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- All Subjects: Violin and piano music
- All Subjects: Music
- Status: Published
ContributorsWatras, Matthew (Performer) / Qualls, Karla J. (Performer) / ASU Library. Music Library (Publisher)
Created1990-12-06
ContributorsBivona, Kathryn (Performer) / Chen, Chia-I (Performer) / ASU Library. Music Library (Publisher)
Created2008-11-23
ContributorsAn, Zhihuan (Performer) / Hsieh, Hsaio-Hsi (Performer) / ASU Library. Music Library (Publisher)
Created2023-11-30
Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
ContributorsMani, Vinesh (Author) / Tsakalis, Konstantinos (Thesis director) / Jayasuriya, Suren (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
ContributorsJo, HyeonSeon (Performer) / ASU Library. Music Library (Publisher)
Created2024-01-20
ContributorsHao, Yun (Performer) / Ridgway, Thomas (Performer) / Zhang, Yudi (Performer) / ASU Library. Music Library (Publisher)
Created2024-02-22
ContributorsQuon, Joyce (Performer) / Campbell, Andrew (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created1999-10-16
ContributorsChang, Ruihong (Performer) / Liu, Mengyu (Performer) / ASU Library. Music Library (Publisher)
Created2019-04-25
Description
This thesis proposes and explores an adapted approach to music recommendation and
event promotion, aimed at providing streaming users more accessibility to local artists,
performers, and events. By focusing specifically on the enrichment of local music ecosystems,
this research is undertaken with the express interest of local music artists, venues, and
streamers in mind. It seeks to serve a multifaceted goal: revitalizing local music scenes by
making them more visible and accessible through streaming platforms; empowering local music artists by providing them with a broader audience and new opportunities for engagement; reconnecting artists with the community, thereby fostering a stronger sense of local identity and cultural cohesion; and providing economic benefit to local venues through increased attendance and engagement. In essence, this thesis intends to harness the power of streaming platforms to rekindle the localized relationships between listeners, artists, and venues, thereby contributing to the renaissance, sustainability, and vibrancy of local music cultures. These outcomes will be achieved through a two-pronged theoretical and practical approach, incorporating Proof of Concept (PoC) algorithm with the Business Model Canvas
(BMC). A Proof of Concept recommendation algorithm was developed as a tool to empirically demonstrate the viability of the proposed strategies. This prototype music recommendation
algorithm was designed and tested with the explicit goal of creating a novel music recommendation algorithm that biased users towards exposure of smaller local artists and
events.
ContributorsBradley, Robert (Author) / Clarkin, Michael (Co-author) / Ellini, Andre (Co-author) / Mancenido, Michelle (Thesis director) / Sirugudi, Kumar (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Natural Sciences (Contributor)
Created2024-05
Description
Tempe City Roots is an upcoming music festival that aims to promote sustainability and community in the City of Tempe. Accessibility has been notably absent from the ideation process for this festival, despite being essential for a sustainable and community-oriented festival. Equity and justice are core to sustainability and disabled people are important members of the Tempe community. I have undergone a thorough research and ideation process to create nine accessibility-centered ideas for Tempe City Roots based on the thoughts and experiences of the disabled community. These ideas would make Tempe City Roots more accessible and inclusive for all, and allow each attendee to have a safe and enjoyable experience.
ContributorsColucci, Melody (Author) / Reeves, James Scott (Thesis director) / Kuhn, Anthony (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05