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Öğe A complex-valued adaptive filter algorithm for system identification problem(Institute of Electrical and Electronics Engineers Inc., 2016) Mengüç, Engin Cemal; Acir, NurettinIn this study, a complex-valued adaptive filter algorithm based on Lyapunov stability theory is presented to solve a system identification problem in the complex domain. The performance of the proposed complex-valued Lyapunov adaptive filter (CLAF) algorithm is improved for the complex-valued system identification problem by integrating the LST into the filter optimization cost. The performance of the proposed algorithm is tested on a complex-valued moving average (MA) system identification problem and compared with the conventional complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) algorithms. The simulation results show that the proposed CLAF algorithm has achieved a faster convergence rate and a lower steady-state MSE performance when compared to the other algorithms. © 2015 Chamber of Electrical Engineers of Turkey.Öğe A Complex-Valued Adaptive Filter Algorithm for System Identification Problem(IEEE, 2015) Mengguc, Engin Cemal; Acir, NurettinIn this study, a complex-valued adaptive filter algorithm based on Lyapunov stability theory is presented to solve a system identification problem in the complex domain. The performance of the proposed complex-valued Lyapunov adaptive filter (CLAF) algorithm is improved for the complex-valued system identification problem by integrating the LST into the filter optimization cost. The performance of the proposed algorithm is tested on a complex-valued moving average (MA) system identification problem and compared with the conventional complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) algorithms. The simulation results show that the proposed CLAF algorithm has achieved a faster convergence rate and a lower steady-state MSE performance when compared to the other algorithms.Öğe A generalized Lyapunov stability theory-based adaptive FIR filter algorithm with variable step sizes(Springer London Ltd, 2017) Menguc, Engin Cemal; Acir, NurettinThis paper presents a novel approach to Lyapunov stability theory-based adaptive filter (LAF) design. The proposed design is based on the minimization of the Euclidean norm of the difference weight vector under negative definiteness constraint defined over a novel linear Lyapunov function. The proposed fixed step size LAF (FSS-LAF) algorithm is first obtained by using the method of Lagrangian multipliers. The FSS-LAF satisfying asymptotic stability in the sense of Lyapunov provides a significant performance gain in the presence of a measurement noise. The stability of the FSS-LAF algorithm is also statistically analyzed in this study. Moreover, gradient variable step size (VSS) algorithms are adapted to the FSS-LAF algorithm to further enhance the performance for the first time in this paper. These VSS algorithms are Benveniste (BVSS), Mathews and Farhang-Ang (FVSS) algorithms. Simulation results on system identification problems show that the bounds of step size for the FSS-LAF algorithm are verified, and especially, the BVSS-LAF and FVSS-LAF algorithms provide a better trade-off between steady-state mean square deviation error and convergence rate than other proposed algorithms.Öğe A modified adaptive IIR filter design via wavelet networks based on Lyapunov stability theory(SPRINGER, 2008) Acir, NurettinIn this paper, we present a wavelet network IIR filtering system satisfying asymptotic stability in the sense of Lyapunov unlike many other gradient descent algorithms based adaptive filtering systems. The proposed system also carries the advantages of the time-frequency specific properties of wavelet networks embedded into the proposed filter dynamics. Two experiments for system identification problems corresponding to the infinite impulse response filter design are proposed. The results verified that the proposed wavelet network infinite impulse response adaptive filtering system not only performs better than gradient descent based algorithms but also performs as good as other stability theory based optimization algorithms.Öğe A Modified Neural Filtering Algorithm for Tracking of Chaotic Signals(IEEE, 2014) Menguc, Engin Cemal; Acir, Nurettin; AlDabass, D; Orsoni, A; Cant, R; Yunus, J; Ibrahim, Z; Saad, IIn this study, a modified neural filtering algorithm is presented for tracking of chaotic signals. A multilayer neural network (MLNN) structure is used in proposed design as a nonlinear adaptive filtering tool. Initially, the MLNN is linearized using Taylor series expansion and then the weight vector update rule is designed by using Lyapunov stability theory (LST) to adaptively update the weights of the MLNN. The tracking capability of the proposed algorithm is improved by using adaptation gain rate parameter "a(k)" which is iteratively adjusted itself depending on sequential tracking errors rate. The tracking ability of the proposed algorithm is tested on two chaotic signals and compared with conventional algorithms. The simulation results have supported that the proposed neural filtering algorithm achieved better performance.Öğe A New Approach to Channel Equalization Problem(IEEE, 2015) Menguc, Engin Cemal; Acir, NurettinIn this study, a new approach based on Lyapunov stability theory (LST) is proposed for channel equalization problem. For the first time, the convergence capability of the proposed algorithm is presented on the channel equalization problem. The proposed approach is compared with normalized least mean square (NLMS) algorithm. Simulation results show that the convergence capability of the proposed algorithm is better than NLMS algorithm. As a result, the proposed approach can effectively be used for the channel equalization problem.Öğe A novel adaptive filter algorithm for tracking of chaotic time series(2011) Mengüç, Engin Cemal; Acir, NurettinIn this study, a novel tracking filter algorithm is proposed satisfying stability in the sense of Lyapunov and is performed which is independent of statistical properties of input. The robustness of the proposed filter is presented by using two benchmark chaotic time series found in the literature. The proposed filter is compared with well known classical filters and performed in a high performance. © 2011 IEEE.Öğe A novel adaptive filter design using Lyapunov stability theory(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2015) Menguc, Engin Cemal; Acir, NurettinThis paper presents a new approach to design an adaptive filter using Lyapunov stability theory. The design procedure is formulated as an inequality constrained optimization problem. Lagrange multiplier theory is used as an optimization tool. Lyapunov stability theory is integrated into the constraint function to satisfy the asymptotic stability of the proposed filtering system. The tracking capability is improved by using a new analytical adaptation gain rate, which has the ability to adaptively adjust itself depending on a sequential tracking square error rate. The fast and robust convergence ability of the proposed algorithm is comparatively examined by simulation examples.Öğe A novel nonlinear adaptive filter design and its implementation with FPGA(2012) Mengüç, Engin Cemal; Acir, NurettinIn this study, a novel nonlinear adaptive filter algorithm is proposed guaranteeing the asymptotic stability in the sense of Lyapunov. The tracking capability of the proposed filter is tested by using a created artificial signal having a finite number of discontinuities. The proposed filter shows high performance both in Matlab environment and its FPGA realization. As a result, realization of the proposed filter with FPGA is confirmed. © 2012 IEEE.Öğe A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis(Pergamon-Elsevier Science Ltd, 2017) Cinar, Salim; Acir, NurettinElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs. (C) 2016 Elsevier Ltd. All rights reserved.Öğe An augmented complex-valued Lyapunov stability theory based adaptive filter algorithm(Elsevier, 2017) Menguc, Engin Cemal; Acir, NurettinA novel augmented complex-valued Lyapunov stability theory (LST) based adaptive filter (ACLAF) algorithm is proposed for the widely linear adaptive filtering of noncircular complex-valued signals. After a candidate Lyapunov function is determined, the design procedure is formulated as an inequality constrained optimization problem by using augmented statistics and LST. Thus, the proposed algorithm has improved the adaptive filtering of noncircular complex-valued signals by a unified framework of the LST and augmented complex statistics. Moreover, we statistically show that the ACLAF algorithm converges to the optimal Wiener solution under stationary environments, the required condition of the step size for the stability of the ACLAF algorithm is obtained by convergence in mean analysis and a new approach. In addition, the variance of the ACLAF algorithm is statically analysed in this study. The performance of the ACLAF algorithm is tested on circular and noncircular benchmark signals and on a real-world non circular wind signal. Simulation results verify that the ACLAF algorithm outperforms complex-valued LST based adaptive filter (CLAF), complex-valued least mean square (CLMS), complex-valued normalized least mean square (CNLMS), augmented CLMS (ACLMS) and augmented CNLMS (ACNLMS) algorithms for adaptive prediction of noncircular signals in terms of prediction gain, convergence rate and mean square error (MSE). Also, the ACLAF algorithm enhances the prediction gain by more than 25% when compared to the other augmented algorithms. (C) 2017 Elsevier B.V. All rights reserved.Öğe Artificial Single Trial Auditory Evoked Potential Signal Generator(IEEE, 2009) Bahtiyar, Yemen Alev; Erkan, Yasemin; Acir, NurettinIn this study, we introduce an artificial single trial auditory evoked potential signal generator which contributes to create the synthetic data set that are mostly used in biomedical engineering applications. The proposed system not only provides the comparison of the methods used in engineering applications but also it provides an objectivity in determining the performance of the novel systems to be designed in other studies. In this study, we have aimed to create synthetic data set by generating single trial auditory evoked potential at any arbitrary SNR level.Öğe Auditory brainstem response classification for threshold detection using estimated evoked potential data: comparison with ensemble averaged data(SPRINGER, 2013) Acir, Nurettin; Erkan, Yasemin; Bahtiyar, Yemen AlevAuditory brainstem response (ABR) has become a routine clinical tool in neurological and audiological assessment. ABR measurement process with ensemble averaging is very time-consuming and uncomfortable for subjects due to the more repetition of single trials. This condition also restricts the wide usability of ABR in clinical applications. Therefore, the reduction in repetitions has a great importance in ABR measurements. In this study, 488 ABR responses are used for creating two different data sets. The first set is created conventionally by ensemble averaging of 1,024 single trials for each ABR pattern. The second set is obtained from the first estimated 64 single trials of the same records for each ABRs. Estimation is realized by using a nonlinear adaptive filtering algorithm. In classification stage, a powerful classifier integrated with a feature selection algorithm is performed for each data set. In result, the classification performance for estimated ABR data with 64 repetitions is better than the classification performance of the ensemble averaged data with 1,024 repetitions. The proposed system is resulted in an accuracy of 96% for estimated ABRs. So, the proposed system can effectively be used for threshold detection in auditory assessment providing a high accuracy. While the obtained results contribute to the practical ABR usage in clinics, the great significance of it arises from the reduction in repetitions via estimation of ABRs.Öğe Auditory Threshold Detection by Classifying Estimated Short Latency Evoked Potentials(IEEE, 2009) Acir, Nurettin; Erkan, Yasemin; Bahtiyar, Yemen AlevShort latency evoked response (SLER) has become a routine clinical tool in neurological and audiological assessment. But, in order to extract SLER from backgroung EEG signal, many repeated single trial measurements are necessary In some cases, these reprtitions are up to 2000. Therefore, measuring period is very time consuming and uncomfortable for subjects. This condition is also limited the SLER usage in clinical applications. In this study, 302 SLER responses extracted by averaging 1024 single trials are used for creating two different data sets. The first set is created from ensemble averaging of 1024 trials for each SLER signals. The second set is obtained from the same single trial measurements by estimating 64 trials of each SLER signal. The support vector machine which is a powerful binary classifier is performed for each data sets for three different feature extraction techniques. In result, the results obtained from estimated data (second data set) classification procedure is better than the results of classical ensemble averaged data set (first data set) with a high accuracy and less time consuming. This results contribute to the SLER usage in clinics more practical than classical ones.Öğe Automatic Removal of Ocular Artefacts in EEG Signal by Using Independent Component Analysis and Artificial Neural Network(IEEE, 2017) CInar, Salim; Menguc, Engin Cemal; Acir, NurettinOcular artefacts caused by eye movements can distort Electroencephalogram (EEG) recordings. It is important to obtain clean EEG signals in diagnosing and interpreting diseases. Meaningful EEG signals should not be distorted during the removal of artefacts. In this study, Independent Component Analysis and Artificial Neural Network were used together to remove ocular artefacts. The method was tested by using the real dataset. The Relative Error (RE) and Correlation Coefficient (CC) was used to test the performance of the method. Relative error = 0.227 0.229 and correlation coefficient = 0.941 0.088 was calculated in the performance analysis. According to the results, the proposed method is successful in removing ocular artefacts in EEG signals.Öğe Automatic Removal of Ocular Artefacts In EEG Signal by Using Independent Component Analysis and Chauvenet Criterion(IEEE, 2015) Cinar, Salim; Acir, NurettinEye movements (saccade, blink and cause artefacts in Electroencephalogram recordings. The ocular artefact can distort the EEG signals. Removal of ocular artefact is important issue in EEG signal analysis. The main task of artefact removal algorithms is to obtain cleaned EEG without losing meaningful EEG signal. The main focus of this work is to remove ocular artefact automatically by using Independent Component Analysis and Chauvenet criterion. The method is tested on real dataset. Relative error and Correlation coefficient are used for the performance test. The performance of the proposed method was Relative error= 0.273 +/- 0.148, Correlation coefficient= 0.943 +/- 0.042 in the dataset. The results show that the porposed method effectively removes ocular artefacts in EEG.Öğe Complex-Valued Least Mean Kurtosis Adaptive Filter Algorithm(IEEE, 2016) Menguc, Engin Cemal; Acir, NurettinIn this study, a complex-valued least mean Kurtosis (CLMK) adaptive filter algorithm is designed for processing complex-valued signals. The performance of the designed algorithm is tested on a complex-valued system identification and compared the complex-valued least mean square (CLMS) and complex-valued normalized least mean square (CNLMS) algorithms. As a result, the CLMK algorithm shows a higher performance than the other algorithms in terms of the convergence rate, mean square error (MSE) and mean square deviation (MSD).Öğe Deep Neural Network Training with iPSO Algorithm(IEEE, 2018) Kosten, Mehmet Muzaffer; Barut, Murat; Acir, NurettinDeep learning-based methods are frequently preferred in many areas in recent years. Another issue, which is as important as deep neural networks applications, is the training of deep neural networks. Although many techniques are proposed in the literature for the training of deep nets, most of these techniques use gradient descent based approaches. In this study, differently from the conventional gradient method, Improved Particle Swam Optimisation (IPSO) algorithm is used for the training of deep neural networks. LeNet-5 network is preferred as network structure and MNIST is utilized as data set. Depending on the number of particles, a performance of up to 96.29% was achieved. In the cases after 20 particles, the average performance was over 90%.Öğe Design of a nonlinear adaptive infinite impulse response filter(IEEE, 2007) Acir, NurettinThis study introduces a wavelet network based adaptive BR filtering system satisfying asymptotic stability in the sense of Lyapunov. The proposed system is also integrated with the advantages of the time-frequency specific properties of wavelet networks in its dynamics. Two well known experiments are proposed. The results are verified that the proposed filtering system not only performs better than conventional gradient descent based algorithms but also performs as good as other stability theory based optimization algorithms.Öğe Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm(SPRINGER, 2013) Acir, NurettinIn this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also for reduction of single-trial numbers in BAEP pattern extraction process. Trial-to-trial variations in auditory evoked potential (AEP) are very important in quantifying dynamical properties of the nervous system and in specifying the group-specific effects in clinical applications. Due to the nonlinear dynamics of the AEP, a nonlinear adaptive filtering technique is considered as a powerful tool for tracking such variations. Therefore, we have designed a wavelet network-based nonlinear adaptive filter (WaNe-NAF) satisfying asymptotic stability in the sense of Lyapunov. The simulation results are verified that the proposed WaNe-NAF can effectively track the trial-to-trial variations. We have also compared the WaNe-NAF with the most widely used ensemble averaging technique using real measured human BAEP data. The WaNe-NAF shows promise for requiring less number of ensembles than conventional ensemble averaging method to attain adequate signal quality. As a result, the proposed filtering system is suggested as a powerful tool in AEP acquisition and processing systems.
