Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Yuanjian Liu"'
Publikováno v:
Sensors, Vol 24, Iss 19, p 6464 (2024)
Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesd
Externí odkaz:
https://doaj.org/article/655bb4f9dcad48789b627b15815487e1
Publikováno v:
Materials Research Express, Vol 11, Iss 4, p 046504 (2024)
Abrasive waterjet peening is a favorable surface treatment method for improving the fatigue resistance of metal materials. An insight into the fatigue crack growth properties of AWJ peened specimens is meaningful for obtaining better strengthening pe
Externí odkaz:
https://doaj.org/article/01489a2053b940ed8a76e7ab3da26863
Publikováno v:
IEEE Access, Vol 9, Pp 51561-51572 (2021)
The efficiency improvements offered by adopting Silicon Carbide (SiC) devices in Electric Vehicle (EV) inverters has been widely reported in various studies. However, it has still not been established whether or when the efficiency benefits can count
Externí odkaz:
https://doaj.org/article/5f2469f2781d415c9cf1fde5088a76dd
Publikováno v:
International Journal of Antennas and Propagation, Vol 2021 (2021)
The millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measure
Externí odkaz:
https://doaj.org/article/adcad6bfd5a24618aeb7d8896b48927f
Publikováno v:
Symmetry, Vol 14, Iss 5, p 1038 (2022)
Scenario identification plays an important role in assisting unmanned aerial vehicle (UAV) cognitive communications. Based on the scenario-dependent channel characteristics, a support vector machine (SVM)-based air-to-ground (A2G) scenario identifica
Externí odkaz:
https://doaj.org/article/20af358b7bd541fdbd4c5fc6cb627380
Publikováno v:
Microwave and Optical Technology Letters. 65:2092-2100
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b4c0bca61debaac1a57fdd9cefa43d5
https://doi.org/10.1002/mop.33662
https://doi.org/10.1002/mop.33662
Publikováno v:
Food Analytical Methods. 16:491-498
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf00dc29966a7c81adf642e56d306838
https://doi.org/10.1007/s12161-022-02436-7
https://doi.org/10.1007/s12161-022-02436-7
Autor:
Yuanjian Liu, Sheng Di, Kai Zhao, Sian Jin, Cheng Wang, Kyle Chard, Dingwen Tao, Ian Foster, Franck Cappello
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 33:4440-4457
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0b021ed81063fbed1f17aec56c38697d
https://doi.org/10.1109/tpds.2022.3194695
https://doi.org/10.1109/tpds.2022.3194695
Publikováno v:
Journal of Sensors. 2022:1-16
Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19c81f2a746387b41079ff650be07cf3
https://doi.org/10.1155/2022/6585800
https://doi.org/10.1155/2022/6585800
