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Filtering by: DOI 10.5194/hess-19-293-2015 Remove constraint DOI: 10.5194/hess-19-293-2015 Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science

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  • Evaluating social media attention interventions

    Creator:
    Gasper, David M.
    Abstract:
    Social media platforms use many techniques to engage users' attention with their platforms, including notifications, popups, and gamification elements. The impact of social media on physical and mental health has been studied, but limited publicly available research exists on how social media users can be helped to disengage from these...
    Resource Type:
    Honors College Thesis
    Full Text:
    Science _____________________________________________________________________ Robin Hess, Committee

  • Toward computer vision for understanding American football in video

    Creator:
    Hess, Robin W.
    Abstract:
    In this work, I examine the problem of understanding American football in video. In particular, I present several mid-level computer vision algorithms that each accomplish a different sub-task within a larger system for annotating, interpreting, and analyzing collections of American football video. The analysis of football video is useful in...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Robin W. Hess for the

  • Relational Networks for Visual Relationship Detection in Images

    Creator:
    Nguyen, Khoi
    Abstract:
    This thesis is about visual relationship detection. This is an important task in computer vision. The goal is to detect all visual relationships in a given image between objects. This thesis presents a new approach to this problem. Our approach does not use an object detector as a common pre-processing...
    Resource Type:
    Masters Thesis
    Full Text:
    Master of Science in Computer Science presented on September 19, 2017. Title: Relational Networks for

  • Power Efficient Architectures for Low Noise Switched-Capacitor Filters and High Accuracy Analog-to-Digital Converters

    Creator:
    Payandehnia, Pedram
    Abstract:
    Filters and data converters are key analog-and-mixed-signal (AMS) building blocks in communication systems, such as software-defined radios and internet-of-things. In this dissertation, novel switched-capacitor filter and analog-to-digital converter (ADC) circuit configurations have been explored which are power efficient and are digital scaling friendly. First, a novel switched-capacitor low-pass filter architecture...
    Resource Type:
    Dissertation
    Full Text:
    the degree of Doctor of Philosophy in Electrical and Computer Engineering presented on November 19

  • Enabling Virtual Globes for A Visual-Auditory User Experience of Spatial Data

    Creator:
    Gune, Aditya N.
    Abstract:
    Geographical datasets are large, complex, and can be difficult for users to navigate and derive meaning from. These datasets, as well as the unique insights derived from them, provide tremendous opportunity for social change -- many of the global challenges humankind is currently facing can benefit from analytics or visualization...
    Resource Type:
    Masters Thesis
    Full Text:
    degree of Master of Science in Computer Science presented on June 19, 2018. Title: Enabling Virtual

  • Uncertainty in Deep Learning with Implicit Neural Networks

    Creator:
    Ratzlaff, Neale
    Abstract:
    The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
    Resource Type:
    Dissertation
    Full Text:
    degree of Doctor of Philosophy in Computer Science presented on July 19 2021. Title: Uncertainty in

  • Optimizing Cryptographic Obfuscation

    Creator:
    Carmer, Brent
    Abstract:
    Cryptographic obfuscation is a powerful tool that makes programs “unintelligible” yet still runnable. It essentially gives programs the ability to keep secrets. The practical applications of obfuscation range from keeping secrets in banking applications to preventing software theft to providing secure messaging applications. The cryptographic applications of obfuscation are also...
    Resource Type:
    Dissertation
    Full Text:
    degree of Doctor of Philosophy in Computer Science presented on September 19, 2018. Title: Optimizing

  • Facilitating Code Comprehension by Annotations in Canvas-Based IDE

    Creator:
    Adeli, Marjan
    Abstract:
    Developers spend a considerable amount of time comprehending code and building accurate mental models of the code. Understanding the relationships between software features within IDEs is difficult, with information split across different visual hierarchies making navigation cumbersome. Canvas-based IDEs mitigate some of the navigation costs by allowing relevant information to...
    Resource Type:
    Masters Thesis
    Full Text:
    degree of Master of Science in Computer Science presented on March 19, 2020. Title: Facilitating Code

  • Atomic Layer Deposition of Multi-Insulator Metal-Insulator-Metal Capacitors

    Creator:
    Austin, Dustin Z.
    Abstract:
    Back end of line (BEOL) metal-insulator-metal capacitors (MIMCAPs) have become a core passive component in modern integrated circuits. International Technology Roadmap for Semiconductors (ITRS) projections for scaling of analog/mixed-signal MIMCAP applications require simultaneously increasing capacitance density while maintaining low leakage current density and low voltage nonlinearity (characterized by the quadratic...
    Resource Type:
    Dissertation
    Full Text:
    Engineering presented on May 19, 2017. Title: Atomic Layer Deposition of Multi-Insulator Metal-Insulator

  • A Study of Temporal Convolutional Neural Networks for Music Language Modeling as Applied to Automatic Music Transcription

    Creator:
    Meyn, Matthew
    Abstract:
    Automatic music transcription (AMT) is the task, given an acoustic representation of music, to recover a symbolic notation of the written notes expressed by the sound. Transcribing music with multiple notes sounding simultaneously is difficult for both humans and machines. Much existing work on AMT has focused on suitable acoustic...
    Resource Type:
    Capstone Project
    Full Text:
    the degree of Master Of Science in Computer Science presented on June 19, 2019. Title: A Study of