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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1002/adma.201906269
|2 doi
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|a pubmed24n1014.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Lee, Gun-Hee
|e verfasserin
|4 aut
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| 245 |
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|a Parallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition, Inspired by the Human Somatosensory System
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 18.11.2020
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|a Date Revised 18.11.2020
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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|a Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S11 ) spectrum, and the applied pressure changes the magnitude of the S11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole-coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure-sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)-based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability
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|a Journal Article
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|a LC passive resonators
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|a electronic skin
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|a machine learning
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|a parallel signal processing
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4 |
|a pressure sensors
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|a Dimethylpolysiloxanes
|2 NLM
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| 650 |
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|a Polymers
|2 NLM
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| 650 |
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|a Pyrroles
|2 NLM
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| 650 |
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|a polypyrrole
|2 NLM
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| 650 |
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|a 30604-81-0
|2 NLM
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|a baysilon
|2 NLM
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|a 63148-62-9
|2 NLM
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| 700 |
1 |
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|a Park, Jin-Kwan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Byun, Junyoung
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jun Chang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kwon, Se Young
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kim, Chobi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jang, Chorom
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sim, Joo Yong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yook, Jong-Gwan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Park, Steve
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 32(2020), 8 vom: 21. Feb., Seite e1906269
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
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|g volume:32
|g year:2020
|g number:8
|g day:21
|g month:02
|g pages:e1906269
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|u http://dx.doi.org/10.1002/adma.201906269
|3 Volltext
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