Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions tha...
In the important domain of array shape calibration, the near-field case poses a challenging problem due to the array response complexity induced by the range effect. In this pape...
Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeat...
One of the main difficulties in computing information theoretic learning (ITL) estimators is the computational complexity that grows quadratically with data. Considerable amount ...
Estimating the coefficients of a noisy polynomial phase signal is important in many fields including radar, biology and radio communications. One approach to estimation attempts...
Robby G. McKilliam, I. Vaughan L. Clarkson, Barry ...
We consider the problem of channel estimation for amplify-andforward (AF) two-way relay networks (TWRNs). The majority of works on this problem develop pilot-based algorithms that...
In MB-OFDM-based UWB systems, the different CFOs for different bands can be obtained by only estimating the OFO. To achieve OFO estimators with wide estimation ranges, we benefit...
We consider the problem of source number estimation in array processing when impulsive noise is present. To combat impulsive noise more effectively, two robust estimators with hig...
In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the e...
This paper presents a family of log-spectral amplitude (LSA) estimators for speech enhancement. Generalized Gamma distributed (GGD) priors are assumed for speech short-time spectr...