The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...
In this correspondence, we propose an iterative "turbo" channel estimation algorithm for known symbol padding (KSP) orthogonal frequency-division multiplexing (OFDM), whe...
This paper proposes a new estimation procedure for the ambiguity function of a non-stationary time series. The stochastic properties of the empirical ambiguity function calculated...
This paper presents an efficient method to compute the maximum likelihood (ML) estimation of the parameters of a complex 2-D sinusoidal, with the complexity order of the FFT. The...
In this paper, we present the Gauss-Newton method as a unified approach to optimizing non-linear noise compensation models, such as vector Taylor series (VTS), data-driven parall...