Challenges in Automation, Robotics and Measurement by Roman Szewczyk, Cezary Zieliński, Malgorzata Kaliczyńska

By Roman Szewczyk, Cezary Zieliński, Malgorzata Kaliczyńska

This e-book offers the set of papers authorized for presentation on the overseas convention Automation, held in Warsaw, 2-4 March of 2016. It provides the examine effects provided by way of best specialists within the fields of commercial automation, regulate, robotics and size concepts. every one bankruptcy provides an intensive research of a selected technical challenge that is frequently via numerical research, simulation, and outline of result of implementation of the answer of a true international challenge. The provided theoretical effects, functional strategies and instructions could be priceless for either researchers operating within the sector of engineering sciences and for practitioners fixing business difficulties.

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Additional info for Challenges in Automation, Robotics and Measurement Techniques: Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland

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Hunek ⋅ A. Shylenko Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Institute of Control and Computer Engineering, Gen. pl © Springer International Publishing Switzerland 2016 R. Szewczyk et al. 1007/978-3-319-29357-8_3 25 26 S. Paszkiel et al. communication channel with fully hardware one. This leads to fast, reliable, universal direct data interface. The most popular and easiest way of implement BCI is to use a electroencephalography (EEG) measurement.

9) we have               E2 A20 A21 A2, i B20 B21 0 x x x u = + + ⋯ + + + + x u i + 1 i i − 1 0 i i + 1 ̄ ui + 2 ̄ ̄ − B21 Ā20 − A21 − A22 0 0 − B̄20 ð3:10Þ If the matrix ½ E2T ̄ Š A20 T T ð3:11Þ is singular then we repeat the procedure. Continuing this procedure after finite number of steps p we obtain               Ep Ap, 0 Ap, 1 Bp, 1 0 Api Bp, 0 ̄ p − 1 ui + p − 1 ̄ 0 xi + 1 = − Ap, ̄ 1 xi + − Ap, ̄ 2 xi − 1 + ⋯ + 0 x0 + 0 ui + − Bp, ̄ 0 ui + 1 + ⋯ + − Bp, Ap, ð3:12Þ ̄ ∈ ℜðn − np Þ × n , j = 0, 1, .

But there are also other methods for future extraction like power spectral density approach with autoregressive coefficients approach and time frequency transformations. There are five recognized categories that cover the most used algorithms in BCI classification systems: linear classifiers, where a classification decision based on the value of linear combinations of the extracted future characteristics. It is one of the fastest methods; nearest-neighbor classifiers, which classify any feature based on the closest training pattern; Bayes classifiers, based on Bayes theorem; neural networks and a combination of different classifiers.

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