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
A Fast Fault Diagnosis Method for RF Front-End Modules Base..:
, In:
2023 IEEE AUTOTESTCON
,
Tang, Xiaoting
;
Liu, Zhen
;
Liang, Jingqun
... - p. 1-5 , 2023
Link:
https://doi.org/10.1109/AUTOTESTCON47464.2023.10296419
RT T1
2023 IEEE AUTOTESTCON
: T1
A Fast Fault Diagnosis Method for RF Front-End Modules Based on Adaptive Signal Decomposition and Deep Neural Network
UL https://suche.suub.uni-bremen.de/peid=ieee-10296419&Exemplar=1&LAN=DE A1 Tang, Xiaoting A1 Liu, Zhen A1 Liang, Jingqun A1 Wu, Kunping A1 Bu, Zhiyuan A1 Chen, Li YR 2023 SN 1558-4550 K1 Radio frequency K1 Fault diagnosis K1 Wireless communication K1 Training K1 Convolution K1 Feature extraction K1 Transceivers K1 MIMO system K1 RF front-end modules K1 fault diagnosis K1 variational mode decomposition K1 Interleaved group convolution K1 deep neural network SP 1 OP 5 LK http://dx.doi.org/https://doi.org/10.1109/AUTOTESTCON47464.2023.10296419 DO https://doi.org/10.1109/AUTOTESTCON47464.2023.10296419 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)