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You searched for +publisher:"Georgia Tech" +contributor:("Davenport, Mark"). Showing records 1 – 30 of 32 total matches.

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Georgia Tech

1. Dedhia, Vaibhav. Scene flow for autonomous navigation.

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

 Today, there are various different paradigms for vision based autonomous navigation: mediated perception approaches that parse an entire scene to make driving decision, a direct… (more)

Subjects/Keywords: Autonomous navigation; CNNs; Computer vision

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APA (6th Edition):

Dedhia, V. (2018). Scene flow for autonomous navigation. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59948

Chicago Manual of Style (16th Edition):

Dedhia, Vaibhav. “Scene flow for autonomous navigation.” 2018. Masters Thesis, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/59948.

MLA Handbook (7th Edition):

Dedhia, Vaibhav. “Scene flow for autonomous navigation.” 2018. Web. 17 Sep 2019.

Vancouver:

Dedhia V. Scene flow for autonomous navigation. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/59948.

Council of Science Editors:

Dedhia V. Scene flow for autonomous navigation. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59948


Georgia Tech

2. Patnaik, Kaushik. Adaptive learning in lasso models.

Degree: MS, Computer Science, 2015, Georgia Tech

 Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern (also known as model selection) in linear models from observations contaminated by… (more)

Subjects/Keywords: Lasso; L1 regression; Adaptive methods; Active learning

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APA (6th Edition):

Patnaik, K. (2015). Adaptive learning in lasso models. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54353

Chicago Manual of Style (16th Edition):

Patnaik, Kaushik. “Adaptive learning in lasso models.” 2015. Masters Thesis, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/54353.

MLA Handbook (7th Edition):

Patnaik, Kaushik. “Adaptive learning in lasso models.” 2015. Web. 17 Sep 2019.

Vancouver:

Patnaik K. Adaptive learning in lasso models. [Internet] [Masters thesis]. Georgia Tech; 2015. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/54353.

Council of Science Editors:

Patnaik K. Adaptive learning in lasso models. [Masters Thesis]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54353


Georgia Tech

3. Cao, Yang. Poisson matrix completion and change-point detection.

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

 Statistical signal processing and machine learning are very important in modern science and engineering. Many theories, methods and techniques are developed to help people extract and… (more)

Subjects/Keywords: Matrix completion; Sequential change-point detection; Robust change detection; Online learning

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APA (6th Edition):

Cao, Y. (2018). Poisson matrix completion and change-point detection. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60195

Chicago Manual of Style (16th Edition):

Cao, Yang. “Poisson matrix completion and change-point detection.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60195.

MLA Handbook (7th Edition):

Cao, Yang. “Poisson matrix completion and change-point detection.” 2018. Web. 17 Sep 2019.

Vancouver:

Cao Y. Poisson matrix completion and change-point detection. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60195.

Council of Science Editors:

Cao Y. Poisson matrix completion and change-point detection. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60195


Georgia Tech

4. Luo, Chenchi. Non-uniform sampling: algorithms and architectures.

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

 Modern signal processing applications emerging in telecommunication and instrumentation industries have placed an increasing demand for ADCs with higher speed and resolution. The most fundamental… (more)

Subjects/Keywords: TIADC; Farrow structure; Compressive sensing; Sparsity; Analog-to-digital converters; Sampling (Statistics); Algorithms; Signal processing; Signal processing Digital techniques

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APA (6th Edition):

Luo, C. (2012). Non-uniform sampling: algorithms and architectures. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/45873

Chicago Manual of Style (16th Edition):

Luo, Chenchi. “Non-uniform sampling: algorithms and architectures.” 2012. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/45873.

MLA Handbook (7th Edition):

Luo, Chenchi. “Non-uniform sampling: algorithms and architectures.” 2012. Web. 17 Sep 2019.

Vancouver:

Luo C. Non-uniform sampling: algorithms and architectures. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/45873.

Council of Science Editors:

Luo C. Non-uniform sampling: algorithms and architectures. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/45873


Georgia Tech

5. Kingravi, Hassan. Reduced-set models for improving the training and execution speed of kernel methods.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 This thesis aims to contribute to the area of kernel methods, which are a class of machine learning methods known for their wide applicability and… (more)

Subjects/Keywords: Machine learning; Kernel methods; Reproducing kernel Hilbert spaces; Adaptive control; Manifold learning; Algorithms; Computer algorithms; Kernel functions; Support vector machines

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APA (6th Edition):

Kingravi, H. (2014). Reduced-set models for improving the training and execution speed of kernel methods. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51799

Chicago Manual of Style (16th Edition):

Kingravi, Hassan. “Reduced-set models for improving the training and execution speed of kernel methods.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/51799.

MLA Handbook (7th Edition):

Kingravi, Hassan. “Reduced-set models for improving the training and execution speed of kernel methods.” 2014. Web. 17 Sep 2019.

Vancouver:

Kingravi H. Reduced-set models for improving the training and execution speed of kernel methods. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/51799.

Council of Science Editors:

Kingravi H. Reduced-set models for improving the training and execution speed of kernel methods. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51799


Georgia Tech

6. Zou, Jun. Social computing for personalization and credible information mining using probabilistic graphical models.

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

 In this dissertation, we address challenging social computing problems in personalized recommender systems and social media information mining. We tap into probabilistic graphical models, including… (more)

Subjects/Keywords: Social computing; Recommender systems; Collaborative filtering; Belief propagation; Probabilistic graphical models

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APA (6th Edition):

Zou, J. (2016). Social computing for personalization and credible information mining using probabilistic graphical models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55646

Chicago Manual of Style (16th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/55646.

MLA Handbook (7th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Web. 17 Sep 2019.

Vancouver:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/55646.

Council of Science Editors:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55646


Georgia Tech

7. Moore, Michael George. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.

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

 The purpose of this work is to improve our ability to extract information from data generated by Poisson and Hawkes processes. Our principal focus is… (more)

Subjects/Keywords: Poisson; Hawkes; Point process; Estimation

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APA (6th Edition):

Moore, M. G. (2018). Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60727

Chicago Manual of Style (16th Edition):

Moore, Michael George. “Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60727.

MLA Handbook (7th Edition):

Moore, Michael George. “Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.” 2018. Web. 17 Sep 2019.

Vancouver:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60727.

Council of Science Editors:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60727


Georgia Tech

8. Fair, Kaitlin Lindsay. A biologically plausible sparse approximation solver on neuromorphic hardware.

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

 We develop a novel design methodology to map the biologically plausible Locally Competitive Algorithm (LCA) to the brain-inspired TrueNorth chip to solve for the sparse… (more)

Subjects/Keywords: Neuromorphic; Bio-inspired; TrueNorth; Sparsity; Sparse approximation

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APA (6th Edition):

Fair, K. L. (2017). A biologically plausible sparse approximation solver on neuromorphic hardware. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59782

Chicago Manual of Style (16th Edition):

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/59782.

MLA Handbook (7th Edition):

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Web. 17 Sep 2019.

Vancouver:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/59782.

Council of Science Editors:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59782


Georgia Tech

9. Gillespie, Stephanie Marie. Analysis of affective states from vocal acoustics in adults with aphasia.

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

 This research analyzed objective vocal acoustic measures of aphasic speech as they related to the detection or prediction of stress, depression, and emotional state in… (more)

Subjects/Keywords: Aphasia; Speech processing; Vocal acoustic; Glottal features; Teager features; Dysarthria

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APA (6th Edition):

Gillespie, S. M. (2017). Analysis of affective states from vocal acoustics in adults with aphasia. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59794

Chicago Manual of Style (16th Edition):

Gillespie, Stephanie Marie. “Analysis of affective states from vocal acoustics in adults with aphasia.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/59794.

MLA Handbook (7th Edition):

Gillespie, Stephanie Marie. “Analysis of affective states from vocal acoustics in adults with aphasia.” 2017. Web. 17 Sep 2019.

Vancouver:

Gillespie SM. Analysis of affective states from vocal acoustics in adults with aphasia. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/59794.

Council of Science Editors:

Gillespie SM. Analysis of affective states from vocal acoustics in adults with aphasia. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59794


Georgia Tech

10. Farajtabar, Mehrdad. Point process modeling and optimization of social networks.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 Online social media such as Facebook and Twitter and communities such as Wikipedia and Stackoverflow turn to become an inseparable part of today's lifestyle. Users… (more)

Subjects/Keywords: Point processes

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APA (6th Edition):

Farajtabar, M. (2018). Point process modeling and optimization of social networks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59858

Chicago Manual of Style (16th Edition):

Farajtabar, Mehrdad. “Point process modeling and optimization of social networks.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/59858.

MLA Handbook (7th Edition):

Farajtabar, Mehrdad. “Point process modeling and optimization of social networks.” 2018. Web. 17 Sep 2019.

Vancouver:

Farajtabar M. Point process modeling and optimization of social networks. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/59858.

Council of Science Editors:

Farajtabar M. Point process modeling and optimization of social networks. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59858


Georgia Tech

11. Tian, Ning. Multichannel blind deconvolution in underwater acoustic channels.

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

 This thesis developed new techniques for solving the multichannel blind deconvolution problem and implemented these techniques in acoustic waveguide multiple environment. We developed a systematic… (more)

Subjects/Keywords: Multichannel blind deconvolution; Low-rank recovery; Cross-convolution methods; Non-convex optimizations

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APA (6th Edition):

Tian, N. (2018). Multichannel blind deconvolution in underwater acoustic channels. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59906

Chicago Manual of Style (16th Edition):

Tian, Ning. “Multichannel blind deconvolution in underwater acoustic channels.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/59906.

MLA Handbook (7th Edition):

Tian, Ning. “Multichannel blind deconvolution in underwater acoustic channels.” 2018. Web. 17 Sep 2019.

Vancouver:

Tian N. Multichannel blind deconvolution in underwater acoustic channels. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/59906.

Council of Science Editors:

Tian N. Multichannel blind deconvolution in underwater acoustic channels. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59906


Georgia Tech

12. Wang, Yichen. Modeling, predicting, and guiding users' temporal behaviors.

Degree: PhD, Mathematics, 2018, Georgia Tech

 The increasing availability and granularity of temporal event data produced from user activities in online media, social networks and health informatics provide new opportunities and… (more)

Subjects/Keywords: Point processes; Hawkes processes; Survival analysis; Low-rank models; Mass transport; Fokker Planck equation; Stochastic optimal control; Reinforcement learning; Social network analysis; Information diffusion; Recommendation systems

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APA (6th Edition):

Wang, Y. (2018). Modeling, predicting, and guiding users' temporal behaviors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60208

Chicago Manual of Style (16th Edition):

Wang, Yichen. “Modeling, predicting, and guiding users' temporal behaviors.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60208.

MLA Handbook (7th Edition):

Wang, Yichen. “Modeling, predicting, and guiding users' temporal behaviors.” 2018. Web. 17 Sep 2019.

Vancouver:

Wang Y. Modeling, predicting, and guiding users' temporal behaviors. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60208.

Council of Science Editors:

Wang Y. Modeling, predicting, and guiding users' temporal behaviors. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60208


Georgia Tech

13. Arumugam, Keerthi Suria Kumar. Covert communication over multi-user channels.

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

 The objective of the proposed research is to characterize the maximum rate at which information can be transmitted reliably to a legitimate receiver over certain… (more)

Subjects/Keywords: Covert communication; Multi user; Multiple access channels; Broadcast channels; Relay channels; Asynchronous

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APA (6th Edition):

Arumugam, K. S. K. (2019). Covert communication over multi-user channels. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61256

Chicago Manual of Style (16th Edition):

Arumugam, Keerthi Suria Kumar. “Covert communication over multi-user channels.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/61256.

MLA Handbook (7th Edition):

Arumugam, Keerthi Suria Kumar. “Covert communication over multi-user channels.” 2019. Web. 17 Sep 2019.

Vancouver:

Arumugam KSK. Covert communication over multi-user channels. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/61256.

Council of Science Editors:

Arumugam KSK. Covert communication over multi-user channels. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61256


Georgia Tech

14. Alaudah, Yazeed. Weakly-supervised semantic labeling of migrated seismic data.

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

 Deep learning has revolutionized the fields of machine learning and computer vision. However, the availability of annotated data to train state-of-the-art deep networks is one… (more)

Subjects/Keywords: Deep learning; weakly supervised learning; seismic data; semantic segmentation; seismic interpretation

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APA (6th Edition):

Alaudah, Y. (2019). Weakly-supervised semantic labeling of migrated seismic data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61719

Chicago Manual of Style (16th Edition):

Alaudah, Yazeed. “Weakly-supervised semantic labeling of migrated seismic data.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/61719.

MLA Handbook (7th Edition):

Alaudah, Yazeed. “Weakly-supervised semantic labeling of migrated seismic data.” 2019. Web. 17 Sep 2019.

Vancouver:

Alaudah Y. Weakly-supervised semantic labeling of migrated seismic data. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/61719.

Council of Science Editors:

Alaudah Y. Weakly-supervised semantic labeling of migrated seismic data. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61719


Georgia Tech

15. Zhou, Fan. Statistical inference for high dimensional data with low rank structure.

Degree: PhD, Mathematics, 2018, Georgia Tech

 We study two major topics on statistical inference for high dimensional data with low rank structure occurred in many machine learning and statistics applications. The… (more)

Subjects/Keywords: Nonparametric statistics; Matrix completion; Low rank; Nuclear norm; Tensor; Singular vector perturbation

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APA (6th Edition):

Zhou, F. (2018). Statistical inference for high dimensional data with low rank structure. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60750

Chicago Manual of Style (16th Edition):

Zhou, Fan. “Statistical inference for high dimensional data with low rank structure.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60750.

MLA Handbook (7th Edition):

Zhou, Fan. “Statistical inference for high dimensional data with low rank structure.” 2018. Web. 17 Sep 2019.

Vancouver:

Zhou F. Statistical inference for high dimensional data with low rank structure. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60750.

Council of Science Editors:

Zhou F. Statistical inference for high dimensional data with low rank structure. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60750


Georgia Tech

16. Rizwan, Muhammad. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.

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

 The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the ASR for a particular speaker or a group of speakers.… (more)

Subjects/Keywords: Speaker adaptation; Adaptive phoneme classification; Deep neural networks; Accent classification

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APA (6th Edition):

Rizwan, M. (2017). Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60171

Chicago Manual of Style (16th Edition):

Rizwan, Muhammad. “Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60171.

MLA Handbook (7th Edition):

Rizwan, Muhammad. “Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.” 2017. Web. 17 Sep 2019.

Vancouver:

Rizwan M. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60171.

Council of Science Editors:

Rizwan M. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/60171


Georgia Tech

17. Carroll, Brandon T. Characterizing acoustic environments with OLAF and ELSA.

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

 The confluence of signal processing and machine learning has created many innovative technologies in popular research areas such as speech recognition. However, many of the… (more)

Subjects/Keywords: Sparse coding; Machine learning; Acoustic environmental monitoring; Novelty detection; Bioacoustics; Poultry

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APA (6th Edition):

Carroll, B. T. (2018). Characterizing acoustic environments with OLAF and ELSA. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61650

Chicago Manual of Style (16th Edition):

Carroll, Brandon T. “Characterizing acoustic environments with OLAF and ELSA.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/61650.

MLA Handbook (7th Edition):

Carroll, Brandon T. “Characterizing acoustic environments with OLAF and ELSA.” 2018. Web. 17 Sep 2019.

Vancouver:

Carroll BT. Characterizing acoustic environments with OLAF and ELSA. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/61650.

Council of Science Editors:

Carroll BT. Characterizing acoustic environments with OLAF and ELSA. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61650

18. Spencer, Timothy Scott. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.

Degree: PhD, Mathematics, 2017, Georgia Tech

 The first part of this dissertation explores the application of dominating operators in harmonic analysis by sparse operators. We present preliminary results on dominating certain… (more)

Subjects/Keywords: Weighted inequalities; Sparse operators; Sparse approximation; 1-bit sensing; Restricted isometry property; Quasi-isometry; Noisy embeddings

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APA (6th Edition):

Spencer, T. S. (2017). Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58730

Chicago Manual of Style (16th Edition):

Spencer, Timothy Scott. “Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/58730.

MLA Handbook (7th Edition):

Spencer, Timothy Scott. “Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.” 2017. Web. 17 Sep 2019.

Vancouver:

Spencer TS. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/58730.

Council of Science Editors:

Spencer TS. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58730

19. Mantzel, William. Parametric estimation of randomly compressed functions.

Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech

 Within the last decade, a new type of signal acquisition has emerged called Compressive Sensing that has proven especially useful in providing a recoverable representation… (more)

Subjects/Keywords: Compressed sensing; Acoustic localization; Matched-field processing; Parametric estimation; Signal processing; Differential equations, Partial; Differential equations; Signal processing Digital techniques; Signal processing Digital techniques Mathematics

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APA (6th Edition):

Mantzel, W. (2013). Parametric estimation of randomly compressed functions. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49053

Chicago Manual of Style (16th Edition):

Mantzel, William. “Parametric estimation of randomly compressed functions.” 2013. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/49053.

MLA Handbook (7th Edition):

Mantzel, William. “Parametric estimation of randomly compressed functions.” 2013. Web. 17 Sep 2019.

Vancouver:

Mantzel W. Parametric estimation of randomly compressed functions. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/49053.

Council of Science Editors:

Mantzel W. Parametric estimation of randomly compressed functions. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49053

20. Tzou, Nicholas. Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 Cost reduction has been and will continue to be a primary driving force in the evolution of hardware design and associated technologies. The objective of… (more)

Subjects/Keywords: Low-cost; Sub-Nyquist; Algorithm; Hardware; Measurement; Multi-rate; Band-interleaved; Undersampling; Jitter; Crosstalk separation; Broadband communication systems Equipment and supplies; Algorithms

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APA (6th Edition):

Tzou, N. (2014). Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51876

Chicago Manual of Style (16th Edition):

Tzou, Nicholas. “Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/51876.

MLA Handbook (7th Edition):

Tzou, Nicholas. “Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement.” 2014. Web. 17 Sep 2019.

Vancouver:

Tzou N. Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/51876.

Council of Science Editors:

Tzou N. Low-cost sub-Nyquist sampling hardware and algorithm co-design for wideband and high-speed signal characterization and measurement. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51876

21. Asif, Muhammad Salman. Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video.

Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech

 This thesis presents compressive sensing algorithms that utilize system dynamics in the sparse signal recovery process. These dynamics may arise due to a time-varying signal,… (more)

Subjects/Keywords: L1 norm minimization; Homotopy; Dynamic MRI; Kalman filter; Streaming technology (Telecommunications); Streaming video; Signal processing; Computer vision; Signal processing Digital techniques Mathematics

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APA (6th Edition):

Asif, M. S. (2013). Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49106

Chicago Manual of Style (16th Edition):

Asif, Muhammad Salman. “Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video.” 2013. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/49106.

MLA Handbook (7th Edition):

Asif, Muhammad Salman. “Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video.” 2013. Web. 17 Sep 2019.

Vancouver:

Asif MS. Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/49106.

Council of Science Editors:

Asif MS. Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49106

22. Tuuk, Peter Benjamin. Compressed sensing in radar with structured interference.

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

 Ground clutter has challenged designers of airborne radar since it was first developed in the 1940's. Since that time, pulse-Doppler processing and space-time adaptive processing… (more)

Subjects/Keywords: Radar; Compressed sensing; Clutter; Covariance estimation

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APA (6th Edition):

Tuuk, P. B. (2017). Compressed sensing in radar with structured interference. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58627

Chicago Manual of Style (16th Edition):

Tuuk, Peter Benjamin. “Compressed sensing in radar with structured interference.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/58627.

MLA Handbook (7th Edition):

Tuuk, Peter Benjamin. “Compressed sensing in radar with structured interference.” 2017. Web. 17 Sep 2019.

Vancouver:

Tuuk PB. Compressed sensing in radar with structured interference. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/58627.

Council of Science Editors:

Tuuk PB. Compressed sensing in radar with structured interference. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58627

23. Gross, Nicholas. An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers.

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

 Narrowband very low frequency (VLF) remote sensing has proven to be a useful tool for characterizing the ionosphere's D-region (60 to 90 km altitude) electron… (more)

Subjects/Keywords: Very low frequency; VLF; Ionosphere; D region; Remote sensing; Waveguide; Narrowband; MSK; Neural network; Synthetic data

…accuracy than NLDN. 1.3 Data Acquisition The Georgia Tech LF Radio Lab has designed a VLF/LF… …kHz Figure 1.3: Map of narrowband VLF transmitters and Georgia Tech LF Radio Lab receivers… 

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APA (6th Edition):

Gross, N. (2018). An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60763

Chicago Manual of Style (16th Edition):

Gross, Nicholas. “An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60763.

MLA Handbook (7th Edition):

Gross, Nicholas. “An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers.” 2018. Web. 17 Sep 2019.

Vancouver:

Gross N. An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60763.

Council of Science Editors:

Gross N. An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60763

24. Xu, Hongteng. Point process-based modeling and analysis of asynchronous event sequences.

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

 Real-world interactions among multiple entities, such as user behaviors in social networks, job hunting and hopping, and diseases and their complications, often exhibit self-triggering and… (more)

Subjects/Keywords: Point process; Hawkes process; correcting process; Granger causality; impact function; infectivity network; multi-task learning; Dirichlet mixture model; structural regularizer; disciriminative learning; doubly-censored data; attractiveness model

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APA (6th Edition):

Xu, H. (2017). Point process-based modeling and analysis of asynchronous event sequences. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58690

Chicago Manual of Style (16th Edition):

Xu, Hongteng. “Point process-based modeling and analysis of asynchronous event sequences.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/58690.

MLA Handbook (7th Edition):

Xu, Hongteng. “Point process-based modeling and analysis of asynchronous event sequences.” 2017. Web. 17 Sep 2019.

Vancouver:

Xu H. Point process-based modeling and analysis of asynchronous event sequences. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/58690.

Council of Science Editors:

Xu H. Point process-based modeling and analysis of asynchronous event sequences. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58690

25. Hale, Matthew Thomas. Mixed centralized/decentralized coordination protocols for multi-agent systems.

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

 This thesis uses a mixture of centralized and decentralized architectures and algorithms to develop coordination strategies for multi-agent systems. Conventionally, centralized and decentralized methods are… (more)

Subjects/Keywords: Network coordination; Multi-agent systems; Privacy; Robotics

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APA (6th Edition):

Hale, M. T. (2017). Mixed centralized/decentralized coordination protocols for multi-agent systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58306

Chicago Manual of Style (16th Edition):

Hale, Matthew Thomas. “Mixed centralized/decentralized coordination protocols for multi-agent systems.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/58306.

MLA Handbook (7th Edition):

Hale, Matthew Thomas. “Mixed centralized/decentralized coordination protocols for multi-agent systems.” 2017. Web. 17 Sep 2019.

Vancouver:

Hale MT. Mixed centralized/decentralized coordination protocols for multi-agent systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/58306.

Council of Science Editors:

Hale MT. Mixed centralized/decentralized coordination protocols for multi-agent systems. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58306

26. Charles, Adam Shabti. Dynamics and correlations in sparse signal acquisition.

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

 One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and… (more)

Subjects/Keywords: Sparsity; Dynamic filtering; Spatial filtering; Hyperspectral imagery; Compressive sensing; Echo state networks; Short-term memory; Network-based optimization

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APA (6th Edition):

Charles, A. S. (2015). Dynamics and correlations in sparse signal acquisition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53592

Chicago Manual of Style (16th Edition):

Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/53592.

MLA Handbook (7th Edition):

Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Web. 17 Sep 2019.

Vancouver:

Charles AS. Dynamics and correlations in sparse signal acquisition. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/53592.

Council of Science Editors:

Charles AS. Dynamics and correlations in sparse signal acquisition. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53592

27. Ahmed, Ali. Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals.

Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech

 Low-dimensional signal structures naturally arise in a large set of applications in various fields such as medical imaging, machine learning, signal, and array processing. A… (more)

Subjects/Keywords: Low-rank recovery; Structured randomness; Blind deconvolution; Efficient sampling; Low-dimensional topology; Signal processing

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APA (6th Edition):

Ahmed, A. (2013). Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50226

Chicago Manual of Style (16th Edition):

Ahmed, Ali. “Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals.” 2013. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/50226.

MLA Handbook (7th Edition):

Ahmed, Ali. “Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals.” 2013. Web. 17 Sep 2019.

Vancouver:

Ahmed A. Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/50226.

Council of Science Editors:

Ahmed A. Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50226

28. Yap, Han Lun. Constrained measurement systems of low-dimensional signals.

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

 The object of this thesis is the study of constrained measurement systems of signals having low-dimensional structure using analytic tools from Compressed Sensing (CS). Realistic… (more)

Subjects/Keywords: Compressed sensing; Manifold embeddings; Stable embeddings; Concentration of measure; Restricted isometry property; Recurrent neural networks; Short-term memory; Takens' embedding; Signal processing; Mathematical optimization; Detectors; Remote sensing; Data compression (Computer science)

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APA (6th Edition):

Yap, H. L. (2012). Constrained measurement systems of low-dimensional signals. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47716

Chicago Manual of Style (16th Edition):

Yap, Han Lun. “Constrained measurement systems of low-dimensional signals.” 2012. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/47716.

MLA Handbook (7th Edition):

Yap, Han Lun. “Constrained measurement systems of low-dimensional signals.” 2012. Web. 17 Sep 2019.

Vancouver:

Yap HL. Constrained measurement systems of low-dimensional signals. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/47716.

Council of Science Editors:

Yap HL. Constrained measurement systems of low-dimensional signals. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/47716

29. Zutty, Jason Paul. Automated machine learning: A biologically inspired approach.

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

 Machine learning is a robust process by which a computer can discover characteristics of underlying data that enable it to create a model for making… (more)

Subjects/Keywords: Machine learning; AutoML; Genetic programing; Automated algorithm design

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APA (6th Edition):

Zutty, J. P. (2018). Automated machine learning: A biologically inspired approach. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60768

Chicago Manual of Style (16th Edition):

Zutty, Jason Paul. “Automated machine learning: A biologically inspired approach.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60768.

MLA Handbook (7th Edition):

Zutty, Jason Paul. “Automated machine learning: A biologically inspired approach.” 2018. Web. 17 Sep 2019.

Vancouver:

Zutty JP. Automated machine learning: A biologically inspired approach. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60768.

Council of Science Editors:

Zutty JP. Automated machine learning: A biologically inspired approach. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60768

30. Massimino, Andrew K. Learning to adapt under practical sensing constraints.

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

 The purpose of this work is to explore the capability of sensing systems to acquire information adaptively when they are subject to practical measurement constraints.… (more)

Subjects/Keywords: Sparsity; Compressive sensing; Adaptive sensing; Constrained sensing; Optimal experimental design; Pairwise comparisons; Recommender systems; Preferences

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APA (6th Edition):

Massimino, A. K. (2018). Learning to adapt under practical sensing constraints. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60776

Chicago Manual of Style (16th Edition):

Massimino, Andrew K. “Learning to adapt under practical sensing constraints.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 17, 2019. http://hdl.handle.net/1853/60776.

MLA Handbook (7th Edition):

Massimino, Andrew K. “Learning to adapt under practical sensing constraints.” 2018. Web. 17 Sep 2019.

Vancouver:

Massimino AK. Learning to adapt under practical sensing constraints. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 17]. Available from: http://hdl.handle.net/1853/60776.

Council of Science Editors:

Massimino AK. Learning to adapt under practical sensing constraints. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60776

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