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pro vyhledávání: '"Todros, Koby"'
Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging architectures integrating deep neural networks (DNNs) with traditional modular receiver processing. While deep receivers were shown to operate reliably in co
Externí odkaz:
http://arxiv.org/abs/2407.09134
Autor:
Shlezinger, Nir, Todros, Koby
The least mean-square (LMS) filter is one of the most common adaptive linear estimation algorithms. In many practical scenarios, and particularly in digital communications systems, the signal of interest (SOI) and the input signal are jointly wide-se
Externí odkaz:
http://arxiv.org/abs/1708.00635
Akademický článek
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Autor:
Todros, Koby
In this paper, we develop a generalization of the Gaussian quasi score test (GQST) for composite binary hypothesis testing. The proposed test, called measure transformed GQST (MT-GQST), is based on the score-function of the measure transformed Gaussi
Externí odkaz:
http://arxiv.org/abs/1610.08278
In this paper, the Gaussian quasi likelihood ratio test (GQLRT) for non-Bayesian binary hypothesis testing is generalized by applying a transform to the probability distribution of the data. The proposed generalization, called measure-transformed GQL
Externí odkaz:
http://arxiv.org/abs/1609.07958
Autor:
Todros, Koby, Hero, Alfred O.
In this paper the Gaussian quasi maximum likelihood estimator (GQMLE) is generalized by applying a transform to the probability distribution of the data. The proposed estimator, called measure-transformed GQMLE (MT-GQMLE), minimizes the empirical Kul
Externí odkaz:
http://arxiv.org/abs/1511.00237
Autor:
Todros, Koby, Hero, Alfred O.
In this paper, we introduce a new framework for robust multiple signal classification (MUSIC). The proposed framework, called robust measure-transformed (MT) MUSIC, is based on applying a transform to the probability distribution of the received sign
Externí odkaz:
http://arxiv.org/abs/1508.01625
Autor:
Todros, Koby, Hero, Alfred O.
In this paper we derive a new framework for independent component analysis (ICA), called measure-transformed ICA (MTICA), that is based on applying a structured transform to the probability distribution of the observation vector, i.e., transformation
Externí odkaz:
http://arxiv.org/abs/1302.0730
