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Description
Tracking moving objects with code isn’t a new concept. There are many computer vision libraries that have functions that can track changes in position very accurately. This allows for computers to be able to provide data about situations that aren’t able to be observed in a reasonable amount of time. For example, tracking hundreds of moving cars over a day would take a lot of time if done by hand, but with code, one can get that data quicker.
This thesis aims to provide a clear, simple, and effective application to track moving objects in a given video, trace their paths, and color-code these paths to see which ones are the most congested. This is to provide an efficient and deployable algorithm to track moving objects. This research was in collaboration with Moog Inc, an aerospace and defense company, to develop an algorithm that would analyze a video of a parking lot and determine the empty parking spaces and the common traffic paths that cars take while in a parking lot. Moog Inc. provides an Optimized Development Environment (ODE) to develop the application. Since the hardware is efficient on power and has a small form factor, the applications that are run on it are very easily deployable and portable, which makes it useful for any environment.
The process of tracking cars in a video is somewhat straightforward as well. It consists of filtering the video, drawing rectangles around each region (car), tracing their paths (movements) and applying a heatmap to that path. Since it isn’t too computationally intensive, it can work well on the ODE. Since the ODE is small and has a portable form factor, this algorithm can be deployed fairly easily.
The heatmap generation was effective in showing the densities of certain paths that cars traveled through. There are also various colormaps that can be used, to provide a clearer idea of the paths. There were attempts to optimize this algorithm by processing every other frame instead, but ultimately the tradeoff between efficiency and accuracy was deemed to be unfavorable. There were still some limitations that this approach had, as initially the algorithm would draw paths between areas that weren’t traversed by cars. While this was fixed in the final result, there are still some slight inaccuracies within the roads. There are also ethical concerns with the use of this software, as Moog Inc. does a lot of work in defense and this software could be used in wartime scenarios. However, this software can be applied to various other scenarios like tracking wildlife in an area to study their habits, or tracking particles to see their density in a given environment. Since the algorithm is ran on a low-powered environment, it can be deployed and tested in many different scenarios without being costly.
ContributorsChandra, Rohan (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Rieckmann, Tyron (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
This study presents a comparative analysis of machine learning models on their ability to determine match outcomes in the English Premier League (EPL), focusing on optimizing prediction accuracy. The research leverages a variety of models, including logistic regression, decision trees, random forests, gradient boosting machines, support vector machines, k-nearest neighbors, and extreme gradient boosting, to predict the outcomes of soccer matches in the EPL. Utilizing a comprehensive dataset from Kaggle, the study uses the Sport Result Prediction CRISP-DM framework for data preparation and model evaluation, comparing the accuracy, precision, recall, F1-score, ROC-AUC score, and confusion matrices of each model used in the study. The findings reveal that ensemble methods, notably Random Forest and Extreme Gradient Boosting, outperform other models in accuracy, highlighting their potential in sports analytics. This research contributes to the field of sports analytics by demonstrating the effectiveness of machine learning in sports outcome prediction, while also identifying the challenges and complexities inherent in predicting the outcomes of EPL matches. This research not only highlights the significance of ensemble learning techniques in handling sports data complexities but also opens avenues for future exploration into advanced machine learning and deep learning approaches for enhancing predictive accuracy in sports analytics.
ContributorsTashildar, Ninad (Author) / Osburn, Steven (Thesis director) / Simari, Gerardo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
3D printing has been taking the world by storm for a couple of decades because of the benefits to increase efficiency in testing and part manufacturing. For a large university, serving a large population and promoting projects related to maker spaces, it is necessary to have a 3D printing lab which can effectively manage student and faculty 3D print requests. At the Arizona State University (ASU) print lab there is an efficiency issue which forces all employees of the lab to manage the printing process basically manually. Employees review the list of requests, download individual print files, and then try to fit them onto a print bed of a 3D printer. Downloading individual files causes the employees' computers to become burdened with several files that they do not need and slows down the 3D print job processing pipeline. Downloading and sorting through files is time consuming and employees spend most of their time in the lab trying to figure out what can fit on a print bed instead of fixing printing issues. If the employees had a way to automate some of this process it would allow for employees to handle more difficult work in the lab in a timely manner. This thesis creates a way for employees at the ASU 3D print lab to easily fill a print bed of a 3D printer without looking at each print part file individually improving the efficiency issue in the lab. This thesis uses a greedy algorithm by the press of a button to sort through the submitted print jobs to fill the print bed of each printer effectively and efficiently. The use of an algorithm reduces the time for employees to process print jobs which allows for employees to tackle harder problems.
ContributorsVilla, Kyle (Author) / Balasooriya, Janaka (Thesis director) / Chavez Echeagaray, Maria Elena (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor)
Created2024-05
Description
Japanese puzzle boxes are exquisite wooden creations that combine artistry, craftsmanship, and the thrill of solving intricate puzzles. Originating in Hakone, Japan, over 100 years ago, these boxes are called "himitsu-bako," which translates to "Personal Secret Box" due to their meticulously designed hidden compartments and complex locking mechanisms. Unlocking these boxes involves a precise sequence of movements, twists, and rotations, turning the process into an interactive and engaging challenge. The goal of this project is to create a puzzle box from scratch while incorporating some of the mechanics from the traditional Japanese puzzle boxes.
ContributorsChallaram, Greeshma (Author) / Beiner, Susan (Thesis director) / Ang-Wanek, Nicole (Committee member) / Barrett, The Honors College (Contributor) / School of Art (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Many coordinate systems are involved in the design and construction of launch vehicles, including Earth-Centered, Earth-Fixed (ECEF), Earth-Centered Inertial (ECI), North-East-Down (NED), and Body Axis. Comprehension of these coordinate systems is vital to the success of these launch vehicles. To improve understanding of these complex coordinate systems, this project developed a virtual reality (VR) model that provides an interactive learning environment. Using the Unity game engine and the Extended Reality Interaction Toolkit, the VR model allows the exploration of these coordinate systems and how they relate to each other in a three-dimensional space. The model was evaluated through a pre-test and post-test study which assessed the effectiveness of the VR model in improving comprehension and familiarity.
ContributorsLee, Brian (Author) / Chavez-Echeagaray, Maria Elena (Thesis director) / Price, Taylor (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
In this thesis, we provide a basis for discovering the local interactions within shortcut bridging - a collective behavior that connects two resources with a distance workforce trade-off. We utilize an evolutionary search framework, EvoSOPS, to discover stochastic algorithms for shortcut bridging in self-organizing particle systems. The method described within this thesis is not fully successful in discovering qualitatively good bridges but provides an insight into what will.
ContributorsGroholski, Matthew (Author) / Daymude, Joshua (Thesis director) / Forrest, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
DescriptionCreating a Scheme Dialect using Modern C++.
ContributorsAl-Qassas, Feras (Author) / Osburn, Steve (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Machine learning continues to grow in applications and its influence is felt across the world. This paper builds off the foundations of machine learning used for sports analysis and its specific implementations in tennis by attempting to predict the winner of ATP men’s singles tennis matches. Tennis provides a unique challenge due to the individual nature of singles and the varying career lengths, experiences, and backgrounds of players from around the globe. Related work has explored prediction with features such as rank differentials, physical characteristics, and past performance. This work expands on the studies by including raw player statistics and relevant environment features. State of the art models such as LightGBM and XGBoost, as well as a standard logistic regression are trained and evaluated against a dataset containing matches from 1991 to 2023. All models surpassed the baseline and each has their own strengths and weaknesses. Future work may involve expanding the feature space to include more robust features such as player profiles and ELO ratings, as well as utilizing deep neural networks to improve understanding of past player performance and better comprehend the context of a given match.
ContributorsBandemer, Nathaniel (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
The purpose of the thesis project is to address the rising issue of fake real estate listings and scams prevalent in listing platforms by developing an advanced program that employs various data verification methods to identify potential fraudulent listings. With the rise of online real estate transactions, the need for establishing trust and credibility between buyer and seller has never been more important. This research will create a system that will protect potential buyers from falling victim to fake listings and shield sellers from purchasing on scam-related platforms.
Through analysis, the program will identify any inconsistency and warning signs that may indicate a fake listing. This thesis project aims to enhance the overall integrity and dependability of real estate listing platforms, fostering a secure environment for buyers and sellers to participate in online property transactions.
ContributorsAguilar, Javier (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05