I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Deep Learning vs. Traditional Learning for Radio Frequency ..:
, In:
2024 IST-Africa Conference (IST-Africa)
,
Otto, Andreas
;
Rananga, Seani
;
Masonta, Moshe
- p. 1-8 , 2024
Link:
https://doi.org/10.23919/IST-Africa63983.2024.10569298
RT T1
2024 IST-Africa Conference (IST-Africa)
: T1
Deep Learning vs. Traditional Learning for Radio Frequency Fingerprinting
UL https://suche.suub.uni-bremen.de/peid=ieee-10569298&Exemplar=1&LAN=DE A1 Otto, Andreas A1 Rananga, Seani A1 Masonta, Moshe YR 2024 SN 2576-8581 K1 Radio frequency K1 Wireless communication K1 Support vector machines K1 Training K1 Adaptation models K1 Accuracy K1 Fingerprint recognition K1 Radio frequency fingerprinting K1 Convolutional neural networks K1 Physical layer security SP 1 OP 8 LK http://dx.doi.org/https://doi.org/10.23919/IST-Africa63983.2024.10569298 DO https://doi.org/10.23919/IST-Africa63983.2024.10569298 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)