Inhibitors and Risk of Venous Thromboembolism: A Systematic Review and Meta-analysis

Data-Driven EEG Band Discovery with Decision Trees

Machine learning in geosciences and remote sensing

Using Machine Learning for the Calibration of Airborne Particulate Sensors

PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

Machine Learning for Light Sensor Calibration

High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD) as a Supplementary Weather Source

Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning

Space-based measurements of HCl: Intercomparison and historical context

Using machine learning to examine the relationship between asthma and absenteeism

Catalytic destruction of stratospheric ozone

Specifying the equatorial ionosphere using CINDI on C/NOFS, COSMIC, and data interpolating empirical orthogonal functions

Combining domain filling with a self-organizing map to analyze multi-species hydrocarbon signatures on a regional scale

Using machine learning to understand the temporal morphology of the PM2.5 annual cycle in East Asia

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