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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- Creators: School of Criminology and Criminal Justice
- Creators: Electrical Engineering Program
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
Description
Criminal Justice is a complex subject matter, and not everyone agrees on the way a criminal justice system ought to function. But one feature that is common to virtually all forms of proposed justice systems is that a true justice system treats people ethically. The question, then, is how a justice system can achieve this. This investigation analyzed two ethical theories, Kantianism and Utilitarianism, to determine which one would be better suited for guiding a criminal justice system on how to treat the people involved ethically. This investigation focused on applying the two theories to the U.S. Criminal Justice System in particular.
Kantianism is a duty-based moral theory in which actions have an intrinsic moral worth. This means certain actions are morally right and other are morally wrong, regardless of the intended or realized consequences. The theory relies on the categorical imperative to judge the morality of certain actions. It states that an action is moral if its maxim can be willed universal law and if it avoids treating people as merely a means. In contrast, Utilitarianism is a consequentialist theory which focuses on the consequences of an action in judging moral worth. In Utilitarianism, the morally correct action is the one which will maximize utility; that is to say, the morally right action is the one which will produce the greatest amount of happiness and minimize the amount of pain for the greatest number of people.
After applying these two theories to moral dilemmas facing the U.S. Criminal Justice System, including the appropriate collection of DNA evidence, the use of police deception, and the use of criminal punishments such as solitary confinement or the death penalty, it was clear that Kantianism was the ethical theory best suited for guiding the system in treating people ethically. This is because Kantianism’s focus on the intrinsic moral worth of an action rather than its consequences leaves less room for ambiguity than does Utilitarianism.
Kantianism is a duty-based moral theory in which actions have an intrinsic moral worth. This means certain actions are morally right and other are morally wrong, regardless of the intended or realized consequences. The theory relies on the categorical imperative to judge the morality of certain actions. It states that an action is moral if its maxim can be willed universal law and if it avoids treating people as merely a means. In contrast, Utilitarianism is a consequentialist theory which focuses on the consequences of an action in judging moral worth. In Utilitarianism, the morally correct action is the one which will maximize utility; that is to say, the morally right action is the one which will produce the greatest amount of happiness and minimize the amount of pain for the greatest number of people.
After applying these two theories to moral dilemmas facing the U.S. Criminal Justice System, including the appropriate collection of DNA evidence, the use of police deception, and the use of criminal punishments such as solitary confinement or the death penalty, it was clear that Kantianism was the ethical theory best suited for guiding the system in treating people ethically. This is because Kantianism’s focus on the intrinsic moral worth of an action rather than its consequences leaves less room for ambiguity than does Utilitarianism.
ContributorsMorett, Xavier Laakea (Author) / Manninen, Bertha (Thesis director) / Kimberly, Kobojek (Committee member) / School of Criminology and Criminal Justice (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
A lab protocol was created in order to introduce arson evidence analysis to students. The procedures dictate a thorough introduction from evidence handling procedures to analysis of common accelerant mass spectrum. The objectives of the lab protocol included classifying and describing various pieces of arson evidence and common accelerants as well as synthesizing information about accelerant composition to interpret GC-MS data output. This would allow the student to experience first-hand what the subsection of arson analysis has to offer in the field of forensic science which could help the student decide on more specialties to study later on. I was unable to run the lab protocol in a laboratory setting, therefore in the future I want to use the lab protocol and receive feedback in order to improve the protocol so the student is receiving the best possible learning outcomes. The experience of creating a lab protocol in forensic science gave myself a greater understanding of what goes on behind an academic learning procedure and more insight on arson evidence analysis.
ContributorsWhitten, Jamison Mckall (Author) / Kobojek, Kimberly (Thesis director) / Armendariz Guajardo, Jose (Committee member) / School of Criminology and Criminal Justice (Contributor) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
President Donald Trump announced his candidacy in June 2015, and the America immediately knew that he was an unorthodox candidate. Early on in his campaign, Trump isolated groups of people and treated them as enemies, but none so consistently as the news media. What began as criticism of "fake news," turned into calling the news media "the opposition party." However, media professionals agree that when the Trump administration called the news media the "enemy of the American people" \u2014 a line had been crossed. In the last two years Trump has denied simple fact and credible journalism countless times. His avid use of social media allows his messages to reach millions of people in moments - which had the potential to be a positive thing. However, Twitter is often where Trump turns to dispute the media, science, fact or anything else that "opposes" him. If Americans cannot believe the news media, cannot believe science, and cannot believe established fact, what can they believe? Allowing one man, in this case, Trump, to become the beacon of truth is dangerous and destructive to democracy. The news media must do their best to recapture the trust and faith of the American people by producing good, honest journalism. Seasoned journalism professionals say that his attacks on the media are likely a facade, just another way to appeal to his base, but that those attacks have the potential to wreak havoc in American society. Regardless of Trump's intentions, the toxicity between him and news media could have consequences that reach far beyond his presidency.
ContributorsPenningroth, Kendra Rene (Author) / Crane, Steve (Thesis director) / Downie, Leonard (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / School of Criminology and Criminal Justice (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
Historically, Supreme Court interpretations of the Constitution of the United States have been significantly important, impacting the lives of every American. This honors thesis seeks to understand the ways in which the Constitution has been interpreted through the lens of political ideology. Using constitutional theory, I explain how the political ideologies of classical liberalism, conservatism, libertarianism, and progressive liberalism have played a role in the interpretations of the First, Second, and Fourth Amendments. I also examine how these ideological interpretations have changed from 1776 to 2017, dividing the history of the United States into four eras: the Founding Era, the Civil War Era, the New Deal Era, and the Modern Era. First, the First Amendment's clauses on religion are examined, where I focus on the separation between church and state as well as the concepts of "establishment" and "free exercise." The First Amendment transitions from classically liberal, to conservative, to progressively liberal and classically liberal, to progressively liberal and libertarian. Next, we look at the Second Amendment's notions of a "militia" and the "right to keep and bear arms." The Second Amendment's interpretations begin classically liberal, then change to classically liberal and progressively liberal, to progressively liberal, to conservative. Finally, the analysis on the Fourth Amendment's "unreasonable searches and seizures" as well as "warrants" lends evidence to ideological interpretations. The Fourth Amendment, like the other two, starts classically liberal for two eras, then becomes libertarian, and finally ends libertarian and conservative. The implications of each of these conclusions are then discussed, with emphasis on public opinion in society during the era in question, the ways in which the ideologies in each era seem to build upon one another, the ideologies of the justices who wrote the opinions, and the ideology of the court.
ContributorsSmith, Charlene Anne (Author) / Lennon, Tara (Thesis director) / Hudson, Jill (Committee member) / New College of Interdisciplinary Arts and Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor) / School of Social and Behavioral Sciences (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
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