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 Humanities, Arts, and Cultural Studies
- Creators: O'Flaherty, Katherine
The primary perspective from which people are depicted in media today is shaped by the male gaze. The male gaze is comprised of patriarchal ideals and relies on the understanding that the spectator or viewer is a standard human being, which heteronormativity tells us is a man. From this perspective, the scope of visual representations of men and women in media has been molded after the hierarchized gender displays within which masculinity has primacy over femininity. By presenting a limited spectrum of behavior acceptable for men and women, the media hegemonically manipulates the social constructs of gender and gendered behavior across all levels of society.
This honors thesis applies semiotic and feminist methodologies to engage visual forms of media through art, film, and social media to challenge the social constructs of gender perpetuated and reinforced by dated stereotypes of gender and gendered behavior. First, the theoretical foundation will provide a framework for semiotic and feminist analysis of visual representations of gender in media. Then, I will present data representing the real-world impact that this social construction of gender has on adolescents in America using The State of Gender Equality for U.S. Adolescents, published by Plan International Inc. I will then bring together the explicated methodologies and evidential data alongside my own experiences as a female consumer of visual media to reveal alternative practices of looking that do not revolve around patriarchal norms, looking for a female gaze. In doing so, I hope to present recourse in the face of persistent use of sexist imagery across all levels of our culture and every medium of visual self-expression by providing tools that can be used to interrogate gendered perceptions and inform self-examination in pursuit of a feminist practice of looking.
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.