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Full-Text Articles in Engineering
Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith
Real-Time Facial Expression Recognition Using Edge Ai Accelerators, Mark Heath Smith
Theses and Dissertations
Facial expression recognition is a popular and challenging area of research in machine learning applications. Facial expressions are critical to human communication and allow us to convey complex thoughts and emotions beyond spoken language. The complexity of facial expressions creates a difficult problem for computer vision systems, especially edge computing systems. Current Deep Learning (DL) methods rely on large-scale Convolutional Neural Networks (CNN) which require millions of floating point operations (FLOPS) to accomplish similar image classification tasks. However, on edge and IoT devices, large-scale convolutional models can cause problems due to memory and power limitations. The intent of this work …
Development Of A National-Scale Big Data Analytics Pipeline To Study The Potential Impacts Of Flooding On Critical Infrastructures And Communities, Nattapon Donratanapat
Development Of A National-Scale Big Data Analytics Pipeline To Study The Potential Impacts Of Flooding On Critical Infrastructures And Communities, Nattapon Donratanapat
Theses and Dissertations
With the rapid development of the Internet of Things (IoT) and Big data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management and decision making. FAIS allows the user …