Fast characterization of two ultrasound longitudinal waves in cancellous bone using an adaptive beamforming technique

2015 ◽  
Vol 137 (4) ◽  
pp. 1683-1692 ◽  
Author(s):  
Hirofumi Taki ◽  
Yoshiki Nagatani ◽  
Mami Matsukawa ◽  
Katsunori Mizuno ◽  
Toru Sato
2021 ◽  
Author(s):  
Luyao Zhu ◽  
Yongjian Zhao ◽  
Yuting Shen ◽  
Feng Gao ◽  
Li Liu ◽  
...  
Keyword(s):  

2006 ◽  
Vol 51 (11) ◽  
pp. 2715-2727 ◽  
Author(s):  
B K Hoffmeister ◽  
C I Jones ◽  
G J Caldwell ◽  
S C Kaste

2011 ◽  
Vol 130 (2) ◽  
pp. EL122-EL127 ◽  
Author(s):  
Katsunori Mizuno ◽  
Yoshiki Nagatani ◽  
Keisuke Yamashita ◽  
Mami Matsukawa

2012 ◽  
Vol 29 (12) ◽  
pp. 1769-1775 ◽  
Author(s):  
Koji Nishimura ◽  
Takuji Nakamura ◽  
Toru Sato ◽  
Kaoru Sato

Abstract Aspect-sensitive backscattering of the atmosphere causes a small error in an effective line-of-sight direction in vertical beam observations leading to a serious degradation of vertical wind estimates due to contamination by horizontal wind components. An adaptive beamforming technique for a multichannel mesosphere–stratosphere–troposphere (MST) radar is presented, which makes it possible to measure the vertical wind velocity with higher accuracy by adaptively generating a countersteered reception beam against an off-vertically shifted echo pattern. The technique employs the norm-constrained direction-constrained minimization of power (NC-DCMP) algorithm, which provides not only robustness but also higher accuracy than the basic direction-constrained minimization of power algorithm in realistic conditions. Although the technique decreases the signal-to-noise ratio, the ratio is controlled and bound at a specified level by the norm constraint. In the case that a decrease of −3 dB is acceptable in a vertical beam observation, for which usually a much higher signal-to-noise ratio is obtained than for oblique beams, the maximum contamination is suppressed to even for the most imbalanced aspect sensitivity.


2019 ◽  
Vol 28 (2) ◽  
pp. 024302 ◽  
Author(s):  
Chengcheng Liu ◽  
Rui Dong ◽  
Boyi Li ◽  
Ying Li ◽  
Feng Xu ◽  
...  

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