Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Energy-Efficient Electoral System for Underwater Sea Turtle Image Recognition

Description
This paper presents work that was done to develop an energy-efficient electoral and frame count system for underwater sea turtle image and video recognition using convolutional neural networks, deep learning framework, and the Python programming language. An underwater sea turtle image recognition program is essential to protect turtles from the…

This paper presents work that was done to develop an energy-efficient electoral and frame count system for underwater sea turtle image and video recognition using convolutional neural networks, deep learning framework, and the Python programming language. An underwater sea turtle image recognition program is essential to protect turtles from the threat of bycatch - sea turtles are accidentally caught when fishermen aim for a different type of underwater species. This underwater image recognition system is used to detect the presence of sea turtles, then different kinds of acoustic and light stimuli are used to warn the turtles of approaching danger to reduce bycatch. This image detection system will be placed on a fishing boat to run on a machine at all times (24 hours and 7 days a week). A live video capture from a low-power underwater camera that is attached to the boat will be sent to the image detection system on the machine to analyze the presence of sea turtles in each frame of the video. To lower the computational time and energy of the machine, an energy-efficient electoral and frame count system is implemented on this image detection system.
ContributorsDeng, Enhong (Author) / Ozev, Sule (Thesis director) / Blain Christen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05

Using Microwaves to Detect Various Substances in a Sample of Water

Description
This research explores the potential use of microwave energy to detect various substances in water, with a focus on water quality assessment and pathogen detection applications. There are many non-thermal effects of microwaves on microorganisms and their resonant frequencies could be used to identify and possibly destroy harmful pathogens, such…

This research explores the potential use of microwave energy to detect various substances in water, with a focus on water quality assessment and pathogen detection applications. There are many non-thermal effects of microwaves on microorganisms and their resonant frequencies could be used to identify and possibly destroy harmful pathogens, such as bacteria and viruses, without heating the water. A wide range of materials, including living organisms like Daphnia and Moina, plants, sand, plastic, and salt, were subjected to microwave measurements to assess their influence on the transmission (S21) measurements. The measurements of the living organisms did not display distinctive resonant frequencies and variations in water volume may be the source of the small measurement differences. Conversely, sand and plastic pellets affected the measurements differently, with their arrangement within the test tube emerging as a significant factor. This study also explores the impact of salinity on measurements, revealing a clear pattern that can be modeled as a series RLC resonator. Although unique resonant frequencies for the tested organisms were not identified, the presented system demonstrates the potential for detecting contaminants based on variations in measurements. Future research may extend this work to include a broader array of organisms and enhance measurement precision.
ContributorsChild, Carson (Author) / Aberle, James (Thesis director) / Blain Christen, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-12