Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 22Citation - Scopus: 26Adaptive Sign Algorithm for Graph Signal Processing(Elsevier, 2022) Yan, Yi; Kuruoğlu, Ercan Engin; Altınkaya, Mustafa AzizEfficient and robust online processing techniques for irregularly structured data are crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. The recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean pth power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student's t, or Laplace distributions.Article Citation - WoS: 1Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection(Elsevier, 2022) Wahdan, Muath A.; Altınkaya, Mustafa AzizIn a wireless sensor network, multilevel quantization is necessary to find a compromise between minimizing the power consumption of sensors and maximizing the detection performance at the fusion center (FC). The previous methods have been using distance measures such as J-divergence and Bhattacharyya distance in this quantization. This work proposes a different approach based on the maximum average entropy of the output of the sensors under both hypotheses and utilizes it in a Neyman-Pearson criterion-based distributed detection scheme to detect a point source. The receiver operating characteristics of the proposed maximum average entropy (MAE) method in quantizing sensor outputs have been evaluated for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent M-ary frequency shift keying communication is used for transmitting MAE based multilevel quantized sensor outputs over a Rayleigh fading channel. The simulation studies show the success of the MAE in the cases of both error-free fusion and where the effect of the wireless channel has been incorporated. As expected, the performance improves as the level of quantization increases and with six-level quantization approaches the performance of non-quantized data transmission.Conference Object Citation - Scopus: 1Kod Bölüşümlü Çoklu Erişim (cdma) İletişiminde Gauss Olmayan Sönümlü Kanal Kestirimi için Pearson Sistemi'ne Dayalı Gözü Kapalı Kaynak Ayrıştırma Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2004) Kalkan, Olcay; Altınkaya, Mustafa AzizIn this work, a Pearson System based-blind source separation method is used for detecting the signal coming to a mobile user which is subject to multiple access interference in a CDMA downlink communication. Considering some fading channel measurements showing that the fading channel coefficients may have an impulsive nature, these coefficients are modeled with an a-stable distribution whose shape parameter a takes values between 1.8 and 1.9. These a values show that the distribution resembles a Gaussian distribution but has a more impulsive nature. Simulation studies show that the conventional MMSE receiver fails in this impulsive fading scenario. Both the independent component analysis (ICA) method using the conventional hyperbolic tangent score function and the Pearson System-based ICA are successful in estimating the channel coefficients and the proposed Pearson System-based ICA method performs faster.Conference Object Citation - Scopus: 1Özilinti Matrisinin Oluşturulma Yönteminin Modele Dayalı Sinüzoidal Parametre Kestirimindeki Etkileri(Institute of Electrical and Electronics Engineers Inc., 2004) Altınkaya, Mustafa AzizAlthough the maximum likelihood method gives the optimum solutions for the parameter estimation problem of the sinusoids embedded in noise, it is computationally difficult since it generally requires to solve nonlinear optimization problems. So some model-based parameter estimators with high frequency resolution property are preferred quite often. In order to find these estimates the first step is usually forming the autocorrelation (AC) matrix. In this work the effects of the method utilized in the generation of the AC matrix on the performances of sinusoidal parameter estimators are investigated. One way of forming the AC matrix is to use a Toeplitz structure with either the biased or the unbiased AC lag estimates as the matrix elements. Another way is to use the socalled "covariance method" in the AC matrix generation. In this method the matrix formed is no longer Toeplitz; but it is still symmetric. We can think of that the Toeplitz AC matrix is a perturbed version of the non-Toeplitz AC matrix. The differences in the performances of the MUSIC spectral estimator with Toeplitz; and non-Toeplitz AC matrix usage is related to the perturbation in the AC matrix estimate. For this purpose the 3 x 3 AC matrix is is utilized in the estimation of the frequency of a single sinusoid using the MUSIC frequency estimator. The accuracy of the perturbation analysis is checked with the simulation results. Additionally, the fact that the performance of an estimator with data windowing and Toeplitz AC matrix generation becomes near to the performance of the same estimator with non-Toeplitz AC matrix is shown with simulation studies.Conference Object Citation - Scopus: 1Asimetrik Alfa-kararlı Kaynakların Enküçük Saçılım Kriteri Kullalınarak Ayrıştırılması(Institute of Electrical and Electronics Engineers Inc., 2006) Altınkaya, Mustafa AzizIn this work, we extend the method which separates symmetric alpha-stable sources using minimum dispersion criterion to the case of skewed alpha-stable mixtures. Thus, a more robust method based on fractional lower order statistics is developed which is capable of separating general alpha-stable sources.Conference Object Kırpılmış ortalamalı gürbüz konum kestiriminde yeni sonuçlar(IEEE, 2014) Altınkaya, Mustafa AzizWhen there are more than necessary distance measurements in localization by distance measurements with closed form estimators, forming smaller subgroups of measurements and averaging the location estimates obtained with these subgroups of measurements makes it possible to eliminate outlier measurements if they are present. In order to eliminate these outlier results, the nearest estimate to the geometric median of estimates is proposed as a reference in this work. Conducted simulation studies show that significant gains can be obtained using geometric median in place of arithmetic average in robust averaging methods.Conference Object Citation - Scopus: 1Optimal Quantization in Decentralized Detection by Maximizing the Average Entropy of the Sensors(Institute of Electrical and Electronics Engineers Inc., 2019) Wahdan, Muath A.; Altınkaya, Mustafa AzizIn a wireless sensor network the sensor outputs are required to be quantized because of energy and bandwidth requirements. We propose such a distributed detection scheme for a point source which is based on Neyman-Pearson criterion where sensor outputs are quantized maximizing the average output entropy of the sensors under both hypotheses. The quantized local outputs are transmitted to a fusion center (FC) where they are used to make a global decision. The performance of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was tested for binary, ternary and quarternary quantization. The effects of the channel from the sensors to the FC is also addressed by simplified channel models. The simulation studies show the success of the MAE method.Article Citation - WoS: 18Citation - Scopus: 19Generalized Bayesian Model Selection for Speckle on Remote Sensing Images(Institute of Electrical and Electronics Engineers Inc., 2019) Karakuş, Oktay; Kuruoğlu, Ercan E.; Altınkaya, Mustafa AzizSynthetic aperture radar (SAR) and ultrasound (US) are two important active imaging techniques for remote sensing, both of which are subject to speckle noise caused by coherent summation of back-scattered waves and subsequent nonlinear envelope transformations. Estimating the characteristics of this multiplicative noise is crucial to develop denoising methods and to improve statistical inference from remote sensing images. In this paper, reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with a wider interpretation and a recently proposed RJMCMC-based Bayesian approach, trans-space RJMCMC, has been utilized. The proposed method provides an automatic model class selection mechanism for remote sensing images of SAR and US where the model class space consists of popular envelope distribution families. The proposed method estimates the correct distribution family, as well as the shape and the scale parameters, avoiding performing an exhaustive search. For the experimental analysis, different SAR images of urban, forest and agricultural scenes, and two different US images of a human heart have been used. Simulation results show the efficiency of the proposed method in finding statistical models for speckle.Article Citation - WoS: 22Citation - Scopus: 27Modelling Impulsive Noise in Indoor Powerline Communication Systems(Springer Verlag, 2020) Karakuş, Oktay; Kuruoğlu, Ercan E.; Altınkaya, Mustafa AzizPowerline communication (PLC) is an emerging technology that has an important role in smart grid systems. Due to making use of existing transmission lines for communication purposes, PLC systems are subject to various noise effects. Among those, the most challenging one is the impulsive noise compared to the background and narrowband noise. In this paper, we present a comparative study on modelling the impulsive noise amplitude in indoor PLC systems by utilising several impulsive distributions. In particular, as candidate distributions, we use the symmetric alpha-Stable (S alpha S), generalised Gaussian, Bernoulli Gaussian and Student's t distribution families as well as the Middleton Class A distribution, which dominates the literature as the impulsive noise model for PLC systems. Real indoor PLC system noise measurements are investigated for the simulation studies, which show that the S alpha S distribution achieves the best modelling success when compared to the other families in terms of the statistical error criteria, especially for the tail characteristics of the measured data sets.Conference Object Citation - Scopus: 2Nonlinear model selection for PARMA processes using RJMCMC(IEEE, 2017) Karakuş, Oktay; Kuruoğlu, Ercan Engin; Altınkaya, Mustafa AzizMany prediction studies using real life measure-ments such as wind speed, power, electricity load and rain-fall utilize linear autoregressive moving average (ARMA) based models due to their simplicity and general character. However, most of the real life applications exhibit nonlinear character and modelling them with linear time series may become problematic. Among nonlinear ARMA models, polynomial ARMA (PARMA) models belong to the class of linear-in-the-parameters. In this paper, we propose a reversible jump Markov chain Monte Carlo (RJMCMC) based complete model estimation method which estimates PARMA models with all their parameters including the nonlinearity degree. The proposed method is unique in the manner of estimating the nonlinearity degree and all other model orders and model coefficients at the same time. Moreover, in this paper, RJMCMC has been examined in an anomalous way by performing transitions between linear and nonlinear model spaces. © EURASIP 2017.