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: Economics Program in CLAS
- Creators: Electrical Engineering Program
- Creators: O'Flaherty, Katherine
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.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.