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All issues Volume 139 (2022) SHS Web Conf., 139 (2022) 03019 References
Open Access
Issue
SHS Web Conf.
Volume 139, 2022
The 4th ETLTC International Conference on ICT Integration in Technical Education (ETLTC2022)
Article Number 03019
Number of page(s) 10
Section Topics in Computer Science
DOI https://doi.org/10.1051/shsconf/202213903019
Published online 13 May 2022
  1. S. Grahn, B. Langbeck, K. Johansen, and B. Backman, “Potential advantages using large anthropomorphic robots in human-robot collaborative, hand guided assembly,” Procedia CIRP 44, 281–286 (2016). [CrossRef] [Google Scholar]
  2. A. Robotics, “Collaborative robots,” https://new.abb.com/products/robotics/collaborative-robots (2010), accessed: 2021-10-08. [Google Scholar]
  3. M. Ababneh, H. Sha’ban, D. AlShalabe, D. Khader, H. Mahameed, and M. AlQudimat, “Gesture controlled mobile robotic arm for elderly and wheelchair people assistance using kinect sensor,” in 2018 15th International Multi-Conference on Systems, Signals Devices (SSD) (2018) pp. 636–641. [CrossRef] [Google Scholar]
  4. K. Li, Y. Fang, Y. Zhou, and H. Liu, “Non-invasive stimulation-based tactile sensation for upper-extremity prosthesis: A review,” IEEE Sensors Journal 17, 2625–2635 (2017). [CrossRef] [Google Scholar]
  5. O. Bionics, “Turning disabilities into superpowers,” https://openbionics.com/ (2021), accessed: 2021-10-08. [Google Scholar]
  6. R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green AI,” Communications of the ACM 63, 54–63 (2020). [CrossRef] [Google Scholar]
  7. E. Strubell, A. Ganesh, and A. McCallum, “Energy and policy considerations for deep learning in NLP,” Computer Research Repository abs/1906.02243 (2019), 1906.02243. [Google Scholar]
  8. C. Lee, S. Sarwar, P. Panda, G. Srinivasan, and K. Roy, “Enabling spike-based backpropagation for training deep neural network architectures,” Frontiers in Neuroscience 14, 119 (2020). [CrossRef] [Google Scholar]
  9. M. Ogbodo, T. Vu, K. Dang, and A. Abdallah, “Light-weight spiking neuron processing core for large-scale 3D-NoC based spiking neural network processing systems,” in 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (Institute of Electrical and Electronics Engineers (IEEE), 2020) pp. 133–139. [CrossRef] [Google Scholar]
  10. R. A. de Lemos, L. A. C. de O. Nogueira, A. M. Ribeiro, L. G. B. Mirisola, M. F. Koyama, E. C. de Paiva, and S. S. Bueno, “Unisensory intra-row navigation strategy for orchards environment based on sensor laser,” (SBA Sociedade Brasileira de Automática, 2018). [Google Scholar]
  11. W. Guo, J. Wang, and S. Wang, “Deep multimodal representation learning: A survey,” IEEE Access 7, 63373–63394 (2019). [CrossRef] [Google Scholar]
  12. J. Lee, P. H. Chang, B. Yu, and M. Jin, “An adaptive PID control for robot manipulators under substantial payload variations,” IEEE Access 8, 162261–162270 (2020). [CrossRef] [Google Scholar]
  13. S. Zhen, Z. Zhao, X. Liu, F. Chen, H. Zhao, and Y.-H. Chen, “A novel practical robust control inheriting PID for scara robot,” IEEE Access 8, 227409–227419 (2020). [CrossRef] [Google Scholar]
  14. C. Sun, C. Liu, X. Feng, and X. Jiao, “Visual servoing of flying robot based on fuzzy adaptive linear active disturbance rejection control,” IEEE Transactions on Circuits and Systems II: Express Briefs 68, 2558–2562 (2021). [CrossRef] [Google Scholar]
  15. R. H. Abiyev, N. Akkaya, and I. Gunsel, “Control of omnidirectional robot using z-number-based fuzzy system,” IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, 238–252 (2019). [CrossRef] [Google Scholar]
  16. P. B. and, “Robotics controller: A literature survey,” International Journal of Engineering Research and Technology 4, 1–7 (2015). [Google Scholar]
  17. A. Linares-Barranco, F. Perez-Peña, A. Jimenez-Fernandez, and E. Chicca, “ED-BioRob: A neuromorphic robotic arm with fpga-based infrastructure for bio-inspired spiking motor controllers,” Frontiers in Neurorobotics 14, 96 (2020). [CrossRef] [Google Scholar]
  18. Z. Zou, R. Zhao, Y. Wu, Z. Yang, L. Tian, S. Wu, G. Wang, Y. Yu, Q. Zhao, M. Chen, J. Pei, F. Chen, Y. Zhang, S. Song, M. Zhao, and L. Shi, “A hybrid and scalable brain-inspired robotic platform,” (Springer Science and Business Media LLC, 2020) p. 18160. [Google Scholar]
  19. H. Blum, A. Dietmüller, M. Milde, J. Conradt, G. Indiveri, and Y. Sandamirskaya, “A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor,” (2017) pp. 1–9. [Google Scholar]
  20. D. SepúLveda, R. Fernández, E. Navas, M. Armada, and P. González-De-Santos, “Robotic aubergine harvesting using dual-arm manipulation,” IEEE Access 8, 121889–121904 (2020). [CrossRef] [Google Scholar]
  21. Q. Huang, J. Lan, and X. Li, “Robotic arm based automatic ultrasound scanning for three-dimensional imaging,” IEEE Transactions on Industrial Informatics 15, 1173–1182 (2019). [CrossRef] [Google Scholar]
  22. D. Banerjee, K. Yu, and G. Aggarwal, “Image rectification software test automation using a robotic arm,” IEEE Access 6, 34075–34085 (2018). [CrossRef] [Google Scholar]
  23. A. Ben Abdallah and K. N. Dang, “Toward robust cognitive 3D brain-inspired cross-paradigm system,” Frontiers in Neuroscience 15, 795 (2021). [CrossRef] [Google Scholar]
  24. O. M. Ikechukwu, K. N. Dang, and A. B. Abdallah, “On the design of a fault-tolerant scalable three dimensional NoC-based digital neuromorphic system with on-chip learning,” IEEE Access 9, 64331–64345 (2021). [CrossRef] [Google Scholar]
  25. M. HARRY and M. JOHN, “Hearing lips and seeing voices,” Nature 264, 746–748 (1976). [CrossRef] [PubMed] [Google Scholar]
  26. E. Ceolini, G. Taverni, M. Payvand, and E. Donati, “Emg and video dataset for sensor fusion based hand gestures recognition | zenodo,” https://zenodo.org/record/3228846#.YiplZnpBxhF (2019), (Accessed on 03/11/2022). [Google Scholar]
  27. M. A. Triplett and G. J. Goodhill, “Probabilistic encoding models for multivariate neural data,” Frontiers in Neural Circuits 13 (2019), 10.3389/fncir.2019.00001. [CrossRef] [Google Scholar]
  28. E. Donati, M. Payvand, N. Risi, R. Krause, and G. Indiveri, “Discrimination of EMG signals using a neuromorphic implementation of a spiking neural network,” IEEE Transactions on Biomedical Circuits and Systems 13, 795–803 (2019). [CrossRef] [Google Scholar]
  29. Y. Ma, B. Chen, P. Ren, N. Zheng, G. Indiveri, and E. Donati, “EMG-based gestures classification using a mixed-signal neuromorphic processing system,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10, 578–587 (2020). [CrossRef] [Google Scholar]
  30. E. Ceolini, C. Frenkel, S. B. Shrestha, G. Taverni, L. Khacef, M. Payvand, and E. Donati, “Hand-gesture recognition based on EMG and eventbased camera sensor fusion: A benchmark in neuromorphic computing,” Frontiers in Neuroscience 14 (2020), 10.3389/fnins.2020.00637. [CrossRef] [Google Scholar]

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