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
Rich Feature Deep Learning Classifier for Multiple Simultan..:
, In:
2021 55th Asilomar Conference on Signals, Systems, and Computers
,
Uppal, Ahsen J.
;
Klein, Jeffrey
;
Cribbs, H. Brown
. - p. 368-371 , 2021
Link:
https://doi.org/10.1109/IEEECONF53345.2021.9723376
RT T1
2021 55th Asilomar Conference on Signals, Systems, and Computers
: T1
Rich Feature Deep Learning Classifier for Multiple Simultaneous Radio Signals
UL https://suche.suub.uni-bremen.de/peid=ieee-9723376&Exemplar=1&LAN=DE A1 Uppal, Ahsen J. A1 Klein, Jeffrey A1 Cribbs, H. Brown A1 Huang, H. Howie YR 2021 SN 2576-2303 K1 Deep learning K1 Frequency modulation K1 RF signals K1 Prototypes K1 Frequency estimation K1 Noise measurement K1 Task analysis SP 368 OP 371 LK http://dx.doi.org/https://doi.org/10.1109/IEEECONF53345.2021.9723376 DO https://doi.org/10.1109/IEEECONF53345.2021.9723376 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)