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Öğe Analysis of the electronic integrate and fire neuron model(Elsevier, 2022) Isik, Ibrahim; Tagluk, Mehmet EminNano-scale devices are thought to intervene in natural life for a variety of responsibilities. For understanding the intrinsic communication of such nano-scale devices, software and hardware modalities have been introduced. Some of these models are of neuro-spike communication systems which employ spiking neuron circuits. In this study, the previously designed electronic integrate and fire circuit inspired by Hodgkin Huxley membrane model is analyzed and interrelated to the Izhikevich's systematic integrate and fire model. The generated action potentials with this model are very similar to the ones generated by real biophysical neurons which are thought as the inter-neuronal ionic transporters of information. The superiority of the analyzed model to the existing models is that it can show pulse trains whose characteristics are almost similar to those produced by nerve cells. The analytical, hardware and simulation results have shown that the model has the potential of employment in the smart nano-scale systems and medical treatment strategies. (c) 2022 Elsevier B.V. All rights reserved.Öğe Beneficial effects of dexpanthenol on mesenteric ischemia and reperfusion injury in experimental rat model(Taylor & Francis Ltd, 2016) Cagin, Yasir Furkan; Atayan, Yahya; Sahin, Nurhan; Parlakpinar, Hakan; Polat, Alaadin; Vardi, Nigar; Tagluk, Mehmet EminBackground and aim It has been reported that intestinal ischemia-reperfusion (I/R) injury results from oxidative stress caused by increased reactive oxygen species. Dexpanthenol (Dxp) is an alcohol analogue with epitelization, anti-inflammatory, antioxidant, and increasing peristalsis activities. In the present study, the aim was to investigate protective and therapeutic effects of Dxp against intestinal I/R injury. Materials and methods Overall, 40 rats were assigned into five groups including one control, one alone Dxp, and three I/R groups (40-min ischemia; followed by 2-h reperfusion). In two I/R groups, Dxp (500mg/kg, i.m.) was given before or during ischemia. The histopathological findings including apoptotic changes, and also tissue and serum biochemical parameters levels, were determined. Oxidative stress and ileum damage were assessed by biochemical and histological examination. In the control (n=8) and alone Dxp (n=8; 500mg/kg, i.m. of Dxp was given at least 30min before recording), groups were incised via laparotomy, and electrical activity was recorded from their intestines. In this experiment, the effect of Dxp on the motility of the intestine was examined by analyzing electrical activity. Results In ileum, oxidant levels were found to be higher, while antioxidant levels were found to be lower in I/R groups when compared with controls. Dxp approximated high levels of oxidants than those in the control group, while it increased antioxidant values compared with I/R groups. Histopathological changes caused by intestinal I/R injury and histological improvements were observed in both groups given Dxp. In the Dxp group, electrical signal activity markedly increased compared with the control group. Conclusions Here, it was seen that Dxp had protective and therapeutic effects on intestinal I/R injury and gastrointestinal system peristaltism.Öğe Classification of Hand Opening/Closing and Fingers by Using Two Channel Surface EMG Signal(Ieee, 2017) Sezgin, Necmettin; Ertugrul, Omer Faruk; Tekin, Ramazan; Tagluk, Mehmet EminIn this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.Öğe Comparison of HDL Coder and System Generator Tools in terms of QPSK Analysis(Ieee, 2017) Isik, Ibrahim; Tagluk, Mehmet EminBER (bit error rate) measurement is an important criterion to analyze digital communication systems. In literature this measurement generally performed through simulation programs like Matlab/Simulink. It is considered that the simulation programs may not represent a real communication system and also they are quite time consuming and expensive. However, modeling communication systems with parallel processing and fast modules such as FPGA (Field Programmable Gate Arrays) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) and performing BER measurements on this modules is much faster, closer to the reality. The main interest of this study is to demonstrate the performance of FPGA based models in communication systems and show their advantages and disadvantages compared to the simulator models. Despite the simple structure of FPGA it sometimes restricts a complete design of the system because of comprising only gates, logic operators, register etc. Simulator models such as the ones designed with Matlab have a huge library which provides flexibility in the design and analysis of the system. Software and hardware developers try to develop new ways such as HDL Coder and System Generator tools to get over these restrictions. But both methods still have some restrictions to design a better communication system. In this paper also this restrictions are investigated detailed. As an example, QPSK (Quadrature Phase Shift Keying) modulation is shown by using System Generator tool in this paper.Öğe Design and development of travelling-wave-frequency-based transmission line fault locator using TMS320 DSP(Inst Engineering Technology-Iet, 2019) Arkan, Muslum; Akmaz, Duzgun; Mamis, Mehmet S.; Tagluk, Mehmet EminThe authors use a TMS320 digital signal processor (TMS320-DSP) to determine fault instants and estimate their location in real time in a laboratory environment. The fault instant is determined via examining the instantaneous differential changes in the line currents. After the fault is detected, the fault location is determined by processing the time-domain transient current waves. First, the travelling-wave frequency is determined by application of the fast Fourier transform to the positive-sequence-component line current after the fault, and subsequently, the fault location is estimated by utilising this frequency. The alternative transients programme (ATP)-electromagnetic transient programme is used to simulate the line currents and create short-circuit fault conditions. Furthermore, LabVIEW software and a National Instruments data acquisition board are used to transform the line currents obtained through the ATP programme into analogue signals. The TMS320-DSP determines the fault in real time and estimates the fault location using the completed software and analogue input signals. Their results indicate that the prototype device designed with the use of the TMS320-DSP is suitable for real-time fault detection.Öğe Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine(Ieee, 2017) Ertugrul, Omer Faruk; Sezgin, Necmettin; Oztekin, Abdulkerim; Tagluk, Mehmet EminEstimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using selected features the required memory, equipment and communication costs are also decreased in real time applications. Feature selection by extreme learning machine method was used in determining relevant features. The short-term power loads of two houses (one of them has a power generation capability) were used in tests and achieved results showed lower error rates were obtained by using less number of features.Öğe Dual effects of melatonin on uterine myoelectrical activity of non-pregnant rats(Galenos Yayincilik, 2014) Simsek, Yavuz; Parlakpinar, Hakan; Turhan, Ugur; Tagluk, Mehmet Emin; Ates, BurhanObjective: In this experimental study, we aimed to investigate the role of melatonin on uterine myoelectrical activity of non-pregnant rats. Material and Methods: Forty-six female rats were assigned to six groups: (1) control; (0.2 mL 0.9% NaCl was injected intravenously (IV), n=6); (2) melatonin applied as 0.4 mg/kg/IV (n=8); (3) melatonin applied as 4 mg/kg/IV (n=8); (4) single dose of oxytocin (100 mU/kg) injected IV (n=8); (5) melatonin (0.4 mg/kg) plus oxytocin (100 mU/kg) (n=8); and (6) melatonin (4 mg/kg) plus oxytocin (100 mU/kg) injected IV (n=8). Each rat underwent a laparotomy, and uterine myoelectrical signals were recorded. The mean spectrum, averaged over the spectral content of signals in each group, was compared. Results: Melatonin induced uterine myoelectrical activity in a dose-dependent manner. Treatment of melatonin after oxytocin suppressed the mean power of the signals. Serum melatonin concentrations were significantly higher in melatonin-treated rats. Conclusion: Melatonin itself at two different dose levels was found to be equally effective in stimulating the uterine electrical signals, although oxytocin-induced uterine electrical activity was suppressed by melatonin. These findings merit further investigations on the possible beneficial role of melatonin in the treatment of conditions associated with abnormal uterine activity.Öğe Effect of receiver shape and volume on the Alzheimer disease for molecular communication via diffusion(Inst Engineering Technology-Iet, 2020) Isik, Ibrahim; Yilmaz, H. Birkan; Demirkol, Ilker; Tagluk, Mehmet EminNano-devices are featured to communicate via molecular interaction, the so-called molecular communication (MC). In MC systems, the information is carried by molecules where the amount of molecules constitutes the level of the signal. In this study, an MC-based system was analysed with different receiver topology and related parameters, such as size, shape, and orientation of receptors on the receiver. Also in the concept of nano-medicine, the effect of amyloid-beta (A(beta)), which is believed as the main cause of Alzheimer disease, on the successful reception ratio of molecules with the proposed receiver models was investigated. It was demonstrated that the cubic receiver model is superior to sphere one in terms of the correct reception ratio of the molecular signal. A cubic model where its edge (not rotated around the centre) is placed across the transmitter demonstrated a better performance in reducing the effect of A(beta) as compared to the sphere model while a cubic model where its corner (rotated around the centre) is placed across the transmitter demonstrated a worse performance than the spherical model. From this expression, it may be concluded that with the adjustment of topological system parameters the probability of successful reception ratio in MC may be possible.Öğe Effects of electromagnetic radiation from 3G mobile phone on heart rate, blood pressure and ECG parameters in rats(Sage Publications Inc, 2012) Colak, Cengiz; Parlakpinar, Hakan; Ermis, Necip; Tagluk, Mehmet Emin; Colak, Cemil; Sarihan, Ediz; Dilek, Omer FarukEffects of electromagnetic energy radiated from mobile phones (MPs) on heart is one of the research interests. The current study was designed to investigate the effects of electromagnetic radiation (EMR) from third-generation (3G) MP on the heart rate (HR), blood pressure (BP) and ECG parameters and also to investigate whether exogenous melatonin can exert any protective effect on these parameters. In this study 36 rats were randomized and evenly categorized into 4 groups: group 1 (3G-EMR exposed); group 2 (3G-EMR exposed + melatonin); group 3 (control) and group 4 (control + melatonin). The rats in groups 1 and 2 were exposed to 3G-specific MP's EMR for 20 days (40 min/day; 20 min active (speech position) and 20 min passive (listening position)). Group 2 was also administered with melatonin for 20 days (5 mg/kg daily during the experimental period). ECG signals were recorded from cannulated carotid artery both before and after the experiment, and BP and HR were calculated on 1st, 3rd and 5th min of recordings. ECG signals were processed and statistically evaluated. In our experience, the obtained results did not show significant differences in the BP, HR and ECG parameters among the groups both before and after the experiment. Melatonin, also, did not exhibit any additional effects, neither beneficial nor hazardous, on the heart hemodynamics of rats. Therefore, the strategy (noncontact) of using a 3G MP could be the reason for ineffectiveness; and use of 3G MP, in this perspective, seems to be safer compared to the ones used in close contact with the head. However, further study is needed for standardization of such an assumption.Öğe Effects of Small-World Rewiring Probability and Noisy Synaptic Conductivity on Slow Waves: Cortical Network(Mit Press, 2017) Tekin, Ramazan; Tagluk, Mehmet EminPhysiological rhythms play a critical role in the functional development of living beings. Many biological functions are executed with an interaction of rhythms produced by internal characteristics of scores of cells. While synchronized oscillations may be associated with normal brain functions, anomalies in these oscillations may cause or relate the emergence of some neurological or neuropsychological pathologies. This study was designed to investigate the effects of topological structure and synaptic conductivity noise on the spatial synchronization and temporal rhythmicity of the waves generated by cells in the network. Because of holding the ability of clustering and randomizing with change of parameters, small-world (SW) network topology was chosen. The oscillatory activity of network was tried out by manipulating an insulated SW, cortical network model whose morphology is very close to real world. According to the obtained results, it was observed that at the optimal probabilistic rates of conductivity noise and rewiring of SW, powerful synchronized oscillatory small waves are generated in relation to the internal dynamics of cells, which are in line with the network's input. These two parameters were observed to be quite effective on the excitation-inhibition balance of the network. Accordingly, it may be suggested that the topological dynamics of SW and noisy synaptic conductivity may be associated with the normal and abnormal development of neurobiological structure.Öğe Estimation of Short-Term Power Load of a Small House by Generalized Behavioural Learning Method(Ieee, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminPower load estimation, especially short-term power load estimation, plays an important role in the management of a power system in terms of system security and electricity costs. Therefore, estimation of short-term power load accurately is a popular research issue. In this paper, the generalized behavioral learning method (GBLM), a method developed based on human's behavioral learning theories, was employed to estimate short-term power load. The datasets that belong to houses B and C were employed in the estimation process. Achieved results by GBLM and extreme learning machine (ELM) ELM were compared. It is showed that GBLM estimates short-term power load with a higher success rate than ELM.Öğe A fast feature selection approach based on extreme learning machine and coefficient of variation(Tubıtak scıentıfıc & technıcal research councıl turkey, ataturk bulvarı no 221, kavaklıdere, ankara, 00000, turkey, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminFeature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.Öğe Fault location determination for transmission lines with different series-compensation levels using transient frequencies(Tubıtak scıentıfıc & technıcal research councıl turkey, ataturk bulvarı no 221, kavaklıdere, ankara, 00000, turkey, 2017) Akmaz, Duzgun; Mamis, Mehmet Salih; Arkan, Muslum; Tagluk, Mehmet EminIn this paper, based on the theory of traveling waves, the fault distances on long transmission lines with various series-compensation levels are determined using transient current and voltage frequencies. Transmission lines with series compensation are modeled using Alternative Transients Program software with frequency-dependent effects on the line included in the simulation. The transient current and voltage signals are obtained from the model. A fast Fourier transform is used for frequency-domain conversion and fault location is estimated from the frequencies of fault-generated harmonics in the transient spectrum. The algorithm is implemented in MATLAB. To investigate the effect of compensation on accuracy, the results are obtained for different series-compensation levels. The undesirable source-inductance effect is removed and estimation accuracy is further improved using a waveform-relaxation method. The method is found to be successful in determining fault location on series-compensated transmission lines. The effects of the compensation level, fault resistance, and phase angle are investigated.Öğe Forecasting financial indicators by generalized behavioral learning method(Springer, 2018) Ertugrul, Omer Faruk; Tagluk, Mehmet EminForecasting financial indicators (indexes/prices) is a complex and a quite difficult issue because they depend on many factors such as political events, financial ratios, and economic variables. Also, the psychological facts or decision-making styles of investors or experts are other major reasons for this difficulty. In this study, a generalized behavioral learning method (GBLM) was employed to forecast financial indicators, which are the indexes/prices of 34 different financial indicators (24 stock indexes, 2 forexes, 3 financial futures, and 5 commodities). The achieved results were compared with the reported results in the literature and the obtained results by artificial neural network, which is widely used and suggested for forecasting financial indicators. These results showed that GBLM can be successfully employed in short-term forecasting financial indicators by detecting hidden market behavior (pattern) from their previous values. Also, the results showed that GBLM has the ability to track the fluctuation and the main trend.Öğe Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method(Springer Heidelberg, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminDetermining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL.Öğe Interference and molecule reception probability analysis in nano/micro scale communication systems using Fick's diffusion law(Gazi Univ, Fac Engineering Architecture, 2022) Isik, Ibrahim; Tagluk, Mehmet Emin; Isik, EsmeRecently, too much afford has been conducted toward development of novel communication techniques (biological inspired) for implementing in nano and micro scale systems inspired from electro-chemical communication systems that naturally used by living beings. One of these techniques is known as nano/micro scale communication (NMSC) in which chemical signals are used as carriers for transmission of information through fluid media. The information carrier particles used in such communication systems consist of biological components such as DNA and protein components. Studies regarding NMSC are considered to highly contribute to the developments in the field of nano-technology which can be used to detect and treatment of the some unsolved illness yet. Therefore, in this study, software based a new NMSC model that could potentially be used in nano-scale systems were developed and analysed in terms of communication performance. Firstly, Diffusion constant which affect the communication performance of the software based NMSC model is derived using some Physics laws such as Fick's. Secondly, different forms of receivers such as sphere, cube and rectangular prism topologies have been tried for increasing the rate of molecule reception and reducing the inter symbol interference of the receiver. It was observed that the signal transmission rate increased and the interference decreased with the use of a cube receiver model. The results obtained from the proposed NMSC model encourages one to think that such receiver models might have the potential for Alzheimer and many illness which cause missing and/or wrong communication of the cells.Öğe A joint generalized exemplar method for classification of massive datasets(Elsevier Science Bv, 2015) Tagluk, Mehmet Emin; Ertugrul, Omer FarukDue to technological improvements, the number and volume of datasets are considerably increasing and bring about the need for additional memory and computational complexity. To work with massive datasets in an efficient way; feature selection, data reduction, rule based and exemplar based methods have been introduced. This study presents a method, which may be called joint generalized exemplar (JGE), for classification of massive data sets. This method aims to enhance the computational performance of NGE by working against nesting and overlapping of hyper-rectangles with reassessing the overlapping parts with the same procedure repeatedly and joining non-overlapped hyper-rectangle sections that falling within the same class. This provides an opportunity to have adaptive decision boundaries, and also employing batch data searching instead of incremental searching. Later, the classification was done in accordance with the distance between each particular query and generalized exemplars. The accuracy and time requirements for classification of synthetic datasets and a benchmark dataset obtained by JGE, NGE and other popular machine learning methods were compared and the achieved results by JGE found acceptable. (C) 2015 Elsevier B.V. All rights reserved.Öğe LEARNING WITH CLASSICAL CONDITIONING(Ieee, 2014) Ertugrul, Omer Faruk; Tagluk, Mehmet EminBehavioral learning theory evaluates human's learning process in terms of observable stimulus and responses. One of the behavioral learning methods is the classical conditioning. The classical conditioning theory proposed by Pavlov concerns the analyses of conditioning a response with a neutral stimulus, inspiring from the relation between natural stimulus and response. In this study the classical conditioning theory is modeled in real-time. The viability of the proposed method to basic principles of classical conditioning, based on stimulus-response relations was achieved and compared to the available computational methods.Öğe A novel machine learning method based on generalized behavioral learning theory(Springer, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminLearning is an important talent for understanding the nature and accordingly controlling behavioral characteristics. Behavioral learning theories are one of the popular learning theories which are built on experimental findings. These theories are widely applied in psychotherapy, psychology, neurology as well as in advertisements and robotics. There is an abundant literature associated with understanding learning mechanism, and various models have been proposed for the realization of learning theories. Nevertheless, none of those models are able to satisfactorily simulate the concept of classical conditioning. In this study, popular behavioral learning theories were firstly simplified and the contentious issues with them were clarified by conducting intuitive experiments. The experimental results and information available in the literature were evaluated, and behavioral learning theories were jointly generalized accordingly. The proposed model, to our knowledge, is the first one that possesses not only modeling all features of classical conditioning but also including all features with behavioral theories such as Pavlov, Watson, Guthrie, Thorndike and Skinner. Also, a microcontroller card (Arduino Mega 2560) was used to validate the applicability of the proposed model in robotics. Obtained results showed that this generalized model has a high capacity for modeling human learning. Then, the proposed learning model was further improved to be utilized as a machine learning method that can continuously learn similar to human being. The result obtained from the use of this method, in terms of computational cost and accuracy, showed that the proposed method can be successfully employed in machine learning, especially for time ordered datasets.Öğe A novel version of k nearest neighbor: Dependent nearest neighbor(Elsevier, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet Emink nearest neighbor (kNN) is one of the basic processes behind various machine learning methods In kNN, the relation of a query to a neighboring sample is basically measured by a similarity metric, such as Euclidean distance. This process starts with mapping the training dataset onto a one-dimensional distance space based on the calculated similarities, and then labeling the query in accordance with the most dominant or mean of the labels of the k nearest neighbors, in classification or regression issues, respectively. The number of nearest neighbors (k) is chosen according to the desired limit of success. Nonetheless, two distinct samples may have equal distances to query but, with different angles in the feature space. The similarity of the query to these two samples needs to be weighted in accordance with the angle going between the query and each of the samples to differentiate between the two distances in reference to angular information. This opinion can be analyzed in the context of dependency and can be utilized to increase the precision of classifier. With this point of view, instead of kNN, the query is labeled according to its nearest dependent neighbors that are determined by a joint function, which is built on the similarity and the dependency. This method, therefore, may be called dependent NN (d-NN). To demonstrate d-NN, it is applied to synthetic datasets, which have different statistical distributions, and 4 benchmark datasets, which are Pima Indian, Hepatitis, approximate Sinc and CASP datasets. Results showed the superiority of d-NN in terms of accuracy and computation cost as compared to other employed popular machine learning methods. (C) 2017 Elsevier B.V. All rights reserved.
