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Sökning: L773:9781728195018

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1.
  • Ahmad, Jawad, 1985-, et al. (författare)
  • Stretchable Pressure Sensor Using Thermoplastic Polyurethane and Conductive Inks
  • 2021
  • Ingår i: Proceedings of IEEE Sensors. - : IEEE. - 9781728195018
  • Konferensbidrag (refereegranskat)abstract
    • The development of wearable health devices is an emerging technology, and pressure sensors have been widely used in several of these applications. Plenty of research within pressure sensors is focused on tactile sensing and artificial skin. In this paper, a highly flexible and stretchable pressure sensor is presented. The sensor comprises stretchable thermoplastic polyurethane (TPU) film as substrate and stretchable conductive inks as electrodes and sensing material. Screen printing is used to fabricate electrodes and pressure sensing components on TPU sheets. Electrical and mechanical properties of the fabricated sensors indicate good mechanical and electrical stability while retaining pressure sensing properties and marginal deterioration even after 100 elongation cycles. The findings show that the presented stretchable pressure sensor has a great potential for usage on surfaces where bending and stretching will occur while retaining nearly all of its electrical and mechanical capabilities. The proposed sensor may be employed as a wearable device to detect human movements.
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2.
  • Winkler, Nicolas P., 1991-, et al. (författare)
  • Using Redundancy in a Sensor Network to Compensate Sensor Failures
  • 2021
  • Ingår i: 2021 IEEE SENSORS. - : IEEE. - 9781728195018 - 9781728195025
  • Konferensbidrag (refereegranskat)abstract
    • Wireless sensor networks provide occupational health experts with valuable information about the distribution of air pollutants in an environment. However, especially low-cost sensors may produce faulty measurements or fail completely. Consequently, not only spatial coverage but also redundancy should be a design criterion for the deployment of a sensor network. For a sensor network deployed in a steel factory, we analyze the correlations between sensors and build machine learning forecasting models, to investigate how well the sensor network can compensate for the outage of sensors. While our results show promising prediction quality of the models, they also indicate the presence of spatially very limited events. We, therefore, conclude that initial measurements with, e.g., mobile units, could help to identify important locations to design redundant sensor networks.
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