By Mirsad Hadžikadić, Samir Avdaković
This quantity spans a variety of technical disciplines and applied sciences, together with complicated platforms, biomedical engineering, electric engineering, strength, telecommunications, mechanical engineering, civil engineering, and desktop technological know-how. The papers integrated during this quantity have been awarded on the foreign Symposium on leading edge and Interdisciplinary purposes of complicated applied sciences (IAT), held in Neum, Bosnia and Herzegovina on June 26 and 27, 2016. This hugely interdisciplinary quantity is dedicated to varied points and kinds of structures. platforms considering is important for effectively development and figuring out man-made, normal, and social structures.
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Additional resources for Advanced Technologies, Systems, and Applications
Int J Eng Technol 13(02):11–17 Karabegović I, Karabegović E, Husak E (2013) Application of service robots in rehabilitation and support of patients. com; 03 Feb 2016 Karabegović I, Karabegović E, Husak E (2012) Application of robotic technology in the textile and clothing industry. In: 5th međunarodno znanastveno-stručno savjetovanje Tekstila znanosti i gospodarstva, 26. org. com. ,Karabegović I. : Robotika, Tehnički fakultet, Bihać, 2002 Analysis of Electroencephalogram on Children with Epilepsy Using Global Wavelet Spectrum Salko Zahirović, Nedis Dautbašić, Maja Muftić Dedović, Smail Zubčević and Samir Avdaković Abstract The electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain and is used as the method of choice for the diagnosis of epilepsy.
Global Wavelet Spectrum is used for discrimination between healthy and epileptic patients. A multiresolution is relation between time and frequency resolution. Signal database set consist of 5 patients: • 2—healthy patients; • 3—patients with epileptic syndrome—study state. 4 Hz which is enough for identiﬁcation of all components of EEG signals. Analysis of Electroencephalogram on Children … 29 Fig. 1 Spectral density in the wavelet transform domain (time frequency domain) The coefﬁcients distribution of a WT is presented in the time–frequency domain, shown on Fig.
Ibrić S, Avdaković S, Omerhodžić I, Suljanović N, Mujčić A (2015) Diagnosis of epilepsy from EEG signals using Hilbert Huang transform. Folia Med Fac Univ Saraeviensis 50(1): 68–73 11. Subasi A, Alkan A, Koklukaya E, Kiymik MK (2007) EEG signal classiﬁcation using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093 12. Subasi A, Ercelebi E (2005) Classiﬁcation of EEG signals using neural network and logistic regression, Comput Methods Programs Biomed 87–99 Target Signatures and Pose Estimation Migdat I.
Advanced Technologies, Systems, and Applications by Mirsad Hadžikadić, Samir Avdaković