Bharana Fernando
YOU?
Author Swipe
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: The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation
The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation Open
The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report o…
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: The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation
The Design and Deployment of a Self-Powered, LoRaWAN-Based IoT Environment Sensor Ensemble for Integrated Air Quality Sensing and Simulation Open
The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report o…
View article: Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches
Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches Open
This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). O…
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: Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In-Situ IoT Sensor Network and Remote Sensing Approaches
Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In-Situ IoT Sensor Network and Remote Sensing Approaches Open
This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). O…
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: Unsupervised Blink Detection Using Eye Aspect Ratio Values
Unsupervised Blink Detection Using Eye Aspect Ratio Values Open
This datastore supports an open-source blink detection software which acts as both an implementation of methods proposed in the (unpublished) paper "Unsupervised Blink Detection Using Eye Aspect Ratio Values" and a validation of the result…
View article: Unsupervised Blink Detection Using Eye Aspect Ratio Values
Unsupervised Blink Detection Using Eye Aspect Ratio Values Open
This datastore supports an open-source blink detection software which acts as both an implementation of methods proposed in the (unpublished) paper "Unsupervised Blink Detection Using Eye Aspect Ratio Values" and a validation of the result…
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…