Analog-digital hybrid computing with SnS


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
19 May 2022
Historique:
received: 27 02 2022
accepted: 04 05 2022
entrez: 19 5 2022
pubmed: 20 5 2022
medline: 20 5 2022
Statut: epublish

Résumé

Algorithms for intelligent drone flights based on sensor fusion are usually implemented using conventional digital computing platforms. However, alternative energy-efficient computing platforms are required for robust flight control in a variety of environments to reduce the burden on both the battery and computing power. In this study, we demonstrated an analog-digital hybrid computing platform based on SnS

Identifiants

pubmed: 35589720
doi: 10.1038/s41467-022-30564-5
pii: 10.1038/s41467-022-30564-5
pmc: PMC9119935
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2804

Subventions

Organisme : National Research Foundation of Korea (NRF)
ID : 2019R1A2C1002491
Organisme : National Research Foundation of Korea (NRF)
ID : 2020R1A6A1A03038540

Informations de copyright

© 2022. The Author(s).

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Auteurs

Shania Rehman (S)

Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.

Muhammad Farooq Khan (MF)

Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.

Hee-Dong Kim (HD)

Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea.

Sungho Kim (S)

Department of Electrical Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea. sungho85kim@sejong.ac.kr.

Classifications MeSH