Flexible pressure sensors have aroused extensive attention in health monitoring, human-computer interaction, soft robotics, and more, as a staple member of wearable electronics. However, a majority of traditional research focuses solely on foundational mechanical sensing tests and ordinary human-motion monitoring, ignoring its other applications in daily life. In this work, a paper-based pressure sensor is prepared by using MXene/bacterial cellulose film with three-dimensional isolation layer structure, and its sensing capability as a wearable sound detector has also been studied. The as-prepared device exhibits great comprehensive mechanical sensing performance as well as accurate detection of human physiological signals. As a sound detector, not only can it recognize different voice signals and sound attributes by monitoring movement of throat muscles, but also it will distinguish a variety of natural sounds through air pressure waves caused by sound transmission (also called sound waves), like the eardrum. Besides, it plays an important role in sound visualization technology because of the ability for capturing and presenting music signals. Moreover, millimeter-scale thickness, lightweight, and degradable raw materials make the sensor convenient and easy to carry, meeting requirements of environmental protection as well.
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http://dx.doi.org/10.1021/acsnano.2c03155 | DOI Listing |
Phys Rev Lett
December 2024
CERN, Geneva, Switzerland.
High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.
View Article and Find Full Text PDFSci Rep
January 2025
Institute for the Future of Human Society, Kyoto University, Kyoto, Japan.
Objective digital measurement of gamblers visiting gambling venues is conducted using cashless cards and facial recognition systems, but these methods are confined within a single gambling venue. Hence, we propose an objective digital measurement method using a transformer, a state-of-the-art machine learning approach, to detect total gambling venue visitations for gamblers who visit multiple gambling venues using sounds in gamblers' environments. We sampled gambling and nongambling event datasets from websites to create a gambling play classifier.
View Article and Find Full Text PDFJ Acoust Soc Am
December 2024
Sea Mammal Research Unit, School of Biology, University of St Andrews, KY16 9TH, St Andrews, United Kingdom.
Passive acoustic monitoring (PAM) is an increasingly popular tool to study vocalising species. The amount of data generated by PAM studies calls for robust automatic classifiers. Deep learning (DL) techniques have been proven effective in identifying acoustic signals in challenging datasets, but due to their black-box nature their underlying biases are hard to quantify.
View Article and Find Full Text PDFJ Acoust Soc Am
December 2024
Department of Natural Sciences, Université du Quebec en Outaouais, Gatineau, Quebec, Canada.
The endangered beluga whale (Delphinapterus leucas) of the St. Lawrence Estuary (SLEB) faces threats from a variety of anthropogenic factors. Since belugas are a highly social and vocal species, passive acoustic monitoring has the potential to deliver, in a non-invasive and continuous way, real-time information on SLEB spatiotemporal habitat use, which is crucial for their monitoring and conservation.
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