Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Shengchang Lan"'
Publikováno v:
Sensors, Vol 21, Iss 1, p 259 (2021)
The purpose of this paper was to investigate the effect of a training state-of-the-art convolution neural network (CNN) for millimeter-wave radar-based hand gesture recognition (MR-HGR). Focusing on the small training dataset problem in MR-HGR, this
Externí odkaz:
https://doaj.org/article/a8fae6e696e242768714f4e47d507bed
Autor:
Haizhuo He, Hongjun Chu, Rui Pan, Linting Ye, Kang Zhang, Weichu Chen, Peng Zhang, Shengchang Lan
Publikováno v:
2022 IEEE MTT-S International Wireless Symposium (IWS).
Publikováno v:
Journal of Physics D: Applied Physics. 56:075502
This paper reports a novel dual-band metasurface structure to harvest electromagnetic energy in the environment efficiently. The unit structure comprises a novel Jerusalem cross on F4B substrate, a centrosymmetric structure which exhibits excellent s
Publikováno v:
2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium).
Publikováno v:
2021 International Symposium on Antennas and Propagation (ISAP).
Publikováno v:
2021 International Symposium on Antennas and Propagation (ISAP).
Publikováno v:
IEICE Transactions on Communications. :233-240
Publikováno v:
IEEE Access, Vol 7, Pp 61251-61258 (2019)
A compact, frequency-reconfigurable dielectric resonator antenna (DRA) with wideband and continuously tunable narrowband states is presented. It is intended for the application scenario in the cognitive radio system. The wideband state is the sensing
Publikováno v:
2021 15th European Conference on Antennas and Propagation (EuCAP).
In this paper, a commercial FMCW radar sensor working at 76 to 81GHz is used on single objects and multiple objects to obtain the IF signal containing vital signs information. Heartbeat and respiratory raw waveform are obtained after a series of digi
Publikováno v:
Electronics
Volume 9
Issue 10
Electronics, Vol 9, Iss 1577, p 1577 (2020)
Volume 9
Issue 10
Electronics, Vol 9, Iss 1577, p 1577 (2020)
We studied continuous sign language recognition using Doppler radar sensors. Four signs in Chinese sign language and American sign language were captured and extracted by complex empirical mode decomposition (CEMD) to obtain spectrograms. Image sharp