WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.yasar.edu.tr/handle/123456789/11289

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(TAYLOR & FRANCIS LTD, 2003) F. Ünlü; Ünlü, F
The Use of Information Technologies in Special Education for Preparation of Individual Education Programs
(WORLD ACAD SCI ENG & TECH-WASET, 2005) Yasar Guneri Sahin; Mehmet Cudi Okur; Sahin, Yasar Guneri; Okur, Mehmet Cudi; C Ardil
In this presentation we discuss the use of information technologies in the area of special education for teaching individuals with learning disabilities. Application software which was developed for this purpose is used to demonstrate the applicability of a database integrated information processing system to alleviate the burden of educators. The software allows the preparation of individualized education programs based on the predefined objectives goals and behaviors.
Citation - WoS: 1
Citation - Scopus: 1
Software-assisted preparation and assessment of individual education plans for disabled individuals
(CURRENT SCIENCE ASSN, 2006) Yasar Guneri Sahin; Sahin, Yasar Guneri
This article deals with the use of a computer software in special education. Preparation of Individual Education Plan (IEP) and assessment of predefined IEP are the most important stages in the area of special education for teaching individuals with learning disabilities. A special application software which was developed for this purpose is used to demonstrate the applicability of a database-integrated information processing system to alleviate the burden on educators. The software allows preparation of individualized education programmes based on predefined objectives and behaviours and assessment of school and family trainings of students. The software has a user-friendly interface and its design includes graphical tools.
Feedback network controls photoreceptor output at the layer of first visual synapses in Drosophila
(ROCKEFELLER UNIV PRESS, 2006) L Zheng; GG Polavieja; V Wolfram; MH Asyali; RC Hardie; M Juusola
At the layer of first visual synapses information from photoreceptors is processed and transmitted towards the brain. In fly compound eye output from photoreceptors (R1 - R6) that share the same visual field is pooled and transmitted via histaminergic synapses to two classes of interneuron large monopolar cells (LMCs) and amacrine cells (ACs). The interneurons also feed back to photoreceptor terminals via numerous ligand-gated synapses yet the significance of these connections has remained a mystery. We investigated the role of feedback synapses by comparing intracellular responses of photoreceptors and LMCs in wild-type Drosophila and in synaptic mutants to light and current pulses and to naturalistic light stimuli. The recordings were further subjected to rigorous statistical and information-theoretical analysis. We show that the feedback synapses form a negative feedback loop that controls the speed and amplitude of photoreceptor responses and hence the quality of the transmitted signals. These results highlight the benefits of feedback synapses for neural information processing and suggest that similar coding strategies could be used in other nervous systems.
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 - 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.
Measure on time scales with mathematica
(SPRINGER-VERLAG BERLIN, 2006) Unal Ufuktepe; Ahmet Yantir; VN Alexandrov; GD VanAlbada; PMA Sloot; J Dongarra
In this paper we study the Lebesgue Delta-measure on time scales. We refer to [3 4] for the main notions and facts from the general measure and Lebesgue Delta integral theory. The objective of this paper is to show how the main concepts of Mathematica can be applied to fundamentals of Lebesgue Delta- and Lebesgue Delta- measure on an arbitrary time scale and also on a discrete time scale whose rule is given by the reader. As the time scale theory is investigated in two parts by means of alpha and rho operators we named the measures on time scales by the set function DMeasure and NMeasure respectively for arbitrary time scales.
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: 77
Citation - Scopus: 82
Feedback network controls photoreceptor output at the layer of first visual synapses in Drosophila
(Rockefeller Univ Press, 2006) Lei Zheng; Gonzalo García De Polavieja; Verena Wolfram; Musa Hakan Asyali; Roger C. Hardie; Mikko A. Juusola; Wolfram, V; Polavieja, GG; Juusola, M; Zheng, L; Asyali, MH; De Polavieja, Gonzalo G.; Hardie, RC
At the layer of first visual synapses information from photoreceptors is processed and transmitted towards the brain. In fly compound eye output from photoreceptors (R1-R6) that share the same visual field is pooled and transmitted via histaminergic synapses to two classes of interneuron large monopolar cells (LMCs) and amacrine cells (ACs). The interneurons also feed back to photoreceptor terminals via numerous ligand-gated synapses yet the significance of these connections has remained a mystery. We investigated the role of feedback synapses by comparing intracellular responses of photoreceptors and LMCs in wild-type Drosophila and in synaptic mutants to light and current pulses and to naturalistic light stimuli. The recordings were further subjected to rigorous statistical and information-theoretical analysis. We show that the feedback synapses form a negative feedback loop that controls the speed and amplitude of photoreceptor responses and hence the quality of the transmitted signals. These results highlight the benefits of feedback synapses for neural information processing and suggest that similar coding strategies could be used in other nervous systems. © The Rockefeller University Press. © 2008 Elsevier B.V. All rights reserved., MEDLINE® is the source for the MeSH terms of this document.
Citation - WoS: 107
Gene expression profile classification: A review
(BENTHAM SCIENCE PUBL LTD, 2006) Musa H. Asyali; Dilek Colak; Omer Demirkaya; Mehmet S. Inan; Asyali, Musa H.; Colak, Dilek; Demirkaya, Omer; Inan, Mehmet S.
In this review we have discussed the class-prediction and discovery methods that are applied to gene expression data along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important issues such as preprocessing of gene expression data curse of dimensionality feature extraction/selection and measuring or estimating classifier performance. We discussed and summarized important properties such as generalizability (sensitivity to overtraining) built-in feature selection ability to report prediction strength and transparency (ease of understanding of the operation) of different class-predictor design approaches to provide a quick and concise reference. We have also covered the topic of biclustering which is an emerging clustering method that processes the entries of the gene expression data matrix in both gene and sample directions simultaneously in detail.
Artificial neural network-based prediction technique for wear loss quantities in Mo coatings
(ELSEVIER SCIENCE SA, 2006) Hakan Cetinel; Hasan Ozturk; Erdal Celik; Bekir Karlik
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. (c) 2006 Elsevier B.V. All rights reserved.
Measure on time scales with Mathematica
(Springer Verlag, 2006) Unal Ufuktepe; Ahmet Yantir; Ufuktepe, Ünal; Yantir, Ahmet
In this paper we study the Lebesgue Δ-measure on time scales. We refer to [3 4] for the main notions and facts from the general measure and Lebesgue Δ integral theory. The objective of this paper is to show how the main concepts of Mathematica can be applied to fundamentals of Lebesgue Δ- and Lebesgue ∇- measure on an arbitrary time scale and also on a discrete time scale whose rule is given by the reader. As the time scale theory is investigated in two parts by means of σ and ρ operators we named the measures on time scales by the set function DMeasure and NMeasure respectively for arbitrary time scales. © Springer-Verlag Berlin Heidelberg 2006. © 2015 Elsevier B.V. All rights reserved.
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.
Research on the middleware of grid GIS - distributed cooperative computing GIS software and its key technologies
(SPIE-INT SOC OPTICAL ENGINEERING, 2007) Yumei Sun; Yu Fang; Bin Chen; P Gong; YX Liu
This paper introduces the concept of grid computing and its characteristics. Basing on analyzing the characteristics of grid computing we apply grid computing to GIS field to construct grid GIS which is the application of grid computing on geographic information system with grid computing as the basic running environment. According to the architecture of grid and integrating the characteristics of GIS we design the architecture of grid GIS which has three layers. They are grid GIS resource layer grid GIS middleware layer and grid GIS application layer. Among them the grid GIS middleware layer is the most important. Then this paper expatiates on our research on grid GIS middleware which is about the design and development of distributed cooperative computing GIS software. The key technologies of the distributed cooperative computing GIS software are discussed which include technology of global spatial resource management spatial data computing task allocation and management system consistency mechanism and system security mechanism. The implementation process is also presented. At last this paper presents the further researches of the distributed cooperative computing GIS software.
Citation - WoS: 5
Citation - Scopus: 8
Distance education techniques to assist skills of tourist guides
(IEEE COMPUTER SOC LEARNING TECHNOLOGY TASK FORCE, 2007) Yasar Guneri Sahin; Sabah Balta; Balta, Sabah; Sahin, Yasar Guneri
This study is a presentation of the usage of distance education technologies in a bid to support face to face education of tourist guide candidates during the training tour. The laws require tourist guide candidates to successfully complete their internship tour and get a certificate. Since the time in this internship period is limited and there are restricting factors such as transportation accommodation, many of the tourism places couldn't be included in this internship period. Besides the lack of experienced and competent guides in the visited places is also another negative factor reducing the quality and efficiency of the training. Technological support of the training of tourist guide candidates would effectively reduce the negativities of traditional education methods. Thus benefiting from computer technologies and audiovisual systems during the internship of tourist guide candidates would result in an increase in the effectiveness and usefulness of the training tour and would make it possible to visit more places in a shorter period. This study is a presentation on how distance education method could be implemented and an attempt to show the benefits could be obtained from implementation of these methods along with the possible problems that are predicted to arise.
Citation - WoS: 30
Citation - Scopus: 35
Determining a continuous marker for sleep depth
(Pergamon-Elsevier Science Ltd, 2007) Musa Hakan Asyali; Richard Barnett Berry; Michael C.K. Khoo; Ayşe Asyali Altinok; Khoo, Michael C.K.; Asyali, Musa H.; Berry, Richard B.; Altinok, Ayse
Detection and quantification of sleep arousals is an important issue as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG non-rapid eye movement sleep is staged into four somewhat rough categories. In this study we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep. In order to develop such a marker we obtained the time-frequency map of two EEG channels around sleep arousals and identified the frequency bands that show the most change during arousals. We then evaluated classification performance of the potential signals for representing the depth of sleep using receiver operating characteristic analysis. Our comparisons based on the area under the curve values revealed that the sum of absolute powers in alpha and beta bands is a good continuous marker to represent the depth of sleep. Higher values of this marker indicate low-quality sleep and vice versa. We believe that use of this marker will lead to a better quantification of sleep quality. © 2007. © 2008 Elsevier B.V. All rights reserved., MEDLINE® is the source for the MeSH terms of this document.
Determining a continuous marker for sleep depth
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Musa H. Asyali; Richard B. Berry; Michael C. K. Khoo; Ayse Altinok
Detection and quantification of sleep arousals is an important issue as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG. non-rapid eye movement sleep is staged into four somewhat rough categories. In this study we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep. In order to develop such a marker we obtained the time-frequency map of two EEG channels around sleep arousals and identified the frequency bands that show the most change during arousals. We then evaluated classification performance of the potential signals for representing the depth of sleep using receiver operating characteristic analysis. Our comparisons based on the area under the curve values revealed that the sum of absolute powers in alpha and beta bands is a good continuous marker to represent the depth of sleep. Higher values of this marker indicate low-quality sleep and vice versa. We believe that use of this marker will lead to a better quantification of sleep quality. (C) 2007 Published by Elsevier Ltd.
Citation - WoS: 44
Citation - Scopus: 62
Frequency domain analysis of power system transients using Welch and Yule-Walker AR methods
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Ahmet Alkan; Ahmet S. Yimaz; Alkan, Ahmet; Yimaz, Ahmet S.; Yilmaz, Ahmet S.
In this study power quality (PQ) signals are analyzed by using Welch (non-parametric) and autoregressive (parametric) spectral estimation methods. The parameters of the autoregressive (AR) model were estimated by using the Yule-Walker method. PQ spectra were then used to compare the applied spectral estimation methods in terms of their frequency resolution and the effects in determination of spectral components. The variations in the shape of the obtained power spectra were examined in order to detect power system transients. Performance of the proposed methods was evaluated by means of power spectral densities (PSDs). Graphical results comparing the performance of the AR method with that of the Welch technique are given. The results demonstrate superior performance of the AR method over the Welch method. (c) 2007 Elsevier Ltd. All rights reserved.
Obtaining Volterra Kernels from Neural Networks
(SPRINGER-VERLAG BERLIN, 2007) Musa H. Asyali; Musa Alci; SI Kim; TS Suh
Both neural networks (NN) and Volterra series (VS) are widely used in nonlinear dynamic system identification. In VS approach the system is modeled using a set of kernel functions that correspond to different order convolutions. Kernels in VS are typically estimated using an orthogonal expansion technique. In this study we discuss the method of obtaining VS representation of nonlinear systems from their NN models as an alternative approach and compare its modeling performances against the popular Laguerre basis expansion (LBE) technique. In LBE approach the critical issues are to select a suitable pole parameter and number of basis functions to be used in the expansions so that the kernels can be accurately represented. We devised novel approaches to address both issues the pole parameter is selected using a systematic optimization approach and the number of basis functions is decided using the minimum description length criterion. Our preliminary results on synthetic data indicate that when used with these provisions LBE yields more accurate kernels estimation results than the NN approach. However LBE is typically used without these provisions in literature. We demonstrate that with its typical use kernels estimated using the LBE approach can be quite misleading even though the estimation error may seem to be reasonable. Therefore we suggest the use NN approach as a reference method to confirm the morphology of the kernels estimated via other approaches including LBE.
Research on the middleware of grid GIS - Distributed cooperative computing GIS software and its key technologies
(SPIE-INT Soc Optical Engineering, 2007) Yumei Sun; Yu Fang; Bin M. Chen; Tohid Ahmed Rana; Sun, Yumei; Chen, Bin; Rana, Tohid Ahmed; Fang, Yu
This paper introduces the concept of grid computing and its characteristics. Basing on analyzing the characteristics of grid computing we apply grid computing to GIS field to construct grid GIS which is the application of grid computing on geographic information system with grid computing as the basic running environment. According to the architecture of grid and integrating the characteristics of GIS we design the architecture of grid GIS which has three layers. They are grid GIS resource layer grid GIS middleware layer and grid GIS application layer. Among them the grid GIS middleware layer is the most important. Then this paper expatiates on our research on grid GIS middleware which is about the design and development of distributed cooperative computing GIS software. The key technologies of the distributed cooperative computing GIS software are discussed which include technology of global spatial resource management spatial data computing task allocation and management system consistency mechanism and system security mechanism. The implementation process is also presented. At last this paper presents the further researches of the distributed cooperative computing GIS software. © 2008 Elsevier B.V. All rights reserved.

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