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Seattle\u27s New Edition
Get PDFMy project is to create and develop the branding of the NBA\u27s newest franchise: The Seattle Grunge. Grunge is a genre of alternative rock that emerged in the mid-1980s particularly in the city of Seattle. With my team, I am looking for seamless cohesion with other logos in the Association while establishing an emotional connection between the fanbase and its city. The project will consist of a complete creation of the team’s logo/branding, court, 3 jersey designs (home/away/city connect), merchandise, season ticket packaging, and a full marketing report (team logo exposure/connection to the city)
SRH Lab Case Study: IA, Mental Models, and Website Labels
Get PDFThis paper discusses the results of a research study investigating Cal Poly students’ mental models regarding the Sexual and Reproductive Health Lab’s (SRH Lab) website. Mental models are beliefs that the user has about a website and how it functions, which affects how users interact with the website (Chan 2024). By tailoring its website to students’ mental models through appropriate nomenclature, the SRH Lab will be able to better engage and disseminate information to students.
52 students participated in a card sorting study, in which they were asked to intuitively sort cards labeled with SRH Lab website labels into different categories. Results demonstrate that regardless of gender, participants consistently sorted cards into four general categories: information about the SRH Lab, outreach, student involvement, and the SRH Lab’s work. The cards that were most challenging for participants to sort were ambiguous or overlapped multiple categories.
As the first of its kind, this research on the SRH Lab’s website provides insight into students’ current understandings and challenges, which will help guide the lab in making decisions about future changes to implement and/or research to conduct
Bandwagon Behavior in Major League Baseball
Get PDFThis study investigates “bandwagon” behavior among Major League Baseball (MLB) fans by analyzing Google search interest data from 2004 to 2019. Drawing on publicly available information from Google Trends, the analysis explores how fluctuations in search activity align with team performance during both the regular season and postseason. Hierarchical linear models are used to estimate expected levels of fan interest based on team performance and market characteristics. Deviations from these expectations during the regular season are interpreted as evidence of bandwagon or anti-bandwagon behavior. A drop-off in interest following playoff elimination is also examined to capture shifts in fan attention within the postseason window. To address scaling limitations inherent in Google Trends, a rescaling and normalization method is developed, enabling consistent comparisons across teams and time periods. This approach includes both monthly data in the regular season and daily data in the postseason, with particular attention paid to the heightened volatility and spikes in postseason interest. The findings reveal substantial variability in fan engagement, with certain teams demonstrating pronounced regular season and postseason bandwagon effects. This research offers a novel framework for quantifying fan loyalty using digital search data
ECG-Based Emotion Recognition Using Machine Learning Algorithms
No full textEmotion recognition using ECG signals has gained traction due to its potential in applications like healthcare, human-computer interaction, and the growing availability of wearable ECG monitors. This study investigates machine learning approaches for classifying emotions based on ECG signals, utilizing K-nearest neighbors (KNN), support vector machines (SVM), and ensemble bagged trees (EBT) as classifiers. Three feature extraction methods are examined – statistical features in the time-domain, signal powers in different frequency bands from the wavelet transform, and scattering coefficients from the wavelet scattering transform. The study evaluates model performance across three diverse databases: YAAD, DECAF, and AMIGOS. Computer simulation results indicate that the approach based on wavelet scattering transform and ensemble bagged trees achieves the highest classification accuracy of 84.6%, 82.3%, and 87.8%, respectively, for the above three databases. This work provides insights into optimal feature extraction methods for ECG-based emotion recognition, offering a comparative analysis of widely used machine learning models and signal transformation technique
Edwards Lifesciences: eSheath+ Scoring Automation
No full textDue to a non-disclosure agreement with our sponsor, Edwards Lifesciences, the contents of the report have been omitted
Cal Poly Battle Bot
Get PDFProblem Statement: Create a competition ready, 250 lb combat robot for the Battle Bots Proving Grounds competition. The robot needs to be capable of withstanding and dealing significant damage in addition to being able to operate while inverted. It will also need to be visually unique compared to existing designs
Synthetic Aperture Radar Processing for Increased Resolution of Englacial Strata
Get PDFGlaciological research relies on a variety of remote-sensing methods to study the internal structure of glaciers and other bodies of ice. Synthetic Aperture Radar (SAR) serves as one method for imaging large cross sections of englacial strata, reducing the need for labor-intensive, costly, and hazardous ice coring expeditions and operations. Characterizing englacial structures using SAR images allows scientists to learn about the ice dynamics of past and present which provides the basis for discoveries about climate history and glacial dynamics. This project explores the benefits of employing various SAR processing methods to raw phase history data acquired through the British Antarctic Survey’s Institute-Moller Antarctic Funding Initiative (IMAFI)
Analyzing “Sex Ed: The College Edition” - A Comprehensive Workshop Increasing Sexual Literacy on Cal Poly’s Campus
Get PDFSex-Education: The College Edition , is a comprehensive and interactive workshop based on Cal Poly student data collected via survey. The workshop aimed to address sexual health knowledge and behavior gaps in the student population and provide sexual education about anatomy, barrier methods, contraception, STIs, and more. The following paper discusses the creation, execution, and efficacy of the workshop that was conducted with seven student groups
Clusters, Trends, and Choices: Feature Selection in Interactive Statistical Graphics
Get PDFExploratory data analysis (EDA) is a method for uncovering the structure and key characteristics of data, often through the use of statistical graphics. These visual tools can reveal patterns and trends, and their effectiveness can be enhanced through interactivity. By enabling users to filter data, zoom, and toggle visual features, interactive plots can accelerate and enrich the EDA process. This study extends a previous graphical study by incorporating an interactive framework. Using a statistical lineup protocol with two target patterns (a linear trend and a clustering trend) participants interacted with plots by toggling various aesthetic features, including cluster coloring, ellipses around clusters, linear trendlines, and regression error bands. Data was collected via an RShiny application, capturing participant demographics, target choices, toggle usage, and reasoning for selections. To assess which graphical features enhanced the detectability of the linear or cluster targets, a generalized linear mixed model was applied. I also analyzed toggle behavior, exploring commonly used feature combinations, timing of toggling actions, user preferences, and individual workflows. Finally, I examined participants’ explanations and strategies to gain insight into how they interacted with and interpreted the visualizations. This study contributes to the understanding of how users engage with interactive graphical tools and how such tools support data interpretation in EDA
Custom Data Acquisition System for the Cal Poly Racing Baja Team
Get PDFThe Cal Poly Racing Baja team relies on data to analyze and improve upon various vehicle systems in an off-road style vehicle. In order to accomplish this, some data logging or data acquisition (DAQ) is required to collect the data. This project explores the use of a controller area network with flexible data rate (CAN FD) bus along with a custom file format to store data on an SD card that can be made easily acceptable to engineers on the team