Researcher:
Babataş, Eren

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PhD Student

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Eren

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Babataş

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Babataş, Eren

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Now showing 1 - 4 of 4
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    Publication
    Sparse bounded component analysis for convolutive mixtures
    (Institute of Electrical and Electronics Engineers (IEEE), 2018) N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Babataş, Eren; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    In this article, we propose a Bounded Component Analysis (BCA) approach for the separation of the convolutive mixtures of sparse sources. The corresponding algorithm is derived from a geometric objective function defined over a completely deterministic setting. Therefore, it is applicable to sources which can be independent or dependent in both space and time dimensions. We show that all global optima of the proposed objective are perfect separators. We also provide numerical examples to illustrate the performance of the algorithm.
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    Publication
    DOA estimation of low altitude target based on bounded component analysis algorithm
    (Institution of Engineering and Technology, 2017) N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Babataş, Eren; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    A recently introduced Blind Source Separation method, called Bounded Component Analysis, is used as preliminary technique to isolate direct path radar wave from ground reflected waves in order to overcome the multipath effect. This method enables the radar to estimate the target angle without any a priori knowledge of the operation environment. The numerical experiments illustrate the potential benefit of the proposed approach relative to classical maximum likelihood method (CMLM) based on free space propagation model.
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    Publication
    Time and frequency based sparse bounded component analysis algorithms for convolutive mixtures
    (Elsevier, 2020) N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Babataş, Eren; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    In this paper, we introduce time-domain and frequency-domain versions of a new Blind Source Separation (BSS) approach to extract bounded magnitude sparse sources from convolutive mixtures. We derive algorithms by maximization of the proposed objective functions that are defined in a completely deterministic framework, and prove that global maximums of the objective functions yield perfect separation under suitable conditions. The derived algorithms can be applied to temporal or spatially dependent sources as well as independent sources. We provide experimental results to demonstrate some benefits of the approach, also including an application on blind speech separation. (C) 2020 Elsevier B.V. All rights reserved.
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    Publication
    An algorithmic framework for sparse bounded component analysis
    (Ieee-Inst Electrical Electronics Engineers Inc, 2018) N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Babataş, Eren; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    Bounded component analysis (BCA) is a recent approach that enables the separation of both dependent and independent signals from their mixtures. This paper introduces a novel deterministic instantaneous BCA framework for the separation of sparse bounded sources. The framework is based on a geometric maximization setting, where the objective function is defined as the volume ratio of two objects, namely, the principal hyperellipsoid and the bounding l(1)-norm ball, defined over the separator output samples. It is shown that all global maxima of this objective are perfect separators. This paper also provides the corresponding iterative algorithms for both real and complex sparse sources. The numerical experiments illustrate the potential benefits of the proposed approach, with applications on image separation and neuron identification.