scholarly journals A Compressed Sensing Based Method for Reducing the Sampling Time of A High Resolution Pressure Sensor Array System

Sensors ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1848 ◽  
Author(s):  
Chenglu Sun ◽  
Wei Li ◽  
Wei Chen
ACS Nano ◽  
2015 ◽  
Vol 9 (3) ◽  
pp. 3143-3150 ◽  
Author(s):  
Mingzeng Peng ◽  
Zhou Li ◽  
Caihong Liu ◽  
Qiang Zheng ◽  
Xieqing Shi ◽  
...  

2020 ◽  
Vol 8 (4) ◽  
pp. 296-307
Author(s):  
Konstantin Krestovnikov ◽  
Aleksei Erashov ◽  
Аleksandr Bykov

This paper presents development of pressure sensor array with capacitance-type unit sensors, with scalable number of cells. Different assemblies of unit pressure sensors and their arrays were considered, their characteristics and fabrication methods were investigated. The structure of primary pressure transducer (PPT) array was presented; its operating principle of array was illustrated, calculated reference ratios were derived. The interface circuit, allowing to transform the changes in the primary transducer capacitance into voltage level variations, was proposed. A prototype sensor was implemented; the dependency of output signal power from the applied force was empirically obtained. In the range under 30 N it exhibited a linear pattern. The sensitivity of the array cells to the applied pressure is in the range 134.56..160.35. The measured drift of the output signals from the array cells after 10,000 loading cycles was 1.39%. For developed prototype of the pressure sensor array, based on the experimental data, the average signal-to-noise ratio over the cells was calculated, and equaled 63.47 dB. The proposed prototype was fabricated of easily available materials. It is relatively inexpensive and requires no fine-tuning of each individual cell. Capacitance-type operation type, compared to piezoresistive one, ensures greater stability of the output signal. The scalability and adjustability of cell parameters are achieved with layered sensor structure. The pressure sensor array, presented in this paper, can be utilized in various robotic systems.


Author(s):  
Abdallah Naser ◽  
Ahmad Lotfi ◽  
Joni Zhong

AbstractHuman distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2  m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.


2008 ◽  
Vol 61 (1) ◽  
pp. 103-116 ◽  
Author(s):  
Hong Jung ◽  
Kyunghyun Sung ◽  
Krishna S. Nayak ◽  
Eung Yeop Kim ◽  
Jong Chul Ye

2015 ◽  
Vol 204 (3) ◽  
pp. 510-518 ◽  
Author(s):  
Hadrien A. Dyvorne ◽  
Ashley Knight-Greenfield ◽  
Cecilia Besa ◽  
Nancy Cooper ◽  
Julio Garcia-Flores ◽  
...  

2021 ◽  
pp. 2100709 ◽  
Author(s):  
Zhengguang Yan ◽  
Liangliang Wang ◽  
Yifan Xia ◽  
Rendong Qiu ◽  
Wenquan Liu ◽  
...  

2021 ◽  
Author(s):  
Juzhong Zhang ◽  
Yuyi Chu ◽  
Zhisen Wang ◽  
Tingfeng Ye ◽  
Liming Cai ◽  
...  

Author(s):  
O Legendre ◽  
H Mathias ◽  
E Martincic ◽  
M Zhang ◽  
J Juillard ◽  
...  

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