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Recent Advances in Tensor Based Signal and Image Processing

The goal of this special issue is to gather contributions that bring advances on tensor decompositions with applications to signal and image processing. Articles are invited which focus on either fundamental aspects of tensor decompositions or on application-oriented problems, or both. Fundamental issues include uniqueness, degeneracy, rank definitions and determination, low-rank approximation, structured tensors, constrained tensor models/decompositions, and algorithms. Application fields include (but are not limited to): modeling/ estimation of wireless communication channels, blind equalization and source separation, transceiver design for MIMO and cooperative communication systems, modeling and identification of non-linear systems, biomedical and genomic signal processing, image processing, and audio/ speech processing.

Edited by: Andre Almeida, Gérard Favier, Martin Haardt, Morten Mørup and Alex Vasilescu

  1. Content type: Research

    At the contention-based synchronization, e.g., initial ranging process, it is crucial to identify multiple users through ranging codes and estimate the corresponding parameters such as timing offset and freque...

    Authors: Sungeun Lee and Xiaoli Ma

    Citation: EURASIP Journal on Advances in Signal Processing 2015 2015:1

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  2. Content type: Research

    In this paper, we address the channel estimation problem for multiple-input multiple-output (MIMO) multi-relay systems exploiting measurements collected at the destination only. Assuming that the source, relay...

    Authors: Xi Han, André LF de Almeida and Zhen Yang

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:163

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  3. Content type: Research

    Semi-symmetric three-way arrays are essential tools in blind source separation (BSS) particularly in independent component analysis (ICA). These arrays can be built by resorting to higher order statistics of t...

    Authors: Lu Wang, Laurent Albera, Amar Kachenoura, Huazhong Shu and Lotfi Senhadji

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:150

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  4. Content type: Review

    In this paper, we present an overview of constrained parallel factor (PARAFAC) models where the constraints model linear dependencies among columns of the factor matrices of the tensor decomposition or, altern...

    Authors: Gérard Favier and André LF de Almeida

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:142

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  5. Content type: Research

    Recordings of neural activity, such as EEG, are an inherent mixture of different ongoing brain processes as well as artefacts and are typically characterised by low signal-to-noise ratio. Moreover, EEG dataset...

    Authors: Borbála Hunyadi, Daan Camps, Laurent Sorber, Wim Van Paesschen, Maarten De Vos, Sabine Van Huffel and Lieven De Lathauwer

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:139

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  6. Content type: Research

    In this paper, we present a canonical polyadic (CP) tensor decomposition isolating the scaling matrix. This has two major implications: (i) the problem conditioning shows up explicitly and could be controlled ...

    Authors: Awatif Rouijel, Khalid Minaoui, Pierre Comon and Driss Aboutajdine

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:128

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  7. Content type: Research

    We present a framework for Tensor-based subspace Tracking via Kronecker-structured projections (TeTraKron). TeTraKron allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based...

    Authors: Yao Cheng, Florian Roemer, Olaa Khatib and Martin Haardt

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:123

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  8. Content type: Research

    This paper proposes an extension of the higher order singular value decomposition (HOSVD), namely the alternative unfolding HOSVD (AU-HOSVD), in order to exploit the correlated information in multidimensional ...

    Authors: Maxime Boizard, Guillaume Ginolhac, Fréderic Pascal and Philippe Forster

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:119

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  9. Content type: Research

    Applications based on electrocardiogram (ECG) signal feature extraction and classification are of major importance to the autodiagnosis of heart diseases. Most studies on ECG classification methods have target...

    Authors: Kai Huang and Liqing Zhang

    Citation: EURASIP Journal on Advances in Signal Processing 2014 2014:2

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