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This century has brought about incredible advancements in technology and academia, changing the workforce and the future leaders that will drive it: students. However, the integration of digital literacy and digital tools in many United States K\u201412 schools is often overlooked. Through "Exploring the Digital World," students, parents, and teachers can follow the creatures of this story-driven program as they learn the importance of digital literacy in the 21st century.
ContributorsRaiton, Joseph Michael (Author) / Fehler, Michelle (Thesis director) / Heywood, William (Committee member) / Barrett, The Honors College (Contributor) / The Design School (Contributor)
Created2015-05
Description
This project is intended to fill gaps in the professional knowledge of music educators in the state of Arizona concerning the pedagogy, content, and importance of a visual education program in the scholastic marching band. It also aims to contribute to the general pool of knowledge surrounding visual education. While music educators are often expected to begin teaching marching band immediately following their graduation, many do not ever receive proper training in the visual aspect of the marching arts. The marching band is the most visible element of a holistic educational music program, and often represents the school to the community and the educator to their administrators. While significant music training is given at the collegiate level, many educators have not had further experience in the marching arts. The author uses his experience in Drum Corps International, as well as in teaching marching band to synthesize research-based practices into a handbook of immediately applicable visual pedagogical information that would be immediately useful to any music educator.
ContributorsGerald, Thomas (Author) / Swoboda, Deanna (Thesis director) / Quamo, Jeff (Committee member) / Barrett, The Honors College (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05
Description
Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have shown promising results; they are carefully crafted and built explicitly for these tasks. However, such geometric methods require extreme fine-tuning and extensive prior knowledge to set up these systems for different scenarios. Classical Geometric approaches also require significant post-processing and optimization to minimize the error between the estimated pose and the global truth. In this body of work, the deep learning model was formed by combining SuperPoint and SuperGlue. The resulting model does not require any prior fine-tuning. It has been trained to enable both outdoor and indoor settings. The proposed deep learning model is applied to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset along with other classical geometric visual odometry models. The proposed deep learning model has not been trained on the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. It is only during experimentation that the deep learning model is first introduced to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. Using the monocular grayscale images from the visual odometer files of the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, through the experiment to test the viability of the models for different sequences. The experiment has been performed on eight different sequences and has obtained the Absolute Trajectory Error and the time taken for each sequence to finish the computation. From the obtained results, there are inferences drawn from the classical and deep learning approaches.
ContributorsVaidyanathan, Venkatesh (Author) / Venkateswara, Hemanth (Thesis advisor) / McDaniel, Troy (Thesis advisor) / Michael, Katina (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this study, the role of attention in facial expression processing is investigated, especially as it relates to fearful facial expressions compared to happy facial expressions. Facial fear processing plays a critical role in human social interactions and survival, and this has previously been studied mainly in animal models. This study, however, was accomplished with the presentation of images of actors with happy and fearful facial expressions in three spatial frequency formats, as it is hypothesized that images at different spatial frequencies may be processed via different pathways. These images were presented to human participants in two experiments. In Experiment I, facial expression was task-relevant as participants were asked to discriminate between “happy” and “fear” expressions with reaction time (measured in seconds) and accuracy recorded. In Experiment II, facial expression was task-irrelevant, as participants were asked simply to discriminate between photographs of males and females, again with reaction time and accuracy recorded. Overall, the results comparing happy and fearful facial expressions in Experiment I were not significant. The results comparing happy and fearful facial expressions in Experiment II exhibited similar insignificant results except for accuracy in certain spatial frequencies, which were found to be significant. These results suggest that fearful facial expressions are processed more accurately than happy facial expressions when attention is focused on other variables in the image rather than when attention is focused on the facial expressions themselves.
ContributorsMcMaster, Hope (Author) / Bae, Gi-Yeul (Thesis director) / Corbin, William (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Department of Psychology (Contributor)
Created2024-05