Cargando…
V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier
Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification al...
Autores principales: | Skiribou, Camelia, Elbahhar, Fouzia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271952/ https://www.ncbi.nlm.nih.gov/pubmed/34201574 http://dx.doi.org/10.3390/s21134286 |
Ejemplares similares
-
Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
por: Noshad, Zainib, et al.
Publicado: (2019) -
Cyclostationary-Based Vital Signs Detection Using Microwave Radar at 2.5 GHz
por: Sekak, Fatima, et al.
Publicado: (2020) -
A random forest classifier for protein–protein docking models
por: Barradas-Bautista, Didier, et al.
Publicado: (2021) -
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
por: Pradier, Léa, et al.
Publicado: (2021) -
Random Bits Forest: a Strong Classifier/Regressor for Big Data
por: Wang, Yi, et al.
Publicado: (2016)