When creating computer vision applications, it is important to have a clear image of what is represented such that further processing has the best representation of the underlying data. A common factor that impacts image quality is blur, caused either by an intrinsic property of the camera lens or by introducing motion while the camera’s shutter is capturing an image. Possible solutions for reducing the impact of blur include cameras with faster shutter speeds or higher resolutions; however, both of these solutions require utilizing more expensive equipment, which is infeasible for instances where images are already captured. This thesis discusses an iterative solution for deblurring an image using an alternating minimization technique through regularization and PSF reconstruction. The alternating minimizer is then used to deblur a sample image of a pumpkin field to demonstrate its capabilities.

We propose a new strategy for blackjack, BB-Player, which leverages Hidden Markov Models (HMMs) in online planning to sample a normalized predicted deck distribution for a partially-informed distance heuristic. Viterbi learning is applied to the most-likely sampled future sequence in each game state to generate transition and emission matrices for this upcoming sequence. These are then iteratively updated with each observed game on a given deck. Ultimately, this process informs a heuristic to estimate the true symbolic distance left, which allows BB-Player to determine the action with the highest likelihood of winning (by opponent bust or blackjack) and not going bust. We benchmark this strategy against six common card counting strategies from three separate levels of difficulty and a randomized action strategy. On average, BB-Player is observed to beat card-counting strategies in win optimality, attaining a 30.00% expected win percentage, though it falls short of beating state-of-the-art methods.

Many studies indicate a positive relationship between fundamentalism and sexual and racial prejudice. Many of these studies use the Religious Fundamentalism Scale (RFS), the Attitudes Towards Homosexuals Scale (ATHS) and the Manitoba Scale. However, there appears to be overlap between RFS and both ATHS and the Manitoba Scale, unaddressed by the literature. This study looked at possible overlaps between RFS and ATHS and between RFS and the Manitoba Scale that could inflate the correlation statistic of fundamentalism and sexual and racial prejudice. The Intratextual Fundamentalism Scale (IFS), a study without authoritarian or apparent prejudice-overlapping items, was also tested for overlap. Results showed two-factor structures—namely fundamentalism and prejudice—with only two items loading to the opposite factor. However, there were many near-zero item loadings. The discussion suggests ways to change these items to increase factor loadings and to change overall measures construct validity. The correlations between fundamentalism and sexual prejudice were not significant before modifying the measures and were small and negative after modifying (modifying measures means removing all crossloaded and near-zero loaded items). The modified fundamentalism and sexual prejudice measures correlations do not follow the literature. This may be due to the sample including sexual orientation minorities and a majority of atheist, agnostic, or ‘nothing in particular’ affiliations. The correlations between fundamentalism and racial prejudice were medium and positive before modifying and were small and positive after modifying. This falls in line with the literature of small and medium positive correlation statistics.

Student sections at college sporting events are an integral part of the collegiate experience. They provide a heightened atmosphere and passion that professional teams can not always attract. They are an exciting social event for students to be a part of a larger community. The student section also represents a new potential market base for athletic departments. If students don't go to games, they have less of an emotional attachment when it comes to giving back to their alma mater in their peak earning years. (Dodd, 2022). Therefore, it is vital to understand the factors that influence a student’s intention to return to future games and, in recent years, the decline in student attendance. There are many variables that contribute to student attendance, so a study was developed to attempt to predict a student’s intention to return to future Arizona State University basketball games. There are multiple factors that are considered when determining the attendance such as the demographics of the student or their level of fandom. In addition, other factors such as social media use can influence a student’s intention to return. A statistical analysis was performed to determine which of these factors are most important in order to build a model to predict intention to return. An exploratory factor analysis will be used to determine which variables of the survey are correlated and measure similar factors. Then regression techniques will help analyze each independent variable to determine their importance and relevance. Through these techniques, it was found that satisfaction of stadium factors, sport club participation, on-campus housing, athlete’s social media, and total attendance positively impact attendance while importance of stadium factors, interest in fan loyalty programs, and employment status negatively impact attendance. The following report will include details of the analysis. The model that was developed will help universities narrow the potential variables that impact student attendance to assist in future research.

Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.

In this study I hope to begin evaluating contemporary young adult literature that focuses on the bereavement of adolescents to see if the novels portray psychologically proven productive coping methods. I hope to initiate a conversation around how complicated bereavement is depicted within young adult literature that will establish a body of research that can be expanded into a further exploration into the young adult literature market. Within my study, I will conduct a psychological literature review on young adult complicated grief and coping mechanisms. Then I will create an instrument of analysis, a rubric/model to evaluate the fidelity of novels based on the research within the literature review. Finally, I will evaluate the depiction of productive adolescent grief coping mechanisms in the recently published novel All My Rage by Saaba Tahir based upon my literary model. Finally, I will write my own short story based upon my research and findings in analyzing the model, seeking to represent methods not seen in the literature or not discussed within research.

This investigation evaluates the most effective time series model to forecast the stock price for companies that started trading during the COVID-19 stock market crash. My research involved the analysis of five companies in the technology industry. I was able to create three different machine-learning models for each company. Each model contained various criteria to determine the efficacy of the model. The AIC and SBC are common metrics among Autoregressive, autoregressive moving averages, and cross-correlation input models. Lower AIC and SBC values indicated better-fitted models. Additionally, I conducted a white-noise test to determine stationarity. This yielded an Auto-correlation graph determining whether the data was non-stationary or stationary. This paper is supplemented by a project plan, exploratory data analysis, methodology, data, results, and challenges section. This has relevance in understanding the overall stock market trend when impacted by a global pandemic.

The main purpose of this project is to create a method for determining the absolute position of an accelerometer. Acceleration and angular speed were obtained from an accelerometer attached to a vehicle as it moves around. As the vehicle moves to collect information the orientation of the accelerometer changes, so a rotation matrix is applied to the data based on the angular change at each time. The angular change and distance are obtained by using the trapezoidal approximation of the integrals. This method was first validated by using simple sets of "true" data which are explicitly known sets of data to compare the results to. Then, an analysis of how different time steps and levels of noise affect the error of the results was performed to determine the optimal time step of 0.1 sec that was then used for the actual tests. The tests that were performed were: a stationary test for uses of calibration, a straight line test to verify a simple test, and a closed loop test to test the accuracy. The graphs for these tests give no indication of the actual paths, so the final results can only show that the data from the accelerometer is too noisy and inaccurate for this method to be used by this sensor. The future work would be to test different ways to get more accurate data and then use it to verify this methods. These ways could include using more sensors to interpolate the data, reducing noise by using a different sensor, or adding a filter. Then, if this method is considered accurate enough, it could be implemented into control systems.