ТОП просматриваемых книг сайта:
Human Communication Technology. Группа авторов
Читать онлайн.Название Human Communication Technology
Год выпуска 0
isbn 9781119752158
Автор произведения Группа авторов
Жанр Программы
Издательство John Wiley & Sons Limited
41. Yin, S. and Qu, Z., Resource allocation in multiuser OFDM systems with wireless information and power transfer. IEEE Commun. Lett., 20, 3, 594–597, Jan. 2016.
42. Chen, Y., Li, Y., Xu, D., Xiao, L., DQN-based power control for IoT transmission against jamming, in: Proc. of the IEEE 87th Vehicular Technology Conference (VTC Spring), pp. 1–5, Porto, Portugal, Jul. 2018.
43. Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y., Bennis, M., Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Internet Things J., 6, 3, 4005–4018, Jun. 2019.
44. He, X., Wang, K., Huang, H., Liu, B., QoE-driven big data architecture for smart city. IEEE Commun. Mag., 56, 2, 88–93, Feb. 2018.
45. Sharma, S.K. and Wang, X., Collaborative distributed Q-learning for RACH congestion minimization in cellular IoT networks. IEEE Commun. Lett., 23, 4, 600–603, Apr. 2019.
46. Wang, K., Wang, Y., Sun, Y., Guo, S., Wu, J., Green industrial Internet of Things architecture: An energy-efficient perspective. IEEE Commun. Mag., 54, 12, 48–54, Dec. 2016.
47. Chen, J. et al., Narrowband Internet of Things: implementations and applications. IEEE Internet Things J., 4, 6, 2309–2314, Dec. 2017.
48. Xu, C., Wang, K., Li, P., Guo, S., Luo, J., Ye, B., Guo, M., Making big data open in edges: A resource-efficient blockchain-based approach. IEEE Trans. Parallel Distrib. Syst., 30, 4, 870–882, Apr. 2019.
49. Newman, P., 2017, The Internet of Things 2017 Report: How the IoT is improving lives to transform the world, 2017, http://www.businessinsider.com/the-internet-of-things-2017.
50. Nitti, M., Girau, R., Atzori, L., Trustworthiness management in the social internet of things. IEEE Trans. Knowl. Data Eng., 26, 5, 1253–1266, 2014.
51. Chen, J., Chen, S., Wang, Q., Cao, B., Feng, G., Hu, J., iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks. IEEE Internet Things J., 6, 4, 7011–7023, Aug. 2019.
1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
2
Brain–Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things
R. Raja Sudharsan* and J. Deny
Department of Electronics and Communication Engineering, School of Electronics and Electrical Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India
Abstract
Enlistment of brain (cerebrum) signals can be arranged by a few techniques, for example, invasive and non-invasive. On the off chance that the biosensor is inserted in the cerebrum, at that point, the invasive procedure, has the advantage of high-frequency parts will estimate clearly and exact, yet because of wellbeing dangers and a few moral angles, they are essentially utilized in animal experimentations. If there should arise an occurrence of non-invasive technique, the surface electrodes are made available at the outer portion of the cerebrum, as per 5 to 15 global norms and standards. This application technique is substantially more likely utilized on people (human beings) since it doesn’t jeopardize them because of the implantation, however, it has the detriment, that the deliberate signals are noisier. This noisy signal can be removed by using a digital filter, named: Finite Impulse Response (FIR). In the previous years, a few electroencephalography headsets have been created not just for clinical use, which is worked from own batteries to guarantee versatile use. Presently some across the board Electroencephalography headsets are being presented, which are additionally reasonable for accomplishing one of a kind created Brain-Computer Interface. This kind of Headsets can be developed with the architecture of the Internet of Robotic Things (IoRT), where it can analyse the incoming electroencephalographic signals for corresponding actions of human beings. These recordings can be sent to the remote area and stored in the server through Bluetooth or Wi-fi mediums using the Gateway. This communication will help the remote person to track the targeted human being. This framework will reduce the latency of the electroencephalography concerning network and speed of data transfer.
Keywords: Electroencephalography (EEG) signals, Internet of Robotic Things (IoRT), headsets, brain–computer interface (BCI), graphical user interface (GUI)
2.1 Introduction
A few papers center around the Internet of Things running from customer situated to modern items. The Internet of Things idea has gotten regular since the start of the 21st century also; it was presented officially in 2005 [1, 2]. The Internet of Things empowers to make data recognized by these articles transmittable, and the items themselves controllable, by utilizing the present system framework [3]. This gives the chance to incorporate the physical world and Information Technology frameworks in a considerably more prominent scope, which prompts the improvement of effectiveness, precision, and financial aspects by insignificant human intercession. Brain–Computer Interface framework-based human–robot test condition is executed utilizing Transmission Control Protocol/Internet Protocol correspondence, where the inactivity of human incitation has been examined.
The Internet of Things innovation gives a few prospects to extending chances of robots, for instance, the use of keen incited gadgets. The IoRT is another idea [4] dependent on the Internet of Things for supporting automated frameworks including mechanical, home robots or other complex programmed frameworks with humanlike aptitudes, where somewhat independent frameworks can speak with one another. These gadgets use specially appointed, neighbourhood, dispersed or fog administered knowledge to upgrade performances and movements in this world considering a few factors, for instance, agreeable, adaptable, security, creation, and coordination angles utilizing data trade, furthermore, information sharing.
At the point when the data checked by the robot isn’t sufficient for the ideal activity, the robot can gather extra data from nature as well as can utilize extra cloud administrations to decide the fitting activity. To accomplish this usefulness a few innovations as depicted in Figure 2.1 must be applied dependent on the information on mechanical technology, mechatronics, digital material science, man-made consciousness, bio-designing, data trade system or collaboration.
The Internet of Robotic Things idea managing the expansion of the Internet of Things and mechanical gadgets to give progressed, versatile, progressively keen, shared, and heterogeneous mechanical abilities utilizing the innovations, among others, Networked Robots, Cloud Mechanical technology, Robot as assistance. To accomplish the referenced points various significant angles must be considered, for example, institutionalization, interoperability, normal engineering/framework configuration including time-fluctuating system inertness, security.
Figure 2.1 Foremost branches of IoRT.
On the Internet of Robotic Things framework, the robot is coordinated into the brilliant condition. Internet of Things innovation, the agreeable robots, and the correspondence of the gear fundamentally add to the computerization and improvement of the