Browsing by Author "Özmen, Atilla"
Now showing items 1-20 of 32
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A numerical method for frequency determination in the astable cellular neural networks with opposite-sign templates
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Publisher and Date:(IEEE, 2006)In this study a numerical method is proposed to determine the oscillation frequencies in the astable cellular neural networks with opposite-sign templates [1]. This method depends on the training of a multilayer perceptron that uses various template coefficients and the correspondant frequency values as inputs and outputs. First of all a frequency surface is obtained from templates and then training samples are picked from this surface in order to apply to multilayer perceptron. The effects of the ...
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Amplitude and Frequency Modulations with Cellular Neural Networks
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Publisher and Date:(Springer, 2015)Amplitude and frequency modulations are still the most popular modulation techniques in data transmission at telecommunication systems such as radio and television broadcasting gsm etc. However the architectures of these individual systems are totally different. In this paper it is shown that a cellular neural network with an opposite-sign template can behave either as an amplitude or a frequency modulator. Firstly a brief information about these networks is given and then the amplitude and frequency ...
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Analytical approaches for the amplitude and frequency computations in the astable cellular neural networks with opposite sign templates
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Publisher and Date:(IEEE, 2007)In this paper, by using surface fitting methods, analytical approaches for amplitudes and frequencies of the x(1,2)(t) "States" in a simple dynamical neural network called "Cellular Neural Network with Opposite Sign Templates" which was proposed by Zou and Nossek [1], are obtained under oscillation conditions. The mentioned explicit expressions are employed in a cellular neural network based, amplitude and frequency tuneable oscillator design.
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Application of deep neural network (DNN) for experimental liquid-liquid equilibrium data of water + butyric acid + 5-methyl-2-hexanone ternary systems
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Publisher and Date:(Elsevier B.V., 2021)LLE data are important for simulation and design of extraction equipment. In this study, deep neural network (DNN) structure was proposed for modelling of the ternary liquid-liquid equilibrium (LLE). LLE data of (water + butyric acid + 5-methyl-2-hexanone) ternaries defined at three different temperatures of 298.2, 308.2, and 318.2 K and P = 101.3 kPa, were obtained experimentally and then correlated with nonrandom two-liquid (NRTL) and universal quasi-chemical (UNIQUAC) models. The performance ...
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Artificial neural network based estimation of sparse multipath channels in OFDM systems
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Publisher and Date:(SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS, 2021-01)In order to increase the transceiver performance in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) system is used to combat inter-symbol-interference. In this work, a channel estimation scheme for an OFDM system in the presence of sparse multipath channel is studied using the artificial neural networks (ANN). By means of ANN's learning capability, it is shown that how to model and obtain a channel estimate and how it allows the proposed technique ...
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Artificial neural network based sparse channel estimation for OFDM systems
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Publisher and Date:(Kadir Has University, 2017)In order to increase the communication quality in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) systems are used to combat inter-symbol-interference (ISI). In this thesis, a channel estimation scheme for the OFDM system in the presence of sparse multipath channel is studied. The channel estimation is done by using the artificial neural networks (ANNs) with Resilient Backpropagation training algorithm. This technique uses the learning capability ...
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Bayesian estimation of discrete-time cellular neural network coefficients
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Publisher and Date:(TUBITAK Scientific & Technical Research Council Turkey, 2017)A new method for finding the network coefficients of a discrete-time cellular neural network (DTCNN) is proposed. This new method uses a probabilistic approach that itself uses Bayesian learning to estimate the network coefficients. A posterior probability density function (PDF) is composed using the likelihood and prior PDFs derived from the system model and prior information respectively. This posterior PDF is used to draw samples with the help of the Metropolis algorithm a special case of the ...
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Caching algorithm implementation for edge computing in IoT network
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Publisher and Date:(Kadir Has University, 2020)The developing IoT concept brings new challenges to the service providers. The architecture of the networks changes to satisfy the needs arising by the large number of connected devices. Edge computing is the new architectural solution that will be used in the IoT networks. This architecture is more dynamic than the cloud computing network where the data can be quickly processed in the different layers of the network without going to the cloud. This will remove the problems faced by cloud computing: ...
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Channel Equalization with Cellular Neural Networks
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Publisher and Date:(IEEE, 2010)In this paper a dynamic neural network structure called Cellular Neural Network (CNN) is employed for the equalization in digital communication. It is shown that this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled simple CNN containing 9 neurons thus having only 19 weight coefficients. Proposed system is compared with linear transversal filters as well as with a Multilayer Perceptron (MLP) ...
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Channel Estimation for Realistic Indoor Optical Wireless Communication in ACO-OFDM Systems
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Publisher and Date:(Springer, 2018)In this paper channel estimation problem in a visible light communication system is considered. The information data is transmitted using asymmetrical clipped optical orthogonal frequency division multiplexing. Channel estimation and symbol detection are performed by the Maximum Likelihood and the Linear Minimum Mean Square Error detection techniques respectively. The system performance is investigated in realistic environment that is simulated using an indoor channel model. Two different channels ...
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Computation of two-variable mixed element network functions
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Publisher and Date:(Kadir Has University, 2017)in this dissertation the algorithm known as “Standard Decomposition Technique (SDT)” is used together with Belevitch’s canonic representation of scattering polynomial for two-port networks operate on high frequency to find the analytical solutions for “Fundamental equation set (FES)”. This FES is extracted by using Belevitch canonic polynomials “ ??(?? ??) ?(?? ??) and ??(?? ??)” used for the description of mixed lumped and distributed lossless two-port cascaded networks in two variables of degree ...
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Correlation of Experimental Liquid-Liquid Equilibrium Data for Ternary Systems Using NRTL and GMDH-Type Neural Network
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Publisher and Date:(Amer Chemical Soc, 2017)In this work liquid liquid equilibrium (LLE) data for the ternary systems (water + propionic acid + solvent) were experimentally obtained at atmospheric pressure and 298.2 K. The ternary systems show type-1 behavior of LLE. Cyclopentane cyclopentanol 2-octanone and dibutyl maleate were chosen as solvent and it has been noted that there are no data in the literature on these ternary systems. The consistency of the experimental tie-line data was checked using the Hand and Othrner-Tobias correlation ...
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Correlation of ternary liquid--liquid equilibrium data using neural network-based activity coefficient model
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Publisher and Date:(Springer, 2014)Liquid--liquid equilibrium (LLE) data are important in chemical industry for the design of separation equipments and it is troublesome to determine experimentally. In this paper a new method for correlation of ternary LLE data is presented. The method is implemented by using a combined structure that uses genetic algorithm (GA)--trained neural network (NN). NN coefficients that satisfy the criterion of equilibrium were obtained by using GA. At the training phase experimental concentration data and ...
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Design and Implementation of a Cellular Neural Network Based Oscillator Circuit
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Publisher and Date:(World Scientific and Engineering Acad and Soc, 2009)In this paper, a novel inductorless oscillator circuit with negative feedbacks, based on a simple version of a "Cellular Neural Network" (CNN) called "CNN with an Opposite Sign Template" (CNN-OST) is designed and implemented. The system is capable of generating quasi-sine oscillations with tuneable amplitude and frequency which can't be provided at the same time in the conventional oscillator circuits.
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Design and implementation of a negative feedback oscillator circuit based on a Cellular Neural Network with an Opposite Sign Template
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Publisher and Date:(2010)In this paper explicit amplitude and frequency expressions for a Cellular Neural Network with an Opposite-Sign Template (CNN-OST) under oscillation condition are derived and a novel inductorless oscillator circuit with negative feedbacks based on this simple structure is designed and implemented. The system is capable of generating quasi-sine signals with tuneable amplitude and frequency which can't be provided at the same time in the classical oscillator circuits.
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Design of low-pass ladder networks with mixed lumped and distributed elements by means of artificial neural networks
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Publisher and Date:(AVES YAYINCILIK, 2003)In this paper, calculation of parameters of low-pass ladder networks with mixed lumped and distributed elements by means of artificial neural networks is given. The results of ANN are compared with the values that are desired. It has been observed that the calculated and the desired values are extremely close to each other. So this algorith can be used to obtain the parameters that will be used to synthesize such circuits.
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Detection of Trojans in integrated circuits
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Publisher and Date:(IEEE, 2012)This paper presents several signal processing approaches in Trojan detection problem in very large scale integrated circuits. Specifically wavelet transforms spectrograms and neural networks are used to analyze power side-channel signals. Trojans in integrated circuits can try to hide themselves and become almost invisible due to process and measurement noises. We demonstrate that our initial results with these techniques are promising in successful detection. Discrete wavelet transforms and ...
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Edge detection using steerable filters and CNN
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Publisher and Date:(European Signal Processing Conference EUSIPCO, 2002)This paper proposes a new approach for edge detection using steerable filters and cellular neural networks (CNNs) where the former yields the local direction of dominant orientation and the latter provides iterative filtering. For this purpose steerable filter coefficients are used in CNN as a B template. The results are compared to the results where only CNN or steerable filters are used. As a result of this study the performance of the system can be improved since iterative filtering property ...
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The Effect of Data Augmentation on ADHD Diagnostic Model using Deep Learning
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Publisher and Date:(IEEE, 2019)Attention Deficit Hyperactivity Disorder (ADHD) is a neuro-behavioral hyperactivity disorder. It is frequently seen in childhood and youth, and lasts a lifetime unless treated.The ADHD classification model should be objective and robust. Correct diagnosis usually depends on the knowledge and experience of health professionals. In this respect, an automated method to be developed for the ADHD classification model is of great importance for clinicians. In this study, the effect of data augmentation ...
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Fault estimation of Trakya and Marmara Sea regions using 2D Gabor filtering [2 boyutlu gabor filtre yöntemi uygulayarak Trakya ve Marmara denizindeki fay hatlarının saptanması]
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Publisher and Date:(2005)In this paper we have applied 2D Gabor filtering to gravity and magnetic anomalies in estimation of discontinuities. Gabor filtering is an effective separation method compared to others having steerable and frequency parameter properties. We have found new faults using Gabor filtering for gravity and magnetic anomalies of Marmara Sea. © 2005 IEEE.
