Sergios Theodoridis
Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces.
Tue, 11/30/2010 - 16:01 — SomNambulAuthors: Sergios Theodoridis, Pantelis Bouboulis
Subjects: Computer Vision and Pattern Recognition
AbstractThe goal of this paper is the development of a novel approach for the problem
of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces
(RKHS). The problem is cast as an optimization task in a RKHS, by taking
advantage of the celebrated semiparametric Representer Theorem. Examples verify
that in the presence of gaussian noise the proposed method performs relatively
well compared to wavelet based technics and outperforms them significantly in
the presence of impulse or mixed noise.Extension of Wirtinger's Calculus to Reproducing Kernel Hilbert Spaces and the Complex Kernel LMS.
Fri, 10/01/2010 - 15:01 — SomNambulAuthors: Sergios Theodoridis, Pantelis Bouboulis
Subjects: Learning
AbstractOver the last decade, kernel methods for nonlinear processing have
successfully been used in the machine learning community. The primary
mathematical tool employed in these methods is the notion of the Reproducing
Kernel Hilbert Space. However, so far, the emphasis has been on batch
techniques. It is only recently, that online techniques have been considered in
the context of adaptive signal processing tasks. Moreover, these efforts have
only been focussed on real valued data sequences.Extension of Wirtinger Calculus in RKH Spaces and the Complex Kernal LMS.
Fri, 05/07/2010 - 15:02 — SomNambulAuthors: Sergios Theodoridis, Pantelis Bouboulis
Subjects: Learning
AbstractOver the last decade, kernel methods for nonlinear processing have
successfully been used in the machine learning community. However, so far, the
emphasis has been on batch techniques. It is only recently, that online
adaptive techniques have been considered in the context of signal processing
tasks. To the best of our knowledge, no kernel-based strategy has been
developed, so far, that is able to deal with complex valued signals. In this
paper, we take advantage of a technique called complexification of real RKHSs
to attack this problem.The Complex Gaussian Kernel LMS algorithm.
Fri, 05/07/2010 - 15:02 — SomNambulAuthors: Sergios Theodoridis, Pantelis Bouboulis
Subjects: Learning
AbstractAlthough the real reproducing kernels are used in an increasing number of
machine learning problems, complex kernels have not, yet, been used, in spite
of their potential interest in applications such as communications. In this
work, we focus our attention on the complex gaussian kernel and its possible
application in the complex Kernel LMS algorithm. In order to derive the
gradients needed to develop the complex kernel LMS (CKLMS), we employ the
powerful tool of Wirtinger's Calculus, which has recently attracted much
attention in the signal processing community.Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study.
Wed, 10/21/2009 - 14:15 — SomNambulAuthors: Dimitris Katselis, Eleftherios Kofidis, Athanasios Rontogiannis, Sergios Theodoridis
Subjects: Information Theory
AbstractIn this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission.Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study.
Wed, 10/21/2009 - 14:15 — SomNambulAuthors: Dimitris Katselis, Eleftherios Kofidis, Athanasios Rontogiannis, Sergios Theodoridis
Subjects: Information Theory
AbstractIn this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission.
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