Shawhin Talebi
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View article: Improvement in the Estimation of Inhaled Concentrations of Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Physiological Responses and Power Spectral Density from an Astrapi Spectrum Analyzer
Improvement in the Estimation of Inhaled Concentrations of Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Physiological Responses and Power Spectral Density from an Astrapi Spectrum Analyzer Open
The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO), which, when inhaled, bring about several changes in the autonomous responses of our body. Our pr…
View article: Improvement in the Estimation of Inhaled Concentration of Carbon Dioxide, Nitrogen Dioxide, Nitric Oxide Using Physiological Responses and Power Spectral Density from an Astrapi Spectrum Analyzer
Improvement in the Estimation of Inhaled Concentration of Carbon Dioxide, Nitrogen Dioxide, Nitric Oxide Using Physiological Responses and Power Spectral Density from an Astrapi Spectrum Analyzer Open
The air we breathe consists of contaminants such as particulate matter (PM), carbon dioxide (CO2), nitrogen dioxide (NO2), nitric oxide (NO) which when inhaled brings about several changes in the autonomous responses of our body. Our previ…
View article: Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping
Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping Open
Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plet…
View article: Unsupervised Characterization of Water Composition with UAV-based Hyperspectral Imaging and Generative Topographic Mapping
Unsupervised Characterization of Water Composition with UAV-based Hyperspectral Imaging and Generative Topographic Mapping Open
Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of…
View article: Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response
Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response Open
Breathing clean air is crucial for maintaining good human health. The air we inhale can significantly impact our physical and mental well-being, influenced by parameters such as particulate matter and gases (e.g. carbon dioxide, carbon mon…
View article: Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning
Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning Open
Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wid…
View article: Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction
Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction Open
Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the reference data needed to calibrate rem…
View article: Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning
Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning Open
In this study, we adopt a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Air pollution has numerous impacts on human health on a variety of time scales. This study…
View article: Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In-Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction
Characterizing Water Composition with an Autonomous Robotic Team Employing Comprehensive In-Situ Sensing, Hyperspectral Imaging, Machine Learning, and Conformal Prediction Open
Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the high-quality reference data needed to …
View article: Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response
Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response Open
Data and code in Jupyter notebook to estimated inhaled NO2 using biometrics of a person for a currently unpublished work titled " Estimating Inhaled Nitrogen Dioxide from Human Biometric Response." By making use of number of biometric vari…
View article: Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response
Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response Open
Data and code in Jupyter notebook to estimated inhaled NO2 using biometrics of a person for a currently unpublished work titled " Estimating Inhaled Nitrogen Dioxide from Human Biometric Response." By making use of number of biometric vari…
View article: Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach
Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach Open
Data and code in form of Jupyter Notebook to accompany an unpublished paper with the title " Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach". This work makes use …
View article: Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach
Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach Open
Data and code in form of Jupyter Notebook to accompany an unpublished paper with the title " Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach". This work makes use …
View article: Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach
Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach Open
Notebook and data to accompany the (unpublished) paper titled "Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach". This work expands a previous study,…
View article: Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach
Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach Open
Notebook and data to accompany the (unpublished) paper titled "Gauging Size Resolved Ambient Particulate Matter Concentration Solely Using Biometric Observations: A Machine Learning and Causal Approach". This work expands a previous study,…
View article: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales Open
The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underly…
View article: Data-Driven EEG Band Discovery with Decision Trees
Data-Driven EEG Band Discovery with Decision Trees Open
Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be u…
View article: Decoding Physical and Cognitive Impacts of PM Concentrations at Ultra-fine Scales
Decoding Physical and Cognitive Impacts of PM Concentrations at Ultra-fine Scales Open
The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues to the underlying…
View article: Unsupervised Blink Detection Using Eye Aspect Ratio Values
Unsupervised Blink Detection Using Eye Aspect Ratio Values Open
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 detect…
View article: Data-Driven EEG Band Discovery with Decision Trees
Data-Driven EEG Band Discovery with Decision Trees Open
Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta.While these bands have been shown to be us…
View article: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales Open
Data, plots, and software to accompany (unpublished) paper: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales. This work uses an ultra-fine, holistic environmental and biometric sensing parad…
View article: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales Open
Data, plots, and software to accompany (unpublished) paper: Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-fine Scales. This work uses an ultra-fine, holistic environmental and biometric sensing parad…
View article: Machine Learning for Light Sensor Calibration
Machine Learning for Light Sensor Calibration Open
Sunlight incident on the Earth’s atmosphere is essential for life, and it is the driving force of a host of photo-chemical and environmental processes, such as the radiative heating of the atmosphere. We report the description and applicat…
View article: Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning
Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning Open
This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous tea…
View article: Autonomous Learning of New Environments With a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning
Autonomous Learning of New Environments With a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning Open
This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous tea…
View article: Using Machine Learning for the Calibration of Airborne Particulate Sensors
Using Machine Learning for the Calibration of Airborne Particulate Sensors Open
Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental…
View article: Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning Open
Shawhin Talebi*, David J. Lary, Lakitha O.H. Wijerante and Tatiana Lary/h3> Author Affiliations Received: July 30, 2019 | Published: August 08, 2019 Corresponding author:Shawhin Talebi, William B. Hanson Center for Space Sciences, Departme…
View article: Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset Open
This page contains the data collected for the paper: Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning (DOI:10.26717/BJSTR.2019.20.003446). The dataset consists of spectral and pupillometric data collected…
View article: Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset Open
This page contains the data collected for the paper: Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning (DOI:10.26717/BJSTR.2019.20.003446). The dataset consists of spectral and pupillometric data collected…