A Multitasking Flexible Sensor via Reservoir Computing.

Seiji Wakabayashi, Takayuki Arie, Seiji Akita, Kohei Nakajima, Kuniharu Takei
Author Information
  1. Seiji Wakabayashi: Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan.
  2. Takayuki Arie: Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan.
  3. Seiji Akita: Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan.
  4. Kohei Nakajima: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, 113-8656, Japan.
  5. Kuniharu Takei: Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka, 599-8531, Japan. ORCID

Abstract

Natural disasters are reported globally, and one source of severe damage to cities is flooding caused by locally heavy rain. Sharing of local weather information can save lives. However, it is difficult to collect local weather information in real-time because such data collection requires bulky, expensive sensors. For local, real-time monitoring of heavy rain and wind, a sensor system should be simple and low-cost so that it can be attached to a variety of surfaces, including roofs, vehicles, and umbrellas. To develop simple, low-cost multitasking sensors located on nonplanar surfaces, a flexible rain sensor to monitor waterdrop volume and wind velocity is devised. To monitor both simultaneously, a laser-induced graphene-based superhydrophobic conductive film is introduced. Using the superhydrophobic surface, water dynamics are measured when waterdrops collide with the sensor surface, and obtained time-series data are processed using "reservoir computing" to extract the volume and velocity from a single sensor as multitasking electronics. As a proof-of-concept, it is shown that the sensor measures continuous, long-term volume and wind-change dynamics. The results demonstrate feasibility of multitasking electronics with reservoir computing to reduce sensor integration complexity with low power consumption for both sensor and signal processing.

Keywords

References

  1. K. Kwon, J. U. Kim, Y. Deng, S. R. Krishnan, J. Choi, H. Jang, K. Lee, C.-J. Su, I. Yoo, Y. Wu, L. Lipschultz, J.-H. Kim, T. S. Chung, D. Wu, Y. Park, T.-I. Kim, R. Ghaffari, S. Lee, Y. Huang, J. A. Rogers, Nat. Electron. 2021, 4, 302.
  2. W. Gao, S. Emaminejad, H. Y. Nyein, S. Challa, K. Chen, A. Peck, H. M. Fahad, H. Ota, H. Shiraki, D. Kiriya, D. H. Lien, G. A. Brooks, R. W. Davis, A. Javey, Nature 2016, 529, 509.
  3. Y. Yang, Y. Song, X. Bo, J. Min, O. S. Pak, L. Zhu, M. Wang, J. Tu, A. Kogan, H. Zhang, T. K. Hsiai, Z. Li, W. Gao, Nat. Biotechnol. 2019, 38, 217.
  4. J. R. Sempionatto, M. Lin, L. Yin, E. De la Paz, K. Pei, T. Sonsa-Ard, A. N. de Loyola Silva, A. A. Khorshed, F. Zhang, N. Tostado, S. Xu, J. Wang, Nat. Biomed. Eng. 2021, 5, 737.
  5. S. Nakata, M. Shiomi, Y. Fujita, T. Arie, S. Akita, K. Takei, Nat. Electron. 2018, 1, 596.
  6. C. Lim, Y. J. Hong, J. Jung, Y. Shin, S.-H. Sunwoo, S. Baik, O. K. Park, S. H. Choi, T. Hyeon, J. H. Kim, S. Lee, D.-H. Kim, Sci. Adv. 2021, 7, eabd3716.
  7. T. Someya, Y. Kato, T. Sekitani, S. Iba, Y. Noguchi, Y. Murase, H. Kawaguchi, T. Sakurai, Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 12321.
  8. S. I. Rich, R. J. Wood, C. Majidi, Nat. Electron. 2018, 1, 102.
  9. Y. C. Lai, J. Deng, R. Liu, Y. C. Hsiao, S. L. Zhang, W. Peng, H. M. Wu, X. Wang, Z. L. Wang, Adv. Mater. 2018, 30, 1801114.
  10. a) S. Wakabayashi, T. Yamaguchi, T. Arie, S. Akita, K. Takei, Carbon 2020, 158, 698;
  11. b) T. Yamaguchi, T. Kashiwagi, T. Arie, S. Akita, K. Takei, Adv. Intell. Syst. 2019, 1, 1900018.
  12. a) J. M. Nassar, S. M. Khan, D. R. Villalva, M. M. Nour, A. S. Almuslem, M. M. Hussain, npj Flexible Electron. 2018, 2, 24;
  13. b) Y. Lu, K. Xu, L. Zhang, M. Deguchi, H. Shishido, T. Arie, R. Pan, A. Hayashi, L. Shen, S. Akita, K. Takei, ACS Nano 2020, 14, 10966.
  14. S. Nie, H. Guo, Y. Lu, J. Zhuo, J. Mo, Z. L. Wang, Adv. Mater. Technol. 2020, 5, 2000454.
  15. M. Lukoševičius, H. Jaeger, Comput. Sci. Rev. 2009, 3, 127.
  16. H. Jaeger, The "Echo State" Approach to Analysing and Training Recurrent Neural Networks, GMD Report, Vol 148, GMD - German National Research Institute for Computer Science, Bonn, Germany 2001.
  17. M. Lukosevicius, in Neural Networks: Tricks of the Trade, Vol 7700, (Eds: G. Montavon, G. B. Orr, K.-R. Müller), Springer, Heidelberg 2012, pp. 659-686.
  18. K. Nakajima, I. Fischer, Reservoir Computing, Springer Nature, Singapore 2021.
  19. H. Jaeger, Short Term Memory in Echo State Networks, GMD Report, Vol 152, GMD - German National Research Institute for Computer Science, Bonn, Germany 2002, p. 152.
  20. K. Nakajima, Jpn. J. Appl. Phys. 2020, 59, 060501.
  21. K. Tanaka, S.-H. Yang, Y. Tokudome, Y. Minami, Y. Lu, T. Arie, S. Akita, K. Takei, K. Nakajima, Adv. Intell. Syst. 2021, 3, 2000174.
  22. K. Tanaka, Y. Tokudome, Y. Minami, S. Honda, T. Nakajima, K. Takei, K. Nakajima, Adv. Intell. Syst. 2021, 4, 2100166.
  23. a) M. Cucchi, C. Gruener, L. Petrauskas, P. Steiner, H. Tseng, A. Fischer, B. Penkovsky, C. Matthus, P. Birkholz, H. Kleemann, K. Leo, Sci. Adv. 2021, 7, eabh0693;
  24. b) Y. Usami, B. van de Ven, D. G. Mathew, T. Chen, T. Kotooka, Y. Kawashima, Y. Tanaka, Y. Otsuka, H. Ohoyama, H. Tamukoh, H. Tanaka, W. G. van der Wiel, T. Matsumoto, Adv. Mater. 2021, 33, 2102688.
  25. S. Kan, K. Nakajima, T. Asai, M. Akai-Kasaya, Adv. Sci. 2021, 9, 2104076.
  26. G. Milano, G. Pedretti, K. Montano, S. Ricci, S. Hashemkhani, L. Boarino, D. Ielmini, C. Ricciardi, Nat. Mater. 2021, 21, 195.
  27. a) G. Van der Sande, D. Brunner, M. C. Soriano, Nanophotonics 2017, 6, 561;
  28. b) J. Moon, W. Ma, J. H. Shin, F. Cai, C. Du, S. H. Lee, W. D. Lu, Nat. Electron. 2019, 2, 480.
  29. J. Torrejon, M. Riou, F. A. Araujo, S. Tsunegi, G. Khalsa, D. Querlioz, P. Bortolotti, V. Cros, K. Yakushiji, A. Fukushima, H. Kubota, S. Yuasa, M. D. Stiles, J. Grollier, Nature 2017, 547, 428.
  30. J. Lin, Z. Peng, Y. Liu, F. Ruiz-Zepeda, R. Ye, E. L. Samuel, M. J. Yacaman, B. I. Yakobson, J. M. Tour, Nat. Commun. 2014, 5, 5714.
  31. K. Xu, Y. Fujita, Y. Lu, S. Honda, M. Shiomi, T. Arie, S. Akita, K. Takei, Adv. Mater. 2021, 33, 2008701.
  32. J. Nasser, J. Lin, L. Zhang, H. A. Sodano, Carbon 2020, 162, 570.
  33. X. Xuan, J. Y. Kim, X. Hui, P. S. Das, H. S. Yoon, J. Y. Park, Biosens. Bioelectron. 2018, 120, 160.
  34. V. Strong, S. Dubin, M. F. El-Kady, A. Lech, Y. Wang, B. H. Weiller, R. B. Kaner, ACS Nano 2012, 6, 1395.
  35. D. X. Luong, K. Yang, J. Yoon, S. P. Singh, T. Wang, C. J. Arnusch, J. M. Tour, ACS Nano 2019, 13, 2579.
  36. Y. Li, D. X. Luong, J. Zhang, Y. R. Tarkunde, C. Kittrell, F. Sargunaraj, Y. Ji, C. J. Arnusch, J. M. Tour, Adv. Mater. 2017, 29, 1700496.
  37. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Neurocomputing 2006, 70, 489.
  38. R. Rahimi, M. Ochoa, W. Yu, B. Ziaie, ACS Appl. Mater. Interfaces 2015, 7, 4463.
  39. S. Harada, T. Arie, S. Akita, K. Takei, BioNanoScience 2014, 4, 301.

Grants

  1. JP18H05472/JSPS KAKENHI
  2. JP22H00594/JSPS KAKENHI
  3. JP-MJCR21U1/JST Accelerated Program
  4. /TEPCO Memorial Foundation

Word Cloud

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