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: Sandra Day O'Connor College of Law
Description
The goal of our project was to determine how to create the most marketable hockey team. To do this, consumer needs, team psychology, and financing were all researched and evaluated. With this information, a business plan was designed around the next NHL expansion team. Two surveys, one for marketing distributed to the general public, and one for team psychology distributed to current and former hockey players were created and sent out, while data for the financing aspect was collected by comparing data from other NHL teams and franchises from different sports. In terms of financials, this comes in lower than average ticket prices, a nice and expensive stadium, the ideal city to generate capital, and sufficient money spent on advertising. Our ticket prices of $140 is based on having a low enough price to generate lots of demand while high enough to make a profit. The $600 million stadium (which will be fully funded) will surely draw a significant crowd. Choosing Seattle as a city is the most ideal to meet these goals and lastly, in meeting with an NHL GM, we determined $4 million in yearly advertising costs as sufficient in creating the most marketable team. Throughout this whole process, we remained data focus. We focused on data from a customized marketing survey, organizational structures, salary cap, and attendance. What our marketing survey results showed us is that our potential fans wanted three characteristics in a hockey team: speed, intensity, and scoring. In looking at organizational structures teams that exemplified these characteristics had a heavy emphasis on development and scouting. So we built our organizational tree around those two ideals. We hired GM Mike Futa, a current director of player personnel for the L.A. Kings, and Head Coach Adam Oates, a current skills development coach for top players to bring those ideals to fruition. In constructing our team we replicated the rules set forth for the Vegas Knights' expansion draft and hypothesized a likely protected list based off of last years lists. As a result we were able to construct a team that statistically out performed the Vegas Knights draft numbers by double, in goals, assists, and points, while also beating them in PIM. Based off of these numbers and an analysis of how goals translate into game attendance we are confident that we have constructed a team that has the highest potential for marketability. For the team psychology area, when creating a roster and scouting players, some of our main findings were that it is important to pursue players who get along well with their teammates and coaching staff, are aggressive, are leaders on the team, and are vocal players who communicate effectively. We also recommended avoiding players who significantly portrayed any "pet-peeve" traits, with the most emphasis placed on "disrespectful toward teammates," and the least emphasis placed on "over-aggression." By following all of these recommendations, we believe the most marketable hockey team possible can be created.
ContributorsQuinn, Colin Christopher (Co-author) / Spigel, Carlos (Co-author) / Meyer, Matt (Co-author) / Eaton, John (Thesis director) / McIntosh, Daniel (Committee member) / Department of Marketing (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
While developing and maintaining a connection between a brand and a customer has always been in the forefront of marketers' agendas, it has become an even more pressing goal as digital trends in marketing surface. Although the idea of using rewards to foster consumer-brand connection has been around for decades, marketers are still struggling to optimize the benefits. How can marketers use rewards to better connect with their customers? Are there certain types of rewards that are more effective than others? Are certain rewards more effective when being implemented under brands of a certain personality type? In a society that values connection and relationship, marketers cannot lose their ability to appreciate customers under digital constraints and to marketplace competition. Through a field study and scenario-based experiment, we explore how and why low conditional vs. high conditional rewards influence consumer-brand connection and the role brand personality plays.
ContributorsBauer, Madelaine Anne (Co-author) / Bryant, Kelly (Co-author) / Lisjak, Monika (Thesis director) / Samper, Adriana (Committee member) / Department of Finance (Contributor) / W.P. Carey School of Business (Contributor) / Department of Marketing (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
There is a disconnect between the way people are taught to find success and happiness, and the results observed. Society teaches us that success will lead to happiness. Instead, it is argued that success is engrained in happiness. Case studies of four, established, successful people: Jack Ma, Elon Musk, Ricardo Semler, and William Gore, have been conducted in order to observe an apparent pattern. This data, coupled with the data from Michael Boehringer's story, is used to formulate a solution to the proposed problem. Each case study is designed to observe characteristics of the individuals that allow them to be successful and exhibit traits of happiness. Happiness will be analyzed in terms of passion and desire to perform consistently. Someone who does what they love, paired with the ability to perform on a regular basis, is considered to be a happy person. The data indicates that there is an observable pattern within the results. From this pattern, certain traits have been highlighted and used to formulate guidelines that will aid someone falling short of success and happiness in their lives. The results indicate that there are simple questions that can guide people to a happier life. Three basic questions are defined: is it something you love, can you see yourself doing this every day and does it add value? If someone can answer yes to all three requirements, the person will be able to find happiness, with success following. These guidelines can be taken and applied to those struggling with unhappiness and failure. By creating such a formula, the youth can be taught a new way of thinking that will help to eliminate these issues, that many people are facing.
ContributorsBoehringer, Michael Alexander (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Management (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Sandra Day O'Connor College of Law (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
Description
In competitive Taekwondo, Electronic Body Protectors (EBPs) are used to register hits made by players during sparring. EBPs are comprised of three main components: chest guard, foot sock, and headgear. This equipment interacts with each other through the use of magnets, electric sensors, transmitters, and a receiver. The receiver is connected to a computer programmed with software to process signals from the transmitter and determine whether or not a competitor scored a point. The current design of EBPs, however, have numerous shortcomings, including sensing false positives, failing to register hits, costing too much, and relying on human judgment. This thesis will thoroughly delineate the operation of the current EBPs used and discuss research performed in order to eliminate these weaknesses.
ContributorsSpell, Valerie Anne (Author) / Kozicki, Michael (Thesis director) / Kitchen, Jennifer (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05