Application of Ica in blind source separation
The fundamental area in this project is Application of Independent Component Analysis (ICA) in Blind Source Separation. ICA is a tool for discovering structure and patterns in data by factoring a multidimensional data distribution into a product of onedimensional, statistically independent component...
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| التنسيق: | Theses and Dissertations |
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2008
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10356/3575 |
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| المؤسسة: | Nanyang Technological University |
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sg-ntu-dr.10356-35752023-07-04T15:44:32Z Application of Ica in blind source separation That Mon Htwe. Yang, Jun School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing The fundamental area in this project is Application of Independent Component Analysis (ICA) in Blind Source Separation. ICA is a tool for discovering structure and patterns in data by factoring a multidimensional data distribution into a product of onedimensional, statistically independent component distributions. Traditional ICA methods, however, can be limited in the flexibility of their decompositions and in the modeling of component distributions. Master of Science (Signal Processing) 2008-09-17T09:32:46Z 2008-09-17T09:32:46Z 2005 2005 Thesis http://hdl.handle.net/10356/3575 Nanyang Technological University application/pdf |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| topic |
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| spellingShingle |
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing That Mon Htwe. Application of Ica in blind source separation |
| description |
The fundamental area in this project is Application of Independent Component Analysis (ICA) in Blind Source Separation. ICA is a tool for discovering structure and patterns in data by factoring a multidimensional data distribution into a product of onedimensional, statistically independent component distributions. Traditional ICA methods, however, can be limited in the flexibility of their decompositions and in the modeling of component distributions. |
| author2 |
Yang, Jun |
| author_facet |
Yang, Jun That Mon Htwe. |
| format |
Theses and Dissertations |
| author |
That Mon Htwe. |
| author_sort |
That Mon Htwe. |
| title |
Application of Ica in blind source separation |
| title_short |
Application of Ica in blind source separation |
| title_full |
Application of Ica in blind source separation |
| title_fullStr |
Application of Ica in blind source separation |
| title_full_unstemmed |
Application of Ica in blind source separation |
| title_sort |
application of ica in blind source separation |
| publishDate |
2008 |
| url |
http://hdl.handle.net/10356/3575 |
| _version_ |
1772826398905335808 |