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dc.contributor.authorBillings, Rachel Maeen
dc.date.accessioned2021-04-07T08:00:19Zen
dc.date.available2021-04-07T08:00:19Zen
dc.date.issued2021-04-06en
dc.identifier.othervt_gsexam:29587en
dc.identifier.urihttp://hdl.handle.net/10919/102957en
dc.description.abstractSince approximately the dawn of the new millennium, neural networks and other machine learning algorithms have become increasingly capable of adeptly performing difficult, dull, and dangerous work conventionally carried out by humans in times of old. As these algorithms become steadily more commonplace in everyday consumer and industry applications, the consideration of how they may be implemented on constrained hardware systems such as smartphones and Internet-of-Things (IoT) peripheral devices in a time- and power- efficient manner while also understanding the scenarios in which they fail is of increasing importance. This work investigates implementations of convolutional neural networks specifically in the context of image inference tasks. Three areas are analyzed: (1) a time- and power-efficient face recognition framework, (2) the development of a COVID-19-related mask classification system suitable for deployment on low-cost, low-power devices, and (3) an investigation into the implementation of spiking neural networks on mobile hardware and their conversion from traditional neural network architectures.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectmachine learningen
dc.subjectneural networksen
dc.subjectimage processingen
dc.subjectmask recognitionen
dc.subjectCOVID-19en
dc.titleOn Efficient Computer Vision Applications for Neural Networksen
dc.typeThesisen
dc.contributor.departmentElectrical Engineeringen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineElectrical Engineeringen
dc.contributor.committeechairMichaels, Alan J.en
dc.contributor.committeememberYu, Guoqiangen
dc.contributor.committeememberAbbott, Amos L.en
dc.description.abstractgeneralThe subject of machine learning and its associated jargon have become ubiquitous in the past decade as industries seek to develop automated tools and applications and researchers continue to develop new methods for artificial intelligence and improve upon existing ones. Neural networks are a type of machine learning algorithm that can make predictions in complex situations based on input data with human-like (or better) accuracy. Real-time, low-power, and low-cost systems using these algorithms are increasingly used in consumer and industry applications, often improving the efficiency of completing mundane and hazardous tasks traditionally performed by humans. The focus of this work is (1) to explore when and why neural networks may make incorrect decisions in the domain of image-based prediction tasks, (2) the demonstration of a low-power, low-cost machine learning use case using a mask recognition system intended to be suitable for deployment in support of COVID-19-related mask regulations, and (3) the investigation of how neural networks may be implemented on resource-limited technology in an efficient manner using an emerging form of computing.en


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