Browsing by Author "Karlik, Bekir"

Now showing 1 - 4 of 4

Citation - WoS: 3
Citation - Scopus: 4
A novel mobile epilepsy warning system
(SPRINGER-VERLAG BERLIN, 2006) Ahmet Alkan; Yasar Guneri Sahin; Bekir Karlik; Alkan, Ahmet; Karlik, Bekir; Sahin, Yasar Guneri; A Sattar; BH Kang
This paper presents a new design of mobile epilepsy warning system for medical application in telemedical environment. Mobile Epilepsy Warning System (MEWS) consists of a wig with a cap equipped with sensors to get Electroencephalogram (EEG) signals a collector which is used for converting signals to data Global Positioning System (GPS) a Personal Digital Assistant (PDA) which has Global System for Mobile (GSM) module and execute Artificial Neural Network (ANN) software to test current patient EEG data with pre-learned data and a calling center for patient assistance or support. The system works as individual sensors obtain EEG signals from patient who has epilepsy and establishes a communication between the patient and Calling Center (CC) in case of an epileptic attack. MEWS learning process has artificial neural network classifier which consists of Multi Layered Perceptron (MLP) neural networks structure and back-propagation training algorithm.
Citation - WoS: 59
Citation - Scopus: 78
Artificial neural network-based prediction technique for wear loss quantities in Mo coatings
(Elsevier Science SA, 2006) Hakan Çetinel; Hasan Öztürk; Erdal Çelik; Bekir Karlik; Çelik, Erdal; Karlik, Bekir; Öztürk, Hasan; Çetinel, Hakan
Mo coated materials are used in automotive aerospace pulp and paper industries in order to protect machine parts against wear and corrosion. In this study the wear amounts of Mo coatings deposited on ductile iron substrates using an atmospheric plasma-spray system were investigated for different loads and environment conditions. The Mo coatings were subjected to sliding wear against AISI 303 counter bodies under dry and acid environments. In a theoretical study cross-sectional microhardness from the surface of the coatings loads environment and friction test durations were chosen as variable parameters in order to determine the amount of wear loss. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results is reasonably good. © 2006 Elsevier B.V. All rights reserved. © 2008 Elsevier B.V. All rights reserved.
Citation - WoS: 13
Medical Image Compression by Using Vector Quantization Neural Network (VQNN)
(ACAD Sciences Czech Republic, Inst Computer Science, 2006) Karlik, Bekir
This paper presents a lossy compression scheme for biomedical images by using a new method. Image data compression using Vector Quantization (VQ) has received a lot of attention because of its simplicity and adaptability. VQ requires the input image to be processed as vectors or blocks of image pixels. The Finite-state vector quantization (FSVQ) is known to give better performance than the memory less vector quantization (VQ). This paper presents a novel combining technique for image compression based on the Hierarchical Finite State Vector Quantization (HFSVQ) and the neural network. The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. The neural network is trained on image pairs consisting of a lossless compression named hierarchical vector quantization. Simulations results are presented that demonstrate improvements in visual quality and peak signal-to-noise ratio of the restored images.
Citation - Scopus: 19
Medical image compression by using vector quantization neural network (VQNN)
(ACAD SCIENCES CZECH REPUBLIC INST COMPUTER SCIENCE, 2006) Bekir Karlik; Karlik, Bekir
This paper presents a lossy compression scheme for biomedical images by using a new method. Image data compression using Vector Quantization (VQ) has received a lot of attention because of its simplicity and adaptability. VQ requires the input image to be processed as vectors or blocks of image pixels. The Finite-state vector quantization (FSVQ) is known to give better performance than the memory less vector quantization (VQ). This paper presents a novel combining technique for image compression based on the Hierarchical Finite State Vector Quantization (HFSVQ) and the neural network. The algorithm performs nonlinear restoration of diffraction-limited images concurrently with quantization. The neural network is trained on image pairs consisting of a lossless compression named hierarchical vector quantization. Simulations results are presented that demonstrate improvements in visual quality and peak signal-to-noise ratio of the restored images.