Researcher:
İnan, Hüseyin Atahan

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Master Student

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Hüseyin Atahan

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İnan

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İnan, Hüseyin Atahan

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2012 - 20156

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    An extended family of bounded component analysis algorithms
    (IEEE Computer Society, 2015) Department of Electrical and Electronics Engineering; N/A; Erdoğan, Alper Tunga; İnan, Hüseyin Atahan; Faculty Member; Master Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 41624; N/A
    Bounded Component Analysis (BCA) is a recent concept proposed as an alternative method for Blind Source Separation problem. BCA enables the separation of dependent as well as independent sources from their mixtures under the practical assumption on source boundedness. This article extends the optimization setting of a recent BCA approach which can be used to produce a variety of BCA algorithms. The article also provides examples of objective functions and the corresponding iterative algorithms. The numerical examples illustrate the advantages of proposed BCA examples regarding the correlated source separation capability over the state of the art ICA based approaches. 1 © 2014 IEEE.
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    A bounded component analysis approach for the separation of convolutive mixtures of dependent and independent sources
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) N/A; N/A; Department of Electrical and Electronics Engineering; İnan, Hüseyin Atahan; Erdoğan, Alper Tunga; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    Bounded Component Analysis is a new framework for Blind Source Separation problem. It allows separation of both dependent and independent sources under the assumption about the magnitude boundedness of sources. This article proposes a novel Bounded Component Analysis optimization setting for the separation of the convolutive mixtures of sources as an extension of a recent geometric framework introduced for the instantaneous mixing problem. It is shown that the global maximizers of this setting are perfect separators. The article also provides the iterative algorithm corresponding to this setting and the numerical examples to illustrate its performance especially for separating convolutive mixtures of sources that are correlated in both space and time dimensions.
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    Adaptive mixture methods based on Bregman divergences
    (IEEE, 2012) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; Kozat, Süleyman Serdar; Dönmez, Mehmet Ali; İnan, Hüseyin Atahan; Faculty Member; Master Student; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 177972; N/A; N/A
    We investigate affinely constrained mixture methods adaptively combining outputs of m constituent filters running in parallel to model a desired signal. We use Bregman divergences and obtain multiplicative updates to train these linear combination weights under the affine constraints. We use the unnormalized relative entropy and the relative entropy that produce the exponentiated gradient update with unnormalized weights (EGU) and the exponentiated gradient update with positive and negative weights (EG), respectively. We carry out the mean and the mean-square transient analysis of the affinely constrained mixtures of m filters using the EGU or EG algorithms. We compare performances of different algorithms through our simulations and illustrate the accuracy of our results.
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    Adaptive mixture methods based on Bregman divergences
    (Academic Press Inc Elsevier Science, 2013) Kozat, Suleyman S.; N/A; N/A; Dönmez, Mehmet Ali; İnan, Hüseyin Atahan; Master Student; Master Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/A
    We investigate adaptive mixture methods that linearly combine outputs of m constituent filters running in parallel to model a desired signal. We use Bregman divergences and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of m constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems. (C) 2012 Published by Elsevier Inc.
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    Convolutive bounded component analysis algorithms for independent and dependent source separation
    (IEEE-inst Electrical Electronics Engineers inc, 2015) N/A; N/A; Department of Electrical and Electronics Engineering; İnan, Hüseyin Atahan; Erdoğan, Alper Tunga; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    Bounded component analysis (BCa) is a framework that can be considered as a more general framework than independent component analysis (ICa) under the boundedness constraint on sources. Using this framework, it is possible to separate dependent as well as independent components from their mixtures. in this paper, As an extension of a recently introduced instantaneous BCa approach, we introduce a family of convolutive BCa criteria and corresponding algorithms. We prove that the global optima of the proposed criteria, under generic BCa assumptions, Are equivalent to a set of perfect separators. the algorithms introduced in this paper are capable of separating not only the independent sources but also the sources that are dependent/correlated in both component (space) and sample (time) dimensions. therefore, under the condition that the sources are bounded, they can be considered as extended convolutive ICa algorithms with additional dependent/correlated source separation capability. Furthermore, they have potential to provide improvement in separation performance, especially for short data records. This paper offers examples to illustrate the space-time correlated source separation capability through a copula distribution-based example. in addition, A frequency-selective Multiple input Multiple Output equalization example demonstrates the clear performance advantage of the proposed BCa approach over the state-of-the-art ICa-based approaches in setups involving convolutive mixtures of digital communication sources.
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    Robust estimation in flat fading channels under bounded channel uncertainties
    (Academic Press Inc Elsevier Science, 2013) N/A; N/A; Department of Electrical and Electronics Engineering; Dönmez, Mehmet Ali; İnan, Hüseyin Atahan; Kozat, Süleyman Serdar; Master Student; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 177972
    We investigate channel equalization problem for time-varying flat fading channels under bounded channel uncertainties. We analyze three robust methods to estimate an unknown signal transmitted through a time-varying flat fading channel. These methods are based on minimizing certain mean-square error criteria that incorporate the channel uncertainties into their problem formulations instead of directly using the inaccurate channel information that is available. We present closed-form solutions to the channel equalization problems for each method and for both zero mean and nonzero mean signals. We illustrate the performances of the equalization methods through simulations. (C) 2013 Elsevier Inc. All rights reserved.