Human Communication Technology. Группа авторов
Читать онлайн книгу.
Table 2.3 Time-varying latency test-3 concerning speed reference and network latencies.
Test | Speed reference latency | Network latency | Total latency |
1 | 4,061.42 ± 17.32 | 0.99 ± 0.02 | 4,062.41 ± 17.34 |
2 | 5,282.33 ± 16.96 | 1.00 ± 0.02 | 5,283.33 ± 16.98 |
3 | 6,106.19 ± 42.46 | 0.97 ± 0.02 | 6,107.16 ± 82.94 |
4 | 7,217.19 ± 19.56 | 0.91 ± 0.02 | 7,218.10 ± 19.58 |
5 | 7,997.36 ± 13.23 | 0.98 ± 0.02 | 7,998.34 ± 13.25 |
2.7 Conclusion
In the article, the Brain–Computer Interface framework intended for human–PC-based control of Internet of Things-based robot (Internet of Robotic Things) unit has been presented, which bolsters current robots with the chances of innovation based on Internet of Things. On account of the performed Brain–Computer Interface framework, what’s more, an Internet of Robotic Things gadget, such challenging condition have been an arrangement, and are reasonable for the acknowledgement of directing both the nearer and farther robots. In this trail condition, execution of humanoid mediation and their inactivity because of Brain–Computer Interface framework have been inspected. As indicated by the encounters of the performed tests, can be expressed, that for the appropriate activity of Brain–Computer Interface framework, trailing patients needed to rehearse the utilization of the gadget in the first place, to arrive at suitable outcomes. Then again, trailing patients is ready to accomplish humanoid intercession just periods of seconds dormancy, even though this inertness didn’t rely upon whether the trailing patients controlling robot legitimately before then again by distant action. The Brain–Computer Interface framework gives appropriate premise to test the innovation, also, on pounded of picked-up outcomes, assurance of sequences for additional upgrades. The Internet of Robotic Things and Brain–Computer Interface can be utilized well in instruction likewise to apply in inventive, troublesome, agreeable learning condition and utilizing current Information and Communication Technology innovation
References
1. Schmitt, S.E., Pargeon, K., Frechette, E.S., Hirsch, L.J., Dalmau, J., Friedman, D., Extreme delta brush: A unique EEG pattern in adults with anti-NMDA receptor encephalitis. Neurology, 79, 11, 1094–1100, 2012.
2. Sudharsan, R.R., Deny, J., Kumaran, E.M., Geege, A.S., An Analysis of Different Biopotential Electrodes Used for Electromyography. 12, 1, 1–7, 2020.
3. Stanski, D.R., Pharmacodynamic modeling of anesthetic EEG drug effects. Annu. Rev. Pharmacol. Toxicol., 32, 1, 423–447, 1992.
4. Gillin, J.C., Duncan, W., Pettigrew, K.D., Frankel, B.L., Snyder, F., Successful separation of depressed, normal, and insomniac subjects by EEG sleep data. Arch. Gen. Psychiatry, 36, 1, 85–90, 1979.
5. Adler, G., Brassen, S., Jajcevic, A., EEG coherence in Alzheimer’s dementia. J. Neural Transm., 110, 9, 1051–1058, 2003.
6. Sudharsan, R.R. and Deny, J., Field Programmable Gate Array (FPGA)-Based Fast and Low-Pass Finite Impulse Response (FIR) Filter, in: Intelligent Computing and Innovation on Data Science, pp. 199–206, 2020.
7. Alvarez, L.A., Moshé, S.L., Belman, A.L., Maytal, J., Resnick, T.J., Keilson, M., EEG and brain death determination in children. Neurology, 38, 2, 227, 1988.
8. Friedberg, J., Shock treatment, brain damage, and memory loss: A neurological perspective. Am. J. Psychiatry, 134, 9, 1010–1014, 1977.
9. Waldert, S., Invasive vs. non-invasive neuronal signals for brain–machine interfaces: Will one prevail? Front. Neurosci., 10, 1–4, 2016.
10. Burchiel, K.J., McCartney, S., Lee, A., Raslan, A.M., Accuracy of deep brain stimulation electrode placement using intraoperative computed tomography without microelectrode recording. J. Neurosurg., 119, 2, 301–306, 2013.
11. Deny, J. and Sudharsan, R.R., Block Rearrangements and TSVs for a Standard Cell 3D IC Placement, in: Intelligent Computing and Innovation on Data Science, pp. 207–214, 2020.
12. Casdagli, M.C., Iasemidis, L.D., Savit, R.S., Gilmore, R.L., Roper, S.N., Sackellares, J.C., Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol., 102, 2, 98–105, 1997.
13. Onal, C. et al., Complications of invasive subdural grid monitoring in children with epilepsy. J. Neurosurg., 98, 5, 1017–1026, 2003.
14. Ball, T., Kern, M., Mutschler, I., Aertsen, A., Schulze-Bonhage, A., Signal quality of simultaneously recorded invasive and non-invasive EEG. Neuroimage, 46, 3, 708–716, 2009.
15. Pinegger, A., Wriessnegger, S.C., Faller, J., Müller-Putz, G.R., Evaluation of different EEG acquisition systems concerning their suitability for building a brain–computer interface: Case studies. Front. Neurosci., 10, 441, 2016.
16. Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A., Ahmad, I., A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Signal Process., 2015, 1, 66, 2015.
17. Hidalgo-Muñoz, A.R., López, M.M., Santos, I.M., Vázquez-Marrufo, M., Lang, E.W., Tomé, A.M., Affective valence detection from EEG signals using wrapper methods. Emotion and Attention Recognition Based on Biological Signals and Images, 12, p. 23, 2017.
18. Dash, M. and Liu, H., Feature selection for classification. Intell. Data Anal., 1, 131–156, 1997.
19. Liu, H. and Yu, L., Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng., 17, 491–502, 2005.
20. Klimesch, W., EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res. Rev., 29, 23, 169–195, 1999.
21. Woehrle, H., Krell, M.M., Straube, S., Kim, S.K., Kirchner, E.A., Kirchner, F., An adaptive spatial filter for user-independent single trial detection of event-related potentials. IEEE Trans. Biomed. Eng., 62, 7, 1696–1705, 2015.
22. Norcia, A.M., Appelbaum, L.G., Ales, J.M., Cottereau,