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
Device-Free Occupant Counting Using Ambient RFID and Deep L..:
, In:
2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT)
,
Xu, Guoyi
;
Kan, Edwin C.
- p. 49-52 , 2024
Link:
https://doi.org/10.1109/WiSNeT59910.2024.10438637
RT T1
2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT)
: T1
Device-Free Occupant Counting Using Ambient RFID and Deep Learning
UL https://suche.suub.uni-bremen.de/peid=ieee-10438637&Exemplar=1&LAN=DE A1 Xu, Guoyi A1 Kan, Edwin C. YR 2024 SN 2473-4624 K1 Deep learning K1 Wireless communication K1 Wireless sensor networks K1 Sensor systems K1 Sensors K1 Convolutional neural networks K1 Received signal strength indicator K1 occupant counting K1 radio-frequency identification (RFID) K1 convolutional neural network (CNN) K1 deep learning SP 49 OP 52 LK http://dx.doi.org/https://doi.org/10.1109/WiSNeT59910.2024.10438637 DO https://doi.org/10.1109/WiSNeT59910.2024.10438637 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)