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University: Georgia Tech

You searched for subject:(Machine learning ). Showing records 1 – 30 of 160 total matches.

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Georgia Tech

1. Hantrakul, Lamtharn. Regressing Dexterous Finger Flexions Using Machine Learning and Multi-channel Single Element Ultrasound Transducers.

Degree: MS, Music, 2018, Georgia Tech

 Human Machine Interfaces or "HMI's" come in many shapes and sizes. The mouse and keyboard is a typical and familiar HMI. In applications such as… (more)

Subjects/Keywords: Machine Learning; Ultrasound

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APA (6th Edition):

Hantrakul, L. (2018). Regressing Dexterous Finger Flexions Using Machine Learning and Multi-channel Single Element Ultrasound Transducers. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61173

Chicago Manual of Style (16th Edition):

Hantrakul, Lamtharn. “Regressing Dexterous Finger Flexions Using Machine Learning and Multi-channel Single Element Ultrasound Transducers.” 2018. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61173.

MLA Handbook (7th Edition):

Hantrakul, Lamtharn. “Regressing Dexterous Finger Flexions Using Machine Learning and Multi-channel Single Element Ultrasound Transducers.” 2018. Web. 23 Sep 2019.

Vancouver:

Hantrakul L. Regressing Dexterous Finger Flexions Using Machine Learning and Multi-channel Single Element Ultrasound Transducers. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61173.

Council of Science Editors:

Hantrakul L. Regressing Dexterous Finger Flexions Using Machine Learning and Multi-channel Single Element Ultrasound Transducers. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61173


Georgia Tech

2. Shegheva, Snejana. A computational model for solving raven’s progressive matrices intelligence test.

Degree: MS, Computer Science, 2018, Georgia Tech

 Graphical models offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling… (more)

Subjects/Keywords: Probabilistic graphical models; Machine learning

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APA (6th Edition):

Shegheva, S. (2018). A computational model for solving raven’s progressive matrices intelligence test. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60191

Chicago Manual of Style (16th Edition):

Shegheva, Snejana. “A computational model for solving raven’s progressive matrices intelligence test.” 2018. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60191.

MLA Handbook (7th Edition):

Shegheva, Snejana. “A computational model for solving raven’s progressive matrices intelligence test.” 2018. Web. 23 Sep 2019.

Vancouver:

Shegheva S. A computational model for solving raven’s progressive matrices intelligence test. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60191.

Council of Science Editors:

Shegheva S. A computational model for solving raven’s progressive matrices intelligence test. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60191


Georgia Tech

3. Mukadam, Mustafa. Structured Learning and Inference for Robot Motion Generation.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 The ability to generate motions that accomplish desired tasks is fundamental to any robotic system. Robots must be able to generate such motions in a… (more)

Subjects/Keywords: Motion Planning; Machine Learning

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APA (6th Edition):

Mukadam, M. (2019). Structured Learning and Inference for Robot Motion Generation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61714

Chicago Manual of Style (16th Edition):

Mukadam, Mustafa. “Structured Learning and Inference for Robot Motion Generation.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61714.

MLA Handbook (7th Edition):

Mukadam, Mustafa. “Structured Learning and Inference for Robot Motion Generation.” 2019. Web. 23 Sep 2019.

Vancouver:

Mukadam M. Structured Learning and Inference for Robot Motion Generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61714.

Council of Science Editors:

Mukadam M. Structured Learning and Inference for Robot Motion Generation. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61714

4. Byun, Byungki. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

 This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having… (more)

Subjects/Keywords: Discriminative learning; Semi-supervised learning; Incremental learning; Image modeling; Multi-view learning; Machine learning; Supervised learning (Machine learning); Boosting (Algorithms)

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APA (6th Edition):

Byun, B. (2012). On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43597

Chicago Manual of Style (16th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/43597.

MLA Handbook (7th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Web. 23 Sep 2019.

Vancouver:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/43597.

Council of Science Editors:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43597


Georgia Tech

5. Na, Taesik. Energy efficient, secure and noise robust deep learning for the internet of things.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 The objective of this research is to design an energy efficient, secure and noise robust deep learning system for the Internet of Things (IoTs). The… (more)

Subjects/Keywords: Deep learning; Adversarial machine learning; Energy efficient training; Noise robust machine learning; IoT

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APA (6th Edition):

Na, T. (2018). Energy efficient, secure and noise robust deep learning for the internet of things. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60293

Chicago Manual of Style (16th Edition):

Na, Taesik. “Energy efficient, secure and noise robust deep learning for the internet of things.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60293.

MLA Handbook (7th Edition):

Na, Taesik. “Energy efficient, secure and noise robust deep learning for the internet of things.” 2018. Web. 23 Sep 2019.

Vancouver:

Na T. Energy efficient, secure and noise robust deep learning for the internet of things. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60293.

Council of Science Editors:

Na T. Energy efficient, secure and noise robust deep learning for the internet of things. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60293


Georgia Tech

6. Berlind, Christopher. New insights on the power of active learning.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in… (more)

Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning

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APA (6th Edition):

Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948

Chicago Manual of Style (16th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 23 Sep 2019.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/53948.

Council of Science Editors:

Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948


Georgia Tech

7. Hermans, Tucker Ryer. Representing and learning affordance-based behaviors.

Degree: PhD, Interactive Computing, 2014, Georgia Tech

 Autonomous robots deployed in complex, natural human environments such as homes and offices need to manipulate numerous objects throughout their deployment. For an autonomous robot… (more)

Subjects/Keywords: Robot learning; Affordance learning; Autonomous robots; Machine learning

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APA (6th Edition):

Hermans, T. R. (2014). Representing and learning affordance-based behaviors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51835

Chicago Manual of Style (16th Edition):

Hermans, Tucker Ryer. “Representing and learning affordance-based behaviors.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/51835.

MLA Handbook (7th Edition):

Hermans, Tucker Ryer. “Representing and learning affordance-based behaviors.” 2014. Web. 23 Sep 2019.

Vancouver:

Hermans TR. Representing and learning affordance-based behaviors. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/51835.

Council of Science Editors:

Hermans TR. Representing and learning affordance-based behaviors. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51835


Georgia Tech

8. Pan, Yunpeng. Learning control via probabilistic trajectory optimization.

Degree: PhD, Aerospace Engineering, 2017, Georgia Tech

 A central problem in the field of robotics is to develop real-time planning and control algorithms for autonomous systems to behave intelligently under uncertainty. While… (more)

Subjects/Keywords: Optimal control; Robotics; Artificial intelligence; Machine learning

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APA (6th Edition):

Pan, Y. (2017). Learning control via probabilistic trajectory optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59278

Chicago Manual of Style (16th Edition):

Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/59278.

MLA Handbook (7th Edition):

Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Web. 23 Sep 2019.

Vancouver:

Pan Y. Learning control via probabilistic trajectory optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/59278.

Council of Science Editors:

Pan Y. Learning control via probabilistic trajectory optimization. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59278


Georgia Tech

9. Hrolenok, Brian Paul. Constructing and evaluating executable models of collective behavior.

Degree: PhD, Computer Science, 2018, Georgia Tech

 Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming… (more)

Subjects/Keywords: Executable models; Machine learning; Multiagent systems

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APA (6th Edition):

Hrolenok, B. P. (2018). Constructing and evaluating executable models of collective behavior. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60751

Chicago Manual of Style (16th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60751.

MLA Handbook (7th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Web. 23 Sep 2019.

Vancouver:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60751.

Council of Science Editors:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60751


Georgia Tech

10. Persson, Nils Erland. Analysis of fibrillar structures for the engineering of polymeric transistors.

Degree: PhD, Chemical and Biomolecular Engineering, 2017, Georgia Tech

 Image processing software was developed and applied to the analysis of polymer nanofiber microstructures in poly(3-hexylthiophene) (P3HT)-based organic field effect transistors. A large database of… (more)

Subjects/Keywords: Organic electronics; Image processing; Machine learning; Fibers

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APA (6th Edition):

Persson, N. E. (2017). Analysis of fibrillar structures for the engineering of polymeric transistors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59187

Chicago Manual of Style (16th Edition):

Persson, Nils Erland. “Analysis of fibrillar structures for the engineering of polymeric transistors.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/59187.

MLA Handbook (7th Edition):

Persson, Nils Erland. “Analysis of fibrillar structures for the engineering of polymeric transistors.” 2017. Web. 23 Sep 2019.

Vancouver:

Persson NE. Analysis of fibrillar structures for the engineering of polymeric transistors. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/59187.

Council of Science Editors:

Persson NE. Analysis of fibrillar structures for the engineering of polymeric transistors. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59187


Georgia Tech

11. Whitaker, Bradley M. Modifying sparse coding to model imbalanced datasets.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 The objective of this research is to explore the use of sparse coding as a tool for unsupervised feature learning to more effectively model imbalanced… (more)

Subjects/Keywords: Sparse coding; Imbalanced data; Machine learning

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APA (6th Edition):

Whitaker, B. M. (2018). Modifying sparse coding to model imbalanced datasets. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59919

Chicago Manual of Style (16th Edition):

Whitaker, Bradley M. “Modifying sparse coding to model imbalanced datasets.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/59919.

MLA Handbook (7th Edition):

Whitaker, Bradley M. “Modifying sparse coding to model imbalanced datasets.” 2018. Web. 23 Sep 2019.

Vancouver:

Whitaker BM. Modifying sparse coding to model imbalanced datasets. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/59919.

Council of Science Editors:

Whitaker BM. Modifying sparse coding to model imbalanced datasets. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59919


Georgia Tech

12. Jeong, Jiwoong. Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features.

Degree: MS, Mechanical Engineering, 2018, Georgia Tech

 Purpose: Glioblastoma (GBM) is the most aggressive cancer with poor prognosis due to its heterogeneity. The purpose of this study is to improve the tissue… (more)

Subjects/Keywords: Delta-radiomics; Machine-Learning; DSC MRI; Glioblastoma

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APA (6th Edition):

Jeong, J. (2018). Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61094

Chicago Manual of Style (16th Edition):

Jeong, Jiwoong. “Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features.” 2018. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61094.

MLA Handbook (7th Edition):

Jeong, Jiwoong. “Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features.” 2018. Web. 23 Sep 2019.

Vancouver:

Jeong J. Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61094.

Council of Science Editors:

Jeong J. Machine-Learning-Based Classification of Gliblastoma Using Dynamic Susceptibility Enhanced MR Image Derived Delta-Radiomic Features. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61094


Georgia Tech

13. Kundu, Abhijit. Urban 3D Scene Understanding from Images.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 Human vision is marvelous in obtaining a structured representation of complex dynamic scenes, such as spatial scene-layout, re-organization of the scene into its constituent objects,… (more)

Subjects/Keywords: computer vision; machine learning; inverse graphics

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APA (6th Edition):

Kundu, A. (2018). Urban 3D Scene Understanding from Images. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61114

Chicago Manual of Style (16th Edition):

Kundu, Abhijit. “Urban 3D Scene Understanding from Images.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61114.

MLA Handbook (7th Edition):

Kundu, Abhijit. “Urban 3D Scene Understanding from Images.” 2018. Web. 23 Sep 2019.

Vancouver:

Kundu A. Urban 3D Scene Understanding from Images. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61114.

Council of Science Editors:

Kundu A. Urban 3D Scene Understanding from Images. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61114


Georgia Tech

14. Zhu, Lijun. Seismic Processing via Machine Learning for Event Detection and Phase Picking.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 A feasible solution for seismic event detection and phase picking is prototyped on an embedded system with seismic sensors using a lightweight convolutional neural network… (more)

Subjects/Keywords: Machine Learning; Seismic; Phase Picking; CNN

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APA (6th Edition):

Zhu, L. (2019). Seismic Processing via Machine Learning for Event Detection and Phase Picking. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61770

Chicago Manual of Style (16th Edition):

Zhu, Lijun. “Seismic Processing via Machine Learning for Event Detection and Phase Picking.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61770.

MLA Handbook (7th Edition):

Zhu, Lijun. “Seismic Processing via Machine Learning for Event Detection and Phase Picking.” 2019. Web. 23 Sep 2019.

Vancouver:

Zhu L. Seismic Processing via Machine Learning for Event Detection and Phase Picking. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61770.

Council of Science Editors:

Zhu L. Seismic Processing via Machine Learning for Event Detection and Phase Picking. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61770


Georgia Tech

15. Zhou, Yi. Stochastic algorithms for distributed optimization and machine learning.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

 In the big data era, machine learning acts as a powerful tool to help us make predictions and decisions. It has strong ties to the… (more)

Subjects/Keywords: Randomized algorithms; Stochastic optimization; Distributed optimization; Machine learning; Distributed machine learning; Finite-sum optimization

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APA (6th Edition):

Zhou, Y. (2018). Stochastic algorithms for distributed optimization and machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60256

Chicago Manual of Style (16th Edition):

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60256.

MLA Handbook (7th Edition):

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Web. 23 Sep 2019.

Vancouver:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60256.

Council of Science Editors:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60256


Georgia Tech

16. Deiss, Olivier. Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms.

Degree: MS, Computer Science, 2018, Georgia Tech

 This thesis focuses on the classification of seizures, together with finding efficient and scalable ways to obtain high-quality datasets in order to train deep neural… (more)

Subjects/Keywords: Labeling; Machine learning; Deep neural networks; Supervised learning

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APA (6th Edition):

Deiss, O. (2018). Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59864

Chicago Manual of Style (16th Edition):

Deiss, Olivier. “Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms.” 2018. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/59864.

MLA Handbook (7th Edition):

Deiss, Olivier. “Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms.” 2018. Web. 23 Sep 2019.

Vancouver:

Deiss O. Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/59864.

Council of Science Editors:

Deiss O. Efficient labeling technique and interpretable deep neural network for the classification of seizures using continuous electroencephalograms. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59864


Georgia Tech

17. Krening, Samantha. Humans Teaching Intelligent Agents with Verbal Instruction.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 The widespread integration of robotics into everyday life requires significant improvement in the underlying machine learning (ML) agents to make them more accessible, customizable, and… (more)

Subjects/Keywords: Robotics; Machine Learning; Interactive Machine Learning; Human-Agent Interaction; Reinforcement Learning; Natural Language Processing; Human-Computer Interaction; Human Factors; Machine Learning Verification

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APA (6th Edition):

Krening, S. (2019). Humans Teaching Intelligent Agents with Verbal Instruction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61232

Chicago Manual of Style (16th Edition):

Krening, Samantha. “Humans Teaching Intelligent Agents with Verbal Instruction.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61232.

MLA Handbook (7th Edition):

Krening, Samantha. “Humans Teaching Intelligent Agents with Verbal Instruction.” 2019. Web. 23 Sep 2019.

Vancouver:

Krening S. Humans Teaching Intelligent Agents with Verbal Instruction. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61232.

Council of Science Editors:

Krening S. Humans Teaching Intelligent Agents with Verbal Instruction. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61232

18. Scholz, Jonathan. Physics-based reinforcement learning for autonomous manipulation.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 With recent research advances, the dream of bringing domestic robots into our everyday lives has become more plausible than ever. Domestic robotics has grown dramatically… (more)

Subjects/Keywords: Machine learning; Robotics; Reinforcement learning

…reasoning in the manner of a physics engine. 3 abstract models in Machine Learning. In this… …methods we present draw from both the robotics and machine learning literatures. We use… …summarize the relevant results from the machine learning and robotics literature. Our main… …6.5.2 NAMO with a Non-Static Constraint . . . . . . . . . . . . . . . . 97 6.5.3 Learning… …65 ix LIST OF FIGURES 1 Pseudo-code for learning forward models. Note that ∆s encodes… 

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APA (6th Edition):

Scholz, J. (2015). Physics-based reinforcement learning for autonomous manipulation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54366

Chicago Manual of Style (16th Edition):

Scholz, Jonathan. “Physics-based reinforcement learning for autonomous manipulation.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/54366.

MLA Handbook (7th Edition):

Scholz, Jonathan. “Physics-based reinforcement learning for autonomous manipulation.” 2015. Web. 23 Sep 2019.

Vancouver:

Scholz J. Physics-based reinforcement learning for autonomous manipulation. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/54366.

Council of Science Editors:

Scholz J. Physics-based reinforcement learning for autonomous manipulation. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54366


Georgia Tech

19. Richard, Antoine. Automated analysis of overhead imagery for habitat segmentation.

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

 The overall objective of our project is to be able to classify the evolution of land usage since the advent of aerial imagery. In practice… (more)

Subjects/Keywords: Deep Learning; Neural Networks; Auto-encoders; habitat segmentation; Computer Vision; Machine Learning; land use detection

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APA (6th Edition):

Richard, A. (2018). Automated analysis of overhead imagery for habitat segmentation. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59291

Chicago Manual of Style (16th Edition):

Richard, Antoine. “Automated analysis of overhead imagery for habitat segmentation.” 2018. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/59291.

MLA Handbook (7th Edition):

Richard, Antoine. “Automated analysis of overhead imagery for habitat segmentation.” 2018. Web. 23 Sep 2019.

Vancouver:

Richard A. Automated analysis of overhead imagery for habitat segmentation. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/59291.

Council of Science Editors:

Richard A. Automated analysis of overhead imagery for habitat segmentation. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59291


Georgia Tech

20. Brough, David. Process-structure linkages with materials knowledge systems.

Degree: PhD, Computational Science and Engineering, 2016, Georgia Tech

 The search for optimal manufacturing process routes that results in the combination of desired properties for any application is a highly dimensional optimization problem due… (more)

Subjects/Keywords: Materials knowledge systems; Multiscale simulations; Machine learning; Data sciences; Phase field

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APA (6th Edition):

Brough, D. (2016). Process-structure linkages with materials knowledge systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56261

Chicago Manual of Style (16th Edition):

Brough, David. “Process-structure linkages with materials knowledge systems.” 2016. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/56261.

MLA Handbook (7th Edition):

Brough, David. “Process-structure linkages with materials knowledge systems.” 2016. Web. 23 Sep 2019.

Vancouver:

Brough D. Process-structure linkages with materials knowledge systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/56261.

Council of Science Editors:

Brough D. Process-structure linkages with materials knowledge systems. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56261


Georgia Tech

21. Powers, Matthew D. Applying inter-layer conflict resolution to hybrid robot control architectures.

Degree: PhD, Computing, 2010, Georgia Tech

 In this document, we propose and examine the novel use of a learning mechanism between the reactive and deliberative layers of a hybrid robot control… (more)

Subjects/Keywords: Robotics; Planning; Machine learning; Architecture; Robots Control systems; Mobile robots

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APA (6th Edition):

Powers, M. D. (2010). Applying inter-layer conflict resolution to hybrid robot control architectures. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33979

Chicago Manual of Style (16th Edition):

Powers, Matthew D. “Applying inter-layer conflict resolution to hybrid robot control architectures.” 2010. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/33979.

MLA Handbook (7th Edition):

Powers, Matthew D. “Applying inter-layer conflict resolution to hybrid robot control architectures.” 2010. Web. 23 Sep 2019.

Vancouver:

Powers MD. Applying inter-layer conflict resolution to hybrid robot control architectures. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/33979.

Council of Science Editors:

Powers MD. Applying inter-layer conflict resolution to hybrid robot control architectures. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33979

22. Ge, Bi. Detecting engagement levels for autism intervention therapy using RGB-D camera.

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

 Our motivation for this work is to develop an autonomous robot system that is able to perform autism intervention therapy. Autism spectrum disorder (ASD) is… (more)

Subjects/Keywords: Robotics; Autism; Machine learning

…Electroencephalogram 3D 3-dimensional CSV Comma separated value SVM Support vector machine KNN K… …Support Vector Machine Support vector machine (SVM) is known for its robustness and… 

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APA (6th Edition):

Ge, B. (2016). Detecting engagement levels for autism intervention therapy using RGB-D camera. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55043

Chicago Manual of Style (16th Edition):

Ge, Bi. “Detecting engagement levels for autism intervention therapy using RGB-D camera.” 2016. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/55043.

MLA Handbook (7th Edition):

Ge, Bi. “Detecting engagement levels for autism intervention therapy using RGB-D camera.” 2016. Web. 23 Sep 2019.

Vancouver:

Ge B. Detecting engagement levels for autism intervention therapy using RGB-D camera. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/55043.

Council of Science Editors:

Ge B. Detecting engagement levels for autism intervention therapy using RGB-D camera. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55043


Georgia Tech

23. Serrano, Miguel M. RAPTr: Robust articulated point-set tracking.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 The objective of this work is to present the Robust Articulated Point-set Tracking (RAPTr) system. It works by synthesizing components from articulated model-based and machine(more)

Subjects/Keywords: Human pose estimation; Machine learning; Clinical gait metrics; Infant pose estimation

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APA (6th Edition):

Serrano, M. M. (2018). RAPTr: Robust articulated point-set tracking. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60730

Chicago Manual of Style (16th Edition):

Serrano, Miguel M. “RAPTr: Robust articulated point-set tracking.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60730.

MLA Handbook (7th Edition):

Serrano, Miguel M. “RAPTr: Robust articulated point-set tracking.” 2018. Web. 23 Sep 2019.

Vancouver:

Serrano MM. RAPTr: Robust articulated point-set tracking. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60730.

Council of Science Editors:

Serrano MM. RAPTr: Robust articulated point-set tracking. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60730


Georgia Tech

24. Zhou, Yang. Innovative mining, processing, and application of big graphs.

Degree: PhD, Computer Science, 2017, Georgia Tech

 With continued advances in science and technology, big graph (or network) data, such as World Wide Web, social networks, academic collaboration networks, transportation networks, telecommunication… (more)

Subjects/Keywords: Big data; Data mining; Parallel and distributed computing; Machine learning; Databases

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APA (6th Edition):

Zhou, Y. (2017). Innovative mining, processing, and application of big graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59173

Chicago Manual of Style (16th Edition):

Zhou, Yang. “Innovative mining, processing, and application of big graphs.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/59173.

MLA Handbook (7th Edition):

Zhou, Yang. “Innovative mining, processing, and application of big graphs.” 2017. Web. 23 Sep 2019.

Vancouver:

Zhou Y. Innovative mining, processing, and application of big graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/59173.

Council of Science Editors:

Zhou Y. Innovative mining, processing, and application of big graphs. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59173


Georgia Tech

25. Sant'anna Dias, Felipe. Mapping future land cover change over large areas of the United State using decision trees.

Degree: MS, Civil and Environmental Engineering, 2015, Georgia Tech

 Climate is one of the primary factors that control vegetation distribution and therefore it is expected that the effects of climate change will have a… (more)

Subjects/Keywords: Climate change; Vegetation distribution; Land cover; Machine learning; Decision trees; C5.0

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APA (6th Edition):

Sant'anna Dias, F. (2015). Mapping future land cover change over large areas of the United State using decision trees. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60398

Chicago Manual of Style (16th Edition):

Sant'anna Dias, Felipe. “Mapping future land cover change over large areas of the United State using decision trees.” 2015. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60398.

MLA Handbook (7th Edition):

Sant'anna Dias, Felipe. “Mapping future land cover change over large areas of the United State using decision trees.” 2015. Web. 23 Sep 2019.

Vancouver:

Sant'anna Dias F. Mapping future land cover change over large areas of the United State using decision trees. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60398.

Council of Science Editors:

Sant'anna Dias F. Mapping future land cover change over large areas of the United State using decision trees. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/60398


Georgia Tech

26. Huang, Cai. K-mer based data structures and heuristics for microbes and cancer.

Degree: PhD, Biology, 2018, Georgia Tech

 Recent technological advances allow for high throughput profiling of biological systems in a cost-efficient manner. The low cost of data generation is leading us to… (more)

Subjects/Keywords: Machine learning; Precision medicine; K-mer; Open source

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APA (6th Edition):

Huang, C. (2018). K-mer based data structures and heuristics for microbes and cancer. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60194

Chicago Manual of Style (16th Edition):

Huang, Cai. “K-mer based data structures and heuristics for microbes and cancer.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/60194.

MLA Handbook (7th Edition):

Huang, Cai. “K-mer based data structures and heuristics for microbes and cancer.” 2018. Web. 23 Sep 2019.

Vancouver:

Huang C. K-mer based data structures and heuristics for microbes and cancer. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/60194.

Council of Science Editors:

Huang C. K-mer based data structures and heuristics for microbes and cancer. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60194


Georgia Tech

27. Dard, Ghislain. Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes.

Degree: MS, Aerospace Engineering, 2019, Georgia Tech

 One of the missions of the Federal Aviation Administration (FAA) is to maintain the safety and efficiency of the National Airspace System (NAS). One way… (more)

Subjects/Keywords: Data fusion; Reroutes; Recommended; FAA; Machine learning; Prediction; Relevancy

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APA (6th Edition):

Dard, G. (2019). Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61294

Chicago Manual of Style (16th Edition):

Dard, Ghislain. “Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes.” 2019. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61294.

MLA Handbook (7th Edition):

Dard, Ghislain. “Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes.” 2019. Web. 23 Sep 2019.

Vancouver:

Dard G. Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes. [Internet] [Masters thesis]. Georgia Tech; 2019. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61294.

Council of Science Editors:

Dard G. Application of data fusion and machine learning to the analysis of the relevancy of recommended flight reroutes. [Masters Thesis]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61294


Georgia Tech

28. Mangortey, Eugene. Predicting the occurrence of ground delay programs and their impact on airport and flight operations.

Degree: MS, Aerospace Engineering, 2019, Georgia Tech

 A flight is delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the… (more)

Subjects/Keywords: Flight delays; Ground delay programs; Machine learning; Aviation big data

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APA (6th Edition):

Mangortey, E. (2019). Predicting the occurrence of ground delay programs and their impact on airport and flight operations. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61288

Chicago Manual of Style (16th Edition):

Mangortey, Eugene. “Predicting the occurrence of ground delay programs and their impact on airport and flight operations.” 2019. Masters Thesis, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61288.

MLA Handbook (7th Edition):

Mangortey, Eugene. “Predicting the occurrence of ground delay programs and their impact on airport and flight operations.” 2019. Web. 23 Sep 2019.

Vancouver:

Mangortey E. Predicting the occurrence of ground delay programs and their impact on airport and flight operations. [Internet] [Masters thesis]. Georgia Tech; 2019. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61288.

Council of Science Editors:

Mangortey E. Predicting the occurrence of ground delay programs and their impact on airport and flight operations. [Masters Thesis]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61288


Georgia Tech

29. Yue, Xiaowei. Engineering-driven Data Analytics for In Situ Process Monitoring of Nanomanufacturing.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

 Carbon Nanotubes (CNTs) buckypaper is a multifunctional platform material with superior mechanical and electrical characteristics. One of the critical roadblocks to scale-up production of high-quality… (more)

Subjects/Keywords: Data analytics; Nanomanufacturing; Carbon nanotube; Mixed effects; Machine learning

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APA (6th Edition):

Yue, X. (2018). Engineering-driven Data Analytics for In Situ Process Monitoring of Nanomanufacturing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61625

Chicago Manual of Style (16th Edition):

Yue, Xiaowei. “Engineering-driven Data Analytics for In Situ Process Monitoring of Nanomanufacturing.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61625.

MLA Handbook (7th Edition):

Yue, Xiaowei. “Engineering-driven Data Analytics for In Situ Process Monitoring of Nanomanufacturing.” 2018. Web. 23 Sep 2019.

Vancouver:

Yue X. Engineering-driven Data Analytics for In Situ Process Monitoring of Nanomanufacturing. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61625.

Council of Science Editors:

Yue X. Engineering-driven Data Analytics for In Situ Process Monitoring of Nanomanufacturing. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61625


Georgia Tech

30. Reinertsen, Erik. Dichotomizing illness from cardiovascular and locomotor activity time series.

Degree: PhD, Biomedical Engineering (Joint GT/Emory Department), 2018, Georgia Tech

 This thesis addresses the issue of automated evaluation of severity of illness in psychiatric populations. In particular, given that both physiology and locomotor activity have… (more)

Subjects/Keywords: signal processing; machine learning; information theory; biomedical informatics

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APA (6th Edition):

Reinertsen, E. (2018). Dichotomizing illness from cardiovascular and locomotor activity time series. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61636

Chicago Manual of Style (16th Edition):

Reinertsen, Erik. “Dichotomizing illness from cardiovascular and locomotor activity time series.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 23, 2019. http://hdl.handle.net/1853/61636.

MLA Handbook (7th Edition):

Reinertsen, Erik. “Dichotomizing illness from cardiovascular and locomotor activity time series.” 2018. Web. 23 Sep 2019.

Vancouver:

Reinertsen E. Dichotomizing illness from cardiovascular and locomotor activity time series. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 23]. Available from: http://hdl.handle.net/1853/61636.

Council of Science Editors:

Reinertsen E. Dichotomizing illness from cardiovascular and locomotor activity time series. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61636

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