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: Department of Management
- 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
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
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
This project seeks to investigate the ways in which the W.P. Carey School of Business, at Arizona State University, can improve student retention and engagement efforts. The analysis is being completed through an audit of the business school's current efforts towards student engagement, an examination of the internal and external environments of business schools across the nation, and a review of scholarly data/research on student retention risk factors and methods for improving engagement. The study highlights what exactly contributes to the success of the W.P. Carey School of Business, concluding with recommendations for how its engagement and retention efforts can be further improved to continue to serve students at a nationally ranked level.
ContributorsStinger, Rio W. (Author) / Hillman, Amy (Thesis director) / Mader, Michael (Committee member) / Division of Teacher Preparation (Contributor) / Department of Management (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of our research was to develop recommendations and/or strategies for Company A's data center group in the context of the server CPU chip industry. We used data collected from the International Data Corporation (IDC) that was provided by our team coaches, and data that is accessible on the internet. As the server CPU industry expands and transitions to cloud computing, Company A's Data Center Group will need to expand their server CPU chip product mix to meet new demands of the cloud industry and to maintain high market share. Company A boasts leading performance with their x86 server chips and 95% market segment share. The cloud industry is dominated by seven companies Company A calls "The Super 7." These seven companies include: Amazon, Google, Microsoft, Facebook, Alibaba, Tencent, and Baidu. In the long run, the growing market share of the Super 7 could give them substantial buying power over Company A, which could lead to discounts and margin compression for Company A's main growth engine. Additionally, in the long-run, the substantial growth of the Super 7 could fuel the development of their own design teams and work towards making their own server chips internally, which would be detrimental to Company A's data center revenue. We first researched the server industry and key terminology relevant to our project. We narrowed our scope by focusing most on the cloud computing aspect of the server industry. We then researched what Company A has already been doing in the context of cloud computing and what they are currently doing to address the problem. Next, using our market analysis, we identified key areas we think Company A's data center group should focus on. Using the information available to us, we developed our strategies and recommendations that we think will help Company A's Data Center Group position themselves well in an extremely fast growing cloud computing industry.
ContributorsJurgenson, Alex (Co-author) / Nguyen, Duy (Co-author) / Kolder, Sean (Co-author) / Wang, Chenxi (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Management (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Women dominate in terms of purchasing power and spending. They hold 60 percent of all US personal income, and those aged 50 years or older have a combined net worth of approximately $19 trillion. Of this group, women between 50 and 70 years old, in particular, are the biggest spenders (Barmann, 2014). More important than their spending power, however, is how satisfied (or dissatisfied) they are with their current purchases. Though women make 85 percent of all consumer purchases, 91 percent of women say, "...advertisers don't understand them," (Barmann, 2014). This makes sense, considering that 50 percent of the products marketed to men are actually purchased by women (Barmann, 2014). Successfully targeting women, especially Baby Boomers (women between 52 and 70 years old), would be a lucrative endeavor, and to better understand the unmet needs of that demographic, exploratory research was needed. In-depth interviews of Baby Boomer women reveals a problem that \u2014 even on a macro level \u2014 has gone unresolved, and has perhaps worsened, throughout written history: the Generation Gap (Bengtson, 1970). To illustrate the depth of the problem, there exist starkly different impressions of younger generations, namely Millennials (born between 1980 and 1995). According to The New Generation Gap by Neil Howe and William Strauss (1992), Baby Boomers view Millennials as unintelligent, entitled "pleasure beasts." In Millennials Rising, also by Howe and Strauss (2000), Millennials are characterized as a generation that is, "...beginning to manifest a wide array of positive social habits that older Americans no longer associate with youth, including a new focus on teamwork, achievement, modesty, and good conduct." These contradictory opinions further support the substantial misunderstanding between generations that surfaced during in-depth interviews. Using the results of in-depth interviews and follow-up questions for idea validation, this thesis presents a potential method for "closing the gap." The goal of this business offering is not to homogenize older and younger generations of women; the goal is to cultivate empathy and connection \u2014 Intergenerational Cohesion \u2014 between them.
ContributorsSeefus, Cole Hawk Gillette (Author) / Gray, Nancy (Thesis director) / Giard, Jacques (Committee member) / Department of Management (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05