Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Yaokun Hu"'
Autor:
Yaokun Hu, Takeshi Toda
Publikováno v:
IEEE Access, Vol 10, Pp 78219-78230 (2022)
Research on radar-based non-contact vital sign monitoring systems is critical during the COVID-19 epidemic. The accuracy of remote vital sign measurements has increased with the advancement of radar technology and various algorithms. Most studies req
Externí odkaz:
https://doaj.org/article/fef34b480ab148549f468f7d743ed9aa
Publikováno v:
IEICE Communications Express. 11:330-335
Autor:
Yaokun HU, Takeshi TODA
Publikováno v:
IEICE Transactions on Communications. :159-167
Publikováno v:
IEICE Communications Express. 10:1009-1014
Publikováno v:
IEICE Communications Express. 10:415-420
Autor:
Takeshi Toda, Yaokun Hu
Publikováno v:
IEICE Communications Express. 10:277-282
Autor:
Yaokun Hu, Takeshi Toda
Publikováno v:
IEICE Communications Express. 10:56-61
Autor:
Allison M. Janda, Michael R. Mathis, Keith D. Aaronson, Hyeon Joo, Sachin Kheterpal, Michael W. Sjoding, Nicholas J. Douville, Yaokun Hu, Michael L. Burns, Kayvan Najarian, Michael D. Maile, Milo Engoren
Publikováno v:
Anesth Analg
Background Heart failure with reduced ejection fraction (HFrEF) is a condition imposing significant health care burden. Given its syndromic nature and often insidious onset, the diagnosis may not be made until clinical manifestations prompt further e
Autor:
Sai Saradha Kalidaikurichi Lakshmanan, V. G. Vinod Vydiswaran, Michael L. Burns, Yaokun Hu, Hyeon Joo
Publikováno v:
JMIR Formative Research, Vol 5, Iss 5, p e22461 (2021)
JMIR Formative Research
JMIR Formative Research
Background Administrative costs for billing and insurance-related activities in the United States are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, deve
Autor:
Hyeon Joo, Michael Burns, Sai Saradha Kalidaikurichi Lakshmanan, Yaokun Hu, V G Vinod Vydiswaran
BACKGROUND Administrative costs for billing and insurance-related activities in the United States are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, deve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1f1cb4d134c376e973a957ae81f2a9e
https://doi.org/10.2196/preprints.22461
https://doi.org/10.2196/preprints.22461