WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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2009 - 200912010 - 20155

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Access Right: Closed AccessAuthor: search.filters.author.Altınkaya, Mustafa Aziz

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Now showing 1 - 6 of 6
  • Conference Object
    Consistency Analysis of Kalman Filter for Modal Analysis of Structures
    (Institute of Electrical and Electronics Engineers Inc., 2009) Tanyer, İlker; Özen, Serdar; Dönmez, Cemalettin; Altınkaya, Mustafa Aziz
    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.
  • Conference Object
    Alpha-Trimmed Means of Multiple Location Estimates
    (Institute of Electrical and Electronics Engineers Inc., 2013) Altınkaya, Mustafa Aziz
    Localization 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, Kağan
    In 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.
  • Conference Object
    A Bayesian Approach for Licence Plate Recognition Developed on a Realistic Simulation Environment
    (Institute of Electrical and Electronics Engineers Inc., 2013) Efeler, Mahmut Cenk; Altınkaya, Mustafa Aziz; Gümüştekin, Şevket
    Template matching is one of the most common methods for license plate recognition. This method discards prior probabilities of license plate codes. The posterior code class probabilities constructed by including the prior probability information are expected to improve the recognition performance. The probability information that needs to be included requires extensive training data, which is quite costly to obtain. In order to generate these training images a license plate image simulator is developed with a realistic noise model. Simulated license plate images are then used to test a Bayesian decision theory based recognition procedure. Test results indicate that, with the inclusion of prior information, significant recognition gain is obtained with respect to standard template matching method at high noise levels.
  • Conference Object
    Citation - WoS: 5
    Estimation 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 Aziz
    Despite 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
    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.