Cargando…
Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction
Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs...
Autores principales: | Zhang, Jie, Xiao, Wendong, Zhang, Sen, Huang, Shoudong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424756/ https://www.ncbi.nlm.nih.gov/pubmed/28420187 http://dx.doi.org/10.3390/s17040879 |
Ejemplares similares
-
Extreme Learning Machine for Heartbeat Classification with Hybrid Time-Domain and Wavelet Time-Frequency Features
por: Xu, Yuefan, et al.
Publicado: (2021) -
Alumina Concentration Detection Based on the Kernel Extreme Learning Machine
por: Zhang, Sen, et al.
Publicado: (2017) -
Machine learning and the quest for objectivity in climate model parameterization
por: Jebeile, Julie, et al.
Publicado: (2023) -
A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces
por: Li, Yanjiao, et al.
Publicado: (2017) -
On the nonexistence of Bilipschitz parameterizations and geometric problems about A$_{\infty}$ weights
por: Semmes, S
Publicado: (1994)