David J Lary

University of Texas at Dallas, Physics, Faculty Member

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Address: Hanson Center for Space Sciences
University of Texas at Dallas
MS WT15, 800 W Campbell Rd
Richardson TX 75080
USA

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Papers by David J Lary

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

BioMedInformatics, Apr 3, 2024

Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and N... more

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Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach

Medical research archives, 2024

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

Zenodo (CERN European Organization for Nuclear Research), Mar 3, 2022

Remotesensing

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectr... more We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a (n -1)-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpreted as relative endmember abundances satisfying both the abundance sum-to-one and abundance non-negativity constraints. Points in this latent space are mapped to reflectance spectra via a flexible function combining linear and non-linear mixing. Due to the probabilistic formulation of the GSM, spectral variability is also estimated by a precision parameter describing the distribution of observed spectra. Model parameters are determined using a generalized expectation-maximization algorithm, which guarantees nonnegativity for extracted endmembers. We first compare the GSM against three varieties of nonnegative matrix factorization (NMF) on a synthetic data set of linearly mixed spectra from the USGS spectral database. Here, the GSM performed favorably for both endmember accuracy and abundance estimation with all non-linear contributions driven to zero by the fitting procedure. In a second experiment, we apply the GTM to model non-linear mixing in real hyperspectral imagery captured over a pond in North Texas. The model accurately identified spectral signatures corresponding to near-shore algae, water, and rhodamine tracer dye introduced into the pond to simulate water contamination by a localized source. Abundance maps generated using the GSM accurately track the evolution of the dye plume as it mixes into the surrounding water.

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Linking NASA Data with Environmental Exposures and Health Outcomes in Theater of War

Abstract : The hypothesis of this study is that a suite of remote sensing data products on atmosp... more Abstract : The hypothesis of this study is that a suite of remote sensing data products on atmospheric aerosols used in their meteorological context and processed by machine learning can provide a daily estimate of the global PM 2.5 abundance. This information is of considerable value to Global Health Surveillance (GHS), providing a capability to routinely estimate troop deployment exposure to elevated levels of particulate matter (PM) globally, significantly contributing to DoD-wide force health protection initiatives. We have exceeded our promised goal and have provided a daily global estimate of the PM2.5 distribution from February 2000 up through the present (we promised from 2006-present).

Cover Feature: Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry (ChemPlusChem 4/2022)

Collection of Czechoslovak Chemical Communications, Mar 21, 2022

Unsupervised Blink Detection Using Eye Aspect Ratio Values

The eyes serve as a window into underlying physical and cognitive processes. Although factors suc... more The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, we present a new unsupervised machine learning blink detection strategy using existing eye-tracking technology. The method is compared to two existing techniques. All three algorithms make use of eye aspect ratio values for blink detection. Accurate and rapid blink detection complements existing eye-tracking research and may provide a new informative index of physical and mental status.

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Dense Urban Environment Dosimetry for Actionable Information and Recording Exposure (DUE DARE)

The views, opinions and/or findings contained in this report are those of the author(s) and shoul... more

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Chemical Data Assimilation &Optimized Earth Observation

EAEJA, Apr 1, 2003

ABSTRACT

Characterizing Urban Air Quality to Provide Actionable Information

AGU Fall Meeting Abstracts, Dec 1, 2017

Stratosphere-Troposphere Exchange of Ozone Observed by Data Assimilation

Stratosphere-Troposphere exchange of ozone at tropopause folds is an important process in the atm... more Stratosphere-Troposphere exchange of ozone at tropopause folds is an important process in the atmosphere. Reconstruction of the evolution of ozone at a tropopause fold by conventional means requires synoptic ozone measurements with a small temporal and spatial separation. Such measurements are rarely obtained. The technique of data assimilation can perform this reconstruction using a small, asynoptic data set. In this project data assimilation was used to reconstruct ozone at a tropopause fold over Western Europe on March 3rd 1995 at low resolution over a period of 48 hours and a region of size 2500 km. The reconstruction was found to be realistic. Future high resolution studies will give a close look at ozone exchange in many observed tropopause folds, a result previously restricted to modelled folds and a few well-studied folds.

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BigData and Machine Learning for Public Health

142nd APHA Annual Meeting and Exposition (November 15 - November 19, 2014), Nov 18, 2014

ABSTRACT BigData should be a key component of a holistic approach to public health. There is incr... more ABSTRACT BigData should be a key component of a holistic approach to public health. There is increasing awareness that health is shaped by more than health care, but the exact causal pathway that links health to health behaviors, socioeconomic conditions, and environmental conditions has been inadequately explored by conventional epidemiologic methods. Existing knowledge and conventional research tools are often insufficient to predict a priori how various environmental, social, psychological, behavioral, and biological factors are interrelated and change over time. Human health is an interdependent multifaceted system. The quantity of data that is now available through new technologies requires different analytic methods and approaches. An exciting new era is dawning where we are using these valuable data together (fully multi- variately) with computational techniques such as machine learning to provide insights for integrative health in the areas of methodology for patient care, scientific discovery, decision support, and policy formulation. This session will showcase new advances for those who would like to leverage the computational BigData revolution for integrative health insight generation and describe some areas of exciting future development. Upon completion attendees will have an appreciation of the tremendous value of using the methodology of BigData and Machine Learning for Integrative Health. This is timely as many Public Health professionals may not be familiar with these tools.

Short-Lived Ozone-Related Compounds

s of Papers Presented at the WMO-IGA C Conference on the Measurement and Assessment of A tmospher... more

Complementary metal oxide semiconductor integrated circuits for rotational spectroscopy

CMOS (Complementary Metal Oxide Semiconductor) transmitter and receiver circuits for rotational s... more CMOS (Complementary Metal Oxide Semiconductor) transmitter and receiver circuits for rotational spectroscopy are demonstrated. The IC’s implemented in 65-nm CMOS consist of a 208-252 GHz transmitter and a 225-280 GHz receiver. Use of CMOS electronics can reduce the cost of electronics for rotational spectrometer application from over $50k to less than $1k. The receiver (RX) includes an on-chip antenna for air-to-chip interface, a 2nd order sub-harmonic down-conversion mixer, a low noise IF amplifier and an amplitude detector. The transmitter (TX) includes an on-chip antenna for chip-to-air interface, Fractional-N synthesizer with a frequency step less than 1 kHz with a built-in frequency shift keying circuit as well as a frequency up-converter to generate the signal at the RF. The integrated circuits were assembled into a rotational spectrometer and utilized to detect numerous gases including Ethanol and Acetone in human breath. It is the first ever demonstration of spectroscopy on pure gases as well as breath using CMOS circuits, and this work paves the way toward a more compact, affordable and efficient rotational spectroscopy system.

Analysis of exhaled human breath via terahertz molecular spectroscopy

We report on our progress in utilizing THz breath sensing in several bio-medical diagnostic appli... more We report on our progress in utilizing THz breath sensing in several bio-medical diagnostic applications. Our work bears promise in applying this technology to non-invasive analysis of blood glucose based on chemical composition of breath, as well as assessment of asthma related airway inflammation. Our most recent testing of CMOS based THz breath sensor, in the evolution of this technology towards compact and affordable implementations, is discussed.

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Using Neural Networks for Instrument Cross-Calibration

AGU Fall Meeting Abstracts, Dec 1, 2007

Neural networks are non-linear non-parametric learning algorithms that are universal approximator... more Neural networks are non-linear non-parametric learning algorithms that are universal approximators. They have proved very useful to us in a variety of applications (Lary et al., 2004, 2007a,b), from the acceleration of expensive code elements to learning the cross-calibration between large earth observing datasets including atmospheric composition, aerosol optical depth, and vegetation indices. We have been using a variety of networks including feed-forward multi-layer perceptron networks trained with the Levenberg-Marquardt algorithm, and neuro-fuzzy networks. The success of the neural networks largely depends on two factors. First, having a training dataset that adequately spans the parameter space. Second, including the variables that explain the variance in the dataset. If these two criteria are met then the neural networks give excellent results as they are universal approximators. We present several examples of neural network cross-calibration.

Fugitive greenhouse gas emissions from shale gas activities - a case study of Dish, TX

AGU Fall Meeting Abstracts, Dec 1, 2012

Using machine learning to examine the relationship between asthma and absenteeism

Environmental Monitoring and Assessment, Jun 1, 2019

In this study, we found that machine learning was able to effectively estimate student learning o... more In this study, we found that machine learning was able to effectively estimate student learning outcomes geo-spatially across all the campuses in a large, urban, independent school district. The machine learning showed that key factors in estimating the student learning outcomes included the number of days students were absent from school. In turn, one of the most important factors in estimating the number of days a student was absent was whether or not the student had asthma. This highlights the importance of environmental public health for student learning outcomes.

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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, Jun 1, 2019

For the period of the Barnett Coordinated Campaign, October 16-31, 2013, hourly concentrations fo... more For the period of the Barnett Coordinated Campaign, October 16-31, 2013, hourly concentrations for 46 volatile organic compounds (VOCs) were recorded at 14 air monitoring stations within the Barnett Shale of North Texas. These measurements are used to identify and analyze multi-species hydrocarbon signatures on a regional scale through the novel combination of two techniques: domain filling with Lagrangian trajectories and the machine learning unsupervised classification algorithm called a self-organizing map (SOM). This combination of techniques is shown to accurately identify concentration enhancements in the lightest measured alkane species at and downwind of the locations of activepermit oil and gas facilities, despite the model having This article is part of the Topical Collection on Topical Collection on Geospatial Technology in Environmental Health Applications

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

Remote Sensing, Aug 20, 2021

This article is an open access article distributed under the terms and conditions of the Creative... more

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