Synchronization and Preprocessing of Hybrid Bistatic SAR Data in the HITCHHIKER Experiment
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: 2014DOI: 10.1109/ICOSP.2014.7015176
Keyword(s): biomedical MRI,feature extraction,graph theory,learning (artificial intelligence),medical image processing,neural nets,time-domain analysis,RS functional connectivity analysis,blood oxygen level dependent signal,brain data classification,brain network analysis,confidence coefficient,dynamic features extraction method,functional magnetic resonance imaging signal,general graph-based static method,glioma diagnosis,glioma patients,machine learning algorithms,neural network classifi
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Synchronization and Preprocessing of Hybrid Bistatic SAR Data in the HITCHHIKER Experiment
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| date accessioned | 2020-03-12T20:11:47Z | |
| date available | 2020-03-12T20:11:47Z | |
| date issued | 2014 | |
| identifier other | 6856781.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1005487 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Synchronization and Preprocessing of Hybrid Bistatic SAR Data in the HITCHHIKER Experiment | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8126933 | |
| subject keywords | biomedical MRI | |
| subject keywords | feature extraction | |
| subject keywords | graph theory | |
| subject keywords | learning (artificial intelligence) | |
| subject keywords | medical image processing | |
| subject keywords | neural nets | |
| subject keywords | time-domain analysis | |
| subject keywords | RS functional connectivity analysis | |
| subject keywords | blood oxygen level dependent signal | |
| subject keywords | brain data classification | |
| subject keywords | brain network analysis | |
| subject keywords | confidence coefficient | |
| subject keywords | dynamic features extraction method | |
| subject keywords | functional magnetic resonance imaging signal | |
| subject keywords | general graph-based static method | |
| subject keywords | glioma diagnosis | |
| subject keywords | glioma patients | |
| subject keywords | machine learning algorithms | |
| subject keywords | neural network classifi | |
| identifier doi | 10.1109/ICOSP.2014.7015176 | |
| journal title | USAR 2014; 10th European Conference on Synthetic Aperture Radar; Proceedings of | |
| filesize | 225858 | |
| citations | 0 | |
| contributor rawauthor | Behner, Florian , Reuter, Simon , Nies, Holger , Loffeld, Otmar |