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Filtering by: Creator Best, Jaclyn P. Remove constraint Creator: Best, Jaclyn P. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science

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  • p-type transparent electronics

    Creator:
    Valencia, Melinda Marie
    Abstract:
    The objective of this thesis is to contribute to the development of p-type materials for transparent electronics applications. Thin films of ®-BaCu2S2, a p-type semi-transparent semiconductor, are fabricated and characterized. ®-BaCu2S2 has a transmittance of 60% to 80 % in the visible portion of the electromagnetic spectrum. The mobility, conductivity,...
    Resource Type:
    Masters Thesis
    Full Text:
    experimentally that Ag and Cu are the best p-TC cation candidates because their d energy levels are closest to

  • Modeling and development of p-type thin-film transistors

    Creator:
    Spiegelberg, Matthew Craig
    Abstract:
    The primary focus of this thesis involves modeling and development of p-type thin-film transistors (p-TFTs), moving towards the realization of a filly transparent thin-film transistor (TTFT). The modeling portion of this thesis emphasizes the development of physics-based TFT models, specifically focusing on the elucidation of non-ideal current-voltage characteristic behavior. A...
    Resource Type:
    Masters Thesis
    Full Text:
    of p-type thin-film transistors (p-TFTs), moving towards the realization of a fully transparent

  • Effectiveness of using two and three-parameter distributions in place of "best-fit distributions" in discrete event simulation models of production lines

    Creator:
    Sharma, Akash
    Abstract:
    This study presents the results of using common two or three-parameter "default" distributions in place of "best fit distributions" in simulations of serial production lines with finite buffers and blocking. The default distributions used instead of the best-fit distribution are chosen such that they are non-negative, unbounded, and can match...
    Resource Type:
    Masters Thesis
    Full Text:
    : Effectiveness of using Two and Three-parameter Distributions in Place of "Best-fit Distributions" in Discrete

  • Fabrication and characterization of p-type CuO / n-type ZnO heterostructure gas sensors prepared by sol-gel processing techniques

    Creator:
    Ravichandran, Ram
    Abstract:
    Increased interest in the field of sensor technology stems from the availability of an inexpensive and robust sensor to detect and quantify the presence of a specific gas. Bulk p-CuO/n-ZnO heterocontact based gas sensors have been shown to exhibit the necessary sensitivity and selectivity characteristics, however, low interfacial CuO/ZnO contact...
    Resource Type:
    Masters Thesis
    Full Text:
    Characterization of p-type CuO / n-type ZnO Heterostructure Gas Sensors Prepared by Sol-Gel Processing Techniques

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
    Resource Type:
    Dissertation
    Full Text:
    Constraints for Sequential Decision Making Abstract approved: Alan P. Fern Writing a program that performs

  • Re-understanding Finite-State Representations of Recurrent Policy Networks

    Creator:
    Danesh, Mohamad H.
    Abstract:
    We propose an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minimization process can obscure a deeper understanding...
    Resource Type:
    Masters Thesis
    Full Text:
    -understanding Finite-State Representations of Recurrent Policy Networks Abstract approved: Alan P. Fern We

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
    Resource Type:
    Dissertation
    Full Text:
    Modes for Sequential Decision Making Abstract approved: Alan P. Fern This thesis considers the

  • Automatic Detection of Possessions and Shots from Raw Basketball Videos

    Creator:
    Terhuja, Vikedo
    Abstract:
    Sports analytics is rapidly evolving today through the use of computer vision systems that automatically extract huge amount of information inherently present in multimedia data without much human assistance. This information can facilitate a better understanding of patterns and strategies in various sports. However, for non-professional teams, due to expense...
    Resource Type:
    Masters Thesis
    Full Text:
    Raw Basketball Videos. Abstract approved: Alan P. Fern Sports analytics is rapidly evolving today

  • Detecting bad smells in spreadsheets

    Creator:
    Asavametha, Atipol
    Abstract:
    Spreadsheets are a widely used end-user programming tool. Field audits have found that 80-90% of spreadsheets created by end users contain textual and formula errors in spreadsheets. Such errors may have severe negative consequences for users in terms of productivity, credibility, or profits. To solve the problem of spreadsheet errors,...
    Resource Type:
    Masters Thesis
    Full Text:
    : ______________________________________________________ Christopher P Scaffidi Spreadsheets are a widely used end-user programming tool

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
    Resource Type:
    Dissertation
    Full Text:
    . Title: Learning and Improving Policies for Probabilistic Planning Problems Abstract approved: Alan P