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In this paper, we introduce a new analytical model for the a-stable probability density function (p.d.f). The new model is based on a corollary of the mixing theorem for symmetric α-stable (SαS) random variables (r.v.) [1] which states that a SαS r.v. can be expressed as the product of a Gaussian r.v. and a positive-stable r.v. We also extend this model to provide an analytical approximation for a...
The array output for a distributed source can be approximated by the superposition of the array response to a large number of closely spaced point sources. In the limit, a distributed source corresponds to an infinite number of point sources. In this approximation, the number of free parameters increases with the number of point sources. In this paper, we show that if the point sources (approximation...
This paper presents a method for the classification of nonlinear systems through the study of the free oscillations in the time-frequency plane, when the measured data are affected by noise. Nonconservative SDOF (Single Degree Of Freedom) oscillators described by a nonlinear second order differential equation are considered. The nonlinearity is due to a nonlinear function of the state variable, which...
A new recursive algorithm is proposed for finding the minimum of an objective function whose gradient is not obtainable directly but is approximated from the noisy observations of the function. The algorithm is based on the simultaneous perturbation stochastic approximation method (SPSA) combined with randomly varying truncations, and provides the estimate, which is convergent under weaker conditions...
The problem of estimating the state of discrete-time linear systems when uncertainties affect the system matrices is addressed. A quadratic cost function is considered, involving a finite number of recent measurements and a prediction vector. This leads to state the estimation problem in the form of a regularized least-squares one with uncertain data. The optimal solution (involving on-line scalar...
Many techniques are currently used for breast abnormality location and breast cancer detection, in particular. One find statistical approaches involving second and/or higher order statistics. The applicability of the filtering by approximated densities (FAD) is here demonstrated. The FAD introduced to alleviate limitations due conventional Kalman modelling, is applied to texture modelling for mammography...
This paper describes and analyses an improved algorithm for hands-free telephony which uses an acoustic echo canceller combined with an additional FIR-filter (called "echo shaping filter") in the sending path of the hands-free telephone. The algorithm controlling the filter is motivated by an approximation of an optimal least squares filter. Simulation results show that the algorithm allows...
The subject of this communication is the compensation of nonlinearities in digital radio links, where the major source of nonlinearity is caused by the High Power Amplifier (HPA), typically working close to its saturation point because of energy constraints. This paper deals with the design of CMAC-based predistorters for application in digital transmission over nonlinear channels with memory. A novel...
We study the output variance of a class of nonlinear filters, called da-filters. In general, it is impossible to obtain an explicit expression of the output variance because of the implicit Input/Output relationship, except for α=1 (median filter), α=2 (mean filter) and α=∞ (midrange filter). In this paper, we develop a new approach to the computation of the filter output variance. It is based on...
This paper studies the design of a set of outgoing radar signals to discriminate between two target classes. We model the reflectivity function of each target by a two-dimensional stochastic process to account for uncertainties and propagation effects. The signals are selected to minimize the expected number of transmissions that are needed to guarantee a given confidence level in the classification...
In many acoustic conditions the recorded speech signals may be severely affected by reverberation, leading to a reduced speech quality and intelligibility. In this paper we focus on a blind speech dereverberation method based on multi-channel linear prediction (MCLP) in the short-time Fourier transform domain, which is typically performed in each frequency bin independently without taking into account...
This paper lies in the lineage of recent works studying the asymptotic behaviour of robust-scatter estimators in the case where the number of observations and the dimension of the population covariance matrix grow at infinity with the same pace. In particular, we analyze the fluctuations of bilinear forms of the robust shrinkage estimator of covariance matrix. We show that this result can be leveraged...
The objective of this paper is to propose a coarse model for coupling of switching noise through lightly doped substrates. This could be achieved by assuming a regular placement of substrate contacts in a digital aggressor. Additionally, an approximation of equal ground bounce in an entire digital aggressor is applied. The proposed model is aimed for use as an estimation before placement, i.e. Before...
In this paper, we consider the phase recovery problem, where a complex signal vector has to be estimated from the knowledge of the modulus of its linear projections, from a naive variational Bayesian point of view. In particular, we derive an iterative algorithm following the minimization of the Kullback-Leibler divergence under the mean-field assumption, and show on synthetic data with random projections...
In this paper, we consider the problem of distributed estimation of node-specific signals in a fully-connected wireless sensor network with multi-sensor nodes. The estimation relies on a data-driven design of a spatial filter, referred to as the generalized eigenvalue decomposition (GEVD)-based multi-channel Wiener filter (MWF). In non-stationary or low-SNR conditions, this GEVD-based MWF has been...
In complex-valued signal processing, estimation algorithms require complete knowledge (or accurate estimation) of the second order statistics, this makes Gaussian processes (GP) well suited for modelling complex signals, as they are designed in terms of covariance functions. Dealing with bivariate signals using GPs require four covariance matrices, or equivalently, two complex matrices. We propose...
Identifying “interesting” features, such as faults, unconformities, and other events in subsurface images is a challenging task in seismic data processing. Existing state-of-the-art methods usually involve manual intervention in the form of a visual inspection by an expert, but this is time-consuming, expensive, and error-prone. In this paper, we propose an efficient, automatic approach for seismic...
Vector Taylor Series (VTS) based model compensation approach has been successfully applied to various robust speech recognition tasks. In this paper, we propose a novel method of variable transformation to calculate the static statistics. In addition, we provide a detailed explanation of VTS and random variable transformations adopted in some recent papers. Experiments on Aurora 4 showed that the...
The approximation of linear time-invariant (LTI) systems by sampling series is an important topic in signal processing. However, the convergence of the approximation series is not guaranteed: there exist stable LTI systems and bandlimited input signals such that the approximation series diverges, regardless of the oversampling factor and the sampling pattern. Recently, it has been shown that this...
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