Crossmark

Deep learning based object tracking in walking droplet and granular intruder experiments

Crossref DOI link: https://doi.org/10.1007/s11554-023-01341-4

Published Online: 2023-07-20

Published Print: 2023-10

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Kara, Erdi

Zhang, George

Williams, Joseph J.

Ferrandez-Quinto, Gonzalo

Rhoden, Leviticus J.

Kim, Maximilian

Kutz, J. Nathan

Rahman, Aminur
Funding

Funding for this research was provided by:

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Division of Chemical, Bioengineering, Environmental, and Transport Systems (2112085)

Air Force Office of Scientific Research (FA9550-19-1-0011)

Air Force Office of Scientific Research (FA9550-19-1-0011)

Air Force Office of Scientific Research (FA9550-19-1-0011)

Air Force Office of Scientific Research (FA9550-19-1-0011)

Air Force Office of Scientific Research (FA9550-19-1-0011)

Air Force Office of Scientific Research (FA9550-19-1-0011)

Air Force Office of Scientific Research (FA9550-19-1-0011)
License Information

Text and Data Mining valid from 2023-07-20

Version of Record valid from 2023-07-20
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Article History

Received: 14 March 2023

Accepted: 5 July 2023

First Online: 20 July 2023

Declarations

:

: The authors declare that they have no conflict of interest.

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