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Filtering by: Creator Waddell, Karen L. Remove constraint Creator: Waddell, Karen L. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Degree Level Master's Remove constraint Degree Level: Master's

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  • Hardware and software methods to improve and accelerate the development cycle of wave energy converters

    Creator:
    Tavakoli, Mike
    Abstract:
    With the ever-increasing demand and competition for global energy resources among nations, it is essential to explore and tap into various energy sources to reduce dependency on global markets. Renewable energy sources offer a significant opportunity to create localized and stable energy supplies. Ocean wave energy can be a promising...
    Resource Type:
    Masters Thesis

  • Methods for short-term prediction of wind speeds in the Pacific Northwest Columbia Gorge wind farm region

    Creator:
    Davidson, James D.
    Abstract:
    Variable electrical generation (VG) sources such as wind farms are an increasing percentage of total electrical generation in the Bonneville Power Administration (BPA) balancing area and are starting to impact the ability of the regional balancing authority to control the electric grid. Wind farms are not dispatchable and challenge historical...
    Resource Type:
    Masters Thesis

  • A methodology to reduce the strain on hydro turbines using advanced life extending control of multiple energy storage systems

    Creator:
    Brosig, Sean G.
    Abstract:
    Over the last 20 years, there has been rapid growth in the amount of installed wind power in the Pacific Northwest, specifically in the Columbia River Gorge. The variable and non-dispatchable nature of this resource requires that it be balanced in some form by other sources on the grid. In...
    Resource Type:
    Masters Thesis

  • Large-Area Nanoplasmonic Structures for Enhanced Fluorescence-Based Biosensing

    Creator:
    Wang, Yi-Chieh
    Abstract:
    An innovative silver nano-dimple arrays (Ag DAs)-integrated microfluidic device was developed to achieve highly sensitive fluorescence-based nucleic acid detection. The Ag DAs were utilized to produce surface plasmon resonance (SPR) for strong fluorescence enhancement. We systematically investigated the plasmon-enhanced fluorescence by controlling the nanostructure dimension of the Ag DA substrate....
    Resource Type:
    Masters Thesis
    Full Text:
    the long particle pair axis and (b) orthogonal to it [66]. O. L. Muskens also investigated a

  • Infant Hand Detection and Tracking

    Creator:
    Mishra, Shridhar
    Abstract:
    Hand detection is a fundamental step for many hand-related computer vision tasks, such as gesture recognition, hand pose estimation, hand sign language translation, and so on. However, robustly detecting hands is a challenging task because of drastic changes in appearance based on finger articulation and changes in lighting conditions, camera...
    Resource Type:
    Masters Thesis
    Full Text:
    , whom I worked within two past years, and other peers from NYU- Brianna Kaplan, Ori Ossmy, and Karen

  • Window Axial Vision Transformer for Image Classification

    Creator:
    Lee, Jisoo
    Abstract:
    Currently, a popular approach to image classification uses the deep Transformer architecture. In a Transformer, the attention mechanism enables the model to learn efficiently with fewer computational resources than the convolutional neural networks (CNNs). In this thesis, we study the sparse attention mechanism widely used in the Transformers developed specifically...
    Resource Type:
    Masters Thesis
    Full Text:
    alignment (MSA) as input. The input of the model is x ∈ RM×L, M is the number of sequences in the MSA and L

  • Local and Global Explanations for Deep Image Classification via Structured Attention Graphs

    Creator:
    Shitole, Vivswan
    Abstract:
    Attention maps are popular tools of explaining the decisions of convolutional neural net-works (CNNs) for image classification. Typically, for each image of interest, a single attention map is produced, which assigns weights to pixels based on their importance to the classification. We argue that a single attention map provides an...
    Resource Type:
    Masters Thesis
    Full Text:
    training DNNs - the backpropagation algorithm. Consider a fully-connected deep neural network having L

  • Iterative reconstruction methods of CT images using a statistical framework

    Creator:
    Delgado, Diana
    Abstract:
    Medical imaging technologies play a vital role in early diagnosis of disease by providing internal images of the human body to medical professionals. Computed Tomography (CT) is currently the most commonly used medical imaging technology because it is easy to use, detectors and scanners are constantly improving, and more importantly,...
    Resource Type:
    Masters Thesis
    Full Text:
    served on my defense committee: Dr. Annette von Jouanne, Dr. Huaping Liu and Dr. Karen Shell, your

  • Optimizing Interconnection Topology using Deep Reinforcement Learning

    Creator:
    Zeng, Kai
    Abstract:
    As the number of nodes in high-performance computing (HPC) systems continues to grow, it becomes increasingly important to design scalable interconnection network topologies. Prior work has shown promise in adding random shortcuts on top of an existing topology to reduce average hop count and network diameter, but has been limited...
    Resource Type:
    Masters Thesis
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 LIST OF FIGURES Figure Page 2.1 High-level organization of the Blue Gene/L supercomputer

  • Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

    Creator:
    Khorram, Saeed
    Abstract:
    In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
    Resource Type:
    Masters Thesis
    Full Text:
    the augmented Lagrangian, L(x, z,y) = f(x) + g(z) + yT (Ax+Bz − c) + ρ 2 ‖Ax+Bz − c‖22 (2.4) where y