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Hyperglycemia Identification Using Ecg In Deep Learning Era, Renato Cordeiro, Nima Karimian, Younghee Park
Hyperglycemia Identification Using Ecg In Deep Learning Era, Renato Cordeiro, Nima Karimian, Younghee Park
Faculty Research, Scholarly, and Creative Activity
A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the …
A Deep Learning Approach To Downscale Geostationary Satellite Imagery For Decision Support In High Impact Wildfires, Nicholas F. Mccarthy, Ali Tohidi, Yawar Aziz, Matt Dennie, Mario Miguel Valero, Nicole Hu
A Deep Learning Approach To Downscale Geostationary Satellite Imagery For Decision Support In High Impact Wildfires, Nicholas F. Mccarthy, Ali Tohidi, Yawar Aziz, Matt Dennie, Mario Miguel Valero, Nicole Hu
Faculty Research, Scholarly, and Creative Activity
Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) …