Quantifying Inhaled Air Quality Using Biometric Responses
Air pollution has numerous impacts on human health on a variety of time […]
Autonomous Environmental Robots
Aided by machine learning, the multi-member UTD robots can navigate environmental sites that […]
Decoding Physical and Cognitive Impacts of PM Concentrations at Ultra-fine Scales
The human body is an incredible and complex sensing system. Environmental factors trigger […]
Data-Driven EEG Band Discovery with Decision Trees
Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on […]
Unsupervised Blink Detection Using Eye Aspect Ratio Values
The eyes serve as a window into underlying physical and cognitive processes. Although […]
Machine Learning for Environmental Sensing
Machine learning has found many applications in Earth Science. These applications range from […]
High Spatial-Temporal PM2.5 Modeling Utilizing Next Generation Weather Radar (NEXRAD)
PM2.5, a type of fine particulate with a diameter equal to or less […]
PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD
In this study, we present a nationwide machine learning model for hourly PM2.5 estimation […]
Machine Learning for Light Sensor Calibration
Sunlight incident on the Earth’s atmosphere is essential for life, and it is […]
Autonomous Learning of New Environments with a Robotic Team
We describe and demonstrates an autonomous robotic team that can rapidly learn the […]
SharedAirDFW
Under the working title of SharedAirDFW over 100 new custom-built air quality monitors […]
UV/Vis+ photochemistry database: Structure, content and applications
The “science-softCon UV/Vis+ Photochemistry Database” (www.photochemistry.org) is a large and comprehensive collection of EUV-VUV-UV–Vis-NIR […]
Using Machine Learning for the Calibration of Airborne Particulate Sensors
Airborne particulates are of particular significance for their human health impacts and their […]
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning
The human body exhibits a variety of autonomic responses. For example, changing light […]
Using machine learning to examine the relationship between asthma and absenteeism
In this study, we found that machine learning was able to effectively estimate […]
Using machine learning to understand the temporal morphology of the PM2.5 annual cycle in East Asia
PM2.5 air pollution is a significant issue for human health all over the world, […]
Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS
In order to examine associations between asthma morbidity and local ambient air pollution […]