Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- Creators: Electrical Engineering Program
- Creators: School of Geographical Sciences and Urban Planning
- Creators: O'Flaherty, Katherine
Description
Breast microcalcifications are a potential indicator of cancerous tumors. Current visualization methods are either uncomfortable or impractical. Impedance measurement studies have been performed, but not in a clinical setting due to a low sensitivity and specificity. We are hoping to overcome this challenge with the development of a highly accurate impedance probe on a biopsy needle. With this technique, microcalcifications and the surrounding tissue could be differentiated in an efficient and comfortable manner than current techniques for biopsy procedures. We have developed and tested a functioning prototype for a biopsy needle using bioimpedance sensors to detect microcalcifications in the human body. In the final prototype a waveform generator sends a sin wave at a relatively low frequency(<1KHz) into the pre-amplifier, which both stabilizes and amplifies the signal. A modified howland bridge is then used to achieve a steady AC current through the electrodes. The voltage difference across the electrodes is then used to calculate the impedance being experienced between the electrodes. In our testing, the microcalcifications we are looking for have a noticeably higher impedance than the surrounding breast tissue, this spike in impedance is used to signal the presence of the calcifications, which are then sampled for examination by radiology.
ContributorsWen, Robert Bobby (Co-author) / Grula, Adam (Co-author) / Vergara, Marvin (Co-author) / Ramkumar, Shreya (Co-author) / Kozicki, Michael (Thesis director) / Ranjani, Kumaran (Committee member) / School of Molecular Sciences (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The El Niño Southern Oscillation (ENSO) consists of a linkage between changes in sea-surface temperatures and atmospheric pressure across the Tropical Pacific. ENSO encompasses three phases: neutral events, warm/El Niño events in which sea-surface temperatures are warmer-than-normal and the pressure gradient decreases across the Equatorial Pacific, and cold/La Niña events in which Tropical Pacific sea-surface temperatures are cooler-than-normal and the pressure gradient increases. Previous studies have determined a connection between variations in ENSO phase and weather patterns across the globe, focusing particularly on surface temperature and precipitation patterns in the United States. However, little research exists that attempts to link changes in ENSO phase with severe weather in Arizona. Therefore, in this study, I analyzed how variations in ENSO phase affect the frequency, intensity, and spatial distribution of four types of severe weather from 1959 to 2016 in Arizona, including a) tornado events, b) severe thunderstorm wind events, c) hail events, and d) heavy rain and flash flood events. I collected data on the Oceanic Niño Index (ONI), a measure of ENSO, as well as storm reports for each severe weather phenomenon dating back to 1959. Then, I analyzed the frequency of each Arizona severe weather event type within each of the twelve annual months and over the entire study period. I also analyzed mean intensity values (Fujita/Enhanced Fujita Scale rating, path width, and path length for tornadoes; hail diameter in millimeters for hail; and wind gust speed for severe thunderstorm wind events) for each severe weather phenomenon, excluding the heavy rain and flash flood events. Finally, I used the Mean Center and Directional Distribution tools in ArcGIS to determine variations in the spatial distribution and mean centers between each ENSO phase for each severe weather event type. I found that ENSO phase, particularly La Niña, does impact the frequency and intensity of tornadoes, hail, thunderstorm wind, and heavy rain/flash flood events in Arizona. However, it appears that ENSO does not affect the spatial distribution of these Arizona severe weather phenomena. These findings attempt to fill in the gap in the literature and could help meteorologists better forecast changes in Arizona severe weather, in turn allowing Arizonans to better prepare for and mitigate the effects of severe weather across the state.
ContributorsGreenwood, Trey Austin (Author) / Cerveny, Randall (Thesis director) / Balling, Robert (Committee member) / School of Geographical Sciences and Urban Planning (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
DescriptionThis project is designed to generate enthusiasm for science among refugee students in hopes of inspiring them to continue learning science as well as to help them with their current understanding of their school science subject matter.
ContributorsSipes, Shannon Paige (Author) / O'Flaherty, Katherine (Thesis director) / Gregg, George (Committee member) / School of Molecular Sciences (Contributor) / Division of Teacher Preparation (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Buck converters are a class of switched-mode power converters often used to step down DC input voltages to a lower DC output voltage. These converters naturally produce a current and voltage ripple at their output due to their switching action. Traditional methods of reducing this ripple have involved adding large discrete inductors and capacitors to filter the ripple, but large discrete components cannot be integrated onto chips. As an alternative to using passive filtering components, this project investigates the use of active ripple cancellation to reduce the peak output ripple. Hysteretic controlled buck converters were chosen for their simplicity of design and fast transient response. The proposed cancellation circuits sense the output ripple of the buck converter and inject an equal ripple exactly out of phase with the sensed ripple. Both current-mode and voltage-mode feedback loops are simulated, and the effectiveness of each cancellation circuit is examined. Results show that integrated active ripple cancellation circuits offer a promising substitute for large discrete filters.
ContributorsWang, Ziyan (Author) / Bakkaloglu, Bertan (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This creative project thesis involves electronic music composition and production, and it uses some elements of algorithmic music composition (through recurrent neural networks). Algorithmic composition techniques are used here as a tool in composing the pieces, but are not the main focus. Thematically, this project explores the analogy between artificial neural networks and neural activity in the brain. This project consists of three short pieces, each exploring these concept in different ways.
ContributorsKarpur, Ajay (Author) / Suzuki, Kotoka (Thesis director) / Ingalls, Todd (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Food’s implication on culture and agriculture challenges agriculture’s identity in the age of the city. As architect and author Carolyn Steel explained, “we live in a world shaped by food, and if we realize that, we can use food as a powerful tool — a conceptual tool, design tool, to shape the world differently. It triggers a new way of thinking about the problem, recognizing that food is not a commodity; it is life, it is culture, it’s us. It’s how we evolved.” If the passage of food culture is dependent upon the capacity for learning and transmitting knowledge to succeeding generations, the learning environments should reflect this tenability in its systematic and architectural approach.
Through an investigation of agriculture and cuisine and its consequential influence on culture, education, and design, the following project intends to reconceptualize the learning environment in order facilitate place-based practices. Challenging our cognitive dissonant relationship with food, the design proposal establishes a food identity through an imposition of urban agriculture and culinary design onto the school environment. Working in conjunction with the New American University’s mission, the design serves as a didactic medium between food, education, and architecture in designing the way we eat.
Through an investigation of agriculture and cuisine and its consequential influence on culture, education, and design, the following project intends to reconceptualize the learning environment in order facilitate place-based practices. Challenging our cognitive dissonant relationship with food, the design proposal establishes a food identity through an imposition of urban agriculture and culinary design onto the school environment. Working in conjunction with the New American University’s mission, the design serves as a didactic medium between food, education, and architecture in designing the way we eat.
ContributorsBone, Nicole (Author) / Rocchi, Elena (Thesis director) / Hejduk, Renata (Committee member) / Robert, Moric (Committee member) / The Design School (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The Phoenix CubeSat is a 3U Earth imaging CubeSat which will take infrared (IR) photos of cities in the United Stated to study the Urban Heat Island Effect, (UHI) from low earth orbit (LEO). It has many different components that need to be powered during the life of its mission. The only power source during the mission will be its solar panels. It is difficult to calculate power generation from solar panels by hand because of the different orientations the satellite will be positioned in during orbit; therefore, simulation will be used to produce power generation data. Knowing how much power is generated is integral to balancing the power budget, confirming whether there is enough power for all the components, and knowing whether there will be enough power in the batteries during eclipse. This data will be used to create an optimal design for the Phoenix CubeSat to accomplish its mission.
ContributorsBarakat, Raymond John (Author) / White, Daniel (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This thesis project examines the likely factors that cause students to drop out of Barrett, the Honors College. Honors literature regarding retention and attrition suggests four areas encompassing individual student attributes and honors program characteristics which may impact a student's decision to stay or leave an Honors College. The primary question in focus is, "Why do students leave the Honors College?" followed by the tertiary questions of, "what can be done to mitigate this occurrence?" and, "how does this affect the quality of an honors education?" Assessing attrition can be broken down into biographical, cognitive-behavioral, socio-environmental, and institutional-instrumental components. Students who graduated with honors and those who did not graduate with honors were assessed on these four components through survey methods and qualitative interviews to investigate specific reasons why students leave the honors program. The results indicated a wide array of reasons impacting student attrition, the most significant being negative perceptions towards (1) honors courses and contracts, (2) difficulty completing a thesis project, and (3) finding little to no value in "graduating with honors." Each of these reasons reflect the institutional-instrumental component of student attrition, making it the most salient group of reasons why students leave the Honors College. The socio-environmental component also influences student attrition through peer influence and academic advisor support, though this was found to be within the context of institutional-instrumental means. This project offers solutions to ameliorate each of the four components of attrition by offering standardized honors contracts and more mandatory honors classes, mandatory thesis preparatory courses instead of workshops, and emphasizing the benefit Barrett gives to students as a whole. These solutions aim at increasing graduation rates for future honors students at Barrett as well as improving the overall quality of an honors education.
ContributorsSanchez, Gilbert Xavier (Author) / Parker, John (Thesis director) / O'Flaherty, Katherine (Committee member) / School of Criminology and Criminal Justice (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05