Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/169413
Title: | Braille recognition by E-skin system based on binary memristive neural network | Authors: | Liu, Y. H. Wang, J. J. Wang, H. Z. Liu, S. Wu, Y. C. Hu, S. G. Yu, Q. Liu, Z. Chen, Tupei Yin, Y. Liu, Y. |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2023 | Source: | Liu, Y. H., Wang, J. J., Wang, H. Z., Liu, S., Wu, Y. C., Hu, S. G., Yu, Q., Liu, Z., Chen, T., Yin, Y. & Liu, Y. (2023). Braille recognition by E-skin system based on binary memristive neural network. Scientific Reports, 13(1), 5437-. https://dx.doi.org/10.1038/s41598-023-31934-9 | Journal: | Scientific Reports | Abstract: | Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system. | URI: | https://hdl.handle.net/10356/169413 | ISSN: | 2045-2322 | DOI: | 10.1038/s41598-023-31934-9 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2023 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Journal Articles |
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s41598-023-31934-9.pdf | 2.69 MB | Adobe PDF | View/Open |
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