Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/45

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Citation - WoS: 15
Citation - Scopus: 15
Rapidly Time-Varying Channel Estimation for Full-Duplex Amplify-And One-Way Relay Networks
(IEEE-INST Electrical Electronics Engineers Inc, 2018) Şenol, Habib; Li, Xiaofeng; Tepedelenlioglu, Cihan
Estimation of both cascaded and residual self-interference (RSI) channels and a new training frame structure are considered for full-duplex (FD) amplify-and-forward (AF) one-way relay networks with rapidly time-varying individual channels. To estimate the RSI and the rapidly time-varying cascaded channels we propose a new training frame structure in which orthogonal training blocks are sent by the source node and delivered to the destination over an FD-AF relay. Exploiting the orthogonality of the training blocks we obtain two decoupled training signal models for the estimation of the RSI and the cascaded channels. We apply linear minimum mean square error (MMSE) based estimators to the cascaded channel as well as RSI channel. In order to investigate the mean square error (MSE) performance of the system we also derive the Bayesian Cramer-Rao lower bound. As another performance benchmark we also assess the symbol error rate (SER) performances corresponding to the estimated and the perfect channel state information available at the receiver side. Computer simulations exhibit the proposed training frame structure and the linear MMSE estimator MSE and SER performances are shown.
Optimizing Neuron Simulation Environment Using Remote Memory Access With Recursive Doubling on Distributed Memory Systems
(Hindawi Ltd, 2016) Shehzad, Danish; Bozkuş, Zeki
Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.
Citation - WoS: 2
Nonuniform Sampling for Detection of Abrupt Changes
(Birkhauser Boston Inc, 2003) Kerestecioğlu, Feza; Tokat, Sezai
In this work detection of abrupt changes in continuous-time linear stochastic systems and selection of the sampling interval to improve the detection performance are considered. Cost functions are proposed to optimize both uniform and nonuniform sampling intervals for the well-known cumulative sum algorithm. Some iterative techniques are presented to make online optimization computationally feasible. It is shown that considerable improvement in the detection performance can be obtained by using nonuniform sampling intervals.
Citation - WoS: 13
Citation - Scopus: 21
Improving Energy-Efficiency of Wsns Through Lefca
(Sage Publications Inc, 2016) Cengiz, Korhan; Dağ, Tamer
Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence the life span and the energy consumption of a WSN have become a challenging research area. According to the existing studies instead of using direct transmission or multihop routing clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. In this paper we propose a low energy fixed clustering algorithm (LEFCA) for WSNs. With LEFCA the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation significantly.
Citation - Scopus: 3
First Impressions on Social Network Sites: Impact of Self-Disclosure Breadth on Attraction
(Academic Conferences and Publishing International Limited, 2017) Baruh, Lemi; Cemacılar, Zeynep; Bisson, Christophe; Chisik, Yoram I.
This paper reports the results of two experiments that investigate the relationship between the quantity of information disclosed on an SNS profile and profile viewers' first impressions of the profile owner. Both experiments utilized a 2 (low quantity of information vs. high quantity of information) by 2 (male vs. female profile) design. In the first experiment (n = 1059), the respondents were randomly assigned to the experimental conditions. The results showed that profile viewers were more favorable to profiles of women. Also, both for female and male SNS profiles, higher quantity of information led to more positive ratings of the profile owner. The second experiment expanded the findings from the first experiment in two ways. First, in the second experiment (n = 320), rather than being randomly assigned to the profile gender condition, the respondents could pick the gender of the profile they would review. Second, informed by previous research on face to face interactions which indicate that quantity of self-disclosure can increase interpersonal attraction by reducing the level of uncertainty about relational outcomes, we tested whether uncertainty reduction mediated the relationship between quantity of information presented in an SNS profile and interpersonal attraction. Female profiles were selected more often than male profiles by both female and male respondents; however, there was no difference in interpersonal attraction ratings that male and female profiles received. Higher quantity of information presented in an SNS profile had a significant impact on interpersonal attraction. The results from the second experiment also indicated that while quantity of information positively influenced profile viewers' perceptions regarding the agreeableness of the profile owner, it did not have an impact on viewers' perceptions regarding the dependability of the profile owner. As predicted, the impact of quantity of information on interpersonal attraction was mediated by a reduction in uncertainty levels.
Citation - WoS: 96
Citation - Scopus: 111
Spinal: Scalable Protein Interaction Network Alignment
(Oxford University Press, 2013) Aladağ, Ahmet Emre; Erten, Cesim
Motivation: Given protein-protein interaction (PPI) networks of a pair of species a pairwise global alignment corresponds to a one-to-one mapping between their proteins. Based on the presupposition that such a mapping provides pairs of functionally orthologous proteins accurately the results of the alignment may then be used in comparative systems biology problems such as function prediction/verification or construction of evolutionary relationships. Results: We show that the problem is NP-hard even for the case where the pair of networks are simply paths. We next provide a polynomial time heuristic algorithm SPINAL which consists of two main phases. In the first coarse-grained alignment phase we construct all pairwise initial similarity scores based on pairwise local neighborhood matchings. Using the produced similarity scores the fine-grained alignment phase produces the final one-to-one mapping by iteratively growing a locally improved solution subset. Both phases make use of the construction of neighborhood bipartite graphs and the contributors as a common primitive. We assess the performance of our algorithm on the PPI networks of yeast fly human and worm. We show that based on the accuracy measures used in relevant work our method outperforms the state-of-the-art algorithms. Furthermore our algorithm does not suffer from scalability issues as such accurate results are achieved in reasonable running times as compared with the benchmark algorithms.
Citation - Scopus: 1
A Synthesis Tool for the Multiplierless Realization of Fir-Based Multirate Dsp Systems
(IEEE, 2000) Yurdakul, Arda
In this study a synthesis tool using a novel multirate folding technique which handles each FIR filter in a multirate DSP system as a single node is developed. A new architecture is presented for the multiplierless realization of a fold of multirate FIR filters. This synthesizer fully exploits the redundancies (i.e. `idle' and `missing' cycles) and common terms in multirate systems without sacrificing from overall system quality to produce multiplierless multirate systems. It also enables the usage of a single clock for all parts of the circuit.
Citation - WoS: 2
Citation - Scopus: 8
Navigation of Autonomous Mobile Robots in Connected Groups
(IEEE, 2008) Cezayirli, Ahmet; Kerestecioğlu, Feza
The navigation of autonomous mobile robots as a group is considered in this paper. Definitions adopted from the graph theory are given to characterize the robot group. A local steering strategy is proposed such that when each robot in the group applies this steering scheme the overall result is that the whole group is displaced without losing its connectivity. This is achieved using only limited-range position sensors and without any communication between the robots.
Non-Preemptive Priority Scheduler With Multiple Thresholds for Network Routers
(Pamukkale Univ, 2018) Dağ, Tamer
The vast variety of applications available and being developed for computer networks have different quality of service requirements. One of the most significant ways to satisfy the needs of the applications is the packet scheduling algorithms employed by the network routers. By allocating router resources to the applications packet schedulers try to improve the quality of service needs of the applications. Thus the delays can be reduced or the reliability of the applications can be increased by reducing packet losses. Priority schedulers are able reduce the delay and losses for high priority applications. On the other hand for low priority applications they introduce the starvation problem. Low priority application packets can face excessive delays and losses. In this paper a non-preemptive priority scheduler with multiple thresholds (PRMT) is proposed. The PRMT scheduler needs only a single queue with predefined threshold levels for different priority applications. The PRMT scheduler eliminates the starvation problem of low priority applications without a significant impact on the high priority applications.
Citation - WoS: 34
Citation - Scopus: 47
Performance of Distributed Estimation Over Unknown Parallel Fading Channels
(IEEE-INST Electrical Electronics Engineers Inc, 2008) Şenol, Habib; Tepedelenlioglu, Cihan
We consider distributed estimation of a source in additive Gaussian noise observed by sensors that are connected to a fusion center with unknown orthogonal (parallel) flat Rayleigh fading channels. We adopt a two-phase approach of i) channel estimation with training and ii) source estimation given the channel estimates and transmitted sensor observations where the total power is fixed. In the second phase we consider both an equal power scheduling among sensors and an optimized choice of powers. We also optimize the percentage of total power that should be allotted for training. We prove that 50% training is optimal for equal power scheduling and at least 50% is needed for optimized power scheduling. For both equal and optimized cases a power penalty of at least 6 dB is incurred compared to the perfect channel case to get the same mean squared error performance for the source estimator. However the diversity order is shown to be unchanged in the presence of channel estimation error. In addition we show that unlike the perfect channel case increasing the number of sensors will lead to an eventual degradation in performance. We approximate the optimum number of sensors as a function of the total power and noise statistics. Simulations corroborate our analytical findings.
Citation - WoS: 27
Citation - Scopus: 45
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy
(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.
Citation - WoS: 14
Force-Directed Approaches To Sensor Localization
(Association for Computing Machinery, 2010) Efrat, Alon; Forrester, David; Iyer, Anand; Kobourov, Stephen G.; Erten, Cesim; Kılış, Ozan
As the number of applications of sensor networks increases so does the interest in sensor network localization that is in recovering the correct position of each node in a network of sensors from partial connectivity information such as adjacency range or angle between neighboring nodes. In this article we consider the anchor-free localization problem in sensor networks that report possibly noisy range information and angular information about the relative order of each sensor's neighbors. Previously proposed techniques seem to successfully reconstruct the original positions of the nodes for relatively small networks with nodes distributed in simple regions. However these techniques do not scale well with network size and yield poor results with nonconvex or nonsimple underlying topology. Moreover the distributed nature of the problem makes some of the centralized techniques inapplicable in distributed settings. To address these problems we describe a multiscale dead-reckoning (MSDR) algorithm that scales well for large networks can reconstruct complex underlying topologies and is resilient to noise. The MSDR algorithm takes its roots from classic force-directed graph layout computation techniques. These techniques are augmented with a multiscale extension to handle the scalability issue and with a dead-reckoning extension to overcome the problems arising with nonsimple topologies. Furthermore we show that the distributed version of the MSDR algorithm performs as well as if not better than its centralized counterpart as shown by the quality of the layout measured in terms of the accuracy of the computed pairwise distances between sensors in the network.
Citation - WoS: 2
Power Control and Resource Allocation in Tdd-Ofdm Based Femtocell Networks With Interference
(IEEE, 2017) Altabbaa, Mhd Tahssin; Arsan, Taner; Panayırcı, Erdal
Femtocell technology is a promising solution for different dilemmas in cellular networks. In femtocell power control the interference experienced by the network is divided into two main tiers according to the type of network whose signal is interfering with another network. In utilizing the functionality of a two-tier network where femtocell technology is deployed a major challenge is in sharing the frequency resource of a macrocell. This paper proposes an enhanced dynamic algorithm bounded by two constraints to optimize the transmission powers of femtocell users in TDD-OFDM based femtocell networks taking into consideration rate enhancement of femtocell mobile stations. We compare our algorithm with the macrocell guard system which allows femtocells to occupy only the subchannels unoccupied by the macrocell.
Citation - WoS: 4
Performance Investigation of Ieee 802.11af Systems Under Realistic Channel Conditions
(IEEE, 2015) Macit, Mustafa Can; Şenol, Habib; Erküçük, Serhat
As the analog TV broadcasting channels have become less frequently used in the last decade there has been a great interest in these frequency bands for the deployment of metropolitan local and personal area networks. Among them the local area network standard IEEE 802.11af defines PHY and MAC layer implementation of such networks in these unused frequency bands also named television white space (TVWS). According to the standard the systems may use contiguous or non-contiguous channels during their operation depending on the channel availability. In this paper we investigate in detail the performance of different operation modes of these systems under realistic channel conditions. While the perfect knowledge of channel would result in similar system performances as the number of in-between-bands occupying the non-contiguous modes is increased the channel estimation performance degrades drastically which is quantified in this study. In addition it is shown that determining the true locations of multipaths heavily relies on the selected channel resolution and has a significant effect on the system performance. Numerical examples are given to demonstrate the effects of both the non-contiguous operation modes and the selected channel resolution.
Citation - WoS: 4
Citation - Scopus: 6
Channel Estimation for Realistic Indoor Optical Wireless Communication in Aco-Ofdm Systems
(Springer, 2018) Özmen, Atilla; Şenol, Habib
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 are produced using the indoor channel model. Symbol error rate (SER) performance of the system with estimated channels is presented for QPSK and 16-QAM digital modulation types and compared with the perfect channel state information. As a mean square error (MSE) performance benchmark for the channel estimator Cramer-Rao lower bound is also derived. MSE and SER performances of the simulation results are presented.
Citation - WoS: 1
An Analysis for the Use of Compressed Sensing Method in Microwave Imaging
(IEEE, 2017) Yiğit, Enes; Tekbaş, Mustafa; Ünal, İlhami; Erdoğan, Sercan; Çalışkan, Cafer
One of the most important problems encountered in microwave imaging methods is intensive data processing traffic that occurs when high resolution and real time tracking is desired. Radar signals can be recovered without loss of data with a randomly selected subset of the measurement data by compression sensing (CS) method which has been popular in recent years. For this reason, in this study, the use and capabilities of the CS method were investigated for tracking moving human, and the target information was correctly determined for the data obtained much below the Nyquist sampling criterion. In this study, it was revealed that the CS method can be developed for target detection and tracking
Citation - WoS: 1
Citation - Scopus: 1
Bayesian Estimation of Discrete-Time Cellular Neural Network Coefficients
(TUBITAK Scientific & Technical Research Council Turkey, 2017) Özer, Hakan Metin; Özmen, Atilla; Şenol, Habib
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 Metropolis--Hastings algorithm where the proposal distribution function is symmetric and resulting samples are then averaged to find the minimum mean square error (MMSE) estimate of the network coefficients. A couple of image processing applications are performed using these estimated parameters and the results are compared with those of some well-known methods.
Citation - Scopus: 4
Pilot-Aided Bayesian Mmse Channel Estimation for Ofdm Systems: Algorithm and Performance Analysis
(2004) Şenol, Habib; Cirpan, Hakan Ali; Panayırcı, Erdal
This paper proposes a computationally efficient pilot-aided minimum mean square error (MMSE) channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover optimal rank reduction is achieved in the proposed approach by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first consider the stochastic Cramer-Rao bound and derive the closed-form expression for the random KL coefficients. We then exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. © 2004 IEEE.
Citation - WoS: 1
Citation - Scopus: 1
Hybrid Mpi Plus Upc Parallel Programming Paradigm on an Smp Cluster
(TUBITAK Scientific & Technical Research Council Turkey, 2012) Bozkuş, Zeki
The symmetric multiprocessing (SMP) cluster system which consists of shared memory nodes with several multicore central processing units connected to a high-speed network to form a distributed memory system is the most widely available hardware architecture for the high-performance computing community. Today the Message Passing Interface (MPI) is the most widely used parallel programming paradigm for SMP clusters in which the MPI provides programming both for an SMP node and among nodes simultaneously. However Unified Parallel C (UPC) is an emerging alternative that supports the partitioned global address space model that can be again employed within and across the nodes of a cluster. In this paper we describe a hybrid parallel programming paradigm that was designed to combine MPI and UPC programming models. This paradigm's objective is to mix the MPI's data locality control and scalability strengths with UPC's fine-grain parallelism and ease of programming to achieve multiple-level parallelism at the SMP cluster which itself has multilevel parallel architecture. Utilizing a proposed hybrid model and comparing MPI-only to UPC-only implementations this paper presents a detailed description of Cannon's algorithm benchmark application with performance results of a random-access benchmark and the Barnes-Hut N-Body simulation. Experiments indicate that the hybrid MPI+UPC model can significantly provide performance increases of up to double in comparison with UPC-only implementation and up to 20% increases in comparison to MPI-only implementation. Furthermore an optimization was achieved that improved the hybrid performance by an additional 20%.
Citation - WoS: 18
Citation - Scopus: 23
Review of Bandwidth Estimation Tools and Application To Bandwidth Adaptive Video Streaming
(IEEE, 2012) Arsan, Taner
Streaming video is very popular in today's best effort delivery networks. Streaming video applications should not only have a good end-to-end transport performance but also have a Quality of Service (QoS) provisioning in network infrastructure. Bandwidth estimation schemes have been used to improve the QoS of multimedia services and video streaming applications. To ensure the video streaming service quality some other components such as adaptive rate allocation and control should be taken into consideration. This paper gives a review of bandwidth estimation tools for wired and wireless networks and then introduces a new bandwidth adaptive architecture for video streaming. © 2012 IEEE.

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