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Full-Text Articles in Engineering
Adaptive Mobile Eeg Noise Cancellation Using 2d Convolutional Autoencoders For Bci Authentication, Tyree Lewis
Adaptive Mobile Eeg Noise Cancellation Using 2d Convolutional Autoencoders For Bci Authentication, Tyree Lewis
USF Tampa Graduate Theses and Dissertations
Electroencephalography (EEG) signals can be used for many purposes and has the potential to be adapted to various systems. When EEG is recorded from users, these studies are performed primarily in an indoor environment, while the user is stationary. This is due to the levels of noise that are experienced when recording EEG data, to minimize errors in the data. This thesis aims to adapt tasks that are performed indoors to an external environment by removing both noise and artefacts in EEG, using a 2D Convolutional Autoencoder (CAE). The data is recorded from subjects is passed into the 2D CAE …
Data-Driven Studies On Social Networks: Privacy And Simulation, Yasanka Sameera Horawalavithana
Data-Driven Studies On Social Networks: Privacy And Simulation, Yasanka Sameera Horawalavithana
USF Tampa Graduate Theses and Dissertations
Social media datasets are fundamental to understanding a variety of phenomena, such as epidemics, adoption of behavior, crowd management, and political uprisings. At the same time, many such datasets capturing computer-mediated social interactions are recorded nowadays by individual researchers or by organizations. However, while the need for real social graphs and the supply of such datasets are well established, the flow of data from data owners to researchers is significantly hampered by privacy risks: even when humans’ identities are removed, or data is anonymized to some extent, studies have proven repeatedly that re-identifying anonymized user identities (i.e., de-anonymization) is doable …
Pain Recognition Performance On A Single Board Computer, Iyonna L. Tynes
Pain Recognition Performance On A Single Board Computer, Iyonna L. Tynes
USF Tampa Graduate Theses and Dissertations
Emotion recognition is a quickly growing field of study due to the increased interest in building systems which can classify and respond to emotions. Recent medical crises, such as the opioid overdose epidemic in the United States and the global COVID-19 pandemic has emphasized the importance of emotion recognition applications is areas like Telehealth services. Considering this, this thesis focuses specifically on pain recognition. The problem of pain recognition is approached from both a hardware and software perspective, as we propose a real-time pain recognition system, from facial images, that is deployed on an NVIDIA Jetson Nano single-board computer. We …