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In the first part of this thesis we are interested in the asymptotic performance analysis of Non-Binary Low-Density Parity-Check (NBLDPC) codes over the Binary Erasure Channel (BEC) decoded via the suboptimal Belief Propagation (BP) decoder as well as the ...
This paper presents a new algorithm for classifying distributions. The algorithm combines the principle of margin maximization and a kernel trick, applied to distributions. Thus, it combines the discriminative power of support vector machines and the well- ...
The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the literature has focused on developing the best uniform threshold or best basis selection. However, not ...
Energy-based detection and estimation are crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present novel Gaussian approximations that are applicable to general energy-based source detection and localization proble ...
The authors studied ultrafast relaxation of localized excited states at Ge-related O deficient centers in SiO2 using femtosecond transient-absorption spectroscopy. The relaxation dynamics exhibits a biexponential decay, which the authors ascribe to the dep ...
Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization ...
Dynamic Magnetic Resonance Imaging (MRI) with contrast media injection is an important tool to study renal perfusion in humans and animals. The goal of this study is to build classifiers for the automatic classification of a kidney as healthy or pathologic ...
Parametric option pricing models are largely used in Finance. These models capture several features of asset price dynamics. However, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that ...
We study the problem of distributed estimation over adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion c ...
Institute of Electrical and Electronics Engineers2008
The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresh- olding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distr ...