Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions.Comment: 4 pages, 3 figure

Reconstruction par transformation parcimonieuse de solutions à alphabet fini de systèmes linéaires sous-déterminés

National audienceNous considérons le problème d'estimer un vecteur à alphabet fini à partir d'un système sous-déterminé y = Af , où A est une matrice aléatoire générique réelle donnée de dimension n × N . Une méthode originale par optimisation convexe est proposée pour reconstruire le vecteur par minimisation L1 . Cette méthode est basée sur une transformation du problème dans un domaine où la solution recherchée est parcimonieuse. Le comportement théorique de cette méthode est donné et illustrée expérimentalement

Sparse Canonical Correlation Analysis Based on Rank-1 Matrix Approximation and its Application for fMRI Signals

International audienceCanonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper a new algorithm for sparse CCA is proposed. This algorithm differs from the existing ones in their derivation which is based on penalized rank one matrix approximation and the orthogonal projectors onto the space spanned by the two sets of multidimensional variables instead of the simple cross-matrix product. The performance and effectiveness of the proposed algorithm are tested on simulated experiments. On these results it can be observed that they outperform the state of the art sparse CCA algorithms

Canonical correlation analysis based on sparse penalty and through rank-1 matrix approximation

Canonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA and smooth or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper two new algorithms for sparse CCA and smooth CCA are proposed. These algorithms differ from the existing ones in their derivation which is based on penalized rank one matrix approximation and the orthogonal projectors onto the space spanned by the columns of the two sets of multidimensional variables instead of the simple cross-matrix product. The performance and effectiveness of the proposed algorithms are tested on simulated experiments. On these results it can be observed that they outperforms the state of the art sparse CCA algorithms

Blind equalization and automatic modulation classification based on PDF fitting

International audienceIn this paper, a completely blind equalizer based on probability density function (pdf) fitting is proposed. It doesn't require any prior information about the transmission channel or the emitted constellation. We also investigate Automatic Modulation Classification (AMC) for Quadrature Amplitude Modulation (QAM) based on the pdf of the equalized signal. We propose three new approaches for AMC. The first employs maximum likelihood functions (ML) of the modulus of real and imaginary parts of the equalized signal. The second is based on the lowest quadratic or Bhattacharyya distance between the estimated pdf of the real and imaginary parts of the equalizer output and the theoretical pdfs of M-QAM modulations. The third approach is based on theoretical pdf dictionnary learning. The performance of the identification scheme is investigated through simulations

A Simple ADMM Solution To Sparse-Modeling-Based Detectors For Massive MIMO Systems

International audienceWe give a simple yet efficient Alternating Direction Method of Multipliers algorithm for solving sparse-modeling-based detectors [7, 9] for massive MIMO systems. Our solution relies on a special reformulation of the associated optimization problem by describing the constraints as a Cartesian power of the probability simplex. Simulation results show that the proposed algorithm is as accurate as the best known solvers (interior point methods), while its complexity remains linear with respect to the size of the system

UNDERDETERMINED BLIND SEPARATION OF AUDIO SOURCES IN TIME-FREQUENCY DOMAIN

International audienceThis paper considers the blind separation of audio sources in the underdetermined case, where we have more sources than sensors. A recent algorithm applies time-frequency distributions (TFDs) to this problem and gives good separation performance in the case where sources are disjoint in the time-frequency (TF) plane. However, in the non-disjoint case, the reconstruction of the signals requires some interpolation at the intersection points in the TF plane. In this paper, we propose a new algorithm that combines the abovementioned method with subspace projection in order to explicitly treat non-disjoint sources. Another contribution of this paper is the estimation of the mixing matrix in the underdetermined case

Adaptive Blind Identification of Sparse SIMO Channels using Maximum a Posteriori Approach

International audienceIn this paper, we are interested in adaptive blind channel identification of sparse single input multiple output (SIMO) systems. A generalized Laplacian distribution is considered to enhance the sparsity of the channel coefficients with a maximum a posteriori (MAP) approach. The resulting cost function is composed of the classical deterministic maximum likelihood (ML) term and an additive $\ell_p$ norm of the channel coefficient vector which represents the sparsity penalization. The proposed adaptive optimization algorithm is based on a simple gradient step. Simulations show that our method outperforms the existing adaptive versions of cross-relation (CR) method

Turbo Detection Based On Sparse Decomposition For Massive MIMO Transmission

International audienceIn this paper, we address the problem of underdetermined massive MIMO detection for QAM constellations. In [1], the authors showed the utility of projecting the signal in a basis of the modulation alphabet, looking for the sparsest vector representation. As an extension of this work and in order to reduce the detection complexity, we present first an equivalent real-valued formulation of the optimization problem, all the more interesting as the modulation order is high. Then we consider an outer forward error correcting (FEC) code and we propose a turbo detection scheme. We focus on the medium SNR value range where detection errors involve adjacent symbols. Based on this hypothesis, we propose a sparse vector formulation to be treated as a soft detection output that can be directly exploited in a symbol-to-binary conversion to feed the FEC decoder with reliable soft input. The FEC decoder output will be exploited to provide a priori information within the detection criterion based on a regularization approach. Simulation results show the efficiency of the proposed scheme in comparison with reference schemes of the state-of-art

Two vertical handcover metrics toward an IEEE 802.11N network

International audienceThis paper deals with two metrics for vertical handover toward an IEEE 802.11n network, estimated from the physical layer instead of the MAC layer. For this reason we dont need to be connected to the network to estimate them. The first metric is related to the channel occupancy rate, and is estimated by the mean of a likelihood function of the observed samples. The second one is related to the collision rate. Using an information theoretic criterion and taking advantage of the OFDMstructure of the signal, we avoid the channel length estimation and decide if a collision occured or not