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.
Filtering by
- Creators: Department of English
- Creators: Electrical Engineering Program
- Creators: Loebenberg, Abby
• Same-sex marriage as the win that cannot be repeated.
Infamously known as the central legal battle for the LGBTQ+ community, same-sex marriage finds itself in many political speeches, campaigns, and social commentaries. Interestingly, after being legalized through a Supreme Court decision in the United States, Same-Sex Marriage finds itself framed as the social inevitability that should not be repeated in politics or any legal shift. In other words, “the gays have won this battle, but not the war.”
• There are risks around the “LGBTQ+ lifestyle” and its careful catering to an elite minority and the mediation through bans.
The risks of the LGBTQ+ “lifestyle” date back far, with many connotations being attached to being LGBTQ+ (AIDS epidemics, etc.). In modern journalism, many media outlets portray LGBTQ+ individuals to be a tiny minority (.001% according to some) that demands the whole society to adhere to their requests. This framework portrays the LGBTQ+ community as oppressors and obsessed advocates that can never “seem to get enough” (ex: more than just marriage). The bans are framed as the neutralizing factor to the catering.
• LGBTQ+ children and topics in academic and social spaces are the extreme degree.
When it comes to LGBTQ+ issues and conversations as they revolve around children, media outlets have some of the most passionate opinions about them. Often portrayed as “the line that shouldn’t be crossed,” LGBTQ+ issues, as they find themselves in schools and other spaces, are thus portrayed as bearable to a certain degree, never completely. Claims of indoctrination are also presented prominently even when institutional efforts are to protect LGBTQ+ kids.
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
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.