34 research outputs found
Stable, Robust and Super Fast Reconstruction of Tensors Using Multi-Way Projections
In the framework of multidimensional Compressed Sensing (CS), we introduce an
analytical reconstruction formula that allows one to recover an th-order
data tensor
from a reduced set of multi-way compressive measurements by exploiting its low
multilinear-rank structure. Moreover, we show that, an interesting property of
multi-way measurements allows us to build the reconstruction based on
compressive linear measurements taken only in two selected modes, independently
of the tensor order . In addition, it is proved that, in the matrix case and
in a particular case with rd-order tensors where the same 2D sensor operator
is applied to all mode-3 slices, the proposed reconstruction
is stable in the sense that the approximation
error is comparable to the one provided by the best low-multilinear-rank
approximation, where is a threshold parameter that controls the
approximation error. Through the analysis of the upper bound of the
approximation error we show that, in the 2D case, an optimal value for the
threshold parameter exists, which is confirmed by our
simulation results. On the other hand, our experiments on 3D datasets show that
very good reconstructions are obtained using , which means that this
parameter does not need to be tuned. Our extensive simulation results
demonstrate the stability and robustness of the method when it is applied to
real-world 2D and 3D signals. A comparison with state-of-the-arts sparsity
based CS methods specialized for multidimensional signals is also included. A
very attractive characteristic of the proposed method is that it provides a
direct computation, i.e. it is non-iterative in contrast to all existing
sparsity based CS algorithms, thus providing super fast computations, even for
large datasets.Comment: Submitted to IEEE Transactions on Signal Processin
A sparse coding approach to inverse problems with application to microwave tomography imaging
Inverse imaging problems that are ill-posed can be encountered across
multiple domains of science and technology, ranging from medical diagnosis to
astronomical studies. To reconstruct images from incomplete and distorted data,
it is necessary to create algorithms that can take into account both, the
physical mechanisms responsible for generating these measurements and the
intrinsic characteristics of the images being analyzed. In this work, the
sparse representation of images is reviewed, which is a realistic, compact and
effective generative model for natural images inspired by the visual system of
mammals. It enables us to address ill-posed linear inverse problems by training
the model on a vast collection of images. Moreover, we extend the application
of sparse coding to solve the non-linear and ill-posed problem in microwave
tomography imaging, which could lead to a significant improvement of the
state-of-the-arts algorithms.Comment: submitted to RevMexAA (conference series
A copula-based method for synthetic microarray data generation
In this work, we propose a copula-based method to generate synthetic
gene expression data that account for marginal and joint probability distributions
features captured from real data. Our method allows us to implant significant
genes in the synthetic dataset in a controlled manner, giving the possibility of
testing new detection algorithms under more realistic environments
Improving Pre-movement Pattern Detection with Filter Bank Selection
Pre-movement decoding plays an important role in movement detection and is
able to detect movement onset with low-frequency electroencephalogram (EEG)
signals before the limb moves. In related studies, pre-movement decoding with
standard task-related component analysis (STRCA) has been demonstrated to be
efficient for classification between movement state and resting state. However,
the accuracies of STRCA differ among subbands in the frequency domain. Due to
individual differences, the best subband differs among subjects and is
difficult to be determined. This study aims to improve the performance of the
STRCA method by a feature selection on multiple subbands and avoid the
selection of best subbands. This study first compares three frequency range
settings (: subbands with equally spaced bandwidths; : subbands whose
high cut-off frequencies are twice the low cut-off frequencies; : subbands
that start at some specific fixed frequencies and end at the frequencies in an
arithmetic sequence.). Then, we develop a mutual information based technique to
select the features in these subbands. A binary support vector machine
classifier is used to classify the selected essential features. The results
show that is a better setting than the other two settings. With the
filter banks in , the classification accuracy of the proposed FBTRCA
achieves 0.87000.1022, which means a significantly improved performance
compared to STRCA (0.82870.1101) as well as to the cross validation and
testing method (0.84310.1078)
A new catalog of HI supershell candidates in the outer part of the Galaxy
Aims. The main goal of this work is to a have a new neutral hydrogen (H i) supershell candidate catalog to analyze their spatial distribution in the Galaxy and to carry out a statistical study of their main properties.
Methods. This catalog was carried out making use of the Leiden-Argentine-Bonn (LAB) survey. The supershell candidates were identified using a combination of two techniques: a visual inspection plus an automatic searching algorithm. Our automatic algorithm is able to detect both closed and open structures.
Results. A total of 566 supershell candidates were identified. Most of them (347) are located in the second Galactic quadrant, while 219 were found in the third one. About 98% of a subset of 190 structures (used to derive the statistical properties of the supershell candidates) are elliptical with a mean weighted eccentricity of 0.8 ± 0.1, and ∼70% have their major axes parallel to the Galactic plane. The weighted mean value of the effective radius of the structures is ∼160 pc. Owing to the ability of our automatic algorithm to detect open structures, we have also identified some “galactic chimney” candidates. We find an asymmetry between the second and third Galactic quadrants in the sense that in the second one we detect structures as far as 32 kpc, while for the 3rd one the farthest structure is detected at 17 kpc. The supershell surface density in the solar neighborhood is ∼8 kpc−2, and decreases as we move farther away form the Galactic center. We have also compared our catalog with those by other authorsInstituto Argentino de RadioastronomíaFacultad de Ciencias Astronómicas y Geofísica
Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
In this paper, the time variation of signals from several SCADA systems of geographically closed turbines are analysed and compared. When operating correctly, they show a clear pattern of joint variation. However, the presence of a failure in one of the turbines causes the signals from the faulty turbine to decouple from the pattern. From this information, SCADA data is used to determine, firstly, how to derive reference signals describing this pattern and, secondly, to compare the evolution of different turbines with respect to this joint variation. This makes it possible to determine whether the behaviour of the assembly is correct, because they maintain the well-functioning patterns, or whether they are decoupled. The presented strategy is very effective and can provide important support for decision making in turbine maintenance and, in the near future, to improve the classification of signals for training supervised normality models. In addition to being a very effective system, it is a low computational cost strategy, which can add great value to the SCADA data systems present in wind farms.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnologies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.b - Per a 2030, ampliar la infraestructura i millorar la tecnologia per tal d’oferir serveis d’energia moderns i sostenibles per a tots els països en desenvolupament, en particular els països menys avançats, els petits estats insulars en desenvolupament i els països en desenvolupament sense litoral, d’acord amb els programes de suport respectiusObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version
Machine Learning Methods with Noisy, Incomplete or Small Datasets
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.Instituto Argentino de Radioastronomí
A Fast Gradient Approximation for Nonlinear Blind Signal Processing
When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsic complexity, the global algorithm is much more slow and hence not useful for our purpose
A Simple Approximation for Fast Nonlinear Deconvolution
When dealing with nonlinear blind deconvolution, complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing or in microarray data analysis. In this paper we propose a simple method to reduce computational time for the inversion of Wiener systems by using a linear approximation in a minimum-mutual information algorithm. Experimental results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased