Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
The purpose of this creative project was to create a stereo sound system in a unique medium. As a team, we decided to integrate a Tesla Coil with a bluetooth audio source. These high frequency, high voltage systems can be configured to emit their electrical discharge in a manner that resembles playing tunes. Originally the idea was to split the audio into left and right, then to further segregate the signals to have a treble, mid, and base emitter for each side. Due to time, budget, and scope constraints, we decided to complete the project with only two coils.<br/><br/>For this project, the team decided to use a solid-state coil kit. This kit was purchased from OneTelsa and would help ensure everyone’s safety and the project’s success. The team developed our own interrupting or driving circuit through reverse-engineering the interrupter provided by oneTesla and discussing with other engineers. The custom interpreter was controlled by the PSoC5 LP and communicated with an audio source through the DFRobot Bluetooth module. Utilizing the left and right audio signals it can drive the two Tesla Coils in stereo to play the music.
The purpose of this creative project was to create a stereo sound system in a unique medium. As a team, we decided to integrate a Tesla Coil with a bluetooth audio source. These high frequency, high voltage systems can be configured to emit their electrical discharge in a manner that resembles playing tunes. Originally the idea was to split the audio into left and right, then to further segregate the signals to have a treble, mid, and base emitter for each side. Due to time, budget, and scope constraints, we decided to complete the project with only two coils.<br/><br/>For this project, the team decided to use a solid-state coil kit. This kit was purchased from OneTelsa and would help ensure everyone’s safety and the project’s success. The team developed our own interrupting or driving circuit through reverse-engineering the interrupter provided by oneTesla and discussing with other engineers. The custom interpreter was controlled by the PSoC5 LP and communicated with an audio source through the DFRobot Bluetooth module. Utilizing the left and right audio signals it can drive the two Tesla Coils in stereo to play the music.
Graduating from college is an important time of life transitions and career development for undergraduates and their future. Future self-identification, the connection between an individual’s current and future self, can negatively predict depression and utilize self-control as a mechanism to achieve later academic goals. Investigating an individual’s future self- identification, depression scores, and behavioral outcomes in the face of the COVID-19 pandemic can help optimize college graduate success in an uncertain world. The present study aimed to (1) determine if earlier future self-identification moderated the changes between later outcomes (e.g., depression, perceived alcohol consumption, and academic and career goals) from pre-COVID-19 to during COVID-19, (2) investigate if psychological resources (e.g., self-control and emotion regulation) had any intermediary effects between earlier future self-identification and later depression and behavioral outcomes during the pandemic, and (3) test for any moderation effects of future self-identification on the relationship between available psychological resources before COVID-19 and during COVID-19. The present research demonstrated that students with greater earlier future self-identification were less likely to change their academic and career goals and were less likely to experience symptoms of depression during the pandemic. Additionally, self-control was demonstrated as an intermediary factor between earlier future self-identification and later academic and career goal changes. These findings may help college graduates develop resilience in other stressful situations.
People use a variety of emotion regulation strategies to cope with difficult situations. Although there is research supporting humor as an effective emotion regulation strategy, less is known about what circumstances lead people to use humor and what negative emotions humor seems to be the most helpful in mitigating. The current study aimed to determine to what extent specific negative emotions lead people to choose humor as an emotion regulation strategy. Participants wrote about a neutral situation and then selected from four card decks with different stimuli (funny, pleasant, awe-inspiring, or neutral). Participants were then randomly assigned to a negative emotion condition (sadness, embarrassment, anxiety, or anger) and wrote about a situation in which they have experienced that specific emotion. They then completed the card selection task again. We compared the number of funny cards chosen between the neutral vs negative emotion trials for each emotion. We found that card selection did not change significantly from the neutral-affect trial to the negative emotion trial across any of the negative emotions. Limitations and future directions are discussed.
A student-focused course was created that provides students with a fundamental knowledge of cobot use and capabilities. Students learn to use a Universal Robots UR3e cobot, first with an exisiting free online training simulator. After completing the online training, students gain hands-on experience completing tasks with a cobot on a custom-built workstation. Two of these workstations were created. Two students trialed the program, and both reported enjoying the program and feeling significantly more confident in their cobot programming abilities.
Visual odometry (VO) plays a crucial role in determining the position and orientation of an autonomous vehicle as it navigates through its environment. However, the performance of visual odometry can be significantly affected by errors in disparity estimation and LIDAR depth measurements. This thesis investigates the use of LIDAR depth correction and Stereo disparity matching, combined with stronger match filtering, to improve the accuracy and reliability of VO estimations. The study utilizes a dataset consisting of a sequence of image frames, ground truth position data, and a range of feature detection, description, and matching techniques. Results indicate that the proposed approach significantly improves the accuracy of VO estimations, providing a valuable contribution to the development of reliable and safe autonomous navigation systems. The proposed method consists of two main components: (1) an advanced disparity matching algorithm to obtain more accurate and robust disparity estimations, and (2) a LIDAR depth correction module that employs a sensor fusion approach to refine the depth information generated by LIDAR sensors. The LIDAR depth correction module combines data from multiple sensors, including LIDAR, camera, and inertial measurement unit (IMU), to produce a more accurate depth estimation. The performance of the proposed approach is evaluated using real-world datasets and benchmark visual odometry challenges. Results demonstrate that the proposed method significantly improves the accuracy and robustness of visual odometry, leading to better localization and navigation performance for autonomous vehicles. This research contributes to the ongoing development of autonomous vehicle technology by addressing critical challenges in visual odometry and offering a practical solution for more accurate and reliable self-localization
