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You searched for +publisher:"University of Arkansas" +contributor:("Michael Gashler"). Showing records 1 – 9 of 9 total matches.

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University of Arkansas

1. Bell, Brent. Efficacy of Multi-Threshold NULL Convention Logic in Low-Power Applications.

Degree: PhD, 2018, University of Arkansas

  In order for an asynchronous design paradigm such as Multi-Threshold NULL Convention Logic (MTNCL) to be adopted by industry, it is important for circuit… (more)

Subjects/Keywords: Asynchronous; Clockless; Digital; MTNCL; Power; Digital Circuits; Power and Energy; VLSI and Circuits, Embedded and Hardware Systems

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APA (6th Edition):

Bell, B. (2018). Efficacy of Multi-Threshold NULL Convention Logic in Low-Power Applications. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2909

Chicago Manual of Style (16th Edition):

Bell, Brent. “Efficacy of Multi-Threshold NULL Convention Logic in Low-Power Applications.” 2018. Doctoral Dissertation, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/2909.

MLA Handbook (7th Edition):

Bell, Brent. “Efficacy of Multi-Threshold NULL Convention Logic in Low-Power Applications.” 2018. Web. 29 Oct 2020.

Vancouver:

Bell B. Efficacy of Multi-Threshold NULL Convention Logic in Low-Power Applications. [Internet] [Doctoral dissertation]. University of Arkansas; 2018. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/2909.

Council of Science Editors:

Bell B. Efficacy of Multi-Threshold NULL Convention Logic in Low-Power Applications. [Doctoral Dissertation]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2909


University of Arkansas

2. Shukla, Manisha. Theatrical Genre Prediction Using Social Network Metrics.

Degree: MS, 2018, University of Arkansas

  With the emergence of digitization, large text corpora are now available online that provide humanities scholars an opportunity to perform literary analysis leveraging the… (more)

Subjects/Keywords: Data Analysis; Data Visualization; Information Retrieval; Shakespeare Social Network; Social Network Analysis; Text Mining; Graphics and Human Computer Interfaces; Numerical Analysis and Scientific Computing; Social Media

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APA (6th Edition):

Shukla, M. (2018). Theatrical Genre Prediction Using Social Network Metrics. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2920

Chicago Manual of Style (16th Edition):

Shukla, Manisha. “Theatrical Genre Prediction Using Social Network Metrics.” 2018. Masters Thesis, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/2920.

MLA Handbook (7th Edition):

Shukla, Manisha. “Theatrical Genre Prediction Using Social Network Metrics.” 2018. Web. 29 Oct 2020.

Vancouver:

Shukla M. Theatrical Genre Prediction Using Social Network Metrics. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/2920.

Council of Science Editors:

Shukla M. Theatrical Genre Prediction Using Social Network Metrics. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2920


University of Arkansas

3. Hammer, Jon C. Improving the Efficacy of Context-Aware Applications.

Degree: PhD, 2018, University of Arkansas

  In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include… (more)

Subjects/Keywords: Computer Vision; Context-aware Computing; Depth-based Positioning; Logical Sensors; Machine Learning; Graphics and Human Computer Interfaces; OS and Networks

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APA (6th Edition):

Hammer, J. C. (2018). Improving the Efficacy of Context-Aware Applications. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2703

Chicago Manual of Style (16th Edition):

Hammer, Jon C. “Improving the Efficacy of Context-Aware Applications.” 2018. Doctoral Dissertation, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/2703.

MLA Handbook (7th Edition):

Hammer, Jon C. “Improving the Efficacy of Context-Aware Applications.” 2018. Web. 29 Oct 2020.

Vancouver:

Hammer JC. Improving the Efficacy of Context-Aware Applications. [Internet] [Doctoral dissertation]. University of Arkansas; 2018. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/2703.

Council of Science Editors:

Hammer JC. Improving the Efficacy of Context-Aware Applications. [Doctoral Dissertation]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2703


University of Arkansas

4. Aklah, Zeyad Tariq. A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly of Custom Accelerators on FPGAs.

Degree: PhD, 2017, University of Arkansas

  The state of the art in design and development flows for FPGAs are not sufficiently mature to allow programmers to implement their applications through… (more)

Subjects/Keywords: Applied sciences; CGRA; FPGA; Overlay; Reconfigurable architectures; System on chip; Virtual architecture; Hardware Systems; Software Engineering

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APA (6th Edition):

Aklah, Z. T. (2017). A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly of Custom Accelerators on FPGAs. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1928

Chicago Manual of Style (16th Edition):

Aklah, Zeyad Tariq. “A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly of Custom Accelerators on FPGAs.” 2017. Doctoral Dissertation, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/1928.

MLA Handbook (7th Edition):

Aklah, Zeyad Tariq. “A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly of Custom Accelerators on FPGAs.” 2017. Web. 29 Oct 2020.

Vancouver:

Aklah ZT. A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly of Custom Accelerators on FPGAs. [Internet] [Doctoral dissertation]. University of Arkansas; 2017. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/1928.

Council of Science Editors:

Aklah ZT. A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly of Custom Accelerators on FPGAs. [Doctoral Dissertation]. University of Arkansas; 2017. Available from: https://scholarworks.uark.edu/etd/1928


University of Arkansas

5. Godfrey, Luke. Neural Decomposition of Time-Series Data for Effective Generalization.

Degree: MS, 2015, University of Arkansas

  We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function… (more)

Subjects/Keywords: Applied sciences; Other Computer Sciences

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APA (6th Edition):

Godfrey, L. (2015). Neural Decomposition of Time-Series Data for Effective Generalization. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1360

Chicago Manual of Style (16th Edition):

Godfrey, Luke. “Neural Decomposition of Time-Series Data for Effective Generalization.” 2015. Masters Thesis, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/1360.

MLA Handbook (7th Edition):

Godfrey, Luke. “Neural Decomposition of Time-Series Data for Effective Generalization.” 2015. Web. 29 Oct 2020.

Vancouver:

Godfrey L. Neural Decomposition of Time-Series Data for Effective Generalization. [Internet] [Masters thesis]. University of Arkansas; 2015. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/1360.

Council of Science Editors:

Godfrey L. Neural Decomposition of Time-Series Data for Effective Generalization. [Masters Thesis]. University of Arkansas; 2015. Available from: https://scholarworks.uark.edu/etd/1360


University of Arkansas

6. Ashmore, Stephen Charles. Evaluating the Intrinsic Similarity between Neural Networks.

Degree: MS, 2015, University of Arkansas

  We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a… (more)

Subjects/Keywords: Applied sciences; Forward bipartite alignment; Machine learning; Neutral network; Artificial Intelligence and Robotics; OS and Networks

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APA (6th Edition):

Ashmore, S. C. (2015). Evaluating the Intrinsic Similarity between Neural Networks. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1395

Chicago Manual of Style (16th Edition):

Ashmore, Stephen Charles. “Evaluating the Intrinsic Similarity between Neural Networks.” 2015. Masters Thesis, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/1395.

MLA Handbook (7th Edition):

Ashmore, Stephen Charles. “Evaluating the Intrinsic Similarity between Neural Networks.” 2015. Web. 29 Oct 2020.

Vancouver:

Ashmore SC. Evaluating the Intrinsic Similarity between Neural Networks. [Internet] [Masters thesis]. University of Arkansas; 2015. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/1395.

Council of Science Editors:

Ashmore SC. Evaluating the Intrinsic Similarity between Neural Networks. [Masters Thesis]. University of Arkansas; 2015. Available from: https://scholarworks.uark.edu/etd/1395


University of Arkansas

7. Alfarhood, Sultan Dawood. Exploiting Semantic Distance in Linked Open Data for Recommendation.

Degree: PhD, 2017, University of Arkansas

  The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure… (more)

Subjects/Keywords: Applied sciences; Linked data; Recommender system; Semantic distance; Similarity; Databases and Information Systems

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APA (6th Edition):

Alfarhood, S. D. (2017). Exploiting Semantic Distance in Linked Open Data for Recommendation. (Doctoral Dissertation). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/1882

Chicago Manual of Style (16th Edition):

Alfarhood, Sultan Dawood. “Exploiting Semantic Distance in Linked Open Data for Recommendation.” 2017. Doctoral Dissertation, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/1882.

MLA Handbook (7th Edition):

Alfarhood, Sultan Dawood. “Exploiting Semantic Distance in Linked Open Data for Recommendation.” 2017. Web. 29 Oct 2020.

Vancouver:

Alfarhood SD. Exploiting Semantic Distance in Linked Open Data for Recommendation. [Internet] [Doctoral dissertation]. University of Arkansas; 2017. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/1882.

Council of Science Editors:

Alfarhood SD. Exploiting Semantic Distance in Linked Open Data for Recommendation. [Doctoral Dissertation]. University of Arkansas; 2017. Available from: https://scholarworks.uark.edu/etd/1882


University of Arkansas

8. Komoni, Erzen. A Continuous Space Generative Model.

Degree: MS, 2018, University of Arkansas

  Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications… (more)

Subjects/Keywords: Continuous Functions; Deep Learning; Generative Models; Machine Learning; Neural Networks; Graphics and Human Computer Interfaces

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APA (6th Edition):

Komoni, E. (2018). A Continuous Space Generative Model. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2668

Chicago Manual of Style (16th Edition):

Komoni, Erzen. “A Continuous Space Generative Model.” 2018. Masters Thesis, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/2668.

MLA Handbook (7th Edition):

Komoni, Erzen. “A Continuous Space Generative Model.” 2018. Web. 29 Oct 2020.

Vancouver:

Komoni E. A Continuous Space Generative Model. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/2668.

Council of Science Editors:

Komoni E. A Continuous Space Generative Model. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2668


University of Arkansas

9. Holliday, James B. Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy.

Degree: MS, 2018, University of Arkansas

  We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C) algorithm that improves training. In 2016 Google’s DeepMind set a… (more)

Subjects/Keywords: Deep Learning; Exploration Strategy; Machine Learning; Reinforcement Learning; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces

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APA (6th Edition):

Holliday, J. B. (2018). Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/2689

Chicago Manual of Style (16th Edition):

Holliday, James B. “Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy.” 2018. Masters Thesis, University of Arkansas. Accessed October 29, 2020. https://scholarworks.uark.edu/etd/2689.

MLA Handbook (7th Edition):

Holliday, James B. “Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy.” 2018. Web. 29 Oct 2020.

Vancouver:

Holliday JB. Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy. [Internet] [Masters thesis]. University of Arkansas; 2018. [cited 2020 Oct 29]. Available from: https://scholarworks.uark.edu/etd/2689.

Council of Science Editors:

Holliday JB. Improving Asynchronous Advantage Actor Critic with a More Intelligent Exploration Strategy. [Masters Thesis]. University of Arkansas; 2018. Available from: https://scholarworks.uark.edu/etd/2689

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