Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136239642> ?p ?o ?g. }
- W3136239642 endingPage "1583" @default.
- W3136239642 startingPage "1570" @default.
- W3136239642 abstract "Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance." @default.
- W3136239642 created "2021-03-29" @default.
- W3136239642 creator A5002266206 @default.
- W3136239642 creator A5004620577 @default.
- W3136239642 creator A5037889492 @default.
- W3136239642 creator A5050194076 @default.
- W3136239642 date "2021-10-01" @default.
- W3136239642 modified "2023-10-06" @default.
- W3136239642 title "Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing" @default.
- W3136239642 cites W1493041716 @default.
- W3136239642 cites W1966133577 @default.
- W3136239642 cites W1966956646 @default.
- W3136239642 cites W2020058391 @default.
- W3136239642 cites W2026752363 @default.
- W3136239642 cites W2028103671 @default.
- W3136239642 cites W2035609997 @default.
- W3136239642 cites W2036374149 @default.
- W3136239642 cites W2037486375 @default.
- W3136239642 cites W2041350515 @default.
- W3136239642 cites W2041556066 @default.
- W3136239642 cites W2052078443 @default.
- W3136239642 cites W2055571910 @default.
- W3136239642 cites W2062994334 @default.
- W3136239642 cites W2083300563 @default.
- W3136239642 cites W2088751297 @default.
- W3136239642 cites W2095534447 @default.
- W3136239642 cites W2099322603 @default.
- W3136239642 cites W2113761410 @default.
- W3136239642 cites W2119056404 @default.
- W3136239642 cites W2121514123 @default.
- W3136239642 cites W2125959037 @default.
- W3136239642 cites W2127045241 @default.
- W3136239642 cites W2133835651 @default.
- W3136239642 cites W2163908603 @default.
- W3136239642 cites W2170164530 @default.
- W3136239642 cites W2171130677 @default.
- W3136239642 cites W2284794734 @default.
- W3136239642 cites W2511197415 @default.
- W3136239642 cites W2547655816 @default.
- W3136239642 cites W2580898283 @default.
- W3136239642 cites W2611467245 @default.
- W3136239642 cites W2738615740 @default.
- W3136239642 cites W2740337843 @default.
- W3136239642 cites W2753005849 @default.
- W3136239642 cites W2767248316 @default.
- W3136239642 cites W2792310407 @default.
- W3136239642 cites W2795198316 @default.
- W3136239642 cites W2879771073 @default.
- W3136239642 cites W2892265014 @default.
- W3136239642 cites W2904178198 @default.
- W3136239642 cites W2913384339 @default.
- W3136239642 cites W2941524667 @default.
- W3136239642 cites W2947434510 @default.
- W3136239642 cites W2948801778 @default.
- W3136239642 cites W2949733326 @default.
- W3136239642 cites W2951259175 @default.
- W3136239642 cites W2957613877 @default.
- W3136239642 cites W2963432090 @default.
- W3136239642 cites W2968496598 @default.
- W3136239642 cites W2974799347 @default.
- W3136239642 cites W2981404803 @default.
- W3136239642 cites W2998993592 @default.
- W3136239642 cites W3003319185 @default.
- W3136239642 cites W3090345340 @default.
- W3136239642 cites W3099844944 @default.
- W3136239642 cites W3104324122 @default.
- W3136239642 doi "https://doi.org/10.1109/tro.2021.3060342" @default.
- W3136239642 hasPublicationYear "2021" @default.
- W3136239642 type Work @default.
- W3136239642 sameAs 3136239642 @default.
- W3136239642 citedByCount "23" @default.
- W3136239642 countsByYear W31362396422021 @default.
- W3136239642 countsByYear W31362396422022 @default.
- W3136239642 countsByYear W31362396422023 @default.
- W3136239642 crossrefType "journal-article" @default.
- W3136239642 hasAuthorship W3136239642A5002266206 @default.
- W3136239642 hasAuthorship W3136239642A5004620577 @default.
- W3136239642 hasAuthorship W3136239642A5037889492 @default.
- W3136239642 hasAuthorship W3136239642A5050194076 @default.
- W3136239642 hasConcept C119599485 @default.
- W3136239642 hasConcept C127413603 @default.
- W3136239642 hasConcept C154945302 @default.
- W3136239642 hasConcept C155175808 @default.
- W3136239642 hasConcept C17829176 @default.
- W3136239642 hasConcept C205372480 @default.
- W3136239642 hasConcept C21200559 @default.
- W3136239642 hasConcept C24326235 @default.
- W3136239642 hasConcept C2780226545 @default.
- W3136239642 hasConcept C31972630 @default.
- W3136239642 hasConcept C41008148 @default.
- W3136239642 hasConcept C46722567 @default.
- W3136239642 hasConcept C50644808 @default.
- W3136239642 hasConcept C90509273 @default.
- W3136239642 hasConceptScore W3136239642C119599485 @default.
- W3136239642 hasConceptScore W3136239642C127413603 @default.
- W3136239642 hasConceptScore W3136239642C154945302 @default.
- W3136239642 hasConceptScore W3136239642C155175808 @default.
- W3136239642 hasConceptScore W3136239642C17829176 @default.