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You searched for subject:(deep learning indoor localization multimodal learning). Showing records 1 – 30 of 61965 total matches.

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University of Dayton

1. Wu, Hongyu. Multimodal Learning and Single Source WiFi Based Indoor Localization.

Degree: MSin Computer Engineering, Electrical and Computer Engineering, 2020, University of Dayton

 With the rapid development of high speed Internet and Internet of Things (IoT) ap-plications, the demand of indoor localization technology is increasing over years. Well-developed… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; deep learning, indoor localization, multimodal learning

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

APA (6th Edition):

Wu, H. (2020). Multimodal Learning and Single Source WiFi Based Indoor Localization. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656

Chicago Manual of Style (16th Edition):

Wu, Hongyu. “Multimodal Learning and Single Source WiFi Based Indoor Localization.” 2020. Masters Thesis, University of Dayton. Accessed September 20, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656.

MLA Handbook (7th Edition):

Wu, Hongyu. “Multimodal Learning and Single Source WiFi Based Indoor Localization.” 2020. Web. 20 Sep 2020.

Vancouver:

Wu H. Multimodal Learning and Single Source WiFi Based Indoor Localization. [Internet] [Masters thesis]. University of Dayton; 2020. [cited 2020 Sep 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656.

Council of Science Editors:

Wu H. Multimodal Learning and Single Source WiFi Based Indoor Localization. [Masters Thesis]. University of Dayton; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1588098876967656


McMaster University

2. Xu, Qiang. Fingerprints for Indoor Localization.

Degree: PhD, 2018, McMaster University

 Location-based services have experienced substantial growth in the last decade. However, despite extensive research efforts, sub-meter location accuracy with low-cost infrastructure continues to be elusive.… (more)

Subjects/Keywords: indoor localization; fingerprint; mobile crowdsensing; active learning

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APA (6th Edition):

Xu, Q. (2018). Fingerprints for Indoor Localization. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/22841

Chicago Manual of Style (16th Edition):

Xu, Qiang. “Fingerprints for Indoor Localization.” 2018. Doctoral Dissertation, McMaster University. Accessed September 20, 2020. http://hdl.handle.net/11375/22841.

MLA Handbook (7th Edition):

Xu, Qiang. “Fingerprints for Indoor Localization.” 2018. Web. 20 Sep 2020.

Vancouver:

Xu Q. Fingerprints for Indoor Localization. [Internet] [Doctoral dissertation]. McMaster University; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/11375/22841.

Council of Science Editors:

Xu Q. Fingerprints for Indoor Localization. [Doctoral Dissertation]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/22841


University of Victoria

3. Hoang, Minh Tu. WiFi fingerprinting based indoor localization with autonomous survey and machine learning.

Degree: Department of Electrical and Computer Engineering, 2020, University of Victoria

 The demand for accurate localization under indoor environments has increased dramatically in recent years. To be cost-effective, most of the localization solutions are based on… (more)

Subjects/Keywords: WiFi fingerprinting; Indoor localization; Machine learning; KNN; RNN; Passive indoor localization; RSSI; CSI

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APA (6th Edition):

Hoang, M. T. (2020). WiFi fingerprinting based indoor localization with autonomous survey and machine learning. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/12091

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hoang, Minh Tu. “WiFi fingerprinting based indoor localization with autonomous survey and machine learning.” 2020. Thesis, University of Victoria. Accessed September 20, 2020. http://hdl.handle.net/1828/12091.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hoang, Minh Tu. “WiFi fingerprinting based indoor localization with autonomous survey and machine learning.” 2020. Web. 20 Sep 2020.

Vancouver:

Hoang MT. WiFi fingerprinting based indoor localization with autonomous survey and machine learning. [Internet] [Thesis]. University of Victoria; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/1828/12091.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hoang MT. WiFi fingerprinting based indoor localization with autonomous survey and machine learning. [Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/12091

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

4. Zia, Noor ul Sehr (author). PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations.

Degree: 2020, Delft University of Technology

 A good action proposal method should generate proposals with high recall and high temporal overlap with groundtruth. The quality of the proposals relies on the… (more)

Subjects/Keywords: Deep Learning; Action localization; Computer vision

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APA (6th Edition):

Zia, N. u. S. (. (2020). PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:505123cb-125b-4877-a159-94f8d49c58e6

Chicago Manual of Style (16th Edition):

Zia, Noor ul Sehr (author). “PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations.” 2020. Masters Thesis, Delft University of Technology. Accessed September 20, 2020. http://resolver.tudelft.nl/uuid:505123cb-125b-4877-a159-94f8d49c58e6.

MLA Handbook (7th Edition):

Zia, Noor ul Sehr (author). “PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations.” 2020. Web. 20 Sep 2020.

Vancouver:

Zia NuS(. PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Sep 20]. Available from: http://resolver.tudelft.nl/uuid:505123cb-125b-4877-a159-94f8d49c58e6.

Council of Science Editors:

Zia NuS(. PUNet: Temporal Action Proposal Generation with Positive Unlabeled Learning using Key Frame Annotations. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:505123cb-125b-4877-a159-94f8d49c58e6


University of Bradford

5. Nassar, Alaa S. N. A hybrid multibiometric system for personal identification based on face and iris traits : the development of an automated computer system for the identification of humans by integrating facial and iris features using localization, feature extraction, handcrafted and deep learning techniques.

Degree: PhD, 2018, University of Bradford

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal… (more)

Subjects/Keywords: Multimodal multibiometric systems; Face recognition; Iris recognition; Iris localization; Deep learning; Feature extraction; Curvelet transform; Fractal dimension; Deep belief network; Convolutional neural network; Personal identification

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APA (6th Edition):

Nassar, A. S. N. (2018). A hybrid multibiometric system for personal identification based on face and iris traits : the development of an automated computer system for the identification of humans by integrating facial and iris features using localization, feature extraction, handcrafted and deep learning techniques. (Doctoral Dissertation). University of Bradford. Retrieved from http://hdl.handle.net/10454/16917

Chicago Manual of Style (16th Edition):

Nassar, Alaa S N. “A hybrid multibiometric system for personal identification based on face and iris traits : the development of an automated computer system for the identification of humans by integrating facial and iris features using localization, feature extraction, handcrafted and deep learning techniques.” 2018. Doctoral Dissertation, University of Bradford. Accessed September 20, 2020. http://hdl.handle.net/10454/16917.

MLA Handbook (7th Edition):

Nassar, Alaa S N. “A hybrid multibiometric system for personal identification based on face and iris traits : the development of an automated computer system for the identification of humans by integrating facial and iris features using localization, feature extraction, handcrafted and deep learning techniques.” 2018. Web. 20 Sep 2020.

Vancouver:

Nassar ASN. A hybrid multibiometric system for personal identification based on face and iris traits : the development of an automated computer system for the identification of humans by integrating facial and iris features using localization, feature extraction, handcrafted and deep learning techniques. [Internet] [Doctoral dissertation]. University of Bradford; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/10454/16917.

Council of Science Editors:

Nassar ASN. A hybrid multibiometric system for personal identification based on face and iris traits : the development of an automated computer system for the identification of humans by integrating facial and iris features using localization, feature extraction, handcrafted and deep learning techniques. [Doctoral Dissertation]. University of Bradford; 2018. Available from: http://hdl.handle.net/10454/16917


University of Waterloo

6. Sahu, Gaurav. Adaptive Fusion Techniques for Effective Multimodal Deep Learning.

Degree: 2020, University of Waterloo

 Effective fusion of data from multiple modalities, such as video, speech, and text, is a challenging task due to the heterogeneous nature of multimodal data.… (more)

Subjects/Keywords: multimodal deep learning; multimodal fusion; generative adversarial networks; multimodal machine translation; speech emotion recognition

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APA (6th Edition):

Sahu, G. (2020). Adaptive Fusion Techniques for Effective Multimodal Deep Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16194

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sahu, Gaurav. “Adaptive Fusion Techniques for Effective Multimodal Deep Learning.” 2020. Thesis, University of Waterloo. Accessed September 20, 2020. http://hdl.handle.net/10012/16194.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sahu, Gaurav. “Adaptive Fusion Techniques for Effective Multimodal Deep Learning.” 2020. Web. 20 Sep 2020.

Vancouver:

Sahu G. Adaptive Fusion Techniques for Effective Multimodal Deep Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/10012/16194.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sahu G. Adaptive Fusion Techniques for Effective Multimodal Deep Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16194

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Sydney

7. Rao, Dushyant. Multimodal learning from visual and remotely sensed data .

Degree: 2015, University of Sydney

 Autonomous vehicles are often deployed to perform exploration and monitoring missions in unseen environments. In such applications, there is often a compromise between the information… (more)

Subjects/Keywords: multimodal; deep learning; machine learning; marine robotics; habitat mapping

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APA (6th Edition):

Rao, D. (2015). Multimodal learning from visual and remotely sensed data . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/15535

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rao, Dushyant. “Multimodal learning from visual and remotely sensed data .” 2015. Thesis, University of Sydney. Accessed September 20, 2020. http://hdl.handle.net/2123/15535.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rao, Dushyant. “Multimodal learning from visual and remotely sensed data .” 2015. Web. 20 Sep 2020.

Vancouver:

Rao D. Multimodal learning from visual and remotely sensed data . [Internet] [Thesis]. University of Sydney; 2015. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2123/15535.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rao D. Multimodal learning from visual and remotely sensed data . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/15535

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

8. Vielzeuf, Valentin. Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels : Deep learning for multimodal and temporal contents analysis.

Degree: Docteur es, Informatique, 2019, Normandie

Notre perception est par nature multimodale, i.e. fait appel à plusieurs de nos sens. Pour résoudre certaines tâches, il est donc pertinent d’utiliser différentes modalités,… (more)

Subjects/Keywords: Données Multimodales; Deep Learning; Multimodal Data; Affective Computing; Transfer Learning

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

APA (6th Edition):

Vielzeuf, V. (2019). Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels : Deep learning for multimodal and temporal contents analysis. (Doctoral Dissertation). Normandie. Retrieved from http://www.theses.fr/2019NORMC229

Chicago Manual of Style (16th Edition):

Vielzeuf, Valentin. “Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels : Deep learning for multimodal and temporal contents analysis.” 2019. Doctoral Dissertation, Normandie. Accessed September 20, 2020. http://www.theses.fr/2019NORMC229.

MLA Handbook (7th Edition):

Vielzeuf, Valentin. “Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels : Deep learning for multimodal and temporal contents analysis.” 2019. Web. 20 Sep 2020.

Vancouver:

Vielzeuf V. Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels : Deep learning for multimodal and temporal contents analysis. [Internet] [Doctoral dissertation]. Normandie; 2019. [cited 2020 Sep 20]. Available from: http://www.theses.fr/2019NORMC229.

Council of Science Editors:

Vielzeuf V. Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels : Deep learning for multimodal and temporal contents analysis. [Doctoral Dissertation]. Normandie; 2019. Available from: http://www.theses.fr/2019NORMC229


Delft University of Technology

9. Mukherjee, A. (author). Analyzing single molecule emission patterns using Deep Learning.

Degree: 2020, Delft University of Technology

The time taken to generate a super-resolution image and the quality of the final synthetic image depends on the performance of the localization algorithm which… (more)

Subjects/Keywords: Localization Microscopy; Deep Learning; 3D Localization; Aberration Estimation

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APA (6th Edition):

Mukherjee, A. (. (2020). Analyzing single molecule emission patterns using Deep Learning. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a3cf2384-4913-495f-8542-37fbbfbb3197

Chicago Manual of Style (16th Edition):

Mukherjee, A (author). “Analyzing single molecule emission patterns using Deep Learning.” 2020. Masters Thesis, Delft University of Technology. Accessed September 20, 2020. http://resolver.tudelft.nl/uuid:a3cf2384-4913-495f-8542-37fbbfbb3197.

MLA Handbook (7th Edition):

Mukherjee, A (author). “Analyzing single molecule emission patterns using Deep Learning.” 2020. Web. 20 Sep 2020.

Vancouver:

Mukherjee A(. Analyzing single molecule emission patterns using Deep Learning. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Sep 20]. Available from: http://resolver.tudelft.nl/uuid:a3cf2384-4913-495f-8542-37fbbfbb3197.

Council of Science Editors:

Mukherjee A(. Analyzing single molecule emission patterns using Deep Learning. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:a3cf2384-4913-495f-8542-37fbbfbb3197


University of Michigan

10. Sohn, Kihyuk. Improving Deep Representation Learning with Complex and Multimodal Data.

Degree: PhD, Electrical Engineering: Systems, 2015, University of Michigan

 Representation learning has emerged as a way to learn meaningful representation from data and made a breakthrough in many applications including visual object recognition, speech… (more)

Subjects/Keywords: deep learning; representation learning; structured output prediction; multimodal learning; Electrical Engineering; Engineering

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APA (6th Edition):

Sohn, K. (2015). Improving Deep Representation Learning with Complex and Multimodal Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113549

Chicago Manual of Style (16th Edition):

Sohn, Kihyuk. “Improving Deep Representation Learning with Complex and Multimodal Data.” 2015. Doctoral Dissertation, University of Michigan. Accessed September 20, 2020. http://hdl.handle.net/2027.42/113549.

MLA Handbook (7th Edition):

Sohn, Kihyuk. “Improving Deep Representation Learning with Complex and Multimodal Data.” 2015. Web. 20 Sep 2020.

Vancouver:

Sohn K. Improving Deep Representation Learning with Complex and Multimodal Data. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2027.42/113549.

Council of Science Editors:

Sohn K. Improving Deep Representation Learning with Complex and Multimodal Data. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113549


Cornell University

11. Sung, Jaeyong. Learning to Manipulate Novel Objects for Assistive Robots.

Degree: PhD, Computer Science, 2017, Cornell University

 The ability to reason about different modalities of information, for the purpose of physical interaction with objects, is a critical skill for assistive robots. For… (more)

Subjects/Keywords: machine learning; Multimodal Data; Robotic Manipulation; Robot Learning; Artificial intelligence; Deep Learning; Computer science; Robotics

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

APA (6th Edition):

Sung, J. (2017). Learning to Manipulate Novel Objects for Assistive Robots. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/51619

Chicago Manual of Style (16th Edition):

Sung, Jaeyong. “Learning to Manipulate Novel Objects for Assistive Robots.” 2017. Doctoral Dissertation, Cornell University. Accessed September 20, 2020. http://hdl.handle.net/1813/51619.

MLA Handbook (7th Edition):

Sung, Jaeyong. “Learning to Manipulate Novel Objects for Assistive Robots.” 2017. Web. 20 Sep 2020.

Vancouver:

Sung J. Learning to Manipulate Novel Objects for Assistive Robots. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/1813/51619.

Council of Science Editors:

Sung J. Learning to Manipulate Novel Objects for Assistive Robots. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/51619


Rochester Institute of Technology

12. Relyea, Robert. Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning.

Degree: MS, Computer Engineering, 2020, Rochester Institute of Technology

  Autonomous agents in any environment require accurate and reliable position and motion estimation to complete their required tasks. Many different sensor modalities have been… (more)

Subjects/Keywords: Computer vision; Deep learning; Localization; Robotics; Self-supervised learning; Sensor fusion

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APA (6th Edition):

Relyea, R. (2020). Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10448

Chicago Manual of Style (16th Edition):

Relyea, Robert. “Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed September 20, 2020. https://scholarworks.rit.edu/theses/10448.

MLA Handbook (7th Edition):

Relyea, Robert. “Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning.” 2020. Web. 20 Sep 2020.

Vancouver:

Relyea R. Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2020 Sep 20]. Available from: https://scholarworks.rit.edu/theses/10448.

Council of Science Editors:

Relyea R. Improving Omnidirectional Camera-Based Robot Localization Through Self-Supervised Learning. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10448


Hong Kong University of Science and Technology

13. Li, Chun Ting CIVL. Development of BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications.

Degree: 2019, Hong Kong University of Science and Technology

 Over the past decades, indoor positioning has been drawing wide attention in different fields of engineering. Indoor positioning technologies are complementary to the mature outdoor… (more)

Subjects/Keywords: Indoor positioning systems (Wireless localization) ; Wireless localization ; Building information modeling ; Machine learning

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APA (6th Edition):

Li, C. T. C. (2019). Development of BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-101629 ; https://doi.org/10.14711/thesis-991012752559303412 ; http://repository.ust.hk/ir/bitstream/1783.1-101629/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Chun Ting CIVL. “Development of BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications.” 2019. Thesis, Hong Kong University of Science and Technology. Accessed September 20, 2020. http://repository.ust.hk/ir/Record/1783.1-101629 ; https://doi.org/10.14711/thesis-991012752559303412 ; http://repository.ust.hk/ir/bitstream/1783.1-101629/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Chun Ting CIVL. “Development of BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications.” 2019. Web. 20 Sep 2020.

Vancouver:

Li CTC. Development of BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2019. [cited 2020 Sep 20]. Available from: http://repository.ust.hk/ir/Record/1783.1-101629 ; https://doi.org/10.14711/thesis-991012752559303412 ; http://repository.ust.hk/ir/bitstream/1783.1-101629/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li CTC. Development of BIM-assisted access point placement optimization and deep learning based multi-floor identification algorithms for enhancing indoor positioning to support construction applications. [Thesis]. Hong Kong University of Science and Technology; 2019. Available from: http://repository.ust.hk/ir/Record/1783.1-101629 ; https://doi.org/10.14711/thesis-991012752559303412 ; http://repository.ust.hk/ir/bitstream/1783.1-101629/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

14. de Jong, Richard (author). Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity.

Degree: 2019, Delft University of Technology

 Persistent surveillance is an urgent proficiency. For security, surveillance cameras are a strong asset as they support the automatic tracking of people and are directly… (more)

Subjects/Keywords: Multimodal; Deep Learning; Human Activity; Classification; micro-Doppler

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

APA (6th Edition):

de Jong, R. (. (2019). Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9e62cbc0-d110-47b9-a0a3-312d6b0eebc8

Chicago Manual of Style (16th Edition):

de Jong, Richard (author). “Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity.” 2019. Masters Thesis, Delft University of Technology. Accessed September 20, 2020. http://resolver.tudelft.nl/uuid:9e62cbc0-d110-47b9-a0a3-312d6b0eebc8.

MLA Handbook (7th Edition):

de Jong, Richard (author). “Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity.” 2019. Web. 20 Sep 2020.

Vancouver:

de Jong R(. Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Sep 20]. Available from: http://resolver.tudelft.nl/uuid:9e62cbc0-d110-47b9-a0a3-312d6b0eebc8.

Council of Science Editors:

de Jong R(. Multimodal Deep Learning for the Classification of Human Activity: Radar and Video data fusion for the classification of human activity. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9e62cbc0-d110-47b9-a0a3-312d6b0eebc8


Princeton University

15. Zhang, Yinda. From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments .

Degree: PhD, 2018, Princeton University

 Understanding the 3D geometry and semantics of real environments is in critically high demand for many applications, such as autonomous driving, robotics, and augmented reality.… (more)

Subjects/Keywords: 3D geometry; Computer vision; Deep learning; Indoor environment; Scene understanding; Semantic

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APA (6th Edition):

Zhang, Y. (2018). From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp011831cn73t

Chicago Manual of Style (16th Edition):

Zhang, Yinda. “From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments .” 2018. Doctoral Dissertation, Princeton University. Accessed September 20, 2020. http://arks.princeton.edu/ark:/88435/dsp011831cn73t.

MLA Handbook (7th Edition):

Zhang, Yinda. “From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments .” 2018. Web. 20 Sep 2020.

Vancouver:

Zhang Y. From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2020 Sep 20]. Available from: http://arks.princeton.edu/ark:/88435/dsp011831cn73t.

Council of Science Editors:

Zhang Y. From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp011831cn73t


Delft University of Technology

16. van Rosmalen, N.C. (author). Positive Class Localization Map: A framework for weakly supervised object localization.

Degree: 2016, Delft University of Technology

To possess a computer algorithm that can perform the popular task of object localization with only weak supervision is valuable for numerous reasons. Often enough… (more)

Subjects/Keywords: weakly supervised; object localization; birds; deep learning; neural networks; convolutional; PCLM; Positive Class Localization Map

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APA (6th Edition):

van Rosmalen, N. C. (. (2016). Positive Class Localization Map: A framework for weakly supervised object localization. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:23a5bdb8-2610-4e01-9586-a6faf34a6cbd

Chicago Manual of Style (16th Edition):

van Rosmalen, N C (author). “Positive Class Localization Map: A framework for weakly supervised object localization.” 2016. Masters Thesis, Delft University of Technology. Accessed September 20, 2020. http://resolver.tudelft.nl/uuid:23a5bdb8-2610-4e01-9586-a6faf34a6cbd.

MLA Handbook (7th Edition):

van Rosmalen, N C (author). “Positive Class Localization Map: A framework for weakly supervised object localization.” 2016. Web. 20 Sep 2020.

Vancouver:

van Rosmalen NC(. Positive Class Localization Map: A framework for weakly supervised object localization. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2020 Sep 20]. Available from: http://resolver.tudelft.nl/uuid:23a5bdb8-2610-4e01-9586-a6faf34a6cbd.

Council of Science Editors:

van Rosmalen NC(. Positive Class Localization Map: A framework for weakly supervised object localization. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:23a5bdb8-2610-4e01-9586-a6faf34a6cbd


Kansas State University

17. Li, Xukun. Disaster tweet text and image analysis using deep learning approaches.

Degree: PhD, Department of Computer Science, 2020, Kansas State University

 Fast analysis of damage information after a disaster can inform responders and aid agencies, accelerate real-time response, and guide the allocation of resources. Once the… (more)

Subjects/Keywords: Deep Learning; Domain Adaptation; Tweet Classification; Disaster Data Analysis; Damage Localization

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APA (6th Edition):

Li, X. (2020). Disaster tweet text and image analysis using deep learning approaches. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/40815

Chicago Manual of Style (16th Edition):

Li, Xukun. “Disaster tweet text and image analysis using deep learning approaches.” 2020. Doctoral Dissertation, Kansas State University. Accessed September 20, 2020. http://hdl.handle.net/2097/40815.

MLA Handbook (7th Edition):

Li, Xukun. “Disaster tweet text and image analysis using deep learning approaches.” 2020. Web. 20 Sep 2020.

Vancouver:

Li X. Disaster tweet text and image analysis using deep learning approaches. [Internet] [Doctoral dissertation]. Kansas State University; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2097/40815.

Council of Science Editors:

Li X. Disaster tweet text and image analysis using deep learning approaches. [Doctoral Dissertation]. Kansas State University; 2020. Available from: http://hdl.handle.net/2097/40815


Georgia State University

18. Liang, Yi. PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES.

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

  Mobile devices are becoming more and more indispensable in people’s daily life. They bring variety of conveniences. However, many privacy issues also arise along… (more)

Subjects/Keywords: Privacy preserving; Localization; Smart devices; Sensors; Data mining; Deep learning

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APA (6th Edition):

Liang, Y. (2018). PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/138

Chicago Manual of Style (16th Edition):

Liang, Yi. “PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES.” 2018. Doctoral Dissertation, Georgia State University. Accessed September 20, 2020. https://scholarworks.gsu.edu/cs_diss/138.

MLA Handbook (7th Edition):

Liang, Yi. “PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES.” 2018. Web. 20 Sep 2020.

Vancouver:

Liang Y. PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES. [Internet] [Doctoral dissertation]. Georgia State University; 2018. [cited 2020 Sep 20]. Available from: https://scholarworks.gsu.edu/cs_diss/138.

Council of Science Editors:

Liang Y. PRIVACY LEAKAGE THROUGH SENSORY DATA ON SMART DEVICES. [Doctoral Dissertation]. Georgia State University; 2018. Available from: https://scholarworks.gsu.edu/cs_diss/138


Boston University

19. Ma, Shugao. Learning space-time structures for action recognition and localization.

Degree: PhD, Computer Science, 2016, Boston University

 In this thesis the problem of automatic human action recognition and localization in videos is studied. In this problem, our goal is to recognize the… (more)

Subjects/Keywords: Computer science; Action localization; Action recognition; Computer vision; Deep learning; Machine learning; Space-time structures

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APA (6th Edition):

Ma, S. (2016). Learning space-time structures for action recognition and localization. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/17720

Chicago Manual of Style (16th Edition):

Ma, Shugao. “Learning space-time structures for action recognition and localization.” 2016. Doctoral Dissertation, Boston University. Accessed September 20, 2020. http://hdl.handle.net/2144/17720.

MLA Handbook (7th Edition):

Ma, Shugao. “Learning space-time structures for action recognition and localization.” 2016. Web. 20 Sep 2020.

Vancouver:

Ma S. Learning space-time structures for action recognition and localization. [Internet] [Doctoral dissertation]. Boston University; 2016. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2144/17720.

Council of Science Editors:

Ma S. Learning space-time structures for action recognition and localization. [Doctoral Dissertation]. Boston University; 2016. Available from: http://hdl.handle.net/2144/17720

20. Kalogeiton, Vasiliki. Localizing spatially and temporally objects and actions in videos.

Degree: PhD, 2018, University of Edinburgh

 The rise of deep learning has facilitated remarkable progress in video understanding. This thesis addresses three important tasks of video understanding: video object detection, joint… (more)

Subjects/Keywords: action localization; action recognition; object detection; video analysis; computer vision; deep learning; machine learning

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APA (6th Edition):

Kalogeiton, V. (2018). Localizing spatially and temporally objects and actions in videos. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/28984

Chicago Manual of Style (16th Edition):

Kalogeiton, Vasiliki. “Localizing spatially and temporally objects and actions in videos.” 2018. Doctoral Dissertation, University of Edinburgh. Accessed September 20, 2020. http://hdl.handle.net/1842/28984.

MLA Handbook (7th Edition):

Kalogeiton, Vasiliki. “Localizing spatially and temporally objects and actions in videos.” 2018. Web. 20 Sep 2020.

Vancouver:

Kalogeiton V. Localizing spatially and temporally objects and actions in videos. [Internet] [Doctoral dissertation]. University of Edinburgh; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/1842/28984.

Council of Science Editors:

Kalogeiton V. Localizing spatially and temporally objects and actions in videos. [Doctoral Dissertation]. University of Edinburgh; 2018. Available from: http://hdl.handle.net/1842/28984


Penn State University

21. Hall, Donald L. Radio Frequency Fingerprinting Devices in Indoor Environments via Vector Sensing and Machine Learning.

Degree: 2018, Penn State University

 This thesis proposes a passive vector sensor for performing radio frequency fingerprinting (RFF) for simultaneous device characterization, orientation estimation, and localization in indoor environments. The… (more)

Subjects/Keywords: Emitter Localization; Emitter Identification; Machine Learning; Vector Sensing; Radio Frequency Fingerprinting; Indoor Environment

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APA (6th Edition):

Hall, D. L. (2018). Radio Frequency Fingerprinting Devices in Indoor Environments via Vector Sensing and Machine Learning. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15871dqh5265

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hall, Donald L. “Radio Frequency Fingerprinting Devices in Indoor Environments via Vector Sensing and Machine Learning.” 2018. Thesis, Penn State University. Accessed September 20, 2020. https://submit-etda.libraries.psu.edu/catalog/15871dqh5265.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hall, Donald L. “Radio Frequency Fingerprinting Devices in Indoor Environments via Vector Sensing and Machine Learning.” 2018. Web. 20 Sep 2020.

Vancouver:

Hall DL. Radio Frequency Fingerprinting Devices in Indoor Environments via Vector Sensing and Machine Learning. [Internet] [Thesis]. Penn State University; 2018. [cited 2020 Sep 20]. Available from: https://submit-etda.libraries.psu.edu/catalog/15871dqh5265.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hall DL. Radio Frequency Fingerprinting Devices in Indoor Environments via Vector Sensing and Machine Learning. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15871dqh5265

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Oregon State University

22. Ghaeini, Mohammad Reza. Event Detection with Forward-Backward Recurrent Neural Networks.

Degree: MS, 2017, Oregon State University

 Automatic event extraction from natural text is an important and challenging task for natural language understanding. Traditional event detection methods heavily rely on manually engineered… (more)

Subjects/Keywords: Deep Learning

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APA (6th Edition):

Ghaeini, M. R. (2017). Event Detection with Forward-Backward Recurrent Neural Networks. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/61576

Chicago Manual of Style (16th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Masters Thesis, Oregon State University. Accessed September 20, 2020. http://hdl.handle.net/1957/61576.

MLA Handbook (7th Edition):

Ghaeini, Mohammad Reza. “Event Detection with Forward-Backward Recurrent Neural Networks.” 2017. Web. 20 Sep 2020.

Vancouver:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/1957/61576.

Council of Science Editors:

Ghaeini MR. Event Detection with Forward-Backward Recurrent Neural Networks. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61576


California State Polytechnic University – Pomona

23. Frank, Hakeem. Gaussian Process Models for Computer Vision.

Degree: MS, Department of Mathematics and Statistics, 2020, California State Polytechnic University – Pomona

 Supervised learning is the task of finding a function f(x) that maps an input x to an output y using observed data. Gaussian process models… (more)

Subjects/Keywords: deep learning

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APA (6th Edition):

Frank, H. (2020). Gaussian Process Models for Computer Vision. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/216857

Chicago Manual of Style (16th Edition):

Frank, Hakeem. “Gaussian Process Models for Computer Vision.” 2020. Masters Thesis, California State Polytechnic University – Pomona. Accessed September 20, 2020. http://hdl.handle.net/10211.3/216857.

MLA Handbook (7th Edition):

Frank, Hakeem. “Gaussian Process Models for Computer Vision.” 2020. Web. 20 Sep 2020.

Vancouver:

Frank H. Gaussian Process Models for Computer Vision. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2020. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/10211.3/216857.

Council of Science Editors:

Frank H. Gaussian Process Models for Computer Vision. [Masters Thesis]. California State Polytechnic University – Pomona; 2020. Available from: http://hdl.handle.net/10211.3/216857


University of Sydney

24. Windrim, Lloyd. Illumination Invariant Deep Learning for Hyperspectral Data .

Degree: 2018, University of Sydney

 Motivated by the variability in hyperspectral images due to illumination and the difficulty in acquiring labelled data, this thesis proposes different approaches for learning illumination… (more)

Subjects/Keywords: Deep learning

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APA (6th Edition):

Windrim, L. (2018). Illumination Invariant Deep Learning for Hyperspectral Data . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/18734

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Windrim, Lloyd. “Illumination Invariant Deep Learning for Hyperspectral Data .” 2018. Thesis, University of Sydney. Accessed September 20, 2020. http://hdl.handle.net/2123/18734.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Windrim, Lloyd. “Illumination Invariant Deep Learning for Hyperspectral Data .” 2018. Web. 20 Sep 2020.

Vancouver:

Windrim L. Illumination Invariant Deep Learning for Hyperspectral Data . [Internet] [Thesis]. University of Sydney; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/2123/18734.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Windrim L. Illumination Invariant Deep Learning for Hyperspectral Data . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/18734

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidad de Cantabria

25. Noriega Puente, Andrea. Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging.

Degree: Máster en Ciencia de Datos, 2019, Universidad de Cantabria

 RESUMEN: El glioma es el tipo de tumor cerebral más común, presentando distintos grados de malignidad y agresividad, así como un pronóstico variable. La gran… (more)

Subjects/Keywords: Deep Learning

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APA (6th Edition):

Noriega Puente, A. (2019). Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging. (Masters Thesis). Universidad de Cantabria. Retrieved from http://hdl.handle.net/10902/17859

Chicago Manual of Style (16th Edition):

Noriega Puente, Andrea. “Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging.” 2019. Masters Thesis, Universidad de Cantabria. Accessed September 20, 2020. http://hdl.handle.net/10902/17859.

MLA Handbook (7th Edition):

Noriega Puente, Andrea. “Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging.” 2019. Web. 20 Sep 2020.

Vancouver:

Noriega Puente A. Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging. [Internet] [Masters thesis]. Universidad de Cantabria; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/10902/17859.

Council of Science Editors:

Noriega Puente A. Segmentación de gliomas en imagen de resonancia magnética multimodal: Glioma segmentation in multimodal magnetic resonance imaging. [Masters Thesis]. Universidad de Cantabria; 2019. Available from: http://hdl.handle.net/10902/17859


California State Polytechnic University – Pomona

26. Shimpi, Shubhangi. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients.

Degree: MS, Department of Computer Science, 2018, California State Polytechnic University – Pomona

 Electroencephalogram (EEG) data includes information of electrical activity of a brain; thus is commonly used to diagnose any underlying neurological condition such as epilepsy. Epileptic… (more)

Subjects/Keywords: deep learning

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APA (6th Edition):

Shimpi, S. (2018). Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/199949

Chicago Manual of Style (16th Edition):

Shimpi, Shubhangi. “Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients.” 2018. Masters Thesis, California State Polytechnic University – Pomona. Accessed September 20, 2020. http://hdl.handle.net/10211.3/199949.

MLA Handbook (7th Edition):

Shimpi, Shubhangi. “Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients.” 2018. Web. 20 Sep 2020.

Vancouver:

Shimpi S. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2018. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/10211.3/199949.

Council of Science Editors:

Shimpi S. Deep Recurrent Neural Networks for Seizure Prediction in Epileptic Patients. [Masters Thesis]. California State Polytechnic University – Pomona; 2018. Available from: http://hdl.handle.net/10211.3/199949


University of Guelph

27. Veres, Matthew. Modeling Grasp Motor Imagery.

Degree: Master of Applied Science, School of Engineering, 2016, University of Guelph

 Humans have an innate ability for performing complex grasping maneuvers, yet transferring this ability to robotics is an extremely daunting task. A primary culprit for… (more)

Subjects/Keywords: Deep Learning; Robotic grasping; Multimodal grasping; Autoencoders; Conditional variational autoencoders; Joint embeddings

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APA (6th Edition):

Veres, M. (2016). Modeling Grasp Motor Imagery. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9983

Chicago Manual of Style (16th Edition):

Veres, Matthew. “Modeling Grasp Motor Imagery.” 2016. Masters Thesis, University of Guelph. Accessed September 20, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9983.

MLA Handbook (7th Edition):

Veres, Matthew. “Modeling Grasp Motor Imagery.” 2016. Web. 20 Sep 2020.

Vancouver:

Veres M. Modeling Grasp Motor Imagery. [Internet] [Masters thesis]. University of Guelph; 2016. [cited 2020 Sep 20]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9983.

Council of Science Editors:

Veres M. Modeling Grasp Motor Imagery. [Masters Thesis]. University of Guelph; 2016. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9983

28. Budnik, Mateusz. Active and deep learning for multimedia : Apprentissage actif et profond pour le multimédia.

Degree: Docteur es, Informatique, 2017, Université Grenoble Alpes (ComUE)

Les thèmes principaux abordés dans cette thèse sont l'utilisation de méthodes d'apprentissage actif et d'apprentissage profond dans le contexte du traitement de documents multimodaux. Les… (more)

Subjects/Keywords: Annotation documents; Multimodaux; Multilingues; Multimédia; Apprentissage profond; Document annotation; Multimodal; Multilingual; Multimedia; Deep learning; 004

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APA (6th Edition):

Budnik, M. (2017). Active and deep learning for multimedia : Apprentissage actif et profond pour le multimédia. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2017GREAM011

Chicago Manual of Style (16th Edition):

Budnik, Mateusz. “Active and deep learning for multimedia : Apprentissage actif et profond pour le multimédia.” 2017. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed September 20, 2020. http://www.theses.fr/2017GREAM011.

MLA Handbook (7th Edition):

Budnik, Mateusz. “Active and deep learning for multimedia : Apprentissage actif et profond pour le multimédia.” 2017. Web. 20 Sep 2020.

Vancouver:

Budnik M. Active and deep learning for multimedia : Apprentissage actif et profond pour le multimédia. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2017. [cited 2020 Sep 20]. Available from: http://www.theses.fr/2017GREAM011.

Council of Science Editors:

Budnik M. Active and deep learning for multimedia : Apprentissage actif et profond pour le multimédia. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2017. Available from: http://www.theses.fr/2017GREAM011

29. LJUNGGREN, ELIN. Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2019, Chalmers University of Technology

 Abdominal Aortic Aneurysm (AAA) is a localized enlargement of the abdominal aorta that can progress to a rupture, which will cause an internal bleeding that… (more)

Subjects/Keywords: multimodal deep learning; abdominal aortic aneurysm (AAA); subaneurysmal aortic dilatation; VGG19; Keras; heatmaps; permutation importance

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APA (6th Edition):

LJUNGGREN, E. (2019). Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

LJUNGGREN, ELIN. “Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation .” 2019. Thesis, Chalmers University of Technology. Accessed September 20, 2020. http://hdl.handle.net/20.500.12380/300040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

LJUNGGREN, ELIN. “Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation .” 2019. Web. 20 Sep 2020.

Vancouver:

LJUNGGREN E. Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2020 Sep 20]. Available from: http://hdl.handle.net/20.500.12380/300040.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

LJUNGGREN E. Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/300040

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Delft University of Technology

30. Smit, M. (author); Chen, Z. (author); Erbaşu, M.A. (author); Gaol, Y.A.L. (author). SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach.

Degree: 2020, Delft University of Technology

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there… (more)

Subjects/Keywords: Point Cloud; Deep Learning; Machine Learning; Neural Network; Indoor Environment; Data Labeling; Semantic Classification; Visualization; Comparison; Case Study; Web Application

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APA (6th Edition):

Smit, M. (author); Chen, Z. (author); Erbaşu, M.A. (author); Gaol, Y. A. L. (. (2020). SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d40d5802-6df9-4505-b01a-35a918373ba4

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Smit, M. (author); Chen, Z. (author); Erbaşu, M.A. (author); Gaol, Y A L (author). “SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach.” 2020. Thesis, Delft University of Technology. Accessed September 20, 2020. http://resolver.tudelft.nl/uuid:d40d5802-6df9-4505-b01a-35a918373ba4.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Smit, M. (author); Chen, Z. (author); Erbaşu, M.A. (author); Gaol, Y A L (author). “SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach.” 2020. Web. 20 Sep 2020.

Vancouver:

Smit, M. (author); Chen, Z. (author); Erbaşu, M.A. (author); Gaol YAL(. SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach. [Internet] [Thesis]. Delft University of Technology; 2020. [cited 2020 Sep 20]. Available from: http://resolver.tudelft.nl/uuid:d40d5802-6df9-4505-b01a-35a918373ba4.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Smit, M. (author); Chen, Z. (author); Erbaşu, M.A. (author); Gaol YAL(. SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach. [Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:d40d5802-6df9-4505-b01a-35a918373ba4

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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