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You searched for +publisher:"Virginia Tech" +contributor:("Batra, Dhruv"). Showing records 1 – 26 of 26 total matches.

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Virginia Tech

1. Byalik, Antuan. Automated Cross-Platform Code Synthesis from Web-Based Programming Resources.

Degree: MS, Computer Science, 2015, Virginia Tech

 For maximal market penetration, popular mobile applications are typically supported on all major platforms, including Android and iOS. Despite the vast differences in the look-and-feel… (more)

Subjects/Keywords: Recommendation Systems; Code Synthesis; Mobile Computing; Android; iOS; Java; Swift

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APA (6th Edition):

Byalik, A. (2015). Automated Cross-Platform Code Synthesis from Web-Based Programming Resources. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/55273

Chicago Manual of Style (16th Edition):

Byalik, Antuan. “Automated Cross-Platform Code Synthesis from Web-Based Programming Resources.” 2015. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/55273.

MLA Handbook (7th Edition):

Byalik, Antuan. “Automated Cross-Platform Code Synthesis from Web-Based Programming Resources.” 2015. Web. 19 Oct 2019.

Vancouver:

Byalik A. Automated Cross-Platform Code Synthesis from Web-Based Programming Resources. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/55273.

Council of Science Editors:

Byalik A. Automated Cross-Platform Code Synthesis from Web-Based Programming Resources. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/55273


Virginia Tech

2. Mahendru, Aroma. Role of Premises in Visual Question Answering.

Degree: MS, Electrical and Computer Engineering, 2017, Virginia Tech

 In this work, we make a simple but important observation questions about images often contain premises  – objects and relationships implied by the question  –… (more)

Subjects/Keywords: Machine Learning; Natural Language Processing; Computer Vision; Artificial Intelligence

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APA (6th Edition):

Mahendru, A. (2017). Role of Premises in Visual Question Answering. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78030

Chicago Manual of Style (16th Edition):

Mahendru, Aroma. “Role of Premises in Visual Question Answering.” 2017. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/78030.

MLA Handbook (7th Edition):

Mahendru, Aroma. “Role of Premises in Visual Question Answering.” 2017. Web. 19 Oct 2019.

Vancouver:

Mahendru A. Role of Premises in Visual Question Answering. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/78030.

Council of Science Editors:

Mahendru A. Role of Premises in Visual Question Answering. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78030


Virginia Tech

3. Cogswell, Michael Andrew. Understanding Representations and Reducing their Redundancy in Deep Networks.

Degree: MS, Computer Science, 2016, Virginia Tech

 Neural networks in their modern deep learning incarnation have achieved state of the art performance on a wide variety of tasks and domains. A core… (more)

Subjects/Keywords: Object Recognition; Overfitting; Computer Vision; Machine Learning

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APA (6th Edition):

Cogswell, M. A. (2016). Understanding Representations and Reducing their Redundancy in Deep Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78167

Chicago Manual of Style (16th Edition):

Cogswell, Michael Andrew. “Understanding Representations and Reducing their Redundancy in Deep Networks.” 2016. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/78167.

MLA Handbook (7th Edition):

Cogswell, Michael Andrew. “Understanding Representations and Reducing their Redundancy in Deep Networks.” 2016. Web. 19 Oct 2019.

Vancouver:

Cogswell MA. Understanding Representations and Reducing their Redundancy in Deep Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/78167.

Council of Science Editors:

Cogswell MA. Understanding Representations and Reducing their Redundancy in Deep Networks. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78167


Virginia Tech

4. Sundareisan, Shashidhar. Making diffusion work for you: Classification sans text, finding culprits and filling missing values.

Degree: MS, Computer Science, 2014, Virginia Tech

 Can we find people infected with the flu virus even though they did not visit a doctor? Can the temporal features of a trending hashtag… (more)

Subjects/Keywords: Data Mining; Social Networks; Epidemiology; Culprits; Missing nodes; Diffusion; Protests; Classification

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APA (6th Edition):

Sundareisan, S. (2014). Making diffusion work for you: Classification sans text, finding culprits and filling missing values. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/49678

Chicago Manual of Style (16th Edition):

Sundareisan, Shashidhar. “Making diffusion work for you: Classification sans text, finding culprits and filling missing values.” 2014. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/49678.

MLA Handbook (7th Edition):

Sundareisan, Shashidhar. “Making diffusion work for you: Classification sans text, finding culprits and filling missing values.” 2014. Web. 19 Oct 2019.

Vancouver:

Sundareisan S. Making diffusion work for you: Classification sans text, finding culprits and filling missing values. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/49678.

Council of Science Editors:

Sundareisan S. Making diffusion work for you: Classification sans text, finding culprits and filling missing values. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/49678


Virginia Tech

5. Banik, Prakriti. Vision and Radar Fusion for Identification of Vehicles in Traffic.

Degree: MS, Electrical and Computer Engineering, 2015, Virginia Tech

 This report presents a method for estimating the presence and duration of preceding and lead vehicle in front of a motorcycle using an object detection… (more)

Subjects/Keywords: radar object validation; car detection; lead vehicle detection; radar-vision fusion

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APA (6th Edition):

Banik, P. (2015). Vision and Radar Fusion for Identification of Vehicles in Traffic. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/55121

Chicago Manual of Style (16th Edition):

Banik, Prakriti. “Vision and Radar Fusion for Identification of Vehicles in Traffic.” 2015. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/55121.

MLA Handbook (7th Edition):

Banik, Prakriti. “Vision and Radar Fusion for Identification of Vehicles in Traffic.” 2015. Web. 19 Oct 2019.

Vancouver:

Banik P. Vision and Radar Fusion for Identification of Vehicles in Traffic. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/55121.

Council of Science Editors:

Banik P. Vision and Radar Fusion for Identification of Vehicles in Traffic. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/55121


Virginia Tech

6. Ailsworth Jr., James William. Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces.

Degree: MS, Mechanical Engineering, 2016, Virginia Tech

 Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been… (more)

Subjects/Keywords: Brain-Computer Interface; Motor Imagery; Common Spatial Patterns; Riemannian Geometry

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APA (6th Edition):

Ailsworth Jr., J. W. (2016). Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71371

Chicago Manual of Style (16th Edition):

Ailsworth Jr., James William. “Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces.” 2016. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/71371.

MLA Handbook (7th Edition):

Ailsworth Jr., James William. “Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces.” 2016. Web. 19 Oct 2019.

Vancouver:

Ailsworth Jr. JW. Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/71371.

Council of Science Editors:

Ailsworth Jr. JW. Comparison and Development of Algorithms for Motor Imagery Classification in EEG- based Brain-Computer Interfaces. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71371


Virginia Tech

7. Agrawal, Harsh. CloudCV: Deep Learning and Computer Vision on the Cloud.

Degree: MS, Electrical and Computer Engineering, 2016, Virginia Tech

 We are witnessing a proliferation of massive visual data. Visual content is arguably the fastest growing data on the web. Photo-sharing websites like Flickr and… (more)

Subjects/Keywords: Deep Learning; Computer Vision; Cloud Computing

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APA (6th Edition):

Agrawal, H. (2016). CloudCV: Deep Learning and Computer Vision on the Cloud. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71381

Chicago Manual of Style (16th Edition):

Agrawal, Harsh. “CloudCV: Deep Learning and Computer Vision on the Cloud.” 2016. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/71381.

MLA Handbook (7th Edition):

Agrawal, Harsh. “CloudCV: Deep Learning and Computer Vision on the Cloud.” 2016. Web. 19 Oct 2019.

Vancouver:

Agrawal H. CloudCV: Deep Learning and Computer Vision on the Cloud. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/71381.

Council of Science Editors:

Agrawal H. CloudCV: Deep Learning and Computer Vision on the Cloud. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71381


Virginia Tech

8. Granstedt, Jason Louis. Data Augmentation with Seq2Seq Models.

Degree: MS, Electrical and Computer Engineering, 2017, Virginia Tech

 Paraphrase sparsity is an issue that complicates the training process of question answering systems: syntactically diverse but semantically equivalent sentences can have significant disparities in… (more)

Subjects/Keywords: Data Augmentation; Seq2Seq; Diverse Beam Search; VQA

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APA (6th Edition):

Granstedt, J. L. (2017). Data Augmentation with Seq2Seq Models. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78315

Chicago Manual of Style (16th Edition):

Granstedt, Jason Louis. “Data Augmentation with Seq2Seq Models.” 2017. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/78315.

MLA Handbook (7th Edition):

Granstedt, Jason Louis. “Data Augmentation with Seq2Seq Models.” 2017. Web. 19 Oct 2019.

Vancouver:

Granstedt JL. Data Augmentation with Seq2Seq Models. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/78315.

Council of Science Editors:

Granstedt JL. Data Augmentation with Seq2Seq Models. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78315


Virginia Tech

9. Mohapatra, Akrit. Natural Language Driven Image Edits using a Semantic Image Manipulation Language.

Degree: MS, Electrical and Computer Engineering, 2018, Virginia Tech

 Language provides us with a powerful tool to articulate and express ourselves! Understanding and harnessing the expressions of natural language can open the doors to… (more)

Subjects/Keywords: Machine Learning; Natural language Processing; Computer Vision

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APA (6th Edition):

Mohapatra, A. (2018). Natural Language Driven Image Edits using a Semantic Image Manipulation Language. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83452

Chicago Manual of Style (16th Edition):

Mohapatra, Akrit. “Natural Language Driven Image Edits using a Semantic Image Manipulation Language.” 2018. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/83452.

MLA Handbook (7th Edition):

Mohapatra, Akrit. “Natural Language Driven Image Edits using a Semantic Image Manipulation Language.” 2018. Web. 19 Oct 2019.

Vancouver:

Mohapatra A. Natural Language Driven Image Edits using a Semantic Image Manipulation Language. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/83452.

Council of Science Editors:

Mohapatra A. Natural Language Driven Image Edits using a Semantic Image Manipulation Language. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83452

10. Pemula, Latha. Low-shot Visual Recognition.

Degree: MS, Electrical and Computer Engineering, 2016, Virginia Tech

 Many real world datasets are characterized by having a long tailed distribution, with several samples for some classes and only a few samples for other… (more)

Subjects/Keywords: Visual Recognition; Object Recognition; Computer Vision; Low-shot Learning

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APA (6th Edition):

Pemula, L. (2016). Low-shot Visual Recognition. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73321

Chicago Manual of Style (16th Edition):

Pemula, Latha. “Low-shot Visual Recognition.” 2016. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/73321.

MLA Handbook (7th Edition):

Pemula, Latha. “Low-shot Visual Recognition.” 2016. Web. 19 Oct 2019.

Vancouver:

Pemula L. Low-shot Visual Recognition. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/73321.

Council of Science Editors:

Pemula L. Low-shot Visual Recognition. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73321

11. Mathialagan, Clint Solomon. VIP: Finding Important People in Images.

Degree: MS, Electrical and Computer Engineering, 2015, Virginia Tech

 People preserve memories of events such as birthdays, weddings, or vacations by capturing photos, often depicting groups of people. Invariably, some individuals in the image… (more)

Subjects/Keywords: Copmuter Vision; Machine Learning; Importance

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APA (6th Edition):

Mathialagan, C. S. (2015). VIP: Finding Important People in Images. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/53706

Chicago Manual of Style (16th Edition):

Mathialagan, Clint Solomon. “VIP: Finding Important People in Images.” 2015. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/53706.

MLA Handbook (7th Edition):

Mathialagan, Clint Solomon. “VIP: Finding Important People in Images.” 2015. Web. 19 Oct 2019.

Vancouver:

Mathialagan CS. VIP: Finding Important People in Images. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/53706.

Council of Science Editors:

Mathialagan CS. VIP: Finding Important People in Images. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/53706

12. Lad, Shrenik. Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations.

Degree: MS, Electrical and Computer Engineering, 2015, Virginia Tech

 Unsupervised image clustering is a challenging and often ill-posed problem. Existing image descriptors fail to capture the clustering criterion well, and more importantly, the criterion… (more)

Subjects/Keywords: Computer Vision; Semi-Supervised Clustering; Attributes; Human-Machine Communication

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APA (6th Edition):

Lad, S. (2015). Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/54002

Chicago Manual of Style (16th Edition):

Lad, Shrenik. “Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations.” 2015. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/54002.

MLA Handbook (7th Edition):

Lad, Shrenik. “Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations.” 2015. Web. 19 Oct 2019.

Vancouver:

Lad S. Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/54002.

Council of Science Editors:

Lad S. Interactively Guiding Semi-Supervised Clustering via Attribute-based Explanations. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/54002

13. Alfadda, Abdullah Ibrahim A. Temporal Frame Difference Using Averaging Filter for Maritime Surveillance.

Degree: MS, Electrical and Computer Engineering, 2015, Virginia Tech

 Video surveillance is an active research area in Computer Vision and Machine Learning. It received a lot of attention in the last few decades. Maritime… (more)

Subjects/Keywords: Image Processing; Machine Learning; Surveillance Systems; Maritime Surveillance

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APA (6th Edition):

Alfadda, A. I. A. (2015). Temporal Frame Difference Using Averaging Filter for Maritime Surveillance. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/56583

Chicago Manual of Style (16th Edition):

Alfadda, Abdullah Ibrahim A. “Temporal Frame Difference Using Averaging Filter for Maritime Surveillance.” 2015. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/56583.

MLA Handbook (7th Edition):

Alfadda, Abdullah Ibrahim A. “Temporal Frame Difference Using Averaging Filter for Maritime Surveillance.” 2015. Web. 19 Oct 2019.

Vancouver:

Alfadda AIA. Temporal Frame Difference Using Averaging Filter for Maritime Surveillance. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/56583.

Council of Science Editors:

Alfadda AIA. Temporal Frame Difference Using Averaging Filter for Maritime Surveillance. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/56583

14. Chavali, Neelima. Object Proposals in Computer Vision.

Degree: MS, Electrical and Computer Engineering, 2015, Virginia Tech

 Object recognition is a central problem in computer vision which deals with both localizing and identifying objects in images. Object proposals have recently become an… (more)

Subjects/Keywords: Object proposals; evaluation; computer vision

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APA (6th Edition):

Chavali, N. (2015). Object Proposals in Computer Vision. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/56590

Chicago Manual of Style (16th Edition):

Chavali, Neelima. “Object Proposals in Computer Vision.” 2015. Masters Thesis, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/56590.

MLA Handbook (7th Edition):

Chavali, Neelima. “Object Proposals in Computer Vision.” 2015. Web. 19 Oct 2019.

Vancouver:

Chavali N. Object Proposals in Computer Vision. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/56590.

Council of Science Editors:

Chavali N. Object Proposals in Computer Vision. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/56590


Virginia Tech

15. Christie, Gordon Andrew. Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization.

Degree: PhD, Electrical and ComputerEngineering, 2017, Virginia Tech

 Autonomous robot missions in unknown environments are challenging. In many cases, the systems involved are unable to use a priori information about the scene (e.g.… (more)

Subjects/Keywords: Scene Understanding; Semantic Segmentation; Unmanned Systems; UAV; UGV; Path Planning

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APA (6th Edition):

Christie, G. A. (2017). Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83807

Chicago Manual of Style (16th Edition):

Christie, Gordon Andrew. “Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/83807.

MLA Handbook (7th Edition):

Christie, Gordon Andrew. “Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization.” 2017. Web. 19 Oct 2019.

Vancouver:

Christie GA. Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/83807.

Council of Science Editors:

Christie GA. Collaborative Unmanned Air and Ground Vehicle Perception for Scene Understanding, Planning and GPS-denied Localization. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/83807


Virginia Tech

16. Lama Salomon, Abraham. Digital State Models for Infrastructure Condition Assessment and Structural Testing.

Degree: PhD, Civil and Environmental Engineering, 2017, Virginia Tech

 This research introduces and applies the concept of digital state models for civil infrastructure condition assessment and structural testing. Digital state models are defined herein… (more)

Subjects/Keywords: Digital state model; Condition assessment; Non-contact measurement; Computer vision; Point cloud; Crack detection; Change detection; Corrosion resistant; Bridge

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APA (6th Edition):

Lama Salomon, A. (2017). Digital State Models for Infrastructure Condition Assessment and Structural Testing. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/84502

Chicago Manual of Style (16th Edition):

Lama Salomon, Abraham. “Digital State Models for Infrastructure Condition Assessment and Structural Testing.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/84502.

MLA Handbook (7th Edition):

Lama Salomon, Abraham. “Digital State Models for Infrastructure Condition Assessment and Structural Testing.” 2017. Web. 19 Oct 2019.

Vancouver:

Lama Salomon A. Digital State Models for Infrastructure Condition Assessment and Structural Testing. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/84502.

Council of Science Editors:

Lama Salomon A. Digital State Models for Infrastructure Condition Assessment and Structural Testing. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/84502


Virginia Tech

17. Wu, Hao. Probabilistic Modeling of Multi-relational and Multivariate Discrete Data.

Degree: PhD, Electrical and Computer Engineering, 2017, Virginia Tech

 Modeling and discovering knowledge from multi-relational and multivariate discrete data is a crucial task that arises in many research and application domains, e.g. text mining,… (more)

Subjects/Keywords: Multivariate Discrete Data; Multi-relational Data; Maximum Entropy Modeling; Subjective Interestingness; Latent Variable Model; Multivariate Poisson Regression; Covariance Estimation.

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APA (6th Edition):

Wu, H. (2017). Probabilistic Modeling of Multi-relational and Multivariate Discrete Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/74959

Chicago Manual of Style (16th Edition):

Wu, Hao. “Probabilistic Modeling of Multi-relational and Multivariate Discrete Data.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/74959.

MLA Handbook (7th Edition):

Wu, Hao. “Probabilistic Modeling of Multi-relational and Multivariate Discrete Data.” 2017. Web. 19 Oct 2019.

Vancouver:

Wu H. Probabilistic Modeling of Multi-relational and Multivariate Discrete Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/74959.

Council of Science Editors:

Wu H. Probabilistic Modeling of Multi-relational and Multivariate Discrete Data. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/74959


Virginia Tech

18. Durbeck, Lisa J. Global Energy Conservation in Large Data Networks.

Degree: PhD, Electrical and ComputerEngineering, 2016, Virginia Tech

 Seven to ten percent of the energy used globally goes towards powering information and communications technology (ICT): the global data- and telecommunications network, the private… (more)

Subjects/Keywords: Computer engineering; Electrical Engineering; System science; energy; networks; computing; communication; ICT; energy-efficiency; routers; in-network processing

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APA (6th Edition):

Durbeck, L. J. (2016). Global Energy Conservation in Large Data Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78291

Chicago Manual of Style (16th Edition):

Durbeck, Lisa J. “Global Energy Conservation in Large Data Networks.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/78291.

MLA Handbook (7th Edition):

Durbeck, Lisa J. “Global Energy Conservation in Large Data Networks.” 2016. Web. 19 Oct 2019.

Vancouver:

Durbeck LJ. Global Energy Conservation in Large Data Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/78291.

Council of Science Editors:

Durbeck LJ. Global Energy Conservation in Large Data Networks. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78291


Virginia Tech

19. Amuru, Saidhiraj. Intelligent Approaches for Communication Denial.

Degree: PhD, Electrical and Computer Engineering, 2015, Virginia Tech

 Spectrum supremacy is a vital part of security in the modern era. In the past 50 years, a great deal of work has been devoted… (more)

Subjects/Keywords: Communication; Denial; Jamming; Learning

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APA (6th Edition):

Amuru, S. (2015). Intelligent Approaches for Communication Denial. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/56695

Chicago Manual of Style (16th Edition):

Amuru, Saidhiraj. “Intelligent Approaches for Communication Denial.” 2015. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/56695.

MLA Handbook (7th Edition):

Amuru, Saidhiraj. “Intelligent Approaches for Communication Denial.” 2015. Web. 19 Oct 2019.

Vancouver:

Amuru S. Intelligent Approaches for Communication Denial. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/56695.

Council of Science Editors:

Amuru S. Intelligent Approaches for Communication Denial. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/56695


Virginia Tech

20. Aly, Sherin Fathy Mohammed Gaber. Techniques for Facial Expression Recognition Using the Kinect.

Degree: PhD, Computer Engineering, 2016, Virginia Tech

 Facial expressions convey non-verbal cues. Humans use facial expressions to show emotions, which play an important role in interpersonal relations and can be of use… (more)

Subjects/Keywords: Facial Expression Recognition; FACS; Dual KDA; EMFACS; Action Units; ASD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Aly, S. F. M. G. (2016). Techniques for Facial Expression Recognition Using the Kinect. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/89220

Chicago Manual of Style (16th Edition):

Aly, Sherin Fathy Mohammed Gaber. “Techniques for Facial Expression Recognition Using the Kinect.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/89220.

MLA Handbook (7th Edition):

Aly, Sherin Fathy Mohammed Gaber. “Techniques for Facial Expression Recognition Using the Kinect.” 2016. Web. 19 Oct 2019.

Vancouver:

Aly SFMG. Techniques for Facial Expression Recognition Using the Kinect. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/89220.

Council of Science Editors:

Aly SFMG. Techniques for Facial Expression Recognition Using the Kinect. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/89220


Virginia Tech

21. Sun, Qing. Greedy Inference Algorithms for Structured and Neural Models.

Degree: PhD, Electrical Engineering, 2018, Virginia Tech

 A number of problems in Computer Vision, Natural Language Processing, and Machine Learning produce structured outputs in high-dimensional space, which makes searching for the global… (more)

Subjects/Keywords: greedy algorithm; natural language processing; graph models; recurrent neural networks; beam search

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APA (6th Edition):

Sun, Q. (2018). Greedy Inference Algorithms for Structured and Neural Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81860

Chicago Manual of Style (16th Edition):

Sun, Qing. “Greedy Inference Algorithms for Structured and Neural Models.” 2018. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/81860.

MLA Handbook (7th Edition):

Sun, Qing. “Greedy Inference Algorithms for Structured and Neural Models.” 2018. Web. 19 Oct 2019.

Vancouver:

Sun Q. Greedy Inference Algorithms for Structured and Neural Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/81860.

Council of Science Editors:

Sun Q. Greedy Inference Algorithms for Structured and Neural Models. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/81860


Virginia Tech

22. Retty, Hema. Load Modeling using Synchrophasor Data for Improved Contingency Analysis.

Degree: PhD, Electrical and ComputerEngineering, 2016, Virginia Tech

 For decades, researchers have sought to make the North American power system as reliable as possible with many security measures in place to include redundancy.… (more)

Subjects/Keywords: Power Systems; Machine Learning; Load Modeling; Neural Networks; Phasor Measurement Unit; PMU

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APA (6th Edition):

Retty, H. (2016). Load Modeling using Synchrophasor Data for Improved Contingency Analysis. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78328

Chicago Manual of Style (16th Edition):

Retty, Hema. “Load Modeling using Synchrophasor Data for Improved Contingency Analysis.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/78328.

MLA Handbook (7th Edition):

Retty, Hema. “Load Modeling using Synchrophasor Data for Improved Contingency Analysis.” 2016. Web. 19 Oct 2019.

Vancouver:

Retty H. Load Modeling using Synchrophasor Data for Improved Contingency Analysis. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/78328.

Council of Science Editors:

Retty H. Load Modeling using Synchrophasor Data for Improved Contingency Analysis. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78328

23. Lin, Xiao. Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks.

Degree: PhD, Electrical and Computer Engineering, 2017, Virginia Tech

 Learning and reasoning with common sense is a challenging problem in Artificial Intelligence (AI). Humans have the remarkable ability to interpret images and text from… (more)

Subjects/Keywords: Common Sense; Multimodal; Visual Question Answering; Image-Caption Ranking; Vision and Language; Active Learning

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APA (6th Edition):

Lin, X. (2017). Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79521

Chicago Manual of Style (16th Edition):

Lin, Xiao. “Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/79521.

MLA Handbook (7th Edition):

Lin, Xiao. “Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks.” 2017. Web. 19 Oct 2019.

Vancouver:

Lin X. Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/79521.

Council of Science Editors:

Lin X. Leveraging Multimodal Perspectives to Learn Common Sense for Vision and Language Tasks. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/79521

24. Ghannam, Sherin Ghannam. Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data.

Degree: PhD, Electrical and Computer Engineering, 2017, Virginia Tech

 The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years… (more)

Subjects/Keywords: Fusion; Multitemporal; Multisensor; Landsat

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ghannam, S. G. (2017). Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81092

Chicago Manual of Style (16th Edition):

Ghannam, Sherin Ghannam. “Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data.” 2017. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/81092.

MLA Handbook (7th Edition):

Ghannam, Sherin Ghannam. “Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data.” 2017. Web. 19 Oct 2019.

Vancouver:

Ghannam SG. Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/81092.

Council of Science Editors:

Ghannam SG. Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/81092

25. Uzair Gilani, Syed. Biodiversity and dynamics of direction finding accuracy in bat biosonar.

Degree: PhD, Electrical and Computer Engineering, 2016, Virginia Tech

 In the biosonar systems of bats, emitted acoustic energy and receiver sensitivity are distributed over direction and frequency through beampattern functions that have diverse and… (more)

Subjects/Keywords: Biodiversity; dynamics; bats

…number of pages: 104 estimated publication date: 03/20/2016 distribution: Virginia Tech Please… …20/2016 distribution: Virginia Tech Please respond directly to the person making the… 

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APA (6th Edition):

Uzair Gilani, S. (2016). Biodiversity and dynamics of direction finding accuracy in bat biosonar. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/65005

Chicago Manual of Style (16th Edition):

Uzair Gilani, Syed. “Biodiversity and dynamics of direction finding accuracy in bat biosonar.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/65005.

MLA Handbook (7th Edition):

Uzair Gilani, Syed. “Biodiversity and dynamics of direction finding accuracy in bat biosonar.” 2016. Web. 19 Oct 2019.

Vancouver:

Uzair Gilani S. Biodiversity and dynamics of direction finding accuracy in bat biosonar. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/65005.

Council of Science Editors:

Uzair Gilani S. Biodiversity and dynamics of direction finding accuracy in bat biosonar. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/65005

26. Jakubisin, Daniel Joseph. Advances in Iterative Probabilistic Processing for Communication Receivers.

Degree: PhD, Electrical and Computer Engineering, 2016, Virginia Tech

 As wireless communication systems continue to push the limits of energy and spectral efficiency, increased demands are placed on the capabilities of the receiver. At… (more)

Subjects/Keywords: Iterative Receivers; Factor Graphs; Belief Propagation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jakubisin, D. J. (2016). Advances in Iterative Probabilistic Processing for Communication Receivers. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71640

Chicago Manual of Style (16th Edition):

Jakubisin, Daniel Joseph. “Advances in Iterative Probabilistic Processing for Communication Receivers.” 2016. Doctoral Dissertation, Virginia Tech. Accessed October 19, 2019. http://hdl.handle.net/10919/71640.

MLA Handbook (7th Edition):

Jakubisin, Daniel Joseph. “Advances in Iterative Probabilistic Processing for Communication Receivers.” 2016. Web. 19 Oct 2019.

Vancouver:

Jakubisin DJ. Advances in Iterative Probabilistic Processing for Communication Receivers. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/10919/71640.

Council of Science Editors:

Jakubisin DJ. Advances in Iterative Probabilistic Processing for Communication Receivers. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/71640

.