Browsing by Subject "Signal filtering and prediction"

Now showing 1 - 20 of 30

Open Access
Boosted LMS-based piecewise linear adaptive filters
(IEEE, 2016) Kari, Dariush; Marivani, Iman; Delibalta, İ.; Kozat, Süleyman Serdar
We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we have several adaptive constituent filters that run in parallel. For each newly received input vector and observation pair, each filter adapts itself based on the performance of the other adaptive filters in the mixture on this current data pair. These relative updates provide the boosting effect such that the filters in the mixture learn a different attribute of the data providing diversity. The outputs of these constituent filters are then combined using adaptive mixture approaches. We provide the computational complexity bounds for the boosted adaptive filters. The introduced methods demonstrate improvement in the performances of conventional adaptive filtering algorithms due to the boosting effect.
Open Access
Chirp filtering in the fractional Fourier domain
(Optical Society of America, 1994-11-10) Dorsch, R. G.; Lohmann, A. W.; Bitran, Y.; Mendlovic, D.; Özaktaş, Haldun M.
In the Wigner domain of a one-dimensional function, a certain chirp term represents a rotated line delta function. On the other hand, a fractional Fourier transform (FRT) can be associated with a rotation of the Wigner-distribution function by an angle connected with the FRT order. Thus with the FRT tool a chirp and a delta function can be transformed one into the other. Taking the chirp as additive noise, the FRT is used for filtering the line delta function in the appropriate fractional Fourier domain. Experimental filtering results for a Gaussian input function, which is modulated by an additive chirp noise, are shown. Excellent agreement between experiments and computer simulations is achieved.
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
Çokyollu ortamda çapraz belirsizlik işlevi-yön bulma tekniğinin başarım analizi
(IEEE, 2008-04) Güldoǧan, Mehmet Burak; Arıkan, Orhan
Bu bildiride, Çapraz Belirsizlik İşlevi-Yön Bulma (CAFDF) tekniğinin çokyollu ortamlardaki sinyallerin zaman gecikmesi, Doppler kayması, geliş yönü(GY) ve genlik kestirimindeki başarımı ile yiiksek çözünürlüklü algoritmalar olan Uzay-Almaşan Genelleşmiş Beklenti-Enbüyüitme (SAGE) ve Çoklu Sinyal Sınıflandırılması(MUSIC)'in sentetik sinyaller iizerindeki başarımları kıyaslanmıştır. Algoritmalann performansları, kök Ortalama Karesel Hata (kOKH) cinsinden degişik işaret Gürültü Oranı (iGO) değerlerinde Monte Carlo denemelerine dayalı olarak sunulmuştur. Sentetik kanallarda istatiksel kıyaslama amaçlı Cramer-Rao alt sınırları eklenmiştir. Simülasyon sonuçları göstermektedir ki, orta ve düşük iGO değerlerinde CAF-DF diğer iki algoritmaya göre üstünlük sağlamaktadır.
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
Cost-efficient approximation of linear systems with repeated and multi-channel filtering configurations
(IEEE, 1998-05) Kutay, Mehmet Alper; Erden, M. F.; Özaktaş, Haldun M.; Arıkan, Orhan; Candan, Ç.; Güleryüz, Ö.
It is possible to obtain either exact realizations or useful approximations of linear systems or matrix-vector products arising in many different applications, by synthesizing them in the form of repeated or multi-channel filtering operations in fractional Fourier domains, resulting in much more efficient implementations with acceptable decreases in accuracy. By varying the number and configuration of filter blocks, which may take the form of arbitrary flow graphs, it is possible to trade off between accuracy and efficiency in the desired manner. The proposed scheme constitutes a systematic way of exploiting the information inherent in the regularity or structure of a given linear system or matrix, even when that structure is not readily apparent.
Open Access
Effects of linear filter on stability and performance of human-in-the-loop model reference adaptive control architectures
(ASME, 2017) Yousefi, Ehsan; Demir, Didem Fatma; Sipahi, R.; Yücelen, T.; Yıldız, Yıldıray
Model reference adaptive control (MRAC) can effectively handle various challenges of the real world control problems including exogenous disturbances, system uncertainties, and degraded modes of operations. In human-in-the-loop settings, MRAC may cause unstable system trajectories. Basing on our recent work on the stability of MRAC-human dynamics, here we follow an optimization based computations to design a linear filter and study whether or not this filter inserted between the human model and MRAC could help remove such instabilities, and potentially improve performance. To this end, we present a mathematical approach to study how the error dynamics of MRAC could favorably or detrimentally influence human operator's error dynamics in performing a certain task. An illustrative numerical example concludes the study.
Open Access
An efficient algorithm to extract components of a composite signal
(IEEE, 2000) Özdemir, A. Kemal; Arıkan, Orhan
An efficient algorithm is proposed to extract components of a composite signal. The proposed approach has two stages of processing in which the time-frequency supports of the individual signal components are identified and then the individual components are estimated by performing a simple time-frequency domain incision on the identified support of the component. The use of a recently proposed time-frequency representation [1] significantly improves the performance of the proposed approach by providing very accurate description on the auto-Wigner terms of the composite signal. Then, simple fractional Fourier domain incision provides reliable estimates for each of the signal components in O(N log N) complexity for a composite signal of duration N.
Open Access
Energetic efficient synthesis of general mutual intensity distribution
(Institute of Physics Publishing, 2000) Zalevsky, Z.; Medlovic, D.; Özaktaş, Haldun M.
The mutual intensity distribution of a light beam may contain available information. The task of encoding a given mutual intensity distribution is addressed in this paper. Various approaches for encoding the mutual intensity function have been previously proposed. However, all of them provide low energetic efficiency and commonly require sophisticated production methods. The idea of using a phase-only filter for performing this synthesis is hereby investigated. The proposed method is numerically examined for the case of placing the mutual intensity generating filter in the fractional Fourier domain.
Open Access
Filtering in fractional Fourier domains and their relation to chirp transforms
(IEEE, 1994-04) Özaktaş, Haldun M.; Barshan, Billur; Onural, Levent; Mendlovic, D.
Fractional Fourier transforms, which are related to chirp and wavelet transforms, lead to the notion of fractional Fourier domains. The concept of filtering of signals in fractional domains is developed, revealing that under certain conditions one can improve upon the special cases of these operations in the conventional space and frequency domains. Because of the ease of performing the fractional Fourier transform optically, these operations are relevant for optical information processing.
Open Access
Fourier transforms of fractional order and their optical interpretation
(Elsevier, 1993) Özaktaş, Haldun M.; Mendlovic, D.
Fourier transforms of fractional order a are defined in a manner such that the common Fourier transform is a special case with order a=1. An optical interpretation is provided in terms of quadratic graded index media and discussed from both wave and ray viewpoints. Fractional Fourier transforms can extend the range of spatial filtering operations.
Open Access
Framework for online superimposed event detection by sequential Monte Carlo methods
(IEEE, 2008-03-04) Urfalıoğlu, Onay; Kuruoğlu, E. E.; Çetin, A. Enis
In this paper, we consider online seperation and detection of superimposed events by applying particle filtering. We concentrate on a model where a background process, represented by a 1D-signal, is superimposed by an Auto-Regressive (AR) 'event signal', but the proposed approach is applicable in a more general setting. The activation and deactivation times of the event-signal are assumed to be unknown. We solve the online detection problem of this superpositional event by extending the state space dimension by one. The additional parameter of the state represents the AR-signal, which is zero when deactivated. Numerical experiments demonstrate the effectiveness of our approach. ©2008 IEEE.
Open Access
Influence function based Gaussianity tests for detection of microcalcifications in mammogram images
(IEEE, 1999-10) Gürcan, M. Nafi; Yardımcı, Y.; Çetin, A. Enis
In this paper, computer-aided diagnosis of microcalcifications in mammogram images is considered. Microcalcification clusters are an early sign of breast cancer. Microcalcifications appear as single bright spots in mammogram images. We propose an effective method for the detection of these abnormalities. The first step of this method is two-dimensional adaptive filtering. The filtering produces an error image which is divided into overlapping square regions. In each square region, a Gaussianity test is applied. Since microcalcifications have an impulsive appearance, they are treated as outliers. In regions with no microcalcifications, the distribution of the error image is almost Gaussian, on the other hand, in regions containing microcalcification clusters, the distribution deviates from Gaussianity. Using the theory of the influence function and sensitivity curves, we develop a Gaussianity test. Microcalcification clusters are detected using the Gaussianity test. Computer simulation studies are presented.
Open Access
Input sequence estimation and blind channel identification in HF communication
(IEEE, 2000) Khames, Mariam; Miled, B. H.; Arıkan, Orhan
A new algorithm is proposed for reliable communication over HF tropospheric links in the presence of rapid channel variations. In the proposed approach, using fractionally space channel outputs, sequential estimation of channel characteristics and input sequence is performed by utilizing subspace tracking and Kalman filtering. Simulation based comparisons with the existing algorithms show that the proposed approaches significantly improve the performance of the communication system and enable us to utilize HF communication in bad conditions.
Open Access
Interframe differential coding of line spectrum frequencies
(IEEE, 1994) Erzin, E.; Çetin, A. Enis
Line spectrum frequencies (LSF's) uniquely represent the linear predictive coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an inter-frame differential coding scheme is presented for the LSF's. The LSF's of the current speech frame are predicted by using both the LSF's of the previous frame and some of the LSF's of the current frame. Then, the difference resulting from prediction is quantized.
Open Access
Interframe differential vector coding of line spectrum frequencies
(IEEE, 1993-04) Erzin, Engin; Çetin, A. Enis
Line Spectrum Frequencies (LSF's) uniquely represent the Linear Predictive Coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an interframe differential coding scheme is presented for the LSF's. The LSF's of the current speech frame are predicted by using both the LSF's of the previous frame and some of the LSF's of the current frame. Then, the difference vector resulting from prediction is vector quantized.
Open Access
Morphological surface profile extraction from multiple sonars
(IEEE, 1998) Başkent, Deniz; Barshan, Billur
This paper presents a novel method for surface profile determination using multiple sensors. Our approach is based on morphological processing techniques to fuse the range data from multiple sensor returns in a manner that directly reveals the target surface profile. The method has the intrinsic ability of suppressing spurious readings due to noise, crosstalk, and higher-order reflections, as well as processing multiple reflections informatively. The algorithm is verified both by simulations and experiments in the laboratory by processing real sonar data obtained from a mobile robot. The results are compared to those obtained from a more accurate structured-light system, which is however more complex and expensive.
Open Access
A multiresolution nonrectangular wavelet representation for two-dimensional signals
(Elsevier, 1993) Çetin, A. Enis
In this paper, a new multiresolution wavelet representation for two-dimensional signals is described. This wavelet representation is based on a nonrectangular decomposition of the frequency domain. The decomposition can be implemented by a digital filter bank. The application of the new representation to the coding of quincunx and rectangularly sampled images is considered and simulation examples are presented.
Open Access
Neural networks based online learning
(IEEE, 2017) Ergen, Tolga; Kozat, Süleyman Serdar
In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and introduce highly efficient particle filtering (PF) based updates, as well as, extended Kalman filter (EKF) based updates. Our PF based training method guarantees convergence to the optimal parameter estimation under certain assumptions. We achieve this performance with a computational complexity in the order of the first order gradient based methods by controlling the number of particles. The experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods.
Open Access
Online distributed nonlinear regression via neural networks
(IEEE, 2017) Ergen, Tolga; Kozat, Süleyman Serdar
In this paper, we study the nonlinear regression problem in a network of nodes and introduce long short term memory (LSTM) based algorithms. In order to learn the parameters of the LSTM architecture in an online manner, we put the LSTM equations into a nonlinear state space form and then introduce our distributed particle filtering (DPF) based training algorithm. Our training algorithm asymptotically achieves the optimal training performance. In our simulations, we illustrate the performance improvement achieved by the introduced algorithm with respect to the conventional methods.