Low-cost hall-effect sensors for real-time monitoring pier scour

Author(s):  
Chen-Chia Chen ◽  
Chih-Chyau Yang ◽  
Ssu-Ying Chen ◽  
Wen-Ching Chen ◽  
Gang-Neng Sung ◽  
...  
Author(s):  
Tingting Yin ◽  
Zhong Yang ◽  
Youlong Wu ◽  
Fangxiu Jia

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.


2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.


2015 ◽  
Vol 47 (3) ◽  
pp. 236-251 ◽  
Author(s):  
Bambang Kuswandi ◽  
Fitria Damayanti ◽  
Jayus Jayus ◽  
Aminah Abdullah ◽  
Lee Yook Heng

Micromachines ◽  
2017 ◽  
Vol 8 (10) ◽  
pp. 292
Author(s):  
Carlos Polanco ◽  
Ignacio Vazquez ◽  
Adrian Martinez-Rivas ◽  
Miguel Arias-Estrada ◽  
Thomas Buhse ◽  
...  

2013 ◽  
Vol 65 (2) ◽  
pp. 103-108 ◽  
Author(s):  
Yuichi Aoyama ◽  
Koichiro Doi ◽  
Kazuo Shibuya ◽  
Harumi Ohta ◽  
Iuko Tsuwa

2011 ◽  
Vol 127 (2) ◽  
pp. 749-754 ◽  
Author(s):  
S. Piermarini ◽  
G. Volpe ◽  
M. Esti ◽  
M. Simonetti ◽  
G. Palleschi

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