Deep learning neural networks for NAO robot control
Deep Learning is one of the solutions to the future of technology. It is a subset of machine learning. With deep learning, it is possible to ‘learn’ and make ‘informed choices’ based on the data it has analysed. With the addition of deep learning to eye trackers, it is possible for the device to und...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78219 |
| _version_ | 1811683019876990976 |
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| author | Tee, Enid Mun Xin |
| author2 | Song Qing |
| author_facet | Song Qing Tee, Enid Mun Xin |
| author_sort | Tee, Enid Mun Xin |
| collection | NTU |
| description | Deep Learning is one of the solutions to the future of technology. It is a subset of machine learning. With deep learning, it is possible to ‘learn’ and make ‘informed choices’ based on the data it has analysed. With the addition of deep learning to eye trackers, it is possible for the device to understand the user’s habits and improve the prediction of the output. In this project, deep learning algorithm is applied to the NTU’s patented eye tracking technology to help improve the stability and the predicted outcome of eye gazes. It will be able to classify certain eye gestures, which allows the user to activate certain commands. The software component will be developed using Microsoft Visual Studio (MVS) as well as Open Source Computer Vision Library (OpenCV). The language used will be C++. For deep learning, a Recurrent Neural Network (RNN) will be developed using Matrix Laboratory (Matlab) as it has built in functions that can support deep learning training easily. |
| first_indexed | 2024-10-01T04:06:05Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78219 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:05Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/782192023-07-07T16:42:45Z Deep learning neural networks for NAO robot control Tee, Enid Mun Xin Song Qing School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Deep Learning is one of the solutions to the future of technology. It is a subset of machine learning. With deep learning, it is possible to ‘learn’ and make ‘informed choices’ based on the data it has analysed. With the addition of deep learning to eye trackers, it is possible for the device to understand the user’s habits and improve the prediction of the output. In this project, deep learning algorithm is applied to the NTU’s patented eye tracking technology to help improve the stability and the predicted outcome of eye gazes. It will be able to classify certain eye gestures, which allows the user to activate certain commands. The software component will be developed using Microsoft Visual Studio (MVS) as well as Open Source Computer Vision Library (OpenCV). The language used will be C++. For deep learning, a Recurrent Neural Network (RNN) will be developed using Matrix Laboratory (Matlab) as it has built in functions that can support deep learning training easily. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-13T07:17:55Z 2019-06-13T07:17:55Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78219 en Nanyang Technological University 52 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Tee, Enid Mun Xin Deep learning neural networks for NAO robot control |
| title | Deep learning neural networks for NAO robot control |
| title_full | Deep learning neural networks for NAO robot control |
| title_fullStr | Deep learning neural networks for NAO robot control |
| title_full_unstemmed | Deep learning neural networks for NAO robot control |
| title_short | Deep learning neural networks for NAO robot control |
| title_sort | deep learning neural networks for nao robot control |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/78219 |
| work_keys_str_mv | AT teeenidmunxin deeplearningneuralnetworksfornaorobotcontrol |