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This paper proposes a new algorithm for image inpainting algorithm based on the matrix rank minimization with nonlinear mapping function. Assuming that each intensity value of a nonlinear mapped image can be modeled by the autoregressive (AR) model, the image inpainting problem is formulated as a kind of the matrix rank minimization problem, and this paper modifies the iterative partial matrix shrinkage...
We present a new random sampling strategy for k-bandlimited signals defined on graphs, based on determinantal point processes (DPP). For small graphs, i.e., in cases where the spectrum of the graph is accessible, we exhibit a DPP sampling scheme that enables perfect recovery of bandlimited signals. For large graphs, i.e., in cases where the graph's spectrum is not accessible, we investigate, both...
This paper addresses the delayed (or anechoic) source separation problem in the case of parameterized deterministic sources. An alternating least square scheme is proposed to estimate the source parameters, the mixing coefficients and the delays. For the challenging delay parameter we adapt a sparse approximation strategy. A first algorithm considers discrete delays; then an extension, inspired by...
Most current state of the art blind image deconvolution methods model the underlying image (either in the image or filter space) using sparsity promoting priors and perform inference, that is, image, blur, and parameter estimation using variational approximation. In this paper we propose the use of the spike-and-slab prior model in the filter space and a variational posterior approximation more expressive...
In this paper we propose a dictionary learning method that builds an over complete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally complexity. To achieve this we consider two factors (both to be learned from data) in order to design the dictionary: an orthonormal component made up of a fixed number of fast fundamental...
This paper describes new algorithms that incorporates the non-uniform norm constraint into the zero-attracting and reweighted modified filtered-x affine projection or pseudo affine projection algorithms for active noise control. The simulations indicate that the proposed algorithms can obtain better performance for primary and secondary paths with various sparseness levels with insignificant numerical...
In this paper, we focus on tracking the signal subspace under a sparsity constraint. More specifically, we propose a two-step approach to solve the considered problem whether the sparsity constraint is on the system weight matrix or on the source signals. The first step uses the OPAST algorithm for an adaptive extraction of an orthonormal basis of the principal subspace, then an estimation of the...
We present a novel online learning paradigm for nonlinear function estimation based on iterative orthogonal projections in an L2 space reflecting the stochastic property of input signals. An online algorithm is built upon the fact that any finite dimensional subspace has a reproducing kernel, which is given in terms of the Gram matrix of its basis. The basis used in the present study involves multiple...
This paper presents a new algorithm for discontinuity location and characterization using A-scan signals from an ultrasonic inspection system. The algorithm is based on solving an inverse problem in which the observation model is different from that traditionally used. In this model, the input vector represents the location of the geometrical center of the discontinuity and the scattering amplitude...
In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed...
In modern wireless networks, optimizing the association between base stations (BSs) and users effectively improves network performance. On the other hand, a frequently changing BS-user association renders considerable operational burden for network management, e.g., it consumes extra power to awaken the deactivated BSs and to support users' switching among BSs. This motivates us to balance the flexibility...
Low-rank tensor approximation algorithms are building blocks in tensor methods for signal processing. In particular, approximations of low multilinear rank (mrank) are of central importance in tensor subspace analysis. This paper proposes a novel non-iterative algorithm for computing a low-mrank approximation, termed sequential low-rank approximation and projection (SeLRAP). Our algorithm generalizes...
Training a support vector machine (SVM) on large data sets is a computationally intensive task. In this paper, we study the problem of selecting a subset of data for training the SVM classifier under requirement that the loss of performance due to training data reduction is low. A function quantifying suitability of a selected subset is proposed, and a greedy algorithm for solving the subset selection...
In this paper, iterative decoding using Belief Propagation λ-min decoding algorithm is considered. In this algorithm check nodes use only the λ lowest-magnitude messages thus simplifying the hardware complexity and reducing memory usage. A parallel-input architecture is proposed for the check node. We focus on the determination of the sought minima in a parallel fashion. Novel simplified circuits...
In this paper, we consider a practical signal transmission application with fixed power budget such as radar/sonar. The system is modeled by a linear equation with the assumption that the signal energy per measurement decreases linearly and the noise energy per measurement increases approximately linearly with the increasing of the number of measurements. Thus the SNR decreases quadratically with...
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