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Filtering by: Creator Scott, Sophia L. M. Remove constraint Creator: Scott, Sophia L. M. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Decade 2020-2029 Remove constraint Decade: 2020-2029 Resource Type Masters Thesis Remove constraint Resource Type: Masters Thesis

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  • Mechanically Robust Additively Manufactured Magnetically Coupled Aperture and Patch Microstrip to Rectangular Waveguide Transition

    Creator:
    Merrill, Scott
    Abstract:
    Advances in additive manufacturing technologies allow for the rapid, inexpensive manufacturing of microwave circuits. Circuits made using additive manufacturing are comparable in performance to circuits produced circuits produced using conventional methods such as printed circuit board fabrication, thin film deposition, machining, and milling. Very little work has been done in...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Scott Merrill for the degree of

  • SocioEconomicMag Meets a Platform for SES-Diverse College Students: A Case Study

    Creator:
    Agarwal, Puja
    Abstract:
    Emerging research shows that individual differences in how people use technology sometimes cluster by socioeconomic status (SES) and that when technology is not socioeconomically inclusive, low-SES individuals may abandon it. To understand how to improve technology’s SES-inclusivity, we present a multi-phase case study on SocioEconomicMag (SESMag), an emerging inspection method...
    Resource Type:
    Masters Thesis
    Full Text:
    Abstract approved: ______________________________________________________ Margaret M Burnett Anita Sarma

  • Towards the Design and Evaluation of Effective Educational Virtual Reality Applications

    Creator:
    Rangarajan, Vaishnavi
    Abstract:
    With the rapid advancement of educational technology and the need for personalized, engaging content to accommodate diverse learning needs, Virtual Reality (VR) holds promises for the present and future. However, VR applications suffer from challenges, including usability concerns, lack of pedagogical value, and evaluation standards. This thesis focuses on two...
    Resource Type:
    Masters Thesis

  • Nucleation Inhibiting Mechanisms in Atomic Layer Deposition of Nanolaminates

    Creator:
    Witsell, Shane Mike’ele Williama
    Abstract:
    Nucleation in atomic layer deposition (ALD) determines how many cycles are required to initiate growth and to form a continuous film. Nucleation has been exploited for the formation of nanoparticles, catalysts, and for area-selective ALD. Although sometimes overlooked or misrepresented in ALD reports, nucleation is also critical for multicomponent ALD...
    Resource Type:
    Masters Thesis
    Full Text:
    3.6. Dylan B. Fast for assistance in XRR and GIXRD scans. Benjamin M. Kupp for assistance in

  • A Multiresolution Analysis of Temporal Logic

    Creator:
    Pelphrey, Richard E.
    Abstract:
    Is it possible to determine whether a signal violates a formula in Signal Temporal Logic (STL), if the monitor only has access to a low-resolution version of the signal? We answer this question affirmatively by demonstrating that temporal logic has a multiresolution structure, which parallels the multiresolution structure of signals....
    Resource Type:
    Masters Thesis
    Full Text:
    ,ℓ := ∑ k φ−j,k(ℓ)φ−j,k(m) and wm,ℓ := ∑ k ψ−j,k(ℓ)ψ−j,k(m) for 0 ≤ ℓ,m ≤ N − 1 77 Express L(p

  • 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:
    Gnd.ii Exp.iii Ptc.i Gnd.ii Exp.iii E1 M 15 S1 M 16 E2 M 8 S2 F 3 E3 M 3 S3 M 5 E4 M 2 S4 M 5 E5 F 3

  • 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:
    ) Pixel-level probability Sn,mip,jp ; (b) Voronoi-based probability S n,m p for the example image in

  • 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

  • Indoor Localization Using Direction of Arrival Approach

    Creator:
    Zhang, Tingwei
    Abstract:
    In this paper, a Direction of Arrival (DOA) based system is proposed. This method searches the direction relative to the array to find where the signal source is located. The proposed system can achieve sub-meter level accuracy with a near real-time update rate. Also, we introduced several refinement methods including...
    Resource Type:
    Masters Thesis
    Full Text:
    set of locations with n candidates is L = L1, L2, L3, ..., LN . For an RSSI value measured as O, the

  • A Benchmarking Study of Unsupervised Anomaly Detection Algorithms

    Creator:
    Emmott, Andrew F.
    Abstract:
    It is common practice in the unsupervised anomaly detection literature to create experimental benchmarks by sampling from existing supervised learning datasets. We seek to improve this practice by identifying four dimensions important to real-world anomaly detection applications --- point difficulty, clusteredness of anomalies, relevance of features, and relative frequency of...
    Resource Type:
    Masters Thesis