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Gaining Scientific And Engineering Insight Into Ground Motion Simulation Through Machine Learning And Approximate Modeling Approaches, Naeem Khoshnevis
Gaining Scientific And Engineering Insight Into Ground Motion Simulation Through Machine Learning And Approximate Modeling Approaches, Naeem Khoshnevis
Electronic Theses and Dissertations
This dissertation presents a series of methods for gaining scientific and engineering insight into earthquake ground motion simulation in three areas: synthetic validation, attenuation modeling, and nonlinear effects estimation. First, I present guidelines to reduce the number of metrics used to evaluate the goodness-of-fit (GOF) between ground motion synthetics and recorded data in an application independent framework. Validation of ground motion simulations is mostly done using metrics that are user- or application-biased. Comparisons between synthetics from regional scale ground motion simulations and recorded data from past earthquakes provide opportunities to approach the problems using data-driven methods. I used a combination …
Automated Artifact Removal And Detection Of Mild Cognitive Impairment From Single Channel Electroencephalography Signals For Real-Time Implementations On Wearables, Saleha Khatun
Electronic Theses and Dissertations
Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has …