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# IEEE Transactions on Signal Processing

## Filter Results

Displaying Results 1 - 25 of 56

Publication Year: 2010, Page(s):C1 - C4
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• ### IEEE Transactions on Signal Processing publication information

Publication Year: 2010, Page(s): C2
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• ### Robust $H_{infty}$ Filtering for a Class of Nonlinear Networked Systems With Multiple Stochastic Communication Delays and Packet Dropouts

Publication Year: 2010, Page(s):1957 - 1966
Cited by:  Papers (150)
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In this paper, the robust H filtering problem is studied for a class of uncertain nonlinear networked systems with both multiple stochastic time-varying communication delays and multiple packet dropouts. A sequence of random variables, all of which are mutually independent but obey Bernoulli distribution, are introduced to account for the randomly occurred communication delays.... View full abstract»

• ### On the Invariance, Coincidence, and Statistical Equivalence of the GLRT, Rao Test, and Wald Test

Publication Year: 2010, Page(s):1967 - 1979
Cited by:  Papers (25)
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Three common techniques to discriminate between alternatives in a binary hypothesis testing problem are: the generalized likelihood ratio test (GLRT), the Rao test, and the Wald test. In this paper, we investigate some characteristics of the corresponding decision statistics and provide their expressions for some problems of particular interest in statistical signal processing. First of all, we fo... View full abstract»

• ### Refining Decisions After Losing Data: The Unlucky Broker Problem

Publication Year: 2010, Page(s):1980 - 1990
Cited by:  Papers (3)
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Consider a standard statistical hypothesis test, leading to a binary decision made by exploiting a certain dataset. Suppose that, later, part of the data is lost, and we want to refine the test by exploiting both the surviving data and the previous decision. What is the best one can do? Such a question, here referred to as the unlucky broker problem, can be addressed by very standard tools from de... View full abstract»

• ### The Estimation of the Fourth-Order Cumulant for Dependent Data: Consistency and Asymptotic Normality

Publication Year: 2010, Page(s):1991 - 1998
Cited by:  Papers (2)
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Let {Xi} be a stationary dependent random process with finite eight-order moments. For broad classes of processes (??-mixing and strongly mixing), we obtain the convergence in probability, with sharp rates, of the estimate of the fourth-order cumulant from n observations {Xi}i=1 n . We also establish the asymptotic distribution of the estimation e... View full abstract»

• ### An Efficient Approach for Two-Dimensional Parameter Estimation of a Single-Tone

Publication Year: 2010, Page(s):1999 - 2009
Cited by:  Papers (25)
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In this paper, parameter estimation of a two-dimensional (2-D) single damped real/complex tone in the presence of additive white Gaussian noise is addressed. By utilizing the rank-one property of the 2-D noise-free data matrix, the damping factor and frequency for each dimension are estimated in a separable manner from the principal left and right singular vectors according to an iterative weighte... View full abstract»

• ### Computationally Efficient Sparse Bayesian Learning via Belief Propagation

Publication Year: 2010, Page(s):2010 - 2021
Cited by:  Papers (17)  |  Patents (1)
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We present a belief propagation (BP)-based sparse Bayesian learning (SBL) algorithm, referred to as the BP-SBL, to recover sparse transform coefficients in large scale compressed sensing problems. BP-SBL is based on a widely used hierarchical Bayesian model, which is turned into a factor graph so that BP can be applied to achieve computational efficiency. We prove that the messages in BP are Gauss... View full abstract»

• ### A Sparse-Interpolated Scheme for Implementing Adaptive Volterra Filters

Publication Year: 2010, Page(s):2022 - 2035
Cited by:  Papers (23)
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In most practical applications, the major drawback for using adaptive Volterra filters is the large number of coefficients to cope with. Several research works discussing strategies to reduce the computational burden of these structures have been presented in the open literature. For such, a common approach has been the use of some type of sparseness in Volterra filter kernels. In this work, a spa... View full abstract»

• ### A PNLMS Algorithm With Individual Activation Factors

Publication Year: 2010, Page(s):2036 - 2047
Cited by:  Papers (34)
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This paper presents a proportionate normalized least-mean-square (PNLMS) algorithm using individual activation factors for each adaptive filter coefficient, instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding adaptive filter coefficients, are recursively updated. This approach leads to a bett... View full abstract»

• ### Efficient NLMS and RLS Algorithms for Perfect and Imperfect Periodic Sequences

Publication Year: 2010, Page(s):2048 - 2059
Cited by:  Papers (10)
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The paper discusses computationally efficient NLMS and RLS algorithms for perfect and imperfect periodic excitation sequences. The most interesting aspect of these algorithms is that they are exact LMS and RLS algorithms suitable for identification and tracking of every linear system and they require a real-time computational effort of just a multiplication, an addition and a subtraction per sampl... View full abstract»

• ### A Procedure to Adapt Filter Banks to Finite-Length Signals

Publication Year: 2010, Page(s):2060 - 2067
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In this paper, we consider the problem of processing finite-length signals by means of critically sampled or oversampled filter banks. Based on a polyphase matrix factorization of the filter bank, we propose a realization adaptation that does not make any assumption about the signal values outside the support of the input signal, contrary to what is done, for instance, when using zero padding, per... View full abstract»

• ### Group Lifting Structures for Multirate Filter Banks I: Uniqueness of Lifting Factorizations

Publication Year: 2010, Page(s):2068 - 2077
Cited by:  Papers (4)
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Group lifting structures are introduced to provide an algebraic framework for studying lifting factorizations of two-channel perfect reconstruction finite-impulse-response (FIR) filter banks. The lifting factorizations generated by a group lifting structure are characterized by Abelian groups of lower and upper triangular lifting matrices, an Abelian group of unimodular gain scaling matrices, and ... View full abstract»

• ### Group Lifting Structures for Multirate Filter Banks II: Linear Phase Filter Banks

Publication Year: 2010, Page(s):2078 - 2087
Cited by:  Papers (4)
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The theory of group lifting structures is applied to linear phase lifting factorizations for the two nontrivial classes of two-channel linear phase perfect reconstruction filter banks, the whole- and half-sample symmetric classes. Group lifting structures defined for the reversible and irreversible classes of whole- and half-sample symmetric filter banks are shown to satisfy the hypotheses of the ... View full abstract»

• ### Design of Regular Wavelets Using a Three-Step Lifting Scheme

Publication Year: 2010, Page(s):2088 - 2101
Cited by:  Papers (12)
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We propose structural multidimensional multichannel filter banks with desirable numbers of vanishing moments for the analysis and synthesis banks. For a two-channel filter bank, we use a three-step lifting scheme as opposed to the conventional two-step lifting method in order to provide more symmetry between the analysis and synthesis filters. We show that the resulting filters have more regularit... View full abstract»

• ### Multirate Filterbank Design: A Relaxed Commutant Lifting Approach

Publication Year: 2010, Page(s):2102 - 2112
Cited by:  Papers (3)
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In this paper, we reformulate the design of the IIR synthesis filters in classical multirate systems as an interpolation problem involving a norm called the P m norm where m is any positive integer. This interpolation problem can be solved using relaxed commutant lifting techniques in operator theory. The P m norm is actually a tradeoff in handling energy... View full abstract»

• ### A New Approach to Pruning Volterra Models for Power Amplifiers

Publication Year: 2010, Page(s):2113 - 2120
Cited by:  Papers (46)
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The objective of this paper is to present an approach to behavioral modeling that can be applied to predict the nonlinear response of power amplifiers with memory. Starting with the discrete-time, complex-baseband full Volterra model, we define a novel methodology that retains only radial branches that can be implemented with one-dimensional finite impulse response filters. This model is subsequen... View full abstract»

• ### Recursive Least Squares Dictionary Learning Algorithm

Publication Year: 2010, Page(s):2121 - 2130
Cited by:  Papers (127)  |  Patents (3)
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We present the recursive least squares dictionary learning algorithm, RLS-DLA, which can be used for learning overcomplete dictionaries for sparse signal representation. Most DLAs presented earlier, for example ILS-DLA and K-SVD, update the dictionary after a batch of training vectors has been processed, usually using the whole set of training vectors as one batch. The training set is used iterati... View full abstract»

• ### Performing Nonlinear Blind Source Separation With Signal Invariants

Publication Year: 2010, Page(s):2131 - 2140
Cited by:  Papers (4)
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Given a time series of multicomponent measurements x(t), the usual objective of nonlinear blind source separation (BSS) is to find a ¿¿source¿¿ time series s(t), comprised of statistically independent combinations of the measured components. In this paper, the source time series is required to have a density function in (s, mathdot s)-space that is equal t... View full abstract»

• ### Bounded Component Analysis of Linear Mixtures: A Criterion of Minimum Convex Perimeter

Publication Year: 2010, Page(s):2141 - 2154
Cited by:  Papers (27)
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This study presents a blind and geometric technique which pursues the linear decomposition of the observations in bounded component signals. The bounded component analysis of the observations relies on the hypotheses of compactness and Cartesian decomposition of the convex support of the vector of component signals, and in the invertibility of the mixture. Assumptions, which in absence of noise, a... View full abstract»

• ### A General Criterion for Analog Tx-Rx Beamforming Under OFDM Transmissions

Publication Year: 2010, Page(s):2155 - 2167
Cited by:  Papers (18)
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In this paper, we study beamforming schemes for a novel MIMO transceiver, which performs adaptive signal combining in the radio-frequency (RF) domain. Assuming perfect channel knowledge at the receiver side, we consider the problem of designing the transmit and receive RF beamformers under orthogonal frequency division multiplexing (OFDM) transmissions. In particular, a general beamforming criteri... View full abstract»

• ### DOA Estimation of Quasi-Stationary Signals With Less Sensors Than Sources and Unknown Spatial Noise Covariance: A Khatri–Rao Subspace Approach

Publication Year: 2010, Page(s):2168 - 2180
Cited by:  Papers (81)
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In real-world applications such as those for speech and audio, there are signals that are nonstationary but can be modeled as being stationary within local time frames. Such signals are generally called quasi-stationary or locally stationary signals. This paper considers the problem of direction-of-arrival (DOA) estimation of quasi-stationary signals. Specifically, in our problem formulation we as... View full abstract»

• ### A Model Reduction Approach for OFDM Channel Estimation Under High Mobility Conditions

Publication Year: 2010, Page(s):2181 - 2193
Cited by:  Papers (40)
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Orthogonal frequency-division multiplexing (OFDM) combines the advantages of high performance and relatively low implementation complexity. However, for reliable coherent detection of the input signal, the OFDM receiver needs accurate channel information. When the channel exhibits fast time variation as it is the case with several recent OFDM-based mobile broadband wireless standards (e.g., WiMAX,... View full abstract»

• ### An Improved Smoothed $ell^0$ Approximation Algorithm for Sparse Representation

Publication Year: 2010, Page(s):2194 - 2205
Cited by:  Papers (42)
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l0 norm based algorithms have numerous potential applications where a sparse signal is recovered from a small number of measurements. The direct l0 norm optimization problem is NP-hard. In this paper we work with the the smoothed l0(SL0) approximation algorithm for sparse representation. We give an upper bound on the run-time estimation error. This upper bound is t... View full abstract»

• ### Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference

Publication Year: 2010, Page(s):2206 - 2217
Cited by:  Papers (23)  |  Patents (1)
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In this paper, we propose novel low-complexity soft-in soft-out (SISO) equalizers using the Markov chain Monte Carlo (MCMC) technique. We develop a bitwise MCMC equalizer (b-MCMC) that adopts a Gibbs sampler to update one bit at a time, as well as a group-wise MCMC (g-MCMC) equalizer where multiple symbols are updated simultaneously. The g-MCMC equalizer is shown to outperform both the b-MCMC and ... View full abstract»

## Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

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## Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens