Altan, Aytaç | Hacıoğlu, Rıfat

Article | 2018 | JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI21 ( 3 ) , pp.559 - 564

The 3D printers widely used in the world are produced in different mechanical and electronic designs. The 3D printers which have various mechanical structures such as cartesian, delta and core (xy, xz) already are used open source code software such as Sprinter, Marlin, Cura 3D and Teacup. The control of the 3D printers is usually done by the classical Propotional-Integral-Derivative (PID) control algorithm. In this study, we have developed for the designed 3D printer a new software by using adaptive PID control algorithm instead of classical PID. Five step motors of the designed 3D printer are controlled by the adaptive PID. In add . . .ition, there are both heating and cooling processes in the extruder system and these processes are controlled by the adaptive PID. The mechanical design uses a belt and pulley drive system which is suitable for accelerated movements. In the system software, 3D Printing Software Pipeline (input model, orientation and positioning, support structures, slicing, path planning, machine instructions) is applied. The control algorithms for extruder and step motors are prepared as separate function files in software implemented in C. It has been observed that the designed software is particularly successful in eliminating errors on the surface of the products Daha fazlası Daha az

Erkaymaz, Okan | Özer, Mahmut | Perc, Matjaž

Article | 2017 | Applied Mathematics and Computation311 , pp.22 - 28

We investigate the performance of two different small-world feedforward neural networks for the diagnosis of diabetes. We use the Pima Indians Diabetic Dataset as input. We have previously shown than the Watts–Strogatz small-world feedforward neural network delivers a better classification performance than conventional feedforward neural networks. Here, we compare this performance further with the one delivered by the Newman–Watts small-world feedforward neural network, and we show that the latter is better still. Moreover, we show that Newman–Watts small-world feedforward neural networks yield the highest output correlation as well . . . as the best output error parameters. © 2017 Elsevier Inc Daha fazlası Daha az

Erkaymaz, Hande | Özer, Mahmut | Orak, İlhami Muharrem

Article | 2015 | Chaos, Solitons and Fractals77 , pp.225 - 229

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The res . . .ults suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately. © 2015 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Erkaymaz, Okan | Özer, Mahmut

Article | 2016 | Chaos, Solitons and Fractals83 , pp.178 - 185

Artificial intelligent systems have been widely used for diagnosis of diseases. Due to their importance, new approaches are attempted consistently to increase the performance of these systems. In this study, we introduce a new approach for diagnosis of diabetes based on the Small-World Feed Forward Artificial Neural Network (SW- FFANN). We construct the small-world network by following the Watts-Strogatz approach, and use this architecture for classifying the diabetes, and compare its performance with that of the regular or the conventional FFANN. We show that the classification performance of the SW-FFANN is better than that of the . . . conventional FFANN. The SW-FFANN approach also results in both the highest output correlation and the best output error parameters. We also perform the accuracy analysis and show that SW-FFANN approach exhibits the highest classifier performance. © 2015 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Özer, Mahmut | İşler, Yalçın | Özer, Halil

Article | 2004 | Computer Methods and Programs in Biomedicine75 ( 1 ) , pp.51 - 57

In this paper, a new computer software package, Yalzer, is introduced for simulating single-compartmental model of neurons. Passive or excitable membranes with voltage-gated ion channels can be modeled, and current clamp and voltage clamp experiments can be simulated. In the Yalzer, first-order differential equations used to define the dynamics of the gate variables and the membrane potential are solved by two separate integration methods with variable time steps: forward Euler and exponential Euler methods. Outputs of the simulation are shown on a spreadsheet template for allowing flexible data manipulation and can be graphically d . . .isplayed. The user can define the model in detail, and examine the excitability of the model and the dynamics of voltage-gated ion channels. The software package addresses to ones who want to run simple simulations of neurons without need to any programming language skills or expensive software. It can also be used for educational purposes. © 2003 Elsevier Ireland Ltd. All rights reserved Daha fazlası Daha az

Uzuntarla, Muhammet | Yılmaz, Ergin | Wagemakers, Alexandre | Özer, Mahmut

Article | 2015 | Communications in Nonlinear Science and Numerical Simulation22 ( 01.Mar ) , pp.367 - 374

Vibrational resonance (VR) is a phenomenon whereby the response of some dynamical systems to a weak low-frequency signal can be maximized with the assistance of an optimal intensity of another high-frequency signal. In this paper, we study the VR in a heterogeneous neural system having a complex network topology. We consider a scale-free network of neurons where the heterogeneity is in the intrinsic excitability of the individual neurons. It is shown that emergence of VR in heterogeneous neuron population requires less energy than a homogeneous population. We also find that electrical coupling strength among neurons plays a key role . . . in determining the weak signal processing capacity of the heterogeneous population. Lastly, we investigate the influence of interneuronal link density on the VR and demonstrate that the energy needed to obtain the resonance grows with the increase in average degree. © 2014 Elsevier B.V Daha fazlası Daha az

Özer, Mahmut | Erdem, Rıza

Article | 2004 | Physica A: Statistical Mechanics and its Applications331 ( 01.Feb ) , pp.51 - 60

Dynamics of voltage-gated ion channels in the excitable cell membranes is formulated by the path probability method of nonequilibrium statistical physics and approaches of the system toward the steady or equilibrium states are presented. For a single-particle noninteractive two-state model, a first-order rate equation or dynamic equation is derived by introducing the path probability rate coefficients which satisfy the detailed balancing relation. Using known parameters for the batrachotoxin (BTX)-modified sodium channels in giand squid axon as an example, the rate equation is solved and voltage dependence of the time constant (?) a . . .nd its temperature effect are investigated. An increase in voltage caused a shift in ? towards shorter durations while increasing temperature caused a shift in time distribution towards longer durations. Results are compared with the kinetic model for the squid axon BTX-modified sodium channels by the cut-open axon technique and a very good agreement is found. © 2003 Elsevier B.V. All rights reserved Daha fazlası Daha az

Yılmaz, Ergin | Özer, Mahmut

Article | 2015 | Physica A: Statistical Mechanics and its Applications421 , pp.455 - 462

We study the effect of the delayed feedback loop on the weak periodic signal detection performance of a stochastic Hodgkin-Huxley neuron. We consider an electrical autapse characterized by its coupling strength and delay time. The stochastic Hodgkin-Huxley neuron exhibits subthreshold oscillations, and thus has an intrinsic time scale with the subthreshold oscillations. Therefore, we investigate the interplay of the subthreshold oscillations, coupling strength and delay time on the weak periodic signal detection. Results indicate that the delayed feedback either enhances or suppresses the weak signal detection depending on its param . . .eters, when compared to that without the feedback. The delayed feedback augments the weak periodic signal detection for the optimal values of the intrinsic noise and the coupling strength when the delay time is close to the integer multiples of the period of the intrinsic oscillations, due to the multiple resonance among the weak signal, the intrinsic oscillations, and the delayed feedback. We analyze the interspike interval histograms and show that the delayed feedback enhances or suppresses the weak periodic signal detection by increasing or decreasing the phase locking (synchronization) between the spiking and the weak periodic signal. We also show that an optimal phase locking is obtained when the delay time is close to the period of the intrinsic oscillations, leading a single dominant time scale in the spike trains. © 2014 Elsevier B.V. All rights reserved Daha fazlası Daha az

Narin, Ali | İşler, Yalçın | Özer, Mahmut | Perc, Matjaž

Article | 2018 | Physica A: Statistical Mechanics and its Applications509 , pp.56 - 65

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-mi . . .nute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events. © 2018 Elsevier B.V Daha fazlası Daha az

Özer, Mahmut | Ekmekçi, N. Hakan

Article | 2005 | Physics Letters, Section A: General, Atomic and Solid State Physics338 ( 2 ) , pp.150 - 154

Ion channel noise that stems from the stochastic nature of the channel has important effects on neuronal dynamics. In this context, we examine the effect of the channel noise on the time-course of recovery from inactivation of sodium channels by using a stochastic extension of the Hodgkin-Huxley model. We show that the channel noise provides both some amount of the non-inactivated channels and a smaller time-course for recovering from inactivation leading to an increased maximal sodium conductance compared to deterministic one. © 2005 Elsevier B.V. All rights reserved.

Özer, Mahmut | Perc, Matjaž | Uzuntarla, Muhammet

Article | 2009 | Physics Letters, Section A: General, Atomic and Solid State Physics373 ( 10 ) , pp.964 - 968

We study the phenomenon of stochastic resonance on Newman-Watts small-world networks consisting of biophysically realistic Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise via voltage-gated ion channels embedded in neuronal membranes. Importantly thereby, the subthreshold periodic driving is introduced to a single neuron of the network, thus acting as a pacemaker trying to impose its rhythm on the whole ensemble. We show that there exists an optimal intensity of intrinsic ion channel noise by which the outreach of the pacemaker extends optimally across the whole network. This stochastic resonance phenomenon can be . . .further amplified via fine-tuning of the small-world network structure, and depends significantly also on the coupling strength among neurons and the driving frequency of the pacemaker. In particular, we demonstrate that the noise-induced transmission of weak localized rhythmic activity peaks when the pacemaker frequency matches the intrinsic frequency of subthreshold oscillations. The implications of our findings for weak signal detection and information propagation across neural networks are discussed. © 2009 Elsevier B.V. All rights reserved Daha fazlası Daha az

Yılmaz, Ergin | Özer, Mahmut

Article | 2013 | Physics Letters, Section A: General, Atomic and Solid State Physics377 ( 18 ) , pp.1301 - 1307

We consider a scale-free network of stochastic HH neurons driven by a subthreshold periodic stimulus and investigate how the collective spiking regularity or the collective temporal coherence changes with the stimulus frequency, the intrinsic noise (or the cell size), the network average degree and the coupling strength. We show that the best temporal coherence is obtained for a certain level of the intrinsic noise when the frequencies of the external stimulus and the subthreshold oscillations of the network elements match. We also find that the collective regularity exhibits a resonance-like behavior depending on both the coupling . . .strength and the network average degree at the optimal values of the stimulus frequency and the cell size, indicating that the best temporal coherence also requires an optimal coupling strength and an optimal average degree of the connectivity. © 2013 Elsevier B.V Daha fazlası Daha az