Researcher:
Kılınç, Deniz

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PhD Student

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Deniz

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Kılınç

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Kılınç, Deniz

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2013 - 201811

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Researcher Of Publications: search.filters.isAuthorOfPublication.e1847d01-902c-44a6-b5e1-225ff497565a

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Now showing 1 - 10 of 11
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    A traffic congestion avoidance algorithm with dynamic road pricing for smart cities
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Soylemezgiller, Fahri; N/A; N/A; Kuşcu, Murat; Kılınç, Deniz; Master Student; PhD Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 316349; N/A
    The traffic congestion problem is a common issue for the residents of metropolises. Although expanding the capacity of transportation systems and stimulating the public transportation may decrease the traffic congestion, they cannot completely solve the traffic congestion problem. As a solution for the worsening traffic congestion problem in urban areas, road pricing systems have been employed. In this paper, we propose a radically different road pricing scheme to prevent and decrease the traffic congestion in metropolises. Unlike designating a small congestion charge zone in a city, we propose to employ a road pricing system over the entire city. Thus, our road pricing system can control the traffic flow in the entire traffic network of the city. Furthermore, the road prices are adjusted dynamically based on the instantaneous traffic densities of each road in the city in order to rapidly and efficiently control the traffic flow and to prevent the traffic congestion. Moreover, we propose to change the road prices according to the past usage statistics of the road by predicting a possible congestion. The simulation results of our road pricing algorithm show that traffic congestion is prevented over the entire traffic network and the traffic densities of the roads are homogenized.
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    Spike timing precision of neuronal circuits
    (Springer, 2018) N/A; N/A; Department of Electrical and Electronics Engineering; Kılınç, Deniz; Demir, Alper; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 3756
    Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.
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    On the maximum coverage area of wireless networked control systems under stability and cost-efficiency constraints
    (IEEE, 2013) N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Kılınç, Deniz; Özger, Mustafa; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647
    The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this paper, we consider a WNCS where the output sensor measurements are transmitted over separate multi-hop wireless ad-hoc subnetworks. The system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the expected state estimation error covariance is bounded, and hence the WNCS is stable. We find the optimum hop-diameter of a multi-hop wireless ad-hoc subnetwork under the constraints of both the stability of the WNCS and the cost-efficiency of the multi-hop wireless network. Furthermore, under these constraints, we derive the maximum total coverage area of the wireless subnetworks. The numerical analyses show that the maximum total coverage area can be increased by appropriately adjusting the number of sensors, the successful packet transmission probability between relay nodes, and the eigenvalues of the system matrix.
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    Noise in neuronal and electronic circuits: a general modeling framework and non-monte carlo simulation techniques
    (Ieee-Inst Electrical Electronics Engineers Inc, 2017) N/A; N/A; Department of Electrical and Electronics Engineering; Kılınç, Deniz; Demir, Alper; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 3756
    The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.
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    Simulation of noise in neurons and neuronal circuits
    (Institute of Electrical and Electronics Engineers (IEEE), 2016) N/A; N/A; Department of Electrical and Electronics Engineering; Kılınç, Deniz; Demir, Alper; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 3756
    Stochastic behavior of ion channels, neurotransmitter release mechanisms and synaptic connections in neurons emerge as a source of variability and noise in neuronal circuits, causing uncertainty in the computations performed by the brain. One can gain insight into this important aspect of brain mechanism via computational modeling. Stochastic behavior in neurons is usually modeled with fine-grained, discrete-state, continuous-time Markov Chains (MCs). Although these models are considered as the golden standard, they become computationally prohibitive in analyzing multi-neuron circuits. Thus, several approximate models, where the random behavior is captured by coarse-grained, continuous-state, continuous-time Stochastic Differential Equations (SDEs), were proposed. In this paper, we first present a general, fine-grained modeling framework based on MC models of ion channels and synaptic processes. We then develop a formalism for automatically generating the corresponding SDE models, based on representing generic/abstract MCs as a set of chemical reactions and by utilizing techniques from stochastic chemical kinetics. With this formalism, we can exploit the sparsity and special structure in the MC models and arrive at compact SDE models. We present results obtained by our neuronal circuit simulator based on the proposed methodology in analyzing stochasticity in neurons and neuronal circuits. We employ numerical simulation techniques that were previously developed for noise in electronic circuits. We point to the use of non Monte Carlo noise analysis techniques for large-scale analysis of noise in the nervous system.
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    PublicationOpen Access
    On the maximum coverage area of wireless networked control systems with maximum cost-efficiency under convergence constraint
    (Institute of Electrical and Electronics Engineers (IEEE), 2015) Kılınç, Deniz; Özger, Mustafa; Akan, Özgür Barış; PhD Student; College of Engineering
    The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this technical note, we consider a WNCS where the output sensor measurements are transmitted over separate heterogeneous multi-hop wireless ad-hoc subnetworks. The observation process is divided into N parts and the system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the estimator of the WNCS converges. We derive the maximum total coverage area of the heterogeneous wireless subnetworks having maximum cost-efficiency under the constraint of the convergence of the WNCS estimator.
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    PublicationOpen Access
    A theoretical modeling and analysis communication via heat flow at nanoscale
    (Institute of Electrical and Electronics Engineers (IEEE), 2014) Kılınç, Deniz; Akan, Özgür Barış; College of Engineering
    Nanonetworks constructed by interconnecting nanodevices using wireless communication allow the nanodevices to perform more complex functions by means of cooperation between them. For the first time in the literature, a novel and physically realizable nanoscale communication technique is introduced: Nanoscale Heat Communication (NHC) in which the heat transfer is used for communication at the nanoscale. The transmitted information is encoded in temperature signals using Magneto-Caloric Effect (MCE) which is the change in temperature of a magnetic material exposed to a varying magnetic field. Thermal energy emitted or absorbed by a transmitter nanodevice is subject to the laws of thermal diffusion which changes the temperature of the communication medium. The transmitted information is decoded by a receiver nanodevice that senses the temperature variations. Using information theoretical analysis, a closed-form expression for the channel capacity is obtained. According to the performance evaluation of the channel capacity, NHC provides a significantly higher capacity communication compared with the existing molecular communication techniques. Therefore, NHC stands as a promising solution to nanoscale communication between nanomachines based on its channel capacity performance, advantages, and possible applications for the emerging field of nanonetworks.
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    PublicationOpen Access
    An information theoretical analysis of nanoscale molecular gap junction communication channel between cardiomyocytes
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Kılınç, Deniz; Akan, Özgür Barış; College of Engineering
    Molecular communication (MC) is a promising paradigm to communicate at nanoscale and it is inspired by nature. One of the MC methods in nature is the gap junction (GJ) communication between cardiomyocytes. The GJ communication is achieved by diffusion of ions through GJ channels between the cells. The transmission of the information is realized by means of the propagation of the action potential (AP) signal. The probabilities of both the AP propagation failure and the spontaneous AP initiation are obtained. For the first time in the literature, the GJ communication channel is modeled and analyzed from the information theoretical perspective to find the communication channel capacity. A closed-form expression is derived for the capacity of the GJ communication channel. The channel capacity, propagation delay, and information transmission rate are analyzed numerically for a three-cell network. The results of the numerical analyses point out a correlation between an increase in the incidence of several cardiac diseases and a decrease in the channel capacity, an increase in the propagation delay, and either an increase or a decrease in the transmission rate. The method that we use and results that are presented may help in the investigation, diagnosis, and treatment of cardiac diseases as well as help in the design of nanodevices communicating via GJ channels.
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    PublicationOpen Access
    Nanoscale magneto-inductive communication
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Kılınç, Deniz; Akan, Özgür Barış; Faculty Member; College of Engineering
    The nanonetworks constructed by interconnecting nanodevices using wireless communication allow nanodevices to perform more complex functions by means of cooperation between them. For the first time in the literature, we introduce a novel nanoscale communication technique: Nanoscale Magneto-Inductive (NMI) communication. The magnetic coupling between nanocoils establishes a communication channel between them. The electromagnetic (EM) waves at nanoscale encounter two problems: high molecular absorption rates and frequency selective channel characteristics. The novel NMI communication solves these problems by introducing low absorption losses and flat channel characteristics. In the paper, we first present the physical model of the point-to-point NMI communication. Then, we introduce the waveguide technique for the NMI communication. To assess the performance of the point-to-point and the waveguide NMI communication methods, we derive path loss expressions for both methods. The results show that using waveguide technique in the NMI communication significantly reduces the path loss and increases feasible communication range. Based on the numerical performance evaluation, the NMI communication stands as a promising solution to nanoscale communication between nanodevices.
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    PublicationOpen Access
    On the maximum coverage area of wireless networked control systems under stability and cost-efficiency constraints
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Kılınç, Deniz; Özger, Mustafa; Akan, Özgür Barış; PhD Student; College of Engineering
    The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this paper, we consider a WNCS where the output sensor measurements are transmitted over separate multi-hop wireless ad-hoc subnetworks. The system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the expected state estimation error covariance is bounded, and hence the WNCS is stable. We find the optimum hop-diameter of a multi-hop wireless ad-hoc subnetwork under the constraints of both the stability of the WNCS and the cost-efficiency of the multi-hop wireless network. Furthermore, under these constraints, we derive the maximum total coverage area of the wireless subnetworks. The numerical analyses show that the maximum total coverage area can be increased by appropriately adjusting the number of sensors, the successful packet transmission probability between relay nodes, and the eigenvalues of the system matrix.