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- Athanasios P. Liavas, Phillip A. Regalia, Jean Pierre Delmas
- IEEE Trans. Signal Processing
- 1999

— A common assumption of blind channel identification methods is that the order of the true channel is known. This information is not available in practice, and we are obliged to estimate the channel order by applying a rank detection procedure to an " overmodeled " data covariance matrix. Information theoretic criteria have been widely suggested approaches… (More)

- Kejun Huang, Nikos D. Sidiropoulos, Athanasios P. Liavas
- IEEE Transactions on Signal Processing
- 2016

We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning. The new framework is a hybrid between alternating optimization (AO) and the alternating direction method of multipliers (ADMM): each matrix factor is updated in turn, using ADMM, hence the name AO-ADMM.… (More)

- C. Papaloukas, Dr D. I. Fotiadis, A. P. Liavas, A. Likas, L. K. Michalis
- Medical and Biological Engineering and Computing
- 2001

A novel method for the detection of ischaemic episodes in long duration ECGs is proposed. It includes noise handling, feature extraction, rule-based beat classification, sliding window classification and ischaemic episode identification, all integrated in a four-stage procedure. It can be executed in real time and is able to provide explanations for the… (More)

- Athanasios P. Liavas, Phillip A. Regalia, Jean Pierre Delmas
- IEEE Trans. Signal Processing
- 1999

— The least-squares and the subspace methods are two well-known approaches for blind channel identification/ equalization. When the order of the channel is known, the algorithms are able to identify the channel, under the so-called length and zero conditions. Furthermore, in the noiseless case, the channel can be perfectly equalized. Less is known about the… (More)

- Athanasios P. Liavas
- IEEE Trans. Communications
- 2005

—We consider minimum mean-square error Tom-linson–Harashima (MMSE-TH) precoding for time-varying frequency-selective channels. We assume that the receiver estimates the channel and sends the channel state information (CSI) estimate to the transmitter through a lossless feedback channel that introduces a certain delay. Thus, the CSI mismatch at the receiver… (More)

- A P Liavas, G V Moustakides, G Henning, E Z Psarakis, P Husar
- IEEE transactions on bio-medical engineering
- 1998

The task of objective perimetry is to scan the visual field and find an answer about the function of the visual system. Flicker-burst stimulation--a physiological sensible combination of transient and steady-state stimulation--is used to generate deterministic sinusoidal responses or visually evoked potentials (VEP's) at the visual cortex, which are derived… (More)

- Athanasios P. Liavas, Despoina Tsipouridou
- IEEE Transactions on Signal Processing
- 2007

We consider two widely referenced trained finite-length linear equalizers, namely, the mismatched minimum mean square error (MMSE) equalizer and the least-squares (LS) equalizer. Using matrix perturbation theory, we express both of them as perturbations of the ideal MMSE equalizer and we derive insightful analytical expressions for their excess mean square… (More)

- George N. Karystinos, Athanasios P. Liavas
- IEEE Transactions on Information Theory
- 2008

The maximization of a full-rank quadratic form over the binary alphabet can be performed through exponential-complexity exhaustive search. However, if the rank of the form is not a function of the problem size, then it can be maximized in polynomial time. By introducing auxiliary spherical coordinates, we show that the rank-deficient quadratic-form… (More)

- Athanasios P. Liavas, Phillip A. Regalia
- IEEE Trans. Signal Processing
- 1999

— We study the nonlinear round-off error accumulation system of the conventional recursive least squares algorithm, and we derive bounds for the relative precision of the computations in terms of the conditioning of the problem and the exponential forgetting factor, which guarantee the numerical stability of the finite-precision implementation of the… (More)

- Athanasios P. Liavas, Nikos D. Sidiropoulos
- ArXiv
- 2014

Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away… (More)