Altınkaya, Mustafa Aziz
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Altinkaya, MA.
Altınkaya, M. A.
Altınkaya, MA.
Altinkaya, M.A.
Altınkaya, Mustafa A.
Altinkaya, Mustafa Aziz
Altinkaya, Mustafa A.
Altinkaya, M. A.
Altınkaya, M. A.
Altınkaya, MA.
Altinkaya, M.A.
Altınkaya, Mustafa A.
Altinkaya, Mustafa Aziz
Altinkaya, Mustafa A.
Altinkaya, M. A.
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Email Address
mustafaaltinkaya@iyte.edu.tr
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03.05. Department of Electrical and Electronics Engineering
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Current Staff
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Documents
32
Citations
335
h-index
7
Documents
28
Citations
280
Scholarly Output
40
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12
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39310/14484
Supervised MSc Theses
5
Supervised PhD Theses
5
WoS Citation Count
276
Scopus Citation Count
326
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0
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4
WoS Citations per Publication
6.90
Scopus Citations per Publication
8.15
Open Access Source
32
Supervised Theses
10
| Journal | Count |
|---|---|
| Digital Signal Processing: A Review Journal | 4 |
| Signal Processing | 4 |
| 2013 21st Signal Processing and Communications Applications Conference, SIU 2013 | 3 |
| 2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings | 2 |
| 2015 23rd European Signal Processing Conference, EUSIPCO 2015 | 2 |
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Conference Object Citation - Scopus: 5Estimation of the Nonlinearity Degree for Polynomial Autoregressiv Processes With Rjmcmc(Institute of Electrical and Electronics Engineers, 2015) Karakuş, Oktay; Kuruoğlu, Ercan Engin; Altınkaya, Mustafa AzizDespite the popularity of linear process models in signal and image processing, various real life phenomena exhibit nonlinear characteristics. Compromising between the realistic and computationally heavy nonlinear models and the simplicity of linear estimation methods, linear in the parameters nonlinear models such as polynomial autoregressive (PAR) models have been accessible analytical tools for modelling such phenomena. In this work, we aim to demonstrate the potentials of Reversible Jump Markov Chain Monte Carlo (RJMCMC) which is a successful statistical tool in model dimension estimation in nonlinear process identification. We explore the capability of RJMCMC in jumping not only between spaces with different dimensions, but also between different classes of models. In particular, we demonstrate the success of RJMCMC in sampling in linear and nonlinear spaces of varying dimensions for the estimation of PAR processes. © 2015 EURASIP.Conference Object Alpha-Trimmed Means of Multiple Location Estimates(Institute of Electrical and Electronics Engineers Inc., 2013) Altınkaya, Mustafa AzizLocalization by distance measurements is a common technique for solving this contemporary problem. The methods which achieve the theoretically optimum solutions have generally iterative structures. That is why when limited computational load is required, suboptimum methods described by closed form formulas like the one of Coope which depends on orthogonal decomposition of sensor coordinates, are preferred. In this method, when there are more than necessary distance measurements required for localization, the location will be found as the arithmetic average of the estimates obtained using the all three-combinations of distance measurements. In the averaging, eliminating the outlier estimates will increase the performance. In this case discarding the estimates making the ratio of alpha which are farthest away from the arithmetic average, one attains the socalled alpha-trimmed mean of the estimates. Applying this technique, the disturbing effects of impulsive mixture of Gaussian contamination are eliminated and similar performances as in the case of Gaussian distance measurements are attained in localization.Conference Object The Effect of Convolutional Encoder Memory on the Sphere Decoding Search Radius in Mimo Systems(Institute of Electrical and Electronics Engineers Inc., 2013) Karakuş, Oktay; Altınkaya, Mustafa Aziz; Kılıçaslan, KağanIn the new generation communication systems Multiple-Input-Multiple-Output systems are frequently used. The processing load of the Maximum Likelihood (ML) Detector which is the optimum detector for these systems, increases exponentially as a function of system dimension and memory due to testing all possible points. Sphere Decoding (SD) method which tests only the probable points, decreases the processing load dramatically. System memory changes by system dimensions and length of the convolutional encoder. This, in turn, affects the radius of the hyper sphere centered at the observation in the observation space at which SD attains the performance of the ML detector. This effect is investigated via simulation studies. In these simulations, it is observed that the radius of the SD is relatively smaller than the one in ML, and the ratio between the radius values varies from 6,61 in the case of memoryless 2x2 MIMO system to 1,02 in the case of 8x8 MIMO system with memory K=10 according to increased antenna numbers and system memory. In addition to these, it is observed that the radius of the hyper sphere is directly proportional to the memory of the encoder.Doctoral Thesis Generalized Bayesian model selection using reversible jump Markov chain Monte Carlo(Izmir Institute of Technology, 2017) Karakuş, Oktay; Altınkaya, Mustafa Aziz; Kuruoğlu, Ercan EnginThe main objective of this thesis is to suggest a general Bayesian framework for model selection based on reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. In particular, we aim to reveal the undiscovered potentials of RJMCMC in model selection applications by exploiting the original formulation to explore spaces of di erent classes or structures and thus, to show that RJMCMC o ers a wider interpretation than just being a trans-dimensional model selection algorithm. The general practice is to use RJMCMC in a trans-dimensional framework e.g. in model estimation studies of linear time series, such as AR and ARMA and mixture processes, etc. In this thesis, we propose a new interpretation on RJMCMC which reveals the undiscovered potentials of the algorithm. This new interpretation, firstly, extends the classical trans-dimensional approach to a much wider meaning by exploring the spaces of linear and nonlinear models in terms of the nonlinear (polynomial) time series models. Polynomial process modelling is followed by the definition of a new type of RJMCMC move that performs transitions between various generic model spaces irrespective of model sizes. Then, we apply this new framework to the identification of Volterra systems with an application of nonlinear channel estimation of an OFDM communication system. The proposed RJMCMC move has been adjusted to explore the spaces of di erent distribution families by matching the common properties of the model spaces such as norm, and this leads us to perform a distribution estimation study of the observed real-life data sets including, impulsive noise in power-line communications, seismic acceleration time series, remote sensing images, etc. Simulation results demonstrate the remarkable performance of the proposed method in nonlinearity degree estimation and in transitions between di erent classes of models. The proposed method uses RJMCMC in an unorthodox way and reveals its potential to be a general estimation method by performing the reversible jump mechanism between spaces of di erent model classes.Conference Object Citation - WoS: 5Estimation of the Nonlinearity Degree for Polynomial Autoregressive Processes With Rjmcmc(Institute of Electrical and Electronics Engineers Inc., 2015) Karakuş, Oktay; Kuruoğlu, Ercan E.; Altınkaya, Mustafa AzizDespite the popularity of linear process models in signal and image processing, various real life phenomena exhibit nonlinear characteristics. Compromising between the realistic and computationally heavy nonlinear models and the simplicity of linear estimation methods, linear in the parameters nonlinear models such as polynomial autoregressive (PAR) models have been accessible analytical tools for modelling such phenomena. In this work, we aim to demonstrate the potentials of Reversible Jump Markov Chain Monte Carlo (RSMCMC) which is a successful statistical tool in model dimension estimation in nonlinear process identification. We explore the capability of RJMCMC in jumping not only between spaces with different dimensions, but also between different classes of models. In particular, we demonstrate the success of RJMCMC in sampling in linear and nonlinear spaces of varying dimensions for the estimation of PAR processes.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.Article Citation - WoS: 9Citation - Scopus: 15Subspace-Based Frequency Estimation of Sinusoidal Signals in Alpha-Stable Noise(Elsevier Ltd., 2002) Altınkaya, Mustafa Aziz; Deliç, Hakan; Sankur, Bülent; Anarım, EminIn the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistical average, the generalized covariation coefficient (GCC). The GCCs of multiple sinusoids for unity moment order in SαS noise attain the same form as the covariance expressions of multiple sinusoids in white Gaussian noise. The subspace-based frequency estimators FLOS-multiple signal classification (MUSIC) and FLOS-Bartlett are applied to the GCC matrix of the data. On the other hand, we show that the multiple sinusoids in SαS noise can also be modeled as a stable autoregressive moving average process approximated by a higher order stable autoregressive (AR) process. Using the GCCs of the data, we obtain FLOS versions of Tufts-Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show that techniques employing lower order statistics are superior to their second-order statistics (SOS)-based counterparts, especially when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS-MUSIC shows a robust performance. It behaves comparably to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution scenarios. Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies.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.Conference Object Citation - Scopus: 2Performance Analysis of Lattice Reduction Aided Mimo Detectors(Institute of Electrical and Electronics Engineers, 2012) Kılıçaslan, Kağan; Altınkaya, Mustafa AzizLattice reduction is a powerful method used in detection and precoding of wireless multiple input-multiple output (MIMO) systems. The basic idea is to consider the channel transfer matrix as a basis for the transmitted symbols. The channel transfer matrix is reduced to a more orthogonal matrix using lattice reduction algorithms. This in turn, improves the performance of conventional MIMO receivers. In this study, it is shown that this performance improvement depends on the modulation order. © 2012 IEEE.Master Thesis Multiuser Detection in Cdma Using Blind Techniques(Izmir Institute of Technology, 2004) Altınsoy, Eşref Olgu; Altınkaya, Mustafa AzizIn code division multiple access (CDMA) systems, blind multiuser detection (MUD) techniques are of great importance, especially for downlinks, since in practice, it may be unrealistic for a mobile user to know the spreading codes of other active users in the channel. Furthermore, blind methods remove the need for training sequences which leads to a gain in the channel bandwidth. Subspace concept in blind MUD is an alternative process to classical and batch blind MUD techniques based on principle component analysis, or independent component analysis (ICA) and ICA-like algorithms, such as joint approximate diagonalization of eigen-matrices (JADE), blind source separation algorithm with reference system, etc. Briefly, the desired signal is searched in the signal subspace instead of the whole space, in this type of detectors. A variation of the subspace-based MUD is reduced-rank MUD in which a smaller subspace of the signal subspace is tracked where the desired signal is contained in. This latter method leads to a performance gain compared to a standard subspace method. In this thesis, blind signal subspace and reduced-rank MUD techniques are investigated, and applied to minimum mean square error (MMSE) detectors with two different iterative subspace tracking algorithms. The performances of these detectors are compared in different scenarios for additive white Gaussian noise and for multipath fading channels as well. With simulation results the superiority of the reduced-rank detector to the signal subspace detector is shown. Additionally, as a new remark for both detectors, it is shown that, using minimum description length criterion in subspace tracking algorithm results in an increase in rank-tracking ability and correspondingly in the final performance. Finally, the performances of these two detectors are compared with MMSE, adaptive MMSE and JADE detectors.