Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Institution Author: search.filters.institutionAuthor.Altınkaya, Mustafa AzizPublisher: search.filters.publisher.Institute of Electrical and Electronics Engineers Inc.

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Now showing 1 - 8 of 8
  • Conference Object
    Citation - Scopus: 1
    Kod 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 Aziz
    In 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: 1
    Özilinti Matrisinin Oluşturulma Yönteminin Modele Dayalı Sinüzoidal Parametre Kestirimindeki Etkileri
    (Institute of Electrical and Electronics Engineers Inc., 2004) Altınkaya, Mustafa Aziz
    Although 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: 1
    Asimetrik Alfa-kararlı Kaynakların Enküçük Saçılım Kriteri Kullalınarak Ayrıştırılması
    (Institute of Electrical and Electronics Engineers Inc., 2006) Altınkaya, Mustafa Aziz
    In 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
    Citation - WoS: 2
    Citation - Scopus: 3
    Long Term Wind Speed Prediction With Polynomial Autoregressive Model
    (Institute of Electrical and Electronics Engineers Inc., 2015) Karakuş, Oktay; Kuruoğlu, Ercan E.; Altınkaya, Mustafa Aziz
    Wind energy is one of the preferred energy generation methods because wind is an important renewable energy source. Prediction of wind speed in a time period, is important due to the one-to-one relationship between wind speed and wind power. Due to the nonlinear character of the wind speed data, nonlinear methods are known to produce better results compared to linear time series methods like Autoregressive (AR), Autoregressive Moving Average (ARMA) in predicting in a period longer than 12 hours. A method is proposed to apply a 48-hour ahead wind speed prediction by using the past wind speed measurements of the (Cesme Peninsula. We proposed to model wind speed data with a Polynomial AR (PAR) model. Coefficients of the models are estimated via linear Least Squares (LS) method and up to 48 hours ahead wind speed prediction is calculated for different models. In conclusion, a better performance is observed for higher than 12-hour ahead wind speed predictions of wind speed data which is modelled with PAR model, than AR and ARMA models.
  • Conference Object
    Citation - Scopus: 1
    Optimal 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 Aziz
    In 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: 18
    Citation - Scopus: 19
    Generalized Bayesian Model Selection for Speckle on Remote Sensing Images
    (Institute of Electrical and Electronics Engineers Inc., 2019) Karakuş, Oktay; Kuruoğlu, Ercan E.; Altınkaya, Mustafa Aziz
    Synthetic 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.
  • Conference Object
    Phase Noise Mitigation in the Autocorrelation Estimates With Data Windowing: the Case of Two Close Sinusoids
    (Institute of Electrical and Electronics Engineers Inc., 2006) Altınkaya, Mustafa Aziz; Anarım, Emin; Sankur, Bülent
    We address the phase noise and the superresolution problem in Toeplitz matrix-based spectral estimates. The Toeplitz autocorrelation (AC) matrix approach in spectral estimation brings in an order of magnitude computational advantage while the price paid is the phase noise that becomes effective at high signal-to-noise ratios (SNR). This noise can be mitigated with windowing the data though some concomitant loss in resolution occurs. The trade-offs between additive noise SNR, resolvability of sinusoids closer than the resolution limit, and behavior of the estimated AC lags and tone frequencies are investigated.
  • Conference Object
    Citation - Scopus: 1
    Removal of the Phase Noise in the Autocorrelation Estimates With Data Windowing
    (Institute of Electrical and Electronics Engineers Inc., 2005) Altınkaya, Mustafa Aziz; Anarım, Emin; Sankur, Bülent
    The sinusoidal frequency estimation from short data records based on Toeplitz autocorrelation (AC) matrix estimates suffer from phase noise. This effect becomes prominent especially when additive noise vanishes becoming a nuisance, that is at high signal-to-noise ratios (SNR). Based on both analytic derivation of the AC lag terms and simulation experiments, we show that data windowing can mitigate the limitations caused by the phase noise. Thus with proper windowing, the variance of the frequency estimate is no more limited by phase noise, but it continues to decrease linearly with the SNR. The cases of the Pisarenko frequency estimator and of MUSIC, both for the single sinusoid case, are analyzed in detail.