Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2.

The use of the Choquet integral in multicriteria decision making problems has gained attention in the last two decades. Despite of its usefulness, there is the issue of how to define the Choquet integral parameters, called capacity coefficients, specially the ones associated with coalitions of criteria. A possible approach to address this issue is based on unsupervised learning, which aims to define such parameters with the goal of mitigating undesirable effects provided by intercriteria relations. However, current unsupervised approaches present some drawbacks, as there is no guarantee that the parameters are equally prioritized in the learning procedure. In this article, we propose a novel unsupervised capacity identification approach which ensures a fair learning for all parameters. Moreover, in comparison with the existing methods, our proposal is less complex in terms of optimization, as it is based on a linear formulation. Experimental results in both synthetic and real datasets attest the applicability of our proposal.

Several practical applications can be formulated as a problem of estimating a source of interest from a set of mixed data collected by different sensors. Although a lot of effort has been done to address the optimization task in signal extraction, there is a lack in the literature on how to evaluate the contribution of each sensor in the extraction process. In this letter, we propose a model-agnostic approach that can be used to interpret both the contribution of each sensor in the estimated source and the interaction effects between them. Our proposal is based on a solution concept from game theory, called Shapley value. Numerical experiments on synthetic and real data attest the use of our proposal in blind source extraction problems.

In dimensionality reduction problems, the adopted technique may produce disparities between the representation errors of different groups. For instance, in the projected space, a specific class can be better represented in comparison with another one. In some situations, this unfair result may introduce ethical concerns. Aiming at overcoming this inconvenience, a fairness measure can be considered when performing dimensionality reduction through Principal Component Analysis. However, a solution that increases fairness tends to increase the overall re-construction error. In this context, this paper proposes to address this trade-off by means of a multi-objective-based approach. For this purpose, we adopt a fairness measure associated with the disparity between the representation errors of different groups. Moreover, we investigate if the solution of a classical Principal Component Analysis can be used to find a fair projection. Numerical experiments attest that a fairer result can be achieved with a very small loss in the overall reconstruction error.

Economic and financial decision-making may cause a significant impact on government, society, and industries. Due to the increasing volume of data, decision science has become an interdisciplinary field of study, supported by efficient methods and models of data analysis. Our contributions lie exactly in the intersection of signal processing, tensorial algebra, and decision science. More precisely, we introduce a novel approach in which the data taken into account in the decision process is modeled as a tensor. Moreover, we apply adaptive prediction methods for aggregating the decision tensor. Results provided by numerical experiments with both synthetic and actual data attest to the efficacy of our proposal in better supporting economic and financial decisions.

In most research studies, much of the information gathered is of qualitative nature. This paper concentrates on items for which multiple rankings exist that shall be combined optimally. This work presents a unsupervised deterministic approach that can be applied to rank-order data for decomposing them into composite signals. On the opposite to other techniques, it does not requite any prior knowledge about the distribution of the signal components. Typically the variables are necessarily dependent because of the inequality relations among them. The mathematical relationships found are of interest in themselves and in the theory of blind source separation (BSS).Multiple rankings of an item set are decomposed into other rankings of these items, each ranking being orthogonal to the others, hence the name of rank-order principal components. We transform the original ranking data into matrices that linearize the optimization problem for its resolution by linear programming.The resolution is sensitive to the distance between the items that is used. Several distances have been tested: the Euclidean distance (Spearman), the rank absolute deviation distance, the Hölder distance, the $\chi ^{2}$ distance, etc. The results are concordant.

Remote sensing satellites allow continuous information acquisition from large areas of the earth and have been intensively applied in a number of applications, from agriculture to defense. A major challenge in remote sensing is that satellite communication systems present bandwidth restrictions and several issues typical of time-variant channels, which justifies the need for signal coding techniques. In that sense, this letter proposes an unequal error protection method for aerospace applications using the recommendations for source and channel coding created by the Consultative Committee for Space Data System (CCSDS) as frameworks. The proposed method makes use of the CCSDS-recommended convolutional code to ensure a channel coding step as low complex as possible, which allows implementation in a wide range of embedded platforms. This letter exploits the natural data division delivered by the compressor to unequally protect the information. The proposed method, which relies on a multiobjective optimization problem, allows one to find rate arrangements that minimize the distortion of the received image for a given value of an average coding rate within a granular range. The system performance is evaluated over an additive white Gaussian noise channel model. The obtained results show that the proposed method presents several advantages over an equal error protection strategy, and paves the way for scenarios with stringent energy and bandwidth constraints.

This letter investigates blind identification and compensation of Hammerstein systems, whose inputs are sparse in the frequency domain. In our analysis, the Hammerstein system comprises an antisymmetric invertible polynomial nonlinear curve followed by a minimum-phase linear system. We show analytically in a simple scenario and empirically in more complex situations that the Hammerstein system is identifiable with high probability, provided that the nonlinear component of the system reduces the sparsity of the input by a sufficiently large amount. Moreover, we propose a practical compensation procedure in the form of the Wiener system that provides a maximally sparse output when cascaded with the given Hammerstein system. Monte Carlo experiments show that the procedure is effective for Hammerstein systems with sufficiently strong nonlinearities submitted to inputs within a wide range of sparsity levels.

The goal of this work is to provide a brief overview of some recent advances in the field of seismic signal processing. In particular, we shall focus on tasks such as multiple attenuation and coherent noise elimination, paying special attention to the application of signal separation methods that are able to take into account prior information such as sparsity, which is ubiquitous in reflection seismic. In addition, we briefly review the application of signal transforms, such as wavelets and curvelets, to process seismic data. This article introduces the special session “Seismic Signal Processing”, which covers other applications and methods not discussed here.

This tutorial paper aims at summarizing some problems, ranging from analytical chemistry to novel chemical sensors, that can be addressed with classical or advanced methods of signal and image processing. We gather them under the denomination of “chemical sensing”. It is meant to introduce the special session “Signal Processing for Chemical Sensing” with a large overview of issues which have been and remain to be addressed in this application domain, including chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial nose, chromatography, mass spectrometry, etc. For enlarging and illustrating the points of view of this tutorial, the invited papers of the session consider other applications (NMR, Raman spectroscopy, recognition of explosive compounds, etc.) addressed by various methods, e.g. source separation, Bayesian, and exploiting typical chemical signal priors like positivity, linearity, unit-concentration or sparsity.

In this work, we investigate the use of a multimodal search framework to deal with a representative formulation of the Sparse Component Analysis (SCA) problem. The proposed method, which employs an artificial immune network in the role of multimodal optimization tool, is explained and tested in different scenarios. The results are promising and indicate the relevance of using global search tool in SCA, as well as the soundness of the immune-inspired proposal.

There is a growing interest in employing digital signal processing methods to compensate the distortions typical of the next generation optical systems. For instance, several works considered the multi-user constant modulus algorithm (MU-CMA) to recover the transmitted signals in Polarization Division Multiplexing (PDM) systems. In this work, the same problem is tackled through independent component analysis methods. The obtained results point out that, differently from the MU-CMA, our proposal is robust against sources loss even when polarization dependent loss is present.

The problem of independent component analysis (ICA) was firstly formulated and studied in the context of real-valued signals and mixing models, but, recently, an extension of this original formulation was proposed to deal with the problem within the framework of finite fields. In this work, we propose a strategy to deal with ICA over these fields that presents two novel features: (i) it is based on the use of a cost function built directly from an estimate of the mutual information and (ii) it employs an artificial immune system to perform the search for efficient separating matrices, in contrast with the existing techniques, which are based on search schemes of an exhaustive character. The new proposal is subject to a comparative analysis based on different simulation scenarios and the work is concluded by an analysis of perspectives of practical application to digital and genomic data mining.

We propose a novel blind source separation method tailored for retrieving baseband signals having different bandwidths. Such a configuration is characterized by the existence of inactive bands in the frequency domain. By exploiting the eigenstructure of the mixtures covariance matrix calculated in these inactive bands, we develop a simple yet efficient extraction procedure that works in an ordered fashion, in which the sources are extracted according to their degree of smoothness. Numerical results attest the viability of the proposal.