Browsing by Subject "Parameter estimation"

Now showing 1 - 20 of 86

Open Access
3-D motion estimation and wireframe adaptation including photometric effects for model-based coding of facial image sequences
(IEEE, 1994-06) Bozdağı, G.; Tekalp, A. M.; Onural, L.
We propose a novel formulation where 3-D global and local motion estimation and the adaptation of a generic wireframe model to a particular speaker are considered simultaneously within an optical flow based framework including the photometric effects of the motion. We use a flexible wireframe model whose local structure is characterized by the normal vectors of the patches which are related to the coordinates of the nodes. Geometrical constraints that describe the propagation of the movement of the nodes are introduced, which are then efficiently utilized to reduce the number of independent structure parameters. A stochastic relaxation algorithm has been used to determine optimum global motion estimates and the parameters describing the structure of the wireframe model. Results with both simulated and real facial image sequences are provided.
Open Access
Accuracy limits of distance estimation in visible light systems with RGB LEDs
(IEEE, 2019-09) Demirel, İlker; Gezici, Sinan
The distance estimation problem is investigated for visible light positioning (VLP) systems with red-green-blue (RGB) light emitting diodes (LEDs). The accuracy limits on distance estimation are calculated in terms of the Cramér-Rao lower bounds (CRLBs) for three different scenarios. Scenario 1 and Scenario 2 correspond to synchronous and asynchronous systems, respectively, with known channel attenuation formulas at the receiver. In Scenario 3, a synchronous systems is considered but channel attenuation formulas are not known at the receiver. The derived CRLB expressions reveal the relations among the distance estimation accuracies in the considered scenarios and provide intuitive explanations for the benefits of using RGB LEDs.
Open Access
Algebraic error analysis of collinear feature points for camera parameter estimation
(Elsevier, 2011-01-04) Urfalioglu, O.; Thormählen, T.; Broszio, H.; Mikulastik, P.; Çetin, A. Enis
In general, feature points and camera parameters can only be estimated with limited accuracy due to noisy images. In case of collinear feature points, it is possible to benefit from this geometrical regularity by correcting the feature points to lie on the supporting estimated straight line, yielding increased accuracy of the estimated camera parameters. However, regarding Maximum-Likelihood (ML) estimation, this procedure is incomplete and suboptimal. An optimal solution must also determine the error covariance of corrected features. In this paper, a complete theoretical covariance propagation analysis starting from the error of the feature points up to the error of the estimated camera parameters is performed. Additionally, corresponding Fisher Information Matrices are determined and fundamental relationships between the number and distance of collinear points and corresponding error variances are revealed algebraically. To demonstrate the impact of collinearity, experiments are conducted with covariance propagation analyses, showing significant reduction of the error variances of the estimated parameters.
Open Access
An alternative method to measure the likelihood of a financial crisis in an emerging market
(Elsevier BV, 2007) Özlale, Ü.; Özcan, K. M.
This paper utilizes an early warning system in order to measure the likelihood of a financial crisis in an emerging market economy. We introduce a methodology, where we can both obtain a likelihood series and analyze the time-varying effects of several macroeconomic variables on this likelihood. Since the issue is analyzed in a non-linear state space framework, the extended Kalman filter emerges as the optimal estimation algorithm. Taking the Turkish economy as our laboratory, the results indicate that both the derived likelihood measure and the estimated time-varying parameters are meaningful and can successfully explain the path that the Turkish economy had followed between 2000 and 2006. The estimated parameters also suggest that overvalued domestic currency, current account deficit and the increase in the default risk increase the likelihood of having an economic crisis in the economy. Overall, the findings in this paper suggest that the estimation methodology introduced in this paper can also be applied to other emerging market economies as well. © 2007 Elsevier B.V. All rights reserved.
Open Access
Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation
(Springer, 2015) Ravens, U.; Katircioglu-Öztürk, D.; Wettwer, E.; Christ, T.; Dobrev, D.; Voigt, N.; Poulet, C.; Loose, S.; Simon, J.; Stein, A.; Matschke, K.; Knaut, M.; Oto, E.; Oto, A.; Güvenir, H. A.
Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.
Open Access
The asymptotic properties of estimates of the parameters of nonlinear time series
(Springer, 2000) Anisimov, V. V.; Keibakh, K. S.
Asymptotic properties of nonlinear time series parameter estimators constructed on trajectories of stochastic systems under stationary and transient conditions are studied with the use of the least-squares method. The investigation method is based on the study of asymptotic properties of extremal sets of random functions. © 2000 Kluwer Academic/Plenum Publishers.
Open Access
Automatic performance evaluation of Web search engines
(Elsevier, 2004) Can, F.; Nuray, R.; Sevdik, A. B.
Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgments involved. However, both for business enterprises and people it is important to know the most effective Web search engines, since such search engines help their users find higher number of relevant Web pages with less effort. Furthermore, this information can be used for several practical purposes. In this study we introduce automatic Web search engine evaluation method as an efficient and effective assessment tool of such systems. The experiments based on eight Web search engines, 25 queries, and binary user relevance judgments show that our method provides results consistent with human-based evaluations. It is shown that the observed consistencies are statistically significant. This indicates that the new method can be successfully used in the evaluation of Web search engines. © 2003 Elsevier Ltd. All rights reserved.
Open Access
Average fisher information optimization for quantized measurements using additive independent noise
(IEEE, 2010) Balkan, Gokce Osman; Gezici, Sinan
Adding noise to nonlinear systems can enhance their performance. Additive noise benefits are observed also in parameter estimation problems based on quantized observations. In this study, the purpose is to find the optimal probability density function of additive noise, which is applied to observations before quantization, in those problems. First, optimal probability density function of noise is formulated in terms of an average Fisher information maximization problem. Then, it is proven that optimal additive "noise" can be represented by a constant signal level. This result, which means that randomization of additive signal levels is not needed for average Fisher information maximization, is supported with two numerical examples. ©2010 IEEE.
Open Access
Bandwidth selection for kernel density estimation using fourier domain constraints
(Institution of Engineering and Technology, 2016) Suhre, A.; Arıkan, Orhan; Çetin, A. Enis
Kernel density estimation (KDE) is widely-used for non-parametric estimation of an underlying density from data. The performance of KDE is mainly dependent on the bandwidth parameter of the kernel. This study presents an alternative method of estimating the bandwidth by incorporating sparsity priors in the Fourier transform domain. By using cross-validation (CV) together with an l1 constraint, the proposed method significantly reduces the under-smoothing effect of traditional CV methods. A solution for all free parameters in the minimisation is proposed, such that the algorithm does not need any additional parameter tuning. Simulation results indicate that the new approach is able to outperform classical and more recent approaches over a set of distributions of interest.
Open Access
BSSFP görüntülemede eliptik sinyal modeline dayali eşzamanlı parametre tahmini
(IEEE, 2018) Keskin, Kübra; Çukur, Tolga
Faz döngülü dengeli kararlı-durum serbest devinim (bSSFP) görüntüleme tekniginde, görüntüde oluşan rezonans dışı frekanslardan kaynaklı sinyal düşüşlerinin uzamsal konumunu kaydırmak amacıyla her çekimde RF darbelerinin fazı degiştirilerek birden fazla çekim yapılmaktadır. Bu çekimlerden yararlanılarak yapayolgudan arındırılmış görüntü elde edilmektedir. Görüntü geriçatımına ek olarak, aynı çekimden elde edilen veriler; sinyali oluşturan dokulara özgü T1 ve T2 releksasyon zamanı gibi parametrelerin tahminleri için de kullanılabilmektedir. Bu çalışmada T1 ve T2 tahminlerinin yapılabilmesi için gereken minimum faz döngüsü sayısı araştırılmıştır ve eliptik sinyal modelindeki geometrik ilişkileri kullanan bir tahmin yöntemi önerilmiştir. Bu yöntemin benzetim sonuçları referans degerler ile birlikte gösterilmiş, görüntü geriçatımı ise mevcut yöntemler ile karşılaştırılmıştır.
Open Access
Chapter 11 - Parametric estimation
(Academic Press, 2023-06-30) Corey, R. M.; Kozat, Süleyman Serdar; Singer, A. C.; Diniz, P. S. R.
An important engineering concept is that of modeling signals and systems in a manner that enables their study, analysis, and control. We seek models that are relatively easy to compute or estimate, yet at the same time provide insight into the salient characteristics of the signals or systems under study. One way to control the complexity of such models is through the use of parametric models. These are models that explicitly depend on a fixed number of parameters. In this chapter, we explore parametric models for signals and systems with a focus on the estimation of these model parameters under a variety of scenarios. Under statistical and deterministic formulations, we begin with models that are linear in their parameters and study both the batch and recursive formulations of these problems. We next apply these methods to problems in spectrum estimation, prediction, and filtering. Nonlinear modeling, universal methods, and order estimation are advanced topics that are also considered.
Open Access
Çokyollu kanal parametre kestirimi için yeni bir dizilim sinyal işleme tekniği
(IEEE, 2007-06) Güldoǧan, Mehmet Burak; Arıkan, Orhan
Bu bildiride, çarpraz belirsizlik işlevinin kullanıldığı yeni bir dizilim sinyal işleme tekniği önerilmektedir. Geliştirilen teknik bir algılayıcı dizilimine gelen sinyallerden herbirinin geliş yönünü (GY), zaman gecikmesini Doppler kaymasını ve genliğini dürümlü bir sekilde kestirir. Önerilen Çarpraz Belirsizlik İşlevi - Yön Bulma (ÇBI-YB) tekniği ile Çoklu Sinyal Sınıflandırması (MUSIC) algoritmasının performansları sentetik sinyaller kullanılarak kök Ortalama Karesel Hata (kOKH) cinsinden değişik işaret Gürültü Oranı (İGO) değerleri için karşılaştırılmıştır. Önerilen tekniğin başarımı kayıt edilmiş çokyollu yüksek-enlem iyonosfer verileri üzerinde irdelenmiştir. Elde edilen sonuçlar, düşük İGO değerlerinde dahi çokyollu sinyal kaynaklarını ayırmada önerilen ÇBİ-YB tekniğinin ciddi başarım artışı sağladığını göstermektedir.
Open Access
Collective oscillations in a two-dimensional Bose-Einstein condensate with a quantized vortex state
(The American Physical Society, 2005) Banerjee, A.; Tanatar, Bilal
We study the effect of lower dimensional geometry on the frequency splitting of the quadrupole oscillations of a harmonically trapped Bose-Einstein condensate due to the presence of a quantized vortex. To study the effect of two-dimensional geometry we consider a pancake-shaped condensate and employ various models for the coupling parameter depending on the thickness of the condensate relative to the value of the scattering length. Using these models and the sum-rule approach we obtain analytical expressions for the frequency splitting. These expressions are valid for positive scattering length and large N. We show that the frequency splitting of the quadrupole oscillations are significantly altered by the reduced dimensionality and also study the evolution of the splitting as the system makes transition from one scattering regime to the other.
Open Access
Comparative study of an EKF-based parameter estimation and a nonlinear optimization-based estimation on PMSM system identification
(MDPI AG, 2021-09-25) Sel, Artun; Sel, Bilgehan; Coskun, Umit; Kasnakoğlu, Cosku
In this study, two different parameter estimation algorithms are studied and compared. Iterated EKF and a nonlinear optimization algorithm based on on-line search methods are imple mented to estimate parameters of a given permanent magnet synchronous motor whose dynamics are assumed to be known and nonlinear. In addition to parameters, initial conditions of the dynami cal system are also considered to be unknown, and that comprises one of the differences of those two algorithms. The implementation of those algorithms for the problem and adaptations of the methods are detailed for some other variations of the problem that are reported in the literature. As for the computational aspect of the study, a convexity study is conducted to obtain the spherical neighborhood of the unknown terms around their correct values in the space. To obtain such a range is important to determine convexity properties of the optimization problem given in the estimation problem. In this study, an EKF-based parameter estimation algorithm and an optimization-based method are designed for a given nonlinear dynamical system. The design steps are detailed, and the efficacies and shortcomings of both algorithms are discussed regarding the numerical simulations.
Open Access
Comparison of the CAF-DF and sage algorithms in multipath channel parameter estimation
(IEEE, 2008-07) Güldoğan, M. Burak; Arıkan, Orhan
In this paper, performance of the recently proposed Cross Ambiguity Function - Direction Finding (CAF-DF) technique is compared with the Space Alternating Generalized Expectation Maximization (SAGE) technique. The CAF-DF, iteratively estimates direction of arrival (DOA), time-delay, Doppler shift and amplitude corresponding to each impinging signal onto an antenna array by utilizing the cross ambiguity function. On synthetic signals, based on Monte Carlo trials, performances of the algoritms are tested in terms of root Mean Squared Error (rMSE) at different Signal-to-Noise Ratios (SNR). Cramer-Rao lower bound is included for statistical comparisons. Simulation results indicate the superior performance of the CAF-DF technique over SAGE technique for low and medium SNR values. © 2008 IEEE.
Open Access
A comparison of two methods for fusing information from a linear array of sonar sensors for obstacle localization
(IEEE, 1995) Arıkan, Orhan; Barshan, Billur
The performance of a commonly employed linear array of sonar sensors is assessed for point-obstacle localization intended for robotics applications. Two different methods of combining time-of-flight information from the sensors are described to estimate the range and azimuth of the obstacle: pairwise estimate method and the maximum likelihood estimator. The variances of the methods are compared to the Cramer-Rao Lower Bound, and their biases are investigated. Simulation studies indicate that in estimating range, both methods perform comparably; in estimating azimuth, maximum likelihood estimate is superior at a cost of extra computation. The results are useful for target localization in mobile robotics.
Open Access
Comprehensive lower bounds on sequential prediction
(IEEE, 2014-09) Vanlı, N. Denizcan; Sayın, Muhammed O.; Ergüt, S.; Kozat, Süleyman S.
We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as regret, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case.
Open Access
A confidence ellipsoid approach for measurement cost minimization under Gaussian noise
(IEEE, 2012-06) Dülek, Berkan; Gezici, Sinan
The well-known problem of estimating an unknown deterministic parameter vector over a linear system subject to additive Gaussian noise is studied from the perspective of minimizing total sensor measurement cost under a constraint on the log volume of the estimation error confidence ellipsoid. A convex optimization problem is formulated for the general case, and a closed form solution is provided when the system matrix is invertible. Furthermore, effects of system matrix uncertainty are discussed by employing a specific but nevertheless practical uncertainty model. Numerical examples are presented to discuss the theoretical results in detail.
Open Access
Cost minimization of measurement devices under estimation accuracy constraints in the presence of Gaussian noise
(Elsevier, 2012) Dulek, B.; Gezici, Sinan
Novel convex measurement cost minimization problems are proposed based on various estimation accuracy constraints for a linear system subject to additive Gaussian noise. Closed form solutions are obtained in the case of an invertible system matrix. In addition, the effects of system matrix uncertainty are studied both from a generic perspective and by employing a specific uncertainty model. The results are extended to the Bayesian estimation framework by treating the unknown parameters as Gaussian distributed random variables. Numerical examples are presented to discuss the theoretical results in detail.
Open Access
Coupled deconvolution for frequency extrapolation of electromagnetic solutions with matrix pencil method
(IEEE, 2005) Gürel, Levent; Yıldırım, Ferhat
Matrix pencil method (MPM) has been widely used to estimate the parameters of complex-exponential based models. An important application is the extrapolation of the frequency-domain solutions of electromagnetic problems. In this paper, we present a mathematical tool, namely, coupled deconvolution, which improves the performance of the MPM-based extrapolation of electromagnetic solutions.