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You searched for subject:( convolutional neural network CNN ). Showing records 1 – 30 of 27266 total matches.

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1. Nordeng, Ian Edward. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.

Degree: MS, Mechanical Engineering, 2018, University of North Dakota

  This work presents a novel system utilizing previously developed convolutional neural network (CNN) architectures to aid in automating maintenance inspections of the dead-end body… (more)

Subjects/Keywords: CNN; Convolutional Neural Network; Inspections; Machine Learning

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APA (6th Edition):

Nordeng, I. E. (2018). Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. (Masters Thesis). University of North Dakota. Retrieved from https://commons.und.edu/theses/2300

Chicago Manual of Style (16th Edition):

Nordeng, Ian Edward. “Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.” 2018. Masters Thesis, University of North Dakota. Accessed September 19, 2019. https://commons.und.edu/theses/2300.

MLA Handbook (7th Edition):

Nordeng, Ian Edward. “Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery.” 2018. Web. 19 Sep 2019.

Vancouver:

Nordeng IE. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. [Internet] [Masters thesis]. University of North Dakota; 2018. [cited 2019 Sep 19]. Available from: https://commons.und.edu/theses/2300.

Council of Science Editors:

Nordeng IE. Dead End Body Component Inspections With Convolutional Neural Networks Using UAS Imagery. [Masters Thesis]. University of North Dakota; 2018. Available from: https://commons.und.edu/theses/2300


NSYSU

2. Wu, Pei-Hsuan. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

 Deep Neural Networks (DNN) widely used in computer vision applications have superior performance in image classification and object detection. However, the huge amount of data… (more)

Subjects/Keywords: CNN hardware accelerator; deep neural network (DNN); convolutional neural network (CNN); machine learning

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APA (6th Edition):

Wu, P. (2018). Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

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):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Thesis, NSYSU. Accessed September 19, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

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

MLA Handbook (7th Edition):

Wu, Pei-Hsuan. “Architecture Design and Implementation of Deep Neural Network Hardware Accelerators.” 2018. Web. 19 Sep 2019.

Vancouver:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Internet] [Thesis]. NSYSU; 2018. [cited 2019 Sep 19]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714.

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

Council of Science Editors:

Wu P. Architecture Design and Implementation of Deep Neural Network Hardware Accelerators. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729118-154714

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


Clemson University

3. Srinivasamurthy, Ravisutha Sakrepatna. Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals.

Degree: MS, Electrical and Computer Engineering (Holcomb Dept. of), 2018, Clemson University

  In recent times, we have seen a surge in usage of Convolutional Neural Networks to solve all kinds of problems - from handwriting recognition… (more)

Subjects/Keywords: CNN; CNN and DSP; Convolutional Neural Network; Deep Learning; Machine Learning; Signal Processing

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APA (6th Edition):

Srinivasamurthy, R. S. (2018). Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2911

Chicago Manual of Style (16th Edition):

Srinivasamurthy, Ravisutha Sakrepatna. “Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals.” 2018. Masters Thesis, Clemson University. Accessed September 19, 2019. https://tigerprints.clemson.edu/all_theses/2911.

MLA Handbook (7th Edition):

Srinivasamurthy, Ravisutha Sakrepatna. “Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals.” 2018. Web. 19 Sep 2019.

Vancouver:

Srinivasamurthy RS. Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals. [Internet] [Masters thesis]. Clemson University; 2018. [cited 2019 Sep 19]. Available from: https://tigerprints.clemson.edu/all_theses/2911.

Council of Science Editors:

Srinivasamurthy RS. Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals. [Masters Thesis]. Clemson University; 2018. Available from: https://tigerprints.clemson.edu/all_theses/2911


NSYSU

4. Wang, Li-chieh. System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework.

Degree: Master, Computer Science and Engineering, 2017, NSYSU

 In recent years, accelerating compute-intensive applications by utilizing FPGA computing resources based on OpenCL interface has received a lot of attention. This scheme cannot only… (more)

Subjects/Keywords: Altera FPGA; convolutional neural network; HOG; CNN; human detection; OpenCL

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APA (6th Edition):

Wang, L. (2017). System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946

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):

Wang, Li-chieh. “System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework.” 2017. Thesis, NSYSU. Accessed September 19, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946.

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

MLA Handbook (7th Edition):

Wang, Li-chieh. “System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework.” 2017. Web. 19 Sep 2019.

Vancouver:

Wang L. System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework. [Internet] [Thesis]. NSYSU; 2017. [cited 2019 Sep 19]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946.

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

Council of Science Editors:

Wang L. System Platform Integration and Kernel Optimizations for Some Embedded Applications Based on Altera OpenCL Framework. [Thesis]. NSYSU; 2017. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0016117-135946

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


University of Dayton

5. Martell, Patrick Keith. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.

Degree: MS(M.S.), Electrical Engineering, 2017, University of Dayton

Convolutional neural networks (CNNs) lack ample methods to improve performance without either adding more input data, modifying existing data, or changing network design. This work… (more)

Subjects/Keywords: Electrical Engineering; Convolutional Neural Network; Polynomial; CNN; Classification; MNIST

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APA (6th Edition):

Martell, P. K. (2017). Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038

Chicago Manual of Style (16th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Masters Thesis, University of Dayton. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

MLA Handbook (7th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Web. 19 Sep 2019.

Vancouver:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Internet] [Masters thesis]. University of Dayton; 2017. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

Council of Science Editors:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Masters Thesis]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038

6. Hodges, Jonathan Lee. Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks.

Degree: PhD, Mechanical Engineering, 2018, Virginia Tech

 Fire dynamics is a complex process involving multi-mode heat transfer, reacting fluid flow, and the reaction of combustible materials. High-fidelity predictions of fire behavior using… (more)

Subjects/Keywords: Wildland; Structure; Fire; Artificial; Neural; Network; Convolutional; CNN

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APA (6th Edition):

Hodges, J. L. (2018). Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86364

Chicago Manual of Style (16th Edition):

Hodges, Jonathan Lee. “Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks.” 2018. Doctoral Dissertation, Virginia Tech. Accessed September 19, 2019. http://hdl.handle.net/10919/86364.

MLA Handbook (7th Edition):

Hodges, Jonathan Lee. “Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks.” 2018. Web. 19 Sep 2019.

Vancouver:

Hodges JL. Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10919/86364.

Council of Science Editors:

Hodges JL. Predicting Large Domain Multi-Physics Fire Behavior Using Artificial Neural Networks. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/86364

7. Jadeglans, Tim. FPGA-implementation av ett neuralt nätverk .

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

 Image recognition is a quickly growing field where convolutional neural networks, CNN, are in the bleeding edge. Today fast GPUs are used which consume a… (more)

Subjects/Keywords: Convolutional Neural Network; CNN; Field Programmable Gate Array; FPGA; Image Recognition

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APA (6th Edition):

Jadeglans, T. (2019). FPGA-implementation av ett neuralt nätverk . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300036

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):

Jadeglans, Tim. “FPGA-implementation av ett neuralt nätverk .” 2019. Thesis, Chalmers University of Technology. Accessed September 19, 2019. http://hdl.handle.net/20.500.12380/300036.

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

MLA Handbook (7th Edition):

Jadeglans, Tim. “FPGA-implementation av ett neuralt nätverk .” 2019. Web. 19 Sep 2019.

Vancouver:

Jadeglans T. FPGA-implementation av ett neuralt nätverk . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/20.500.12380/300036.

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

Council of Science Editors:

Jadeglans T. FPGA-implementation av ett neuralt nätverk . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/300036

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


Linköping University

8. Helén, Ludvig. Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy.

Degree: Software and Systems, 2017, Linköping University

  The company Ericsson is taking steps towards embracing automating techniques and applying them to their product development cycle. Ericsson wants to apply machine learning… (more)

Subjects/Keywords: error handling; cnn; convolutional neural network; text classification; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Helén, L. (2017). Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139204

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):

Helén, Ludvig. “Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy.” 2017. Thesis, Linköping University. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139204.

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

MLA Handbook (7th Edition):

Helén, Ludvig. “Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy.” 2017. Web. 19 Sep 2019.

Vancouver:

Helén L. Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy. [Internet] [Thesis]. Linköping University; 2017. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139204.

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

Council of Science Editors:

Helén L. Automating Text Categorization with Machine Learning : Error Responsibility in a multi-layer hierarchy. [Thesis]. Linköping University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139204

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


Utah State University

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Khasgiwala, Anuj. “Word Recognition in Nutrition Labels with Convolutional Neural Network.” 2018. Masters Thesis, Utah State University. Accessed September 19, 2019. https://digitalcommons.usu.edu/etd/7101.

MLA Handbook (7th Edition):

Khasgiwala, Anuj. “Word Recognition in Nutrition Labels with Convolutional Neural Network.” 2018. Web. 19 Sep 2019.

Vancouver:

Khasgiwala A. Word Recognition in Nutrition Labels with Convolutional Neural Network. [Internet] [Masters thesis]. Utah State University; 2018. [cited 2019 Sep 19]. Available from: https://digitalcommons.usu.edu/etd/7101.

Council of Science Editors:

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


Virginia Tech

10. Bianchi, Eric Loran. COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges.

Degree: MS, Civil and Environmental Engineering, 2019, Virginia Tech

 Common Objects in Context for bridge inspection (COCO-Bridge) was introduced for use by unmanned aircraft systems (UAS) to assist in GPS denied environments, flight-planning, and… (more)

Subjects/Keywords: Convolutional neural network; bridge inspection; UAS; CNN; Artificial Intelligence; Augmented Reality; Deep Learning; Machine Learning

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APA (6th Edition):

Bianchi, E. L. (2019). COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/87588

Chicago Manual of Style (16th Edition):

Bianchi, Eric Loran. “COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges.” 2019. Masters Thesis, Virginia Tech. Accessed September 19, 2019. http://hdl.handle.net/10919/87588.

MLA Handbook (7th Edition):

Bianchi, Eric Loran. “COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges.” 2019. Web. 19 Sep 2019.

Vancouver:

Bianchi EL. COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10919/87588.

Council of Science Editors:

Bianchi EL. COCO-Bridge: Common Objects in Context Dataset and Benchmark for Structural Detail Detection of Bridges. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/87588


University of Dayton

11. Stanton, Jamie Alyssa. Detecting Image Forgery with Color Phenomenology.

Degree: MS(M.S.), Electrical Engineering, 2019, University of Dayton

 We propose a method that is designed to detect manipulations in images based on the phenomenology of color. Segmented regions of the image are converted… (more)

Subjects/Keywords: Electrical Engineering; Engineering; Image Forensics; Deep Learning; Convolutional Neural Network; CNN; Chromaticity; Multimedia Forensics

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APA (6th Edition):

Stanton, J. A. (2019). Detecting Image Forgery with Color Phenomenology. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572

Chicago Manual of Style (16th Edition):

Stanton, Jamie Alyssa. “Detecting Image Forgery with Color Phenomenology.” 2019. Masters Thesis, University of Dayton. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572.

MLA Handbook (7th Edition):

Stanton, Jamie Alyssa. “Detecting Image Forgery with Color Phenomenology.” 2019. Web. 19 Sep 2019.

Vancouver:

Stanton JA. Detecting Image Forgery with Color Phenomenology. [Internet] [Masters thesis]. University of Dayton; 2019. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572.

Council of Science Editors:

Stanton JA. Detecting Image Forgery with Color Phenomenology. [Masters Thesis]. University of Dayton; 2019. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572


Australian National University

12. Zhang, Haoyang. Learning to Generate and Refine Object Proposals .

Degree: 2018, Australian National University

 Visual object recognition is a fundamental and challenging problem in computer vision. To build a practical recognition system, one is first confronted with high computation… (more)

Subjects/Keywords: object proposal; object candidate; object detection; object instance segmentation; convolutional neural network (CNN)

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APA (6th Edition):

Zhang, H. (2018). Learning to Generate and Refine Object Proposals . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/143520

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):

Zhang, Haoyang. “Learning to Generate and Refine Object Proposals .” 2018. Thesis, Australian National University. Accessed September 19, 2019. http://hdl.handle.net/1885/143520.

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

MLA Handbook (7th Edition):

Zhang, Haoyang. “Learning to Generate and Refine Object Proposals .” 2018. Web. 19 Sep 2019.

Vancouver:

Zhang H. Learning to Generate and Refine Object Proposals . [Internet] [Thesis]. Australian National University; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/1885/143520.

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

Council of Science Editors:

Zhang H. Learning to Generate and Refine Object Proposals . [Thesis]. Australian National University; 2018. Available from: http://hdl.handle.net/1885/143520

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


KTH

13. Huss, Anders. Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models.

Degree: Computer Science and Communication (CSC), 2015, KTH

The increasing energy consumption is one of the greatest environmental challenges of our time. Residential buildings account for a considerable part of the total… (more)

Subjects/Keywords: NILM; disaggregation; neural network; convolutional neural network; CNN; HMM; HSMM; Other Computer and Information Science; Annan data- och informationsvetenskap

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APA (6th Edition):

Huss, A. (2015). Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200

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):

Huss, Anders. “Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models.” 2015. Thesis, KTH. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200.

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

MLA Handbook (7th Edition):

Huss, Anders. “Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models.” 2015. Web. 19 Sep 2019.

Vancouver:

Huss A. Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models. [Internet] [Thesis]. KTH; 2015. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200.

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

Council of Science Editors:

Huss A. Hybrid Model Approach to Appliance Load Disaggregation : Expressive appliance modelling by combining convolutional neural networks and hidden semi Markov models. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179200

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


Uppsala University

14. Heidari, Jawid. Classifying Material Defects with Convolutional Neural Networks and Image Processing.

Degree: Division of Systems and Control, 2019, Uppsala University

  Fantastic progress has been made within the field of machine learning and deep neural networks in the last decade. Deep convolutional neural networks (CNN)… (more)

Subjects/Keywords: artificial neural network; CNN; machine learning; convolutional neural network; image processing; Information Systems; Systemvetenskap, informationssystem och informatik

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APA (6th Edition):

Heidari, J. (2019). Classifying Material Defects with Convolutional Neural Networks and Image Processing. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387797

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):

Heidari, Jawid. “Classifying Material Defects with Convolutional Neural Networks and Image Processing.” 2019. Thesis, Uppsala University. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387797.

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

MLA Handbook (7th Edition):

Heidari, Jawid. “Classifying Material Defects with Convolutional Neural Networks and Image Processing.” 2019. Web. 19 Sep 2019.

Vancouver:

Heidari J. Classifying Material Defects with Convolutional Neural Networks and Image Processing. [Internet] [Thesis]. Uppsala University; 2019. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387797.

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

Council of Science Editors:

Heidari J. Classifying Material Defects with Convolutional Neural Networks and Image Processing. [Thesis]. Uppsala University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387797

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


California State University – Sacramento

15. Rawlani, Bhagyashree. Distracted driver detection using Capsule Network.

Degree: MS, Computer Science, 2019, California State University – Sacramento

Convolutional neural networks are generally assumed to be the best neural networks for classifying images. In November 2017, Geoffrey Hinton et al. introduced another neural(more)

Subjects/Keywords: Convolutional neural network

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

APA (6th Edition):

Rawlani, B. (2019). Distracted driver detection using Capsule Network. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/207659

Chicago Manual of Style (16th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Masters Thesis, California State University – Sacramento. Accessed September 19, 2019. http://hdl.handle.net/10211.3/207659.

MLA Handbook (7th Edition):

Rawlani, Bhagyashree. “Distracted driver detection using Capsule Network.” 2019. Web. 19 Sep 2019.

Vancouver:

Rawlani B. Distracted driver detection using Capsule Network. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10211.3/207659.

Council of Science Editors:

Rawlani B. Distracted driver detection using Capsule Network. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/207659


Uppsala University

16. Ezpeleta, Emilio Vega. Identifying illicit graphic in the online community using the neural network framework.

Degree: Statistics, 2017, Uppsala University

  In this paper two convolutional neural networks are estimated to classify whether an image contains a swastika or not. The images are gathered from… (more)

Subjects/Keywords: Convolutional neural network; CNN; Neural networks; Image recognition; Statistical learning; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA (6th Edition):

Ezpeleta, E. V. (2017). Identifying illicit graphic in the online community using the neural network framework. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325810

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):

Ezpeleta, Emilio Vega. “Identifying illicit graphic in the online community using the neural network framework.” 2017. Thesis, Uppsala University. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325810.

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

MLA Handbook (7th Edition):

Ezpeleta, Emilio Vega. “Identifying illicit graphic in the online community using the neural network framework.” 2017. Web. 19 Sep 2019.

Vancouver:

Ezpeleta EV. Identifying illicit graphic in the online community using the neural network framework. [Internet] [Thesis]. Uppsala University; 2017. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325810.

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

Council of Science Editors:

Ezpeleta EV. Identifying illicit graphic in the online community using the neural network framework. [Thesis]. Uppsala University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325810

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


University of Sydney

17. Wang, Dongang. Action Recognition in Multi-view Videos .

Degree: 2018, University of Sydney

 A long-lasting goal in the field of artificial intelligence is to develop agents that can perceive and understand the rich visual world around us. With… (more)

Subjects/Keywords: Convolutional Neural Network (CNN); Computer Vision; Multi-view Action Recognition; Dividing and Aggregating Network (DA-Net)

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

APA (6th Edition):

Wang, D. (2018). Action Recognition in Multi-view Videos . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/19740

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):

Wang, Dongang. “Action Recognition in Multi-view Videos .” 2018. Thesis, University of Sydney. Accessed September 19, 2019. http://hdl.handle.net/2123/19740.

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

MLA Handbook (7th Edition):

Wang, Dongang. “Action Recognition in Multi-view Videos .” 2018. Web. 19 Sep 2019.

Vancouver:

Wang D. Action Recognition in Multi-view Videos . [Internet] [Thesis]. University of Sydney; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2123/19740.

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

Council of Science Editors:

Wang D. Action Recognition in Multi-view Videos . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/19740

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


Brno University of Technology

18. Fischer, Martin. Detekce graffiti tagů v obraze .

Degree: 2019, Brno University of Technology

 Cílem této práce je porovnat různé přístupy počítačového vidění se záměrem automatické detekce graffiti tagů v obraze. Za tímto účelem byly v řešení použity modely… (more)

Subjects/Keywords: detekce objektů; graffiti tagy; konvoluční neuronové sítě; Faster R-CNN; R-FCN; Mask R-CNN; EAST detektor; CCNN; Counting CNN; object detection; graffiti tags; convolutional neural network; Faster R-CNN; R-FCN; Mask R-CNN; EAST detector; CCNN; Counting CNN

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

APA (6th Edition):

Fischer, M. (2019). Detekce graffiti tagů v obraze . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180108

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):

Fischer, Martin. “Detekce graffiti tagů v obraze .” 2019. Thesis, Brno University of Technology. Accessed September 19, 2019. http://hdl.handle.net/11012/180108.

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

MLA Handbook (7th Edition):

Fischer, Martin. “Detekce graffiti tagů v obraze .” 2019. Web. 19 Sep 2019.

Vancouver:

Fischer M. Detekce graffiti tagů v obraze . [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/11012/180108.

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

Council of Science Editors:

Fischer M. Detekce graffiti tagů v obraze . [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180108

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


Texas State University – San Marcos

19. Chen, Xinbo. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 In recent years, convolutional neural network (CNN) has been widely used to improve the training time and accuracy of image recognition applications. These CNNs are… (more)

Subjects/Keywords: Neural network; CNN; Energy efficiency

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APA (6th Edition):

Chen, X. (2016). Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6853

Chicago Manual of Style (16th Edition):

Chen, Xinbo. “Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed September 19, 2019. https://digital.library.txstate.edu/handle/10877/6853.

MLA Handbook (7th Edition):

Chen, Xinbo. “Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.” 2016. Web. 19 Sep 2019.

Vancouver:

Chen X. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2019 Sep 19]. Available from: https://digital.library.txstate.edu/handle/10877/6853.

Council of Science Editors:

Chen X. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6853


Linköping University

20. Mathiesen, Jarle. Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds.

Degree: Human-Centered systems, 2018, Linköping University

  Applying machine learning techniques for real-time detection and tracking of objects in very high-resolution video is a problem that has not been extensively studied.… (more)

Subjects/Keywords: tracking; object tracking; kalman filter; deep learning; remote tower; convolutional neural network; cnn; real-time; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Mathiesen, J. (2018). Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148848

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):

Mathiesen, Jarle. “Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds.” 2018. Thesis, Linköping University. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148848.

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

MLA Handbook (7th Edition):

Mathiesen, Jarle. “Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds.” 2018. Web. 19 Sep 2019.

Vancouver:

Mathiesen J. Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds. [Internet] [Thesis]. Linköping University; 2018. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148848.

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

Council of Science Editors:

Mathiesen J. Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds. [Thesis]. Linköping University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148848

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


University of Kentucky

21. Li, Chao. WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK.

Degree: 2019, University of Kentucky

 Weld joint penetration determination is the key factor in welding process control area. Not only has it directly affected the weld joint mechanical properties, like… (more)

Subjects/Keywords: Gas tungsten arc welding (GTAW); computer vision; weld penetration; machine learning; convolutional neural network (CNN); Electrical and Computer Engineering; Industrial Engineering

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APA (6th Edition):

Li, C. (2019). WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK. (Doctoral Dissertation). University of Kentucky. Retrieved from https://uknowledge.uky.edu/ece_etds/133

Chicago Manual of Style (16th Edition):

Li, Chao. “WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK.” 2019. Doctoral Dissertation, University of Kentucky. Accessed September 19, 2019. https://uknowledge.uky.edu/ece_etds/133.

MLA Handbook (7th Edition):

Li, Chao. “WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK.” 2019. Web. 19 Sep 2019.

Vancouver:

Li C. WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK. [Internet] [Doctoral dissertation]. University of Kentucky; 2019. [cited 2019 Sep 19]. Available from: https://uknowledge.uky.edu/ece_etds/133.

Council of Science Editors:

Li C. WELD PENETRATION IDENTIFICATION BASED ON CONVOLUTIONAL NEURAL NETWORK. [Doctoral Dissertation]. University of Kentucky; 2019. Available from: https://uknowledge.uky.edu/ece_etds/133


Brno University of Technology

22. Bafrnec, Matúš. Automatická 3D segmentace obrazu mozku .

Degree: 2018, Brno University of Technology

 Táto bakalárska práca popisuje návrh a implementáciu systému na automatickú 3D segmentáciu mozgu založeného na konvolučných neurónových sieťach. Prvá časť práce je venovaná krátkej histórii… (more)

Subjects/Keywords: 3D; CNN; hlboké učenie; konvolučná neurónová sieť; mozog; MRI; obraz; rekurentný; segmentácia; U-Net; 3D; CNN; deep learning; convolutional neural network; brain; MRI; image; recurrent; segmentation; U-Net

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APA (6th Edition):

Bafrnec, M. (2018). Automatická 3D segmentace obrazu mozku . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/82365

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):

Bafrnec, Matúš. “Automatická 3D segmentace obrazu mozku .” 2018. Thesis, Brno University of Technology. Accessed September 19, 2019. http://hdl.handle.net/11012/82365.

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

MLA Handbook (7th Edition):

Bafrnec, Matúš. “Automatická 3D segmentace obrazu mozku .” 2018. Web. 19 Sep 2019.

Vancouver:

Bafrnec M. Automatická 3D segmentace obrazu mozku . [Internet] [Thesis]. Brno University of Technology; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/11012/82365.

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

Council of Science Editors:

Bafrnec M. Automatická 3D segmentace obrazu mozku . [Thesis]. Brno University of Technology; 2018. Available from: http://hdl.handle.net/11012/82365

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


KTH

23. Holm, Noah. Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks.

Degree: Geoinformatics, 2018, KTH

  The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22… (more)

Subjects/Keywords: crime prediction; crime forecasting; residential burglary; deep convolutional neural network; CNN; long short-term memory; LSTM; recurrent neural network; Other Civil Engineering; Annan samhällsbyggnadsteknik

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APA (6th Edition):

Holm, N. (2018). Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952

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):

Holm, Noah. “Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks.” 2018. Thesis, KTH. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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

MLA Handbook (7th Edition):

Holm, Noah. “Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks.” 2018. Web. 19 Sep 2019.

Vancouver:

Holm N. Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks. [Internet] [Thesis]. KTH; 2018. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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

Council of Science Editors:

Holm N. Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952

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


Linköping University

24. Magnusson, Filip. Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle.

Degree: Software and Systems, 2018, Linköping University

  With self-driving cars on the horizon, vehicle autonomy and its problems is a hot topic. In this study we are using convolutional neural networks… (more)

Subjects/Keywords: computer vision; machine learning; autonomous car; self-driving; neural network; convolutional neural network; cnn; datorseende; maskininlärning; neuronnätverk; självkörande; Computer Vision and Robotics (Autonomous Systems); Datorseende och robotik (autonoma system)

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APA (6th Edition):

Magnusson, F. (2018). Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450

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):

Magnusson, Filip. “Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle.” 2018. Thesis, Linköping University. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450.

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

MLA Handbook (7th Edition):

Magnusson, Filip. “Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle.” 2018. Web. 19 Sep 2019.

Vancouver:

Magnusson F. Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle. [Internet] [Thesis]. Linköping University; 2018. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450.

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

Council of Science Editors:

Magnusson F. Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle. [Thesis]. Linköping University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450

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

25. Habrman, David. Face Recognition with Preprocessing and Neural Networks.

Degree: Faculty of Science & Engineering, 2016, Linköping UniversityLinköping University

  Face recognition is the problem of identifying individuals in images. This thesis evaluates two methods used to determine if pairs of face images belong… (more)

Subjects/Keywords: CNN; neural network; convolutional neural network; face recognition; preprocessing; eigenfaces

…viii 4.2.1 CNN Implementation . . . . . . . . . . . . . . . . . . 4.2.2 Neural Network… …4.3.1 CNN Evaluation . . . . . . . . . . . . . . . . . . . . . 4.3.2 Neural Network with… …Convolutional neural networks (CNN) are the state-of-the-art method for face recognition… …37 Notation ABBREVIATIONS Abbreviation CNN LFW PCA ReLU ROC Description Convolutional… …neural network Labeled faces in the wild Principal component analysis Rectified linear unit… 

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APA (6th Edition):

Habrman, D. (2016). Face Recognition with Preprocessing and Neural Networks. (Thesis). Linköping UniversityLinköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128704

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):

Habrman, David. “Face Recognition with Preprocessing and Neural Networks.” 2016. Thesis, Linköping UniversityLinköping University. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128704.

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

MLA Handbook (7th Edition):

Habrman, David. “Face Recognition with Preprocessing and Neural Networks.” 2016. Web. 19 Sep 2019.

Vancouver:

Habrman D. Face Recognition with Preprocessing and Neural Networks. [Internet] [Thesis]. Linköping UniversityLinköping University; 2016. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128704.

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

Council of Science Editors:

Habrman D. Face Recognition with Preprocessing and Neural Networks. [Thesis]. Linköping UniversityLinköping University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128704

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


University of Saskatchewan

26. Wang, Yi 1989-. Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography.

Degree: 2018, University of Saskatchewan

 Without performing biopsy that could lead physical damages to nerves and vessels, Computerized Tomography (CT) is widely used to diagnose the lung cancer due to… (more)

Subjects/Keywords: convolutional neural network; pulmonary nodule

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APA (6th Edition):

Wang, Y. 1. (2018). Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/10844

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):

Wang, Yi 1989-. “Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography.” 2018. Thesis, University of Saskatchewan. Accessed September 19, 2019. http://hdl.handle.net/10388/10844.

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

MLA Handbook (7th Edition):

Wang, Yi 1989-. “Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography.” 2018. Web. 19 Sep 2019.

Vancouver:

Wang Y1. Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography. [Internet] [Thesis]. University of Saskatchewan; 2018. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/10388/10844.

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

Council of Science Editors:

Wang Y1. Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography. [Thesis]. University of Saskatchewan; 2018. Available from: http://hdl.handle.net/10388/10844

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


University of Illinois – Urbana-Champaign

27. Yeh, Raymond Alexander. Stable and symmetric convolutional neural network.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max-pooling, and Relu non-linearity are stable to additive noise. Second, we explore… (more)

Subjects/Keywords: convolutional neural network; deep learning

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APA (6th Edition):

Yeh, R. A. (2016). Stable and symmetric convolutional neural network. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/92687

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):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed September 19, 2019. http://hdl.handle.net/2142/92687.

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

MLA Handbook (7th Edition):

Yeh, Raymond Alexander. “Stable and symmetric convolutional neural network.” 2016. Web. 19 Sep 2019.

Vancouver:

Yeh RA. Stable and symmetric convolutional neural network. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2142/92687.

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

Council of Science Editors:

Yeh RA. Stable and symmetric convolutional neural network. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/92687

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


University of Illinois – Urbana-Champaign

28. Zhu, Tianyilin. Lipreading with convolutional and recurrent neural network models.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 Lip reading is the process of speech recognition from solely visual information. The goal of this thesis is to perform a silence vs. speech classification,… (more)

Subjects/Keywords: Lipreading; Convolutional neural network

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

APA (6th Edition):

Zhu, T. (2017). Lipreading with convolutional and recurrent neural network models. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97763

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):

Zhu, Tianyilin. “Lipreading with convolutional and recurrent neural network models.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed September 19, 2019. http://hdl.handle.net/2142/97763.

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

MLA Handbook (7th Edition):

Zhu, Tianyilin. “Lipreading with convolutional and recurrent neural network models.” 2017. Web. 19 Sep 2019.

Vancouver:

Zhu T. Lipreading with convolutional and recurrent neural network models. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Sep 19]. Available from: http://hdl.handle.net/2142/97763.

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

Council of Science Editors:

Zhu T. Lipreading with convolutional and recurrent neural network models. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97763

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


KTH

29. Kerkhof, Jan van de. Convolutional Neural Networks for Named Entity Recognition in Images of Documents.

Degree: Computer Science and Communication (CSC), 2016, KTH

  This work researches named entity recognition (NER) with respect to images of documents with a domain-specific layout, by means of Convolutional Neural Networks (CNNs).… (more)

Subjects/Keywords: Convolutional Neural Networks; Faster R-CNN; Named Entity Recognition; Images; Documents

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

APA (6th Edition):

Kerkhof, J. v. d. (2016). Convolutional Neural Networks for Named Entity Recognition in Images of Documents. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191213

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):

Kerkhof, Jan van de. “Convolutional Neural Networks for Named Entity Recognition in Images of Documents.” 2016. Thesis, KTH. Accessed September 19, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191213.

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

MLA Handbook (7th Edition):

Kerkhof, Jan van de. “Convolutional Neural Networks for Named Entity Recognition in Images of Documents.” 2016. Web. 19 Sep 2019.

Vancouver:

Kerkhof Jvd. Convolutional Neural Networks for Named Entity Recognition in Images of Documents. [Internet] [Thesis]. KTH; 2016. [cited 2019 Sep 19]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191213.

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

Council of Science Editors:

Kerkhof Jvd. Convolutional Neural Networks for Named Entity Recognition in Images of Documents. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191213

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


Iowa State University

30. Hamdan, Muhammad K A. VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT).

Degree: 2018, Iowa State University

Convolutional Neural Network (CNN), a popular machine learning algorithm, has been proven as a highly accurate and effective algorithm that has been used in a… (more)

Subjects/Keywords: Auto Generation; CNN; Convolutional Neural Networks; FPGA; HLS; VHDL; Engineering

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

APA (6th Edition):

Hamdan, M. K. A. (2018). VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT). (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16368

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):

Hamdan, Muhammad K A. “VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT).” 2018. Thesis, Iowa State University. Accessed September 19, 2019. https://lib.dr.iastate.edu/etd/16368.

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

MLA Handbook (7th Edition):

Hamdan, Muhammad K A. “VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT).” 2018. Web. 19 Sep 2019.

Vancouver:

Hamdan MKA. VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT). [Internet] [Thesis]. Iowa State University; 2018. [cited 2019 Sep 19]. Available from: https://lib.dr.iastate.edu/etd/16368.

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

Council of Science Editors:

Hamdan MKA. VHDL auto-generation tool for optimized hardware acceleration of convolutional neural networks on FPGA (VGT). [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16368

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

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