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You searched for +publisher:"Utah State University" +contributor:("Haitao Wang"). Showing records 1 – 17 of 17 total matches.

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Utah State University

1. Andrews, Aaron M. Minimizing Aggregate Movements for Interval Coverage.

Degree: MS, Computer Science, 2014, Utah State University

  We present an efficient algorithm for solving an interval coverage problem. Given n intervals of the same length on a line L and a… (more)

Subjects/Keywords: Minimizing Aggregate Movements; Interval Coverage; Aggregate Movements; Computer Sciences

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APA (6th Edition):

Andrews, A. M. (2014). Minimizing Aggregate Movements for Interval Coverage. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/3893

Chicago Manual of Style (16th Edition):

Andrews, Aaron M. “Minimizing Aggregate Movements for Interval Coverage.” 2014. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/3893.

MLA Handbook (7th Edition):

Andrews, Aaron M. “Minimizing Aggregate Movements for Interval Coverage.” 2014. Web. 09 May 2021.

Vancouver:

Andrews AM. Minimizing Aggregate Movements for Interval Coverage. [Internet] [Masters thesis]. Utah State University; 2014. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/3893.

Council of Science Editors:

Andrews AM. Minimizing Aggregate Movements for Interval Coverage. [Masters Thesis]. Utah State University; 2014. Available from: https://digitalcommons.usu.edu/etd/3893


Utah State University

2. Dontham, Madhavi Reddy. Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform.

Degree: MS, Electrical and Computer Engineering, 2016, Utah State University

  Online crowdfunding platforms provide project creators with new opportunities for seeking funds from people in the world. But reaching a fund-raising goal or making… (more)

Subjects/Keywords: Electrical and Computer Engineering

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APA (6th Edition):

Dontham, M. R. (2016). Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/4958

Chicago Manual of Style (16th Edition):

Dontham, Madhavi Reddy. “Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform.” 2016. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/4958.

MLA Handbook (7th Edition):

Dontham, Madhavi Reddy. “Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform.” 2016. Web. 09 May 2021.

Vancouver:

Dontham MR. Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform. [Internet] [Masters thesis]. Utah State University; 2016. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/4958.

Council of Science Editors:

Dontham MR. Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform. [Masters Thesis]. Utah State University; 2016. Available from: https://digitalcommons.usu.edu/etd/4958


Utah State University

3. Hou, Yantian. Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques.

Degree: PhD, Computer Science, 2016, Utah State University

  In my dissertation, we focus on the wireless network coexistence problem with advanced physical-layer techniques. For the first part, we study the problem of… (more)

Subjects/Keywords: Computer Sciences; Physical Sciences and Mathematics

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APA (6th Edition):

Hou, Y. (2016). Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/4993

Chicago Manual of Style (16th Edition):

Hou, Yantian. “Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques.” 2016. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/4993.

MLA Handbook (7th Edition):

Hou, Yantian. “Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques.” 2016. Web. 09 May 2021.

Vancouver:

Hou Y. Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques. [Internet] [Doctoral dissertation]. Utah State University; 2016. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/4993.

Council of Science Editors:

Hou Y. Interference Mitigation in Multi-Hop Wireless Networks with Advanced Physical-Layer Techniques. [Doctoral Dissertation]. Utah State University; 2016. Available from: https://digitalcommons.usu.edu/etd/4993


Utah State University

4. Lascano, Jorge Edison. A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing.

Degree: PhD, Computer Science, 2017, Utah State University

  Networking protocols have been developed throughout time following layered architectures such as the Open Systems Interconnection model and the Internet model. These protocols are… (more)

Subjects/Keywords: pattern language; application-level communication protocols; improvement; computer science education; cloud computing; Computer Sciences

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APA (6th Edition):

Lascano, J. E. (2017). A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/6547

Chicago Manual of Style (16th Edition):

Lascano, Jorge Edison. “A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing.” 2017. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/6547.

MLA Handbook (7th Edition):

Lascano, Jorge Edison. “A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing.” 2017. Web. 09 May 2021.

Vancouver:

Lascano JE. A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing. [Internet] [Doctoral dissertation]. Utah State University; 2017. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/6547.

Council of Science Editors:

Lascano JE. A Pattern Language for Designing Application-Level Communication Protocols and the Improvement of Computer Science Education through Cloud Computing. [Doctoral Dissertation]. Utah State University; 2017. Available from: https://digitalcommons.usu.edu/etd/6547


Utah State University

5. Le, Minh. Universal Mobile Service Execution Framework for Device-To-Device Collaborations.

Degree: PhD, Computer Science, 2018, Utah State University

  There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or… (more)

Subjects/Keywords: p2p mobile networks; edge computing; cross-platform; group-to-group; mobile remote method invocation; Computer Sciences

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APA (6th Edition):

Le, M. (2018). Universal Mobile Service Execution Framework for Device-To-Device Collaborations. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7032

Chicago Manual of Style (16th Edition):

Le, Minh. “Universal Mobile Service Execution Framework for Device-To-Device Collaborations.” 2018. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7032.

MLA Handbook (7th Edition):

Le, Minh. “Universal Mobile Service Execution Framework for Device-To-Device Collaborations.” 2018. Web. 09 May 2021.

Vancouver:

Le M. Universal Mobile Service Execution Framework for Device-To-Device Collaborations. [Internet] [Doctoral dissertation]. Utah State University; 2018. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7032.

Council of Science Editors:

Le M. Universal Mobile Service Execution Framework for Device-To-Device Collaborations. [Doctoral Dissertation]. Utah State University; 2018. Available from: https://digitalcommons.usu.edu/etd/7032


Utah State University

6. Zhang, Jingru. Geometric Facility Location Problems on Uncertain Data.

Degree: PhD, Computer Science, 2017, Utah State University

  Facility location, as an important topic in computer science and operations research, is concerned with placing facilities for "serving" demand points (each representing a… (more)

Subjects/Keywords: Algorithms; computational geometry; facility location; k-center; uncertain data; Computer Sciences

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APA (6th Edition):

Zhang, J. (2017). Geometric Facility Location Problems on Uncertain Data. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/6337

Chicago Manual of Style (16th Edition):

Zhang, Jingru. “Geometric Facility Location Problems on Uncertain Data.” 2017. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/6337.

MLA Handbook (7th Edition):

Zhang, Jingru. “Geometric Facility Location Problems on Uncertain Data.” 2017. Web. 09 May 2021.

Vancouver:

Zhang J. Geometric Facility Location Problems on Uncertain Data. [Internet] [Doctoral dissertation]. Utah State University; 2017. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/6337.

Council of Science Editors:

Zhang J. Geometric Facility Location Problems on Uncertain Data. [Doctoral Dissertation]. Utah State University; 2017. Available from: https://digitalcommons.usu.edu/etd/6337


Utah State University

7. Suryavanshi, Chetna. Query AutoAwesome.

Degree: MS, Computer Science, 2019, Utah State University

  This research investigates how to improve legacy queries. Legacy queries are queries that programmers have coded and are used in applications. A database application… (more)

Subjects/Keywords: Query; SQL; improve; algorithm; select; Computer Sciences

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APA (6th Edition):

Suryavanshi, C. (2019). Query AutoAwesome. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7546

Chicago Manual of Style (16th Edition):

Suryavanshi, Chetna. “Query AutoAwesome.” 2019. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7546.

MLA Handbook (7th Edition):

Suryavanshi, Chetna. “Query AutoAwesome.” 2019. Web. 09 May 2021.

Vancouver:

Suryavanshi C. Query AutoAwesome. [Internet] [Masters thesis]. Utah State University; 2019. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7546.

Council of Science Editors:

Suryavanshi C. Query AutoAwesome. [Masters Thesis]. Utah State University; 2019. Available from: https://digitalcommons.usu.edu/etd/7546


Utah State University

8. Gupta, Chelsi. Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples.

Degree: MS, Computer Science, 2019, Utah State University

  The beekeepers need to inspect their hives regularly in order to protect them from various stressors. Manual inspection of hives require a lot of… (more)

Subjects/Keywords: Feature Selection; Machine Learning; BeePi; EBM; PyAudioAnalysis features; Computer Sciences

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APA (6th Edition):

Gupta, C. (2019). Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7564

Chicago Manual of Style (16th Edition):

Gupta, Chelsi. “Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples.” 2019. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7564.

MLA Handbook (7th Edition):

Gupta, Chelsi. “Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples.” 2019. Web. 09 May 2021.

Vancouver:

Gupta C. Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples. [Internet] [Masters thesis]. Utah State University; 2019. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7564.

Council of Science Editors:

Gupta C. Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples. [Masters Thesis]. Utah State University; 2019. Available from: https://digitalcommons.usu.edu/etd/7564


Utah State University

9. Goyal, Aayush. Temporal JSON.

Degree: MS, Computer Science, 2019, Utah State University

  JavaScript Object Notation (JSON) is a format for representing data. In this thesis we show how to capture the history of changes to a… (more)

Subjects/Keywords: temporal databases; web services; versioning; JSON; Computer Sciences

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APA (6th Edition):

Goyal, A. (2019). Temporal JSON. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7653

Chicago Manual of Style (16th Edition):

Goyal, Aayush. “Temporal JSON.” 2019. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7653.

MLA Handbook (7th Edition):

Goyal, Aayush. “Temporal JSON.” 2019. Web. 09 May 2021.

Vancouver:

Goyal A. Temporal JSON. [Internet] [Masters thesis]. Utah State University; 2019. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7653.

Council of Science Editors:

Goyal A. Temporal JSON. [Masters Thesis]. Utah State University; 2019. Available from: https://digitalcommons.usu.edu/etd/7653


Utah State University

10. Amlathe, Prakhar. Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine.

Degree: MS, Computer Science, 2018, Utah State University

  Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since… (more)

Subjects/Keywords: BeePi; Machine Learning; Honeybee; Audio Classifier; Beehive Monitoring; Computer Sciences

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APA (6th Edition):

Amlathe, P. (2018). Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7050

Chicago Manual of Style (16th Edition):

Amlathe, Prakhar. “Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine.” 2018. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7050.

MLA Handbook (7th Edition):

Amlathe, Prakhar. “Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine.” 2018. Web. 09 May 2021.

Vancouver:

Amlathe P. Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine. [Internet] [Masters thesis]. Utah State University; 2018. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7050.

Council of Science Editors:

Amlathe P. Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine. [Masters Thesis]. Utah State University; 2018. Available from: https://digitalcommons.usu.edu/etd/7050


Utah State University

11. Khasgiwala, Anuj. Word Recognition in Nutrition Labels with Convolutional Neural Network.

Degree: MS, Computer Science, 2018, Utah State University

  Nowadays, everyone is very busy and running around trying to maintain a balance between their work life and family, as the working hours are… (more)

Subjects/Keywords: Convolutional Neural Network; CNN; Word Recognition; Text Recognition; Deep Learning; Computer Sciences

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APA (6th Edition):

Khasgiwala, A. (2018). Word Recognition in Nutrition Labels with Convolutional Neural Network. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7101

Chicago Manual of Style (16th Edition):

Khasgiwala, Anuj. “Word Recognition in Nutrition Labels with Convolutional Neural Network.” 2018. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7101.

MLA Handbook (7th Edition):

Khasgiwala, Anuj. “Word Recognition in Nutrition Labels with Convolutional Neural Network.” 2018. Web. 09 May 2021.

Vancouver:

Khasgiwala A. Word Recognition in Nutrition Labels with Convolutional Neural Network. [Internet] [Masters thesis]. Utah State University; 2018. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7101.

Council of Science Editors:

Khasgiwala A. Word Recognition in Nutrition Labels with Convolutional Neural Network. [Masters Thesis]. Utah State University; 2018. Available from: https://digitalcommons.usu.edu/etd/7101


Utah State University

12. Peng, Liang. Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.

Degree: PhD, Computer Science, 2017, Utah State University

  This dissertation develops a novel system for object recognition in videos. The input of the system is a set of unconstrained videos containing a… (more)

Subjects/Keywords: Object Detection; Object Recognition; Hierarchical; Temporal; Deep Neural Networks; Computer Sciences

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APA (6th Edition):

Peng, L. (2017). Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/6531

Chicago Manual of Style (16th Edition):

Peng, Liang. “Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.” 2017. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/6531.

MLA Handbook (7th Edition):

Peng, Liang. “Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks.” 2017. Web. 09 May 2021.

Vancouver:

Peng L. Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks. [Internet] [Doctoral dissertation]. Utah State University; 2017. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/6531.

Council of Science Editors:

Peng L. Object Recognition in Videos Utilizing Hierarchical and Temporal Objectness with Deep Neural Networks. [Doctoral Dissertation]. Utah State University; 2017. Available from: https://digitalcommons.usu.edu/etd/6531


Utah State University

13. Rubaiat, Samia. Exploring the Feasibility of Introducing Alternative Fuel Vehicles into Fleet.

Degree: MS, Civil and Environmental Engineering, 2020, Utah State University

  Transportation is one of the most significant contributing sectors to emissions and consequently air pollution in the United States. Many state and private fleet… (more)

Subjects/Keywords: alternative fuel vehicle; fleet replacement; electric vehicle; Civil and Environmental Engineering; Engineering; Transportation Engineering

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APA (6th Edition):

Rubaiat, S. (2020). Exploring the Feasibility of Introducing Alternative Fuel Vehicles into Fleet. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/8002

Chicago Manual of Style (16th Edition):

Rubaiat, Samia. “Exploring the Feasibility of Introducing Alternative Fuel Vehicles into Fleet.” 2020. Masters Thesis, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/8002.

MLA Handbook (7th Edition):

Rubaiat, Samia. “Exploring the Feasibility of Introducing Alternative Fuel Vehicles into Fleet.” 2020. Web. 09 May 2021.

Vancouver:

Rubaiat S. Exploring the Feasibility of Introducing Alternative Fuel Vehicles into Fleet. [Internet] [Masters thesis]. Utah State University; 2020. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/8002.

Council of Science Editors:

Rubaiat S. Exploring the Feasibility of Introducing Alternative Fuel Vehicles into Fleet. [Masters Thesis]. Utah State University; 2020. Available from: https://digitalcommons.usu.edu/etd/8002


Utah State University

14. Javanmardi, Mohammadreza. Deep Learning and Optimization in Visual Target Tracking.

Degree: PhD, Computer Science, 2021, Utah State University

  Visual tracking is the process of estimating states of a moving object in a dynamic frame sequence. It has been considered as one of… (more)

Subjects/Keywords: Convolutional Neural Network; Adversarial Learning; Optimization; Visual Tracking; Computer Sciences; Physical Sciences and Mathematics; Theory and Algorithms

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APA (6th Edition):

Javanmardi, M. (2021). Deep Learning and Optimization in Visual Target Tracking. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/8017

Chicago Manual of Style (16th Edition):

Javanmardi, Mohammadreza. “Deep Learning and Optimization in Visual Target Tracking.” 2021. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/8017.

MLA Handbook (7th Edition):

Javanmardi, Mohammadreza. “Deep Learning and Optimization in Visual Target Tracking.” 2021. Web. 09 May 2021.

Vancouver:

Javanmardi M. Deep Learning and Optimization in Visual Target Tracking. [Internet] [Doctoral dissertation]. Utah State University; 2021. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/8017.

Council of Science Editors:

Javanmardi M. Deep Learning and Optimization in Visual Target Tracking. [Doctoral Dissertation]. Utah State University; 2021. Available from: https://digitalcommons.usu.edu/etd/8017


Utah State University

15. Farzaneh, Amir Hossein. Facial Expression Recognition in the Wild Using Convolutional Neural Networks.

Degree: PhD, Computer Science, 2020, Utah State University

  Facial Expression Recognition (FER) is the task of predicting a specific facial expression given a facial image. FER has demonstrated remarkable progress due to… (more)

Subjects/Keywords: facial expression recognition; wild; convolutional neural network; deep learning; discriminant; loss function; attention; adaptive; emotion; Computer Sciences

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APA (6th Edition):

Farzaneh, A. H. (2020). Facial Expression Recognition in the Wild Using Convolutional Neural Networks. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7851

Chicago Manual of Style (16th Edition):

Farzaneh, Amir Hossein. “Facial Expression Recognition in the Wild Using Convolutional Neural Networks.” 2020. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7851.

MLA Handbook (7th Edition):

Farzaneh, Amir Hossein. “Facial Expression Recognition in the Wild Using Convolutional Neural Networks.” 2020. Web. 09 May 2021.

Vancouver:

Farzaneh AH. Facial Expression Recognition in the Wild Using Convolutional Neural Networks. [Internet] [Doctoral dissertation]. Utah State University; 2020. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7851.

Council of Science Editors:

Farzaneh AH. Facial Expression Recognition in the Wild Using Convolutional Neural Networks. [Doctoral Dissertation]. Utah State University; 2020. Available from: https://digitalcommons.usu.edu/etd/7851


Utah State University

16. Xu, Fei. Visual Saliency Estimation and Its Applications.

Degree: PhD, Computer Science, 2020, Utah State University

  The human visual system can automatically emphasize some parts of the image and ignore the other parts when seeing an image or a scene.… (more)

Subjects/Keywords: Visual Saliency Estimation; Tumor Saliency Estimation; Breast Anatomy Modeling; Semantic Breast Anatomy; Adaptive-center Bias; Computer Sciences

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APA (6th Edition):

Xu, F. (2020). Visual Saliency Estimation and Its Applications. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7820

Chicago Manual of Style (16th Edition):

Xu, Fei. “Visual Saliency Estimation and Its Applications.” 2020. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7820.

MLA Handbook (7th Edition):

Xu, Fei. “Visual Saliency Estimation and Its Applications.” 2020. Web. 09 May 2021.

Vancouver:

Xu F. Visual Saliency Estimation and Its Applications. [Internet] [Doctoral dissertation]. Utah State University; 2020. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7820.

Council of Science Editors:

Xu F. Visual Saliency Estimation and Its Applications. [Doctoral Dissertation]. Utah State University; 2020. Available from: https://digitalcommons.usu.edu/etd/7820

17. Mansourbeigi, Seyed M-H. Sheaf Theory as a Foundation for Heterogeneous Data Fusion.

Degree: PhD, Computer Science, 2018, Utah State University

  A major impediment to scientific progress in many fields is the inability to make sense of the huge amounts of data that have been… (more)

Subjects/Keywords: Simplicial Complex; Sheaf; Stalks; Cohomology; Topological Data Analysis; Computer Sciences

…Minghui Jiang, Dr. Haitao Wang, Dr. Zhaohu Nie, and Dr. Jia Zhao, for their great comments and… …State University. Their help, support and friendship are really helpful during my PhD program… …the Computer Science, Electrical Engineering, Physics, and Mathematics departments at Utah… 

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APA (6th Edition):

Mansourbeigi, S. M. (2018). Sheaf Theory as a Foundation for Heterogeneous Data Fusion. (Doctoral Dissertation). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/7363

Chicago Manual of Style (16th Edition):

Mansourbeigi, Seyed M-H. “Sheaf Theory as a Foundation for Heterogeneous Data Fusion.” 2018. Doctoral Dissertation, Utah State University. Accessed May 09, 2021. https://digitalcommons.usu.edu/etd/7363.

MLA Handbook (7th Edition):

Mansourbeigi, Seyed M-H. “Sheaf Theory as a Foundation for Heterogeneous Data Fusion.” 2018. Web. 09 May 2021.

Vancouver:

Mansourbeigi SM. Sheaf Theory as a Foundation for Heterogeneous Data Fusion. [Internet] [Doctoral dissertation]. Utah State University; 2018. [cited 2021 May 09]. Available from: https://digitalcommons.usu.edu/etd/7363.

Council of Science Editors:

Mansourbeigi SM. Sheaf Theory as a Foundation for Heterogeneous Data Fusion. [Doctoral Dissertation]. Utah State University; 2018. Available from: https://digitalcommons.usu.edu/etd/7363

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