RF-Ear: Contactless Multi-device Vibration Sensing and Identification Using COTS RFID

Author(s):  
Panlong Yang ◽  
Yuanhao Feng ◽  
Jie Xiong ◽  
Ziyang Chen ◽  
Xiang-Yang Li
Keyword(s):  
2021 ◽  
pp. 103037
Author(s):  
Maria Valero ◽  
Fangyu Li ◽  
Liang Zhao ◽  
Chi Zhang ◽  
Jose Garrido ◽  
...  

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1637
Author(s):  
Miroslav Mrlík ◽  
Josef Osička ◽  
Martin Cvek ◽  
Markéta Ilčíková ◽  
Peter Srnec ◽  
...  

This paper is focused on the comparative study of the vibration sensing capabilities of poly(vinylidene fluoride) (PVDF) sheets. The main parameters such as molecular weight, initial sample thickness, stretching and poling were systematically applied, and their impact on sensing behavior was examined. The mechanical properties of prepared sheets were investigated via tensile testing on the samples with various initial thicknesses. The transformation of the α-phase to the electro-active β-phase was analyzed using FTIR after applying stretching and poling procedures as crucial post-processing techniques. As a complementary method, the XRD was applied, and it confirmed the crystallinity data resulting from the FTIR analysis. The highest degree of phase transformation was found in the PVDF sheet with a moderate molecular weight (Mw of 275 kDa) after being subjected to the highest axial elongation (500%); in this case, the β-phase content reached approximately 90%. Finally, the vibration sensing capability was systematically determined, and all the mentioned processing/molecular parameters were taken into consideration. The whole range of the elongations (from 50 to 500%) applied on the PVDF sheets with an Mw of 180 and 275 kDa and an initial thickness of 0.5 mm appeared to be sufficient for vibration sensing purposes, showing a d33 piezoelectric charge coefficient from 7 pC N−1 to 9.9 pC N−1. In terms of the d33, the PVDF sheets were suitable regardless of their Mw only after applying the elongation of 500%. Among all the investigated samples, those with an initial thickness of 1.0 mm did not seem to be suitable for vibration sensing purposes.


Author(s):  
Yue Zhang ◽  
Zhizhang Hu ◽  
Susu Xu ◽  
Shijia Pan

AbstractIn this paper, we introduce AutoQual, a mobile-based assessment scheme for infrastructure sensing task performance prediction under new deployment environments. With the growth of the Internet-of-Things (IoT), many non-intrusive sensing systems have been explored for various indoor applications, such as structural vibration sensing. This indirect sensing approach’s learning performance is prone to deployment variance when signals propagate through the environment. As a result, current systems heavily rely on expert knowledge and manual assessment to achieve effective deployments and high sensing task performance. In order to mitigate this expert effort, we propose to systematically study factors that reflect deployment environment characteristics and methods to measure them autonomously. We present AutoQual that measures a series of assessment factors (AFs) reflecting how the deployment environment impacts the system performance. AutoQual outputs a task-oriented sensing quality (TSQ) score by integrating measured AFs trained from known deployments as a prediction of untested system’s performance. In addition, AutoQual achieves this assessment without manual effort by leveraging co-located mobile sensing context to extract structural vibration signal for processing automatically. We evaluate AutoQual by using it to predict untested systems’ performance over multiple sensing tasks. We conduct real-world experiments and investigate 48 deployments in 11 environments. AutoQual achieves less than 0.10 average absolute error when auto-assessing multiple tasks at untested deployments, which shows a $$\le 0.018$$ ≤ 0.018 absolute error difference compared to the manual assessment approach.


2018 ◽  
Vol 23 (1) ◽  
pp. 179-189 ◽  
Author(s):  
Tadayoshi Aoyama ◽  
Makoto Chikaraishi ◽  
Akimasa Fujiwara ◽  
Liang Li ◽  
Mingjun Jiang ◽  
...  

2016 ◽  
Vol 35 (7) ◽  
pp. 600-604 ◽  
Author(s):  
Tim Dean ◽  
Tim Brice ◽  
Arthur Hartog ◽  
Ed Kragh ◽  
Daniele Molteni ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document