Numerical analysis of an industrial power saving mechanism in LTE
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: 2014DOI: 10.1109/ICPR.2014.397
Keyword(s): Gabor filters,Gaussian processes,face recognition,image resolution,matrix algebra,mixture models,probability,BioID datasets,Caltech-101 datasets,GMM,bioinspired multiresolution Gabor features,class-specific object part learning,complex-valued Gabor filter responses,computer vision problems,face recognition,facial landmark detection,generative learning,landmark specific likelihoods,object detection,object landmark localisation,object representation,pose estimation,probabilis
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Numerical analysis of an industrial power saving mechanism in LTE
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| contributor author | Fowler, S. , Baravdish, G. , Di Yuan | |
| date accessioned | 2020-03-12T20:40:52Z | |
| date available | 2020-03-12T20:40:52Z | |
| date issued | 2014 | |
| identifier other | 6883575.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1021683?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Numerical analysis of an industrial power saving mechanism in LTE | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8146378 | |
| subject keywords | Gabor filters | |
| subject keywords | Gaussian processes | |
| subject keywords | face recognition | |
| subject keywords | image resolution | |
| subject keywords | matrix algebra | |
| subject keywords | mixture models | |
| subject keywords | probability | |
| subject keywords | BioID datasets | |
| subject keywords | Caltech-101 datasets | |
| subject keywords | GMM | |
| subject keywords | bioinspired multiresolution Gabor features | |
| subject keywords | class-specific object part learning | |
| subject keywords | complex-valued Gabor filter responses | |
| subject keywords | computer vision problems | |
| subject keywords | face recognition | |
| subject keywords | facial landmark detection | |
| subject keywords | generative learning | |
| subject keywords | landmark specific likelihoods | |
| subject keywords | object detection | |
| subject keywords | object landmark localisation | |
| subject keywords | object representation | |
| subject keywords | pose estimation | |
| subject keywords | probabilis | |
| identifier doi | 10.1109/ICPR.2014.397 | |
| journal title | ommunications (ICC), 2014 IEEE International Conference on | |
| filesize | 276316 | |
| citations | 0 |