Mustafa A. Altınkaya’nın Yayınları
Journal Articles |
Yan, Y; Kuruoglu, EE; Altinkaya, MA Adaptive sign algorithm for graph signal processing Journal Article Signal Processing, 200 , pp. 108662–108662, 2022. @article{pop00001z, title = {Adaptive sign algorithm for graph signal processing}, author = {Y Yan and EE Kuruoglu and MA Altinkaya}, year = {2022}, date = {2022-01-01}, journal = {Signal Processing}, volume = {200}, pages = {108662--108662}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Wahdan, MA; Altınkaya, MA Maximum average entropy-based quantization of local observations for distributed detection Journal Article Digital Signal Processing, 123 , pp. 103427–103427, 2022. @article{pop00002r, title = {Maximum average entropy-based quantization of local observations for distributed detection}, author = {MA Wahdan and MA Altınkaya}, year = {2022}, date = {2022-01-01}, journal = {Digital Signal Processing}, volume = {123}, pages = {103427--103427}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Achim, A; Altınkaya, MA Cauchy–Rician Model for Backscattering in Urban SAR Images Journal Article IEEE Geoscience and Remote Sensing Letters, 19 , pp. 1–5, 2022. @article{pop00003w, title = {Cauchy–Rician Model for Backscattering in Urban SAR Images}, author = {O Karakuş and EE Kuruoğlu and A Achim and MA Altınkaya}, year = {2022}, date = {2022-01-01}, journal = {IEEE Geoscience and Remote Sensing Letters}, volume = {19}, pages = {1--5}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Altınkaya, MA Modelling impulsive noise in indoor powerline communication systems Journal Article Signal, Image and Video Processing, 14 , pp. 1655–1661, 2020. @article{pop00001o, title = {Modelling impulsive noise in indoor powerline communication systems}, author = {O Karakuş and EE Kuruoğlu and MA Altınkaya}, year = {2020}, date = {2020-01-01}, journal = {Signal, Image and Video Processing}, volume = {14}, pages = {1655--1661}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Wahdan, MA; Altınkaya, MA Optimal Quantization in Decentralized Detection by Maximizing the Average Entropy of the Sensors Journal Article 2019 27th Signal Processing and Communications Applications Conference (SIU …, 2019. @article{pop00002j, title = {Optimal Quantization in Decentralized Detection by Maximizing the Average Entropy of the Sensors}, author = {MA Wahdan and MA Altınkaya}, year = {2019}, date = {2019-01-01}, journal = {2019 27th Signal Processing and Communications Applications Conference (SIU …}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Wahdan, MA; Altınkaya, MA Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection Journal Article arXiv preprint arXiv:1912., 4548 , 2019. @article{pop00003o, title = {Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection}, author = {MA Wahdan and MA Altınkaya}, year = {2019}, date = {2019-01-01}, journal = {arXiv preprint arXiv:1912.}, volume = {4548}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Altınkaya, MA Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling Journal Article Signal Processing, 153 , pp. 396–410, 2018. @article{pop00010d, title = {Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling}, author = {O Karakuş and EE Kuruoğlu and MA Altınkaya}, year = {2018}, date = {2018-01-01}, journal = {Signal Processing}, volume = {153}, pages = {396--410}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Altınkaya, MA Generalized Bayesian Model Selection for Speckle on Remote Sensing Images Journal Article IEEE Transactions on Image Processing, 28 (4), pp. 1748–1758, 2018. @article{pop00015e, title = {Generalized Bayesian Model Selection for Speckle on Remote Sensing Images}, author = {O Karakuş and EE Kuruoğlu and MA Altınkaya}, year = {2018}, date = {2018-01-01}, journal = {IEEE Transactions on Image Processing}, volume = {28}, number = {4}, pages = {1748--1758}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, Oktay; Kuruoğlu, Ercan E; Altınkaya, Mustafa A One-day ahead wind speed/power prediction based on polynomial autoregressive model Journal Article IET Renewable Power Generation, 2017, ISSN: 1752-1416. @article{Karakus2017, title = {One-day ahead wind speed/power prediction based on polynomial autoregressive model}, author = {Oktay Karakuş and Ercan E Kuruoğlu and Mustafa A Altınkaya}, doi = {10.1049/iet-rpg.2016.0972}, issn = {1752-1416}, year = {2017}, date = {2017-01-01}, journal = {IET Renewable Power Generation}, abstract = {textcopyright The Institution of Engineering and Technology 2017. Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Ceşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.}, keywords = {}, pubstate = {published}, tppubtype = {article} } textcopyright The Institution of Engineering and Technology 2017. Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of Ceşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h. |
Karakuş, O; Kuruoğlu, EE; Altınkaya, MA Bayesian Volterra system identification using reversible jump MCMC algorithm Journal Article Signal Processing, 141 , pp. 125–136, 2017. @article{pop00005eb, title = {Bayesian Volterra system identification using reversible jump MCMC algorithm}, author = {O Karakuş and EE Kuruoğlu and MA Altınkaya}, year = {2017}, date = {2017-01-01}, journal = {Signal Processing}, volume = {141}, pages = {125--136}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Altinkaya, MA Nonlinear model selection for PARMA processes using RJMCMC Journal Article 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2056–2060, 2017. @article{pop00006e, title = {Nonlinear model selection for PARMA processes using RJMCMC}, author = {O Karakuş and EE Kuruoğlu and MA Altinkaya}, year = {2017}, date = {2017-01-01}, journal = {2017 25th European Signal Processing Conference (EUSIPCO)}, pages = {2056--2060}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Altınkaya, MA Beyond Trans-dimensional RJMCMC: Application to Impulsive Data Modeling Journal Article arXiv preprint arXiv:1711., 3633 , 2017. @article{pop00021e, title = {Beyond Trans-dimensional RJMCMC: Application to Impulsive Data Modeling}, author = {O Karakuş and EE Kuruoğlu and MA Altınkaya}, year = {2017}, date = {2017-01-01}, journal = {arXiv preprint arXiv:1711.}, volume = {3633}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Kuruoğlu, EE; Altinkaya, MA Estimation of the nonlinearity degree for polynomial autoregressive processes with RJMCMC Journal Article 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 953–957, 2015. @article{pop00004cb, title = {Estimation of the nonlinearity degree for polynomial autoregressive processes with RJMCMC}, author = {O Karakuş and EE Kuruoğlu and MA Altinkaya}, year = {2015}, date = {2015-01-01}, journal = {2015 23rd European Signal Processing Conference (EUSIPCO)}, pages = {953--957}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altınkaya, Mustafa A Benefits of averaging lateration estimates obtained using overlapped subgroups of sensor data Journal Article Digital Signal Processing: A Review Journal, 2014, ISSN: 10512004. @article{Altnkaya2014, title = {Benefits of averaging lateration estimates obtained using overlapped subgroups of sensor data}, author = {Mustafa A Altınkaya}, doi = {10.1016/j.dsp.2013.09.004}, issn = {10512004}, year = {2014}, date = {2014-01-01}, journal = {Digital Signal Processing: A Review Journal}, abstract = {In this paper, we suggest averaging lateration estimates obtained using overlapped subgroups of distance measurements as opposed to obtaining a single lateration estimate from all of the measurements directly if a redundant number of measurements are available. Least squares based closed form equations are used in the lateration. In the case of Gaussian measurement noise the performances are similar in general and for some subgroup sizes marginal gains are attained. Averaging laterations method becomes especially beneficial if the lateration estimates are classified as useful or not in the presence of outlier measurements whose distributions are modeled by a mixture of Gaussians (MOG) pdf. A new modified trimmed mean robust averager helps to regain the performance loss caused by the outliers. If the measurement noise is Gaussian, large subgroup sizes are preferable. On the contrary, in robust averaging small subgroup sizes are more effective for eliminating measurements highly contaminated with MOG noise. The effect of high-variance noise was almost totally eliminated when robust averaging of estimates is applied to QR decomposition based location estimator. The performance of this estimator is just 1 cm worse in root mean square error compared to the Cramér–Rao lower bound (CRLB) on the variance both for Gaussian and MOG noise cases. Theoretical CRLBs in the case of MOG noise are derived both for time of arrival and time difference of arrival measurement data.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this paper, we suggest averaging lateration estimates obtained using overlapped subgroups of distance measurements as opposed to obtaining a single lateration estimate from all of the measurements directly if a redundant number of measurements are available. Least squares based closed form equations are used in the lateration. In the case of Gaussian measurement noise the performances are similar in general and for some subgroup sizes marginal gains are attained. Averaging laterations method becomes especially beneficial if the lateration estimates are classified as useful or not in the presence of outlier measurements whose distributions are modeled by a mixture of Gaussians (MOG) pdf. A new modified trimmed mean robust averager helps to regain the performance loss caused by the outliers. If the measurement noise is Gaussian, large subgroup sizes are preferable. On the contrary, in robust averaging small subgroup sizes are more effective for eliminating measurements highly contaminated with MOG noise. The effect of high-variance noise was almost totally eliminated when robust averaging of estimates is applied to QR decomposition based location estimator. The performance of this estimator is just 1 cm worse in root mean square error compared to the Cramér–Rao lower bound (CRLB) on the variance both for Gaussian and MOG noise cases. Theoretical CRLBs in the case of MOG noise are derived both for time of arrival and time difference of arrival measurement data. |
Altınkaya, MA Alpha-trimmed means of multiple location estimates Journal Article 2013 21st Signal Processing and Communications Applications Conference (SIU …, 2013. @article{pop00016c, title = {Alpha-trimmed means of multiple location estimates}, author = {MA Altınkaya}, year = {2013}, date = {2013-01-01}, journal = {2013 21st Signal Processing and Communications Applications Conference (SIU …}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Karakuş, O; Altınkaya, MA; Kılıçaslan, K The effect of convolutional encoder memory on the sphere decoding search radius in MIMO systems Journal Article 2013 21st Signal Processing and Communications Applications Conference (SIU …, 2013. @article{pop00023d, title = {The effect of convolutional encoder memory on the sphere decoding search radius in MIMO systems}, author = {O Karakuş and MA Altınkaya and K Kılıçaslan}, year = {2013}, date = {2013-01-01}, journal = {2013 21st Signal Processing and Communications Applications Conference (SIU …}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Efeler, MC; Altınkaya, MA; Gümüştekin, Ş A Bayesian approach for licence plate recognition developed on a realistic simulation environment Journal Article 2013 21st Signal Processing and Communications Applications Conference (SIU …, 2013. @article{pop00024d, title = {A Bayesian approach for licence plate recognition developed on a realistic simulation environment}, author = {MC Efeler and MA Altınkaya and Ş Gümüştekin}, year = {2013}, date = {2013-01-01}, journal = {2013 21st Signal Processing and Communications Applications Conference (SIU …}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Sertatil, Cem; Altinkaya, Mustafa A; Raoof, Kosai A novel acoustic indoor localization system employing CDMA Journal Article Digital Signal Processing: A Review Journal, 2012, ISSN: 10512004. @article{Sertatil2012, title = {A novel acoustic indoor localization system employing CDMA}, author = {Cem Sertatil and Mustafa A Altinkaya and Kosai Raoof}, doi = {10.1016/j.dsp.2011.12.001}, issn = {10512004}, year = {2012}, date = {2012-01-01}, journal = {Digital Signal Processing: A Review Journal}, abstract = {Nowadays outdoor location systems have been used extensively in all fields of human life from military applications to daily life. However, these systems cannot operate in indoor applications. Hence, this paper considers a novel indoor location system that aims to locate an object within an accuracy of about 2 cm using ordinary and inexpensive off-the-shelf devices and that was designed and tested in an office room to evaluate its performance. In order to compute the distance between the transducers (speakers) and object to be localized (microphone), time-of-arrival measurements of acoustic signals consisting of Binary Phase Shift Keying modulated Gold sequences are performed. This DS-CDMA scheme assures accurate distance measurements and provides immunity to noise and interference. Two methods have been proposed for location estimation. The first method takes the average of four location estimates obtained by trilateration technique. In the second method, only a single robust position estimate is obtained using three distances while the least reliable fourth distance measurement is not taken into account. The system's performance is evaluated at positions from two height levels using system parameters determined by preliminary experiments. The precision distributions in the work area and the precision versus accuracy plots depict the system performance. The proposed system provides location estimates of better than 2 cm accuracy with 99% precision. textcopyright 2011 Elsevier Inc. All rights reserved.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Nowadays outdoor location systems have been used extensively in all fields of human life from military applications to daily life. However, these systems cannot operate in indoor applications. Hence, this paper considers a novel indoor location system that aims to locate an object within an accuracy of about 2 cm using ordinary and inexpensive off-the-shelf devices and that was designed and tested in an office room to evaluate its performance. In order to compute the distance between the transducers (speakers) and object to be localized (microphone), time-of-arrival measurements of acoustic signals consisting of Binary Phase Shift Keying modulated Gold sequences are performed. This DS-CDMA scheme assures accurate distance measurements and provides immunity to noise and interference. Two methods have been proposed for location estimation. The first method takes the average of four location estimates obtained by trilateration technique. In the second method, only a single robust position estimate is obtained using three distances while the least reliable fourth distance measurement is not taken into account. The system's performance is evaluated at positions from two height levels using system parameters determined by preliminary experiments. The precision distributions in the work area and the precision versus accuracy plots depict the system performance. The proposed system provides location estimates of better than 2 cm accuracy with 99% precision. textcopyright 2011 Elsevier Inc. All rights reserved. |
Altinkaya, MA On “A Flexible Window Function for Spectral Analysis”[Letter to Editor] Journal Article IEEE Signal Processing Magazine, 28 (4), pp. 7–13, 2011. @article{pop00025d, title = {On “A Flexible Window Function for Spectral Analysis”[Letter to Editor]}, author = {MA Altinkaya}, year = {2011}, date = {2011-01-01}, journal = {IEEE Signal Processing Magazine}, volume = {28}, number = {4}, pages = {7--13}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altınkaya, MA On" A flexible window function for spectral analysis" Journal Article IEEE, 2011. @article{pop00026c, title = {On" A flexible window function for spectral analysis"}, author = {MA Altınkaya}, year = {2011}, date = {2011-01-01}, journal = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altinkaya, Mustafa A; Anarim, Emin; Sankur, Bülent Phase dependence mitigation for autocorrelation-based frequency estimation Journal Article Digital Signal Processing: A Review Journal, 2008, ISSN: 10512004. @article{Altinkaya2008, title = {Phase dependence mitigation for autocorrelation-based frequency estimation}, author = {Mustafa A Altinkaya and Emin Anarim and Bülent Sankur}, doi = {10.1016/j.dsp.2007.02.004}, issn = {10512004}, year = {2008}, date = {2008-01-01}, journal = {Digital Signal Processing: A Review Journal}, abstract = {The sinusoidal frequency estimation from short data records based on Toeplitz autocorrelation (AC) matrix estimates suffer from the dependence on the initial phases of the sinusoid(s). This effect becomes prominent when the impact of additive noise vanishes, 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 dependence. Thus with proper windowing, the variance of the frequency estimate is no more eclipsed by phase dependence, but it continues to decrease linearly with increasing SNR. The study covers both the cases of a single sinusoid and two sinusoids closely spaced in the frequency with the Pisarenko frequency estimator, MUSIC and principal component autoregressive frequency estimators. The trade-offs between the spectral broadening and the achieved minimum variance level due to the data window are analyzed in detail. textcopyright 2007 Elsevier Inc. All rights reserved.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The sinusoidal frequency estimation from short data records based on Toeplitz autocorrelation (AC) matrix estimates suffer from the dependence on the initial phases of the sinusoid(s). This effect becomes prominent when the impact of additive noise vanishes, 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 dependence. Thus with proper windowing, the variance of the frequency estimate is no more eclipsed by phase dependence, but it continues to decrease linearly with increasing SNR. The study covers both the cases of a single sinusoid and two sinusoids closely spaced in the frequency with the Pisarenko frequency estimator, MUSIC and principal component autoregressive frequency estimators. The trade-offs between the spectral broadening and the achieved minimum variance level due to the data window are analyzed in detail. textcopyright 2007 Elsevier Inc. All rights reserved. |
Altınkaya, MA; Anarım, E; Sankur, B Removal of the phase noise in the autocorrelation estimates with data windowing Journal Article 2005 13th European Signal Processing Conference, pp. 1–4, 2005. @article{pop00018f, title = {Removal of the phase noise in the autocorrelation estimates with data windowing}, author = {MA Altınkaya and E Anarım and B Sankur}, year = {2005}, date = {2005-01-01}, journal = {2005 13th European Signal Processing Conference}, pages = {1--4}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altinkaya, MA Effects of the autocorrelation matrix generation method on the model-based sinusoidal parameter estimators Journal Article Proceedings of the IEEE 12th Signal Processing and Communications …, 2004. @article{pop00019g, title = {Effects of the autocorrelation matrix generation method on the model-based sinusoidal parameter estimators}, author = {MA Altinkaya}, year = {2004}, date = {2004-01-01}, journal = {Proceedings of the IEEE 12th Signal Processing and Communications …}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Kalkan, C; Altinkaya, MA Pearson system-based blind source separation for estimating non-Gaussian fading channels in CDMA communication Journal Article Proceedings of the IEEE 12th Signal Processing and Communications …, 2004. @article{pop00029c, title = {Pearson system-based blind source separation for estimating non-Gaussian fading channels in CDMA communication}, author = {C Kalkan and MA Altinkaya}, year = {2004}, date = {2004-01-01}, journal = {Proceedings of the IEEE 12th Signal Processing and Communications …}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altinkaya, Mustafa A; Delic, Hakan; Sankur, Bülent; Anarim, Emin Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise Journal Article Signal Processing, 2002, ISSN: 01651684. @article{Altinkaya2002, title = {Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise}, author = {Mustafa A Altinkaya and Hakan Delic and Bülent Sankur and Emin Anarim}, doi = {10.1016/S0165-1684(02)00313-4}, issn = {01651684}, year = {2002}, date = {2002-01-01}, journal = {Signal Processing}, abstract = {In 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 S$alpha$S 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 S$alpha$S 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. textcopyright 2002 Elsevier Science B.V. All rights reserved.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In 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 S$alpha$S 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 S$alpha$S 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. textcopyright 2002 Elsevier Science B.V. All rights reserved. |
Altınkaya, MA; Deliç, H; Sankur, B; Anarım, E; Swami, A; Sadler, BM; Gini, F; ..., Special Section on Signal Processing with Heavy-tailed Models Journal Article Signal Processing, 82 (12), 2002. @article{pop00031d, title = {Special Section on Signal Processing with Heavy-tailed Models}, author = {MA Altınkaya and H Deliç and B Sankur and E Anarım and A Swami and BM Sadler and F Gini and ...}, year = {2002}, date = {2002-01-01}, journal = {Signal Processing}, volume = {82}, number = {12}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altınkaya, MA; Sankur, B; Anarım, E Performance of prefiltered model-based frequency estimators Journal Article TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 6 (1), pp. 23–36, 1998. @article{pop00020g, title = {Performance of prefiltered model-based frequency estimators}, author = {MA Altınkaya and B Sankur and E Anarım}, year = {1998}, date = {1998-01-01}, journal = {TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES}, volume = {6}, number = {1}, pages = {23--36}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altinkaya, MA; Deliç, H; Sankur, B; Anarim, E Frequency estimation of sinusoidal signals in alpha-stable noise using subspace techniques Journal Article Proceedings of 8th Workshop on Statistical Signal and Array Processing, pp. 234–237, 1996. @article{pop00008d, title = {Frequency estimation of sinusoidal signals in alpha-stable noise using subspace techniques}, author = {MA Altinkaya and H Deliç and B Sankur and E Anarim}, year = {1996}, date = {1996-01-01}, journal = {Proceedings of 8th Workshop on Statistical Signal and Array Processing}, pages = {234--237}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Altinkaya, MA; Anarim, E; Sankur, B Effects of prefiltering on model based tone frequency estimators Journal Article Proceedings of MELECON'94. Mediterranean Electrotechnical Conference, pp. 92–95, 1994. @article{pop00014e, title = {Effects of prefiltering on model based tone frequency estimators}, author = {MA Altinkaya and E Anarim and B Sankur}, year = {1994}, date = {1994-01-01}, journal = {Proceedings of MELECON'94. Mediterranean Electrotechnical Conference}, pages = {92--95}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Inproceedings |
Karakuş, Oktay; Kuruoǧlu, Ercan E; Altinkaya, Mustafa A Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity Inproceedings European Signal Processing Conference, 2016, ISSN: 22195491. @inproceedings{Karakus2016, title = {Bayesian estimation of polynomial moving average models with unknown degree of nonlinearity}, author = {Oktay Karakuş and Ercan E Kuruoǧlu and Mustafa A Altinkaya}, doi = {10.1109/EUSIPCO.2016.7760507}, issn = {22195491}, year = {2016}, date = {2016-01-01}, booktitle = {European Signal Processing Conference}, abstract = {textcopyright 2016 IEEE. Various real world phenomena such as optical communication channels, power amplifiers and movement of sea vessels exhibit nonlinear characteristics. The nonlinearity degree of such systems is assumed to be known as a general intention. In this paper, we contribute to the literature with a Bayesian estimation method based on reversible jump Markov chain Monte Carlo (RJMCMC) for polynomial moving average (PMA) models. Our use of RJMCMC is novel and unique in the way of estimating both model memory and the nonlinearity degree. This offers greater flexibility to characterize the models which reflect different nonlinear characters of the measured data. In this study, we aim to demonstrate the potentials of RJMCMC in the identification for PMA models due to its potential of exploring nonlinear spaces of different degrees by sampling.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } textcopyright 2016 IEEE. Various real world phenomena such as optical communication channels, power amplifiers and movement of sea vessels exhibit nonlinear characteristics. The nonlinearity degree of such systems is assumed to be known as a general intention. In this paper, we contribute to the literature with a Bayesian estimation method based on reversible jump Markov chain Monte Carlo (RJMCMC) for polynomial moving average (PMA) models. Our use of RJMCMC is novel and unique in the way of estimating both model memory and the nonlinearity degree. This offers greater flexibility to characterize the models which reflect different nonlinear characters of the measured data. In this study, we aim to demonstrate the potentials of RJMCMC in the identification for PMA models due to its potential of exploring nonlinear spaces of different degrees by sampling. |
Karakus, Oktay; Kuruoglu, Ercan E; Altinkaya, Mustafa A Long term wind speed prediction with polynomial autoregressive model Inproceedings 2015. @inproceedings{Karakus2015, title = {Long term wind speed prediction with polynomial autoregressive model}, author = {Oktay Karakus and Ercan E Kuruoglu and Mustafa A Altinkaya}, doi = {10.1109/siu.2015.7129907}, year = {2015}, date = {2015-01-01}, abstract = {textcopyright 2015 IEEE. 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 Çeşme 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.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } textcopyright 2015 IEEE. 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 Çeşme 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. |
Altinkaya, Mustafa A New results in robust location estimation with trimmed averages Inproceedings 2014. @inproceedings{Altinkaya2014, title = {New results in robust location estimation with trimmed averages}, author = {Mustafa A Altinkaya}, doi = {10.1109/siu.2014.6830700}, year = {2014}, date = {2014-01-01}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Kilicaslan, Kagan; Altinkaya, Mustafa A Performance analysis of lattice reduction aided MIMO detectors Inproceedings 2012. @inproceedings{Kilicaslan2012, title = {Performance analysis of lattice reduction aided MIMO detectors}, author = {Kagan Kilicaslan and Mustafa A Altinkaya}, doi = {10.1109/siu.2012.6204731}, year = {2012}, date = {2012-01-01}, abstract = {Lattice 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.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Lattice 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. |
Altinkaya, Mustafa A; Kuruoglu, Ercan E Modeling enzymatic reactions via chemical Langevin-Levy equation Inproceedings 2012. @inproceedings{Altinkaya2012, title = {Modeling enzymatic reactions via chemical Langevin-Levy equation}, author = {Mustafa A Altinkaya and Ercan E Kuruoglu}, doi = {10.1109/siu.2012.6204746}, year = {2012}, date = {2012-01-01}, abstract = {Chemical Langevin Equation (CLE) describes a useful approximation in stochastic modeling of chemical reactions. CLE-based $tau$-leaping algoritm updates the quantities of every molecule in a reaction system with a period of $tau$, firing every reaction in the system so many times that the concentration of each molecule can be assumed to remain in the current concentration state. Substituting the Brownian motion in the CLE with a Levy flight, one might expect the CLE to converge more rapidly. This work shows that alpha (Levy)-stable increments can be used in $tau$-leaping, demonstrating it with the example of a detailed kinetic model describing the enzymatic transgalactosylation reaction during lactulose hydrolysis. textcopyright 2012 IEEE.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Chemical Langevin Equation (CLE) describes a useful approximation in stochastic modeling of chemical reactions. CLE-based $tau$-leaping algoritm updates the quantities of every molecule in a reaction system with a period of $tau$, firing every reaction in the system so many times that the concentration of each molecule can be assumed to remain in the current concentration state. Substituting the Brownian motion in the CLE with a Levy flight, one might expect the CLE to converge more rapidly. This work shows that alpha (Levy)-stable increments can be used in $tau$-leaping, demonstrating it with the example of a detailed kinetic model describing the enzymatic transgalactosylation reaction during lactulose hydrolysis. textcopyright 2012 IEEE. |
Tanyer, Ilker; Ozen, Serdar; Donmez, Cemalettin; Altinkaya, Mustafa Aziz Consistency analysis of Kalman Filter for Modal Analysis of Structures Inproceedings 2009. @inproceedings{Tanyer2009, title = {Consistency analysis of Kalman Filter for Modal Analysis of Structures}, author = {Ilker Tanyer and Serdar Ozen and Cemalettin Donmez and Mustafa Aziz Altinkaya}, doi = {10.1109/siu.2009.5136464}, year = {2009}, date = {2009-01-01}, abstract = {In this paper, consistency analysis of Kalman filter for modal analysis of structural systems is made. As a future work, A fundamental modal analysis algorithm, eigensystem realization algorithm(ERA) will be used with Kalman filters together to make a modal parameter estimation for a structural system. By applying ERA to the impulse response measurements taken from the structure, a state-space representation will be written. Kalman filter will be used as a state estimator in this study and it will have a critical role on minimizing the measurement noise. Before using Kalman filter with ERA, a consistency analysis of Kalman filter is made for artificial impulse response data of the structural system.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In this paper, consistency analysis of Kalman filter for modal analysis of structural systems is made. As a future work, A fundamental modal analysis algorithm, eigensystem realization algorithm(ERA) will be used with Kalman filters together to make a modal parameter estimation for a structural system. By applying ERA to the impulse response measurements taken from the structure, a state-space representation will be written. Kalman filter will be used as a state estimator in this study and it will have a critical role on minimizing the measurement noise. Before using Kalman filter with ERA, a consistency analysis of Kalman filter is made for artificial impulse response data of the structural system. |
Altinkaya, Mustafa A; Anarim, Emin; Sankur, B??lent Phase noise mitigation in the autocorrelation estimates with data windowing: The case of two close sinusoids Inproceedings European Signal Processing Conference, 2006, ISSN: 22195491. @inproceedings{Altinkaya2006, title = {Phase noise mitigation in the autocorrelation estimates with data windowing: The case of two close sinusoids}, author = {Mustafa A Altinkaya and Emin Anarim and B??lent Sankur}, issn = {22195491}, year = {2006}, date = {2006-01-01}, booktitle = {European Signal Processing Conference}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |