Open Access
Issue
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
Article Number 01013
Number of page(s) 8
Section Electronics and Electical Engineering
DOI https://doi.org/10.1051/e3sconf/202339901013
Published online 12 July 2023
  1. B. John, “Section 6.3 diver hand signals,” in The Professional Divers’ s Handbook, 2nd ed. Gosport, U.K.: Submex, 2005, ch. 6, sec. 3, p. 252. [Google Scholar]
  2. I. Kvasic’, N. Miökovic’, and Z. Vukic’, “Convolutional neural network architectures for sonar-based diver detection and tracking,” in Proc. OCEANS, Jun. 2019, pp. 1–6, DOI: 10.1109/OCEANSE.2019.8867461. [Google Scholar]
  3. B. T. Kelly, L. A. Roskin, D. T. Kirkendall, and K. P. Speer, “Shoulder muscle activation during aquatic and dry land exercises in nonimpaired subjects,” J. Orthopaedic Sports Phys. Therapy, vol. 30, no. 4, pp. 204–210, Apr. 2000, DOI: 10.2519/jospt.2000.30.4.204. [CrossRef] [PubMed] [Google Scholar]
  4. J. Pan, Y. Luo, Y. Li, C.-K. Tham, C.-H. Heng, and A. V.-Y. Thean, “A wireless multi- channel capacitive sensor system for efficient glovebased gesture recognition with Al at the edge,” IEEE Trans. Circuits Syst. Il, Exp. Briefs, vol. 67, no. 9, pp. 1624–1628, Sep. 2020, DOI: 10.1109rrcs11.2020.3010318. [Google Scholar]
  5. T. Fan et al., “Analog sensing and computing systems with low power consumption for gesture recognition,” Adv. Intell. Syst., vol. 3, no. 1, Jan. 2021, Art. no. 2000184, DOI: 10.1002/aisy.202000184. [Google Scholar]
  6. X. Huang, Q. Wang, S. Zang, J. Wan, G. Yang, Y. Huang, and X. Ren, “Tracing the motion of finger joints for gesture recognition via sewing RGO-coated fibers onto a textile glove,” IEEE Sensors J., vol. 19, no. 20, pp. 9504–9511, Oct. 2019, DOI: 10.1109/JSEN.2019.2924797. [CrossRef] [Google Scholar]
  7. M. K. Bhuyan, A. K. Talukdar, P. Gupta, and R. H. Laskar, “Low cost data glove for hand gesture recognition by finger bend measurement,” in Proc. Int. Conf. Wireless Commun. Signal Process. Netw. (WiSPNET), Aug. 2020, pp. 25–31, DOI: 10.1109/WiSPNET48689.2020.9198521. [Google Scholar]
  8. D.-J. Li, Y.-Y. Li, J.-X. Li, and Y. Fu, “Gesture recognition based on BP neural network improved by chaotic genetic algorithm,” Int. J. Autom. Comput., vol. 15, no. 3, pp. 267–276, 2018, DOI: 10.1007/s11633-017-1107-6. [CrossRef] [Google Scholar]
  9. O. Makaussov, M. Krassavin, M. Zhabinets, and S. Fazli, “A low-cost, IMU-based real-time on device gesture recognition glove,” in Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC), Oct. 2020, pp. 3346–3351, DOI: 10.1109/SMC42975.2020.9283231. [Google Scholar]
  10. M. Lee and J. Bae, “Deep learning based real-time recognition of dynamic finger gestures using a data glove,” IEEE Access, vol. 8, pp. 219923–219933, 2020, DOI: 10.1109/ACCESS.2020.3039401. [CrossRef] [Google Scholar]
  11. Rajesh G., Narayanan R., Srivatsan K., Parthiban S., Raajini X.M., (2021), “Hybrid Neural Network for Handwritten Mathematical Expression Recognition system”, International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2021, Vol., no., pp.-. DOI: 10.1109/ITSS-IoE53029.2021.9615300 [Google Scholar]
  12. Meshach W.T., Hemajothi S., Anita E.A.M., (2022), “Retraction Note to: Real-time facial expression recognition for affect identification using multi-dimensional SVM (Journal of Ambient Intelligence and Humanized Computing, (2020), 12, (6355-6365), 10.1007/s12652-020-02221-6)”, Journal of Ambient Intelligence and Humanized Computing, Vol., no., pp.-. DOI: 10.1007/s12652-022-04015-4 [Google Scholar]
  13. Subburam S., Selvakumar S., Geetha S., (2018), “High performance reversible data hiding scheme through multilevel histogram modification in lifting integer wavelet transform”, Multimedia Tools and Applications, Vol. 77, no. 6, pp. 7071–7095. DOI: 10.1007/s11042-017-4622-0 [CrossRef] [Google Scholar]
  14. Umapathy K., Balaji V., Duraisamy V., Saravanakumar S.S., (2015), “Performance of wavelet based medical image fusion on FPGA using high level language C”, Jurnal Teknologi, Vol. 76, no. 12, pp. 105–109. DOI: 10.11113/jt.v76.5888 [CrossRef] [Google Scholar]
  15. Shirley D.R.A., Sundari V.K., Sheeba T.B., Rani S.S., (2021), “Analysis of IoT-Enabled Intelligent Detection and Prevention System for Drunken and Juvenile Drive Classification”, EAI/Springer Innovations in Communication and Computing, Vol., no., pp. 183–200. DOI: 10.1007/978-3-030-59897-6_10 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.