Artículos (Teoría de la Señal y Comunicaciones)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11422

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A Reduced-Complexity Direct Learning Architecture for Digital Predistortion Through Iterative Pseudoinverse Calculation
(Institute of Electrical and Electronics Engineers (IEEE), 2021-08) Becerra González, Juan Antonio; Pérez Hernández, Abraham; Madero Ayora, María José; Crespo Cadenas, Carlos; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TIC158: Sistemas de Radiotelecomunicación
In this letter, a novel approach is proposed for digital predistortion (DPD) with direct learning architecture (DLA). Regression of a Volterra behavioral model requires the pseudoinverse of a matrix, which needs many resources due to the inverse operation when the Moore-Penrose pseudoinverse is used. This work substitutes the pseudoinverse calculation by a polynomial expansion (PE) method to obtain a polynomial expansion direct learning architecture (PE-DLA), which attains a pseudoinverse in an iterative fashion avoiding the inverse operation and consequently reducing the algorithm computational complexity. Experimental results show that the number of iterations in the PE-DLA affects the convergence speed. The proposal is benchmarked against other state-of-the-art approaches such as the classic DLA and the covariance matrix DLA (CM-DLA) in the DPD of a commercial class AB power amplifier, concluding that the linearization performance of the current proposal is equivalent to others while featuring simple operations.
Generalized Exponentiated Gradient Algorithms and Their Application to On-Line Portfolio Selection
(Institute of Electrical and Electronics Engineers (IEEE), 2024-12-19) Cichocki, Andrzej; Cruces Álvarez, Sergio Antonio; Sarmiento Vega, María Auxiliadora; Tanaka, Toshihisa; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Plan Andaluz de Investigación, Desarrollo e Innovación 2020 (PAIDI); Universidad de Sevilla. TIC246: Tecnologías de aprendizaje automático y procesado digital de la información.
Stochastic gradient descent (SGD) and exponentiated gradient (EG) update methods are widely used in signal processing and machine learning. This study introduces a novel family of generalized Exponentiated Gradient updates (EGAB) derived from the alpha-beta (AB) divergence regularization. The EGAB framework provides enhanced flexibility for processing data with varying distributions, thanks to the tunable hyperparameters of the AB divergence. We explore the applicability of these updates in online portfolio selection (OLPS) for financial markets with the goal of developing algorithms that achieve high risk-adjusted returns, even under relatively high transaction costs. The proposed EGAB algorithms are developed using constrained gradient optimization with regularization terms, demonstrating their versatility in OLPS by unifying the directional search of various algorithms and enabling interpolation between them. Our analysis and extensive computer simulations reveal that EGAB updates outperform existing OLPS algorithms, delivering good results on several performance metrics, such as cumulative return, average excess return, Sharpe ratio, and Calmar ratio, especially when transaction costs are significant. In conclusion, this study introduces a new family of exponentiated gradient updates and demonstrates their flexibility and effectiveness through extensive simulations across a wide range of real-world financial datasets.
Complex Gaussian Processes for Regression and Their Connection to WLMMSE
(Institute of Electrical and Electronics Engineers (IEEE), 2024-12) Boloix Tortosa, Rafael; Murillo Fuentes, Juan José; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; European Commission (EC); Universidad de Sevilla. TIC155: Tratamiento de Señales y Comunicaciones
The Gaussian process (GP) is a well-established Bayesian nonparametric tool for inference in nonlinear estimation problems. When GPs are used for regression, the goal is to estimate a target signal y from an input vector x without assuming that they are linearly related, but with a probabilistic model p(y|x) that is Gaussian distributed. Therefore, GPs can be understood as a natural nonlinear extension to MMSE estimation. For real-valued GPs, this has been analyzed in the existing literature, and it is concluded that they are the natural nonlinear Bayesian extension to the linear minimum mean-squared error (LMMSE) estimation. In this letter, we show that, consequently, complex-valued GP regression (GPR) models are the natural nonlinear Bayesian extension of the widely linear minimum mean squared-error (WLMMSE) estimation. As in the real-valued case, complex-valued GPs are able to better model many regression problems by making use of the information that the complementary kernel or pseudo-kernel provides.
Features identification for automatic burn classification
(Elsevier, 2015-12) Serrano Gotarredona, María del Carmen ; Boloix Tortosa, Rafael; Gómez Cía, Tomás; Acha Piñero, Begoña; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Junta de Andalucía; Universidad de Sevilla. TIC155: Tratamiento de Señales y Comunicaciones
Purpose In this paper an automatic system to diagnose burn depths based on colour digital photographs is presented. Justification: There is a low success rate in the determination of burn depth for inexperienced surgeons (around 50%), which rises to the range from 64 to 76% for experienced surgeons. In order to establish the first treatment, which is crucial for the patient evolution, the determination of the burn depth is one of the main steps. As the cost of maintaining a Burn Unit is very high, it would be desirable to have an automatic system to give a first assessment in local medical centres or at the emergency, where there is a lack of specialists. Method To this aim a psychophysical experiment to determine the physical characteristics that physicians employ to diagnose a burn depth is described. A Multidimensional Scaling Analysis (MDS) is then applied to the data obtained from the experiment in order to identify these physical features. Subsequently, these characteristics are translated into mathematical features. Finally, via a classifier (Support Vector Machine) and a feature selection method, the discriminant power of these mathematical features to distinguish among burn depths is analysed, and the subset of features that better estimates the burn depth is selected. Results A success rate of 79.73% was obtained when burns were classified as those which needed grafts and those which did not. Conclusions Results validate the ability of the features extracted from the psychophysical experiment to classify burns into their depths.
Implementation of Alternating Direction Method of Multipliers for Solving Power Amplifier Linearization Problem: Theoretical Foundations and Proof of Concept
(Institute of Electrical and Electronics Engineers (IEEE), 2024) Marqués Valderrama, Elías; Becerra González, Juan Antonio; Madero Ayora, María José; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TIC158: Sistemas de Radiocomunicación
In this work, a formulation of alternating direction method of multipliers (ADMM) for addressing power amplifiers (PA) modeling and linearization problems is presented. The proposal consists on leveraging the implicit redundancy of the equations in order to achieve a distributed architecture. A detailed theoretical formulation of the method is provided in order to get a better comprehension of its advantages and the approach to integrating the technique in the standard direct learning architecture scheme for digital predistortion. In addition, a discussion on how the implicit regularization helps to deal with numerical problems is presented. It is proven that the implicit regression for the modeling and linearization of PAs can be carried out in a distributed fashion with similar accuracy, enabling the use of resource-constrained devices for digital predistortion. Furthermore, a computational assessment is presented for measuring in terms of arithmetic operations how simpler the devices that implement the proposed ADMM can be, compared with the classical least squares (LS) data-centralized approach. A proof of concept with three scenarios is included: the first two scenarios feature a commercial PA driven by 5G-NR signals with a bandwidth of 30 MHz, and the third scenario involves a commercial Doherty PA with a 100-MHz signal. In a first scenario, experimental results show that an ADMM implementation of a digital predistorter can achieve the same performance as the classical LS solution. The benefits of the proposed regularization are examined by assessing ADMM in a second scenario characterized by significant numerical instabilities. Finally, the robustness of the technique is illustrated through the linearization of the Doherty PA with a 100-MHz OFDM signal.
Smart Bioimpedance Device for the Assessment of Peripheral Muscles in Patients with COPD
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-07) Naranjo Hernández, David; Reina Tosina, Luis Javier; Roa Romero, Laura María; Barbarov-Rostán, Gerardo; Ortega Ruiz, Francisco; Cejudo Ramos, Pilar; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Junta de Andalucía; Fundación Mutua Madrileña; Universidad de Sevilla. TIC203: Ingeniería Biomédica
Muscle dysfunction and muscle atrophy are common complications resulting from Chronic Obstructive Pulmonary Disease (COPD). The evaluation of the peripheral muscles can be carried out through the assessment of their structural components from ultrasound images or their functional components through isometric and isotonic strength tests. This evaluation, performed mainly on the quadriceps muscle, is not only of great interest for diagnosis, prognosis and monitoring of COPD, but also for the evaluation of the benefits of therapeutic interventions. In this work, bioimpedance spectroscopy technology is proposed as a low-cost and easy-to-use alternative for the evaluation of peripheral muscles, becoming a feasible alternative to ultrasound images and strength tests for their application in routine clinical practice. For this purpose, a laboratory prototype of a bioimpedance device has been adapted to perform segmental measurements in the quadriceps region. The validation results obtained in a pseudo-randomized study in patients with COPD in a controlled clinical environment which involved 33 volunteers confirm the correlation and correspondence of the bioimpedance parameters with respect to the structural and functional parameters of the quadriceps muscle, making it possible to propose a set of prediction equations. The main contribution of this manuscript is the discovery of a linear relationship between quadriceps muscle properties and the bioimpedance Cole model parameters, reaching a correlation of 0.69 and an average error of less than 0.2 cm regarding the thickness of the quadriceps estimations from ultrasound images, and a correlation of 0.77 and an average error of 3.9 kg regarding the isometric strength of the quadriceps muscle.
LDA via L1-PCA of Whitened Data
(Institute of Electrical and Electronics Engineers (IEEE), 2020) Martín Clemente, Rubén; Zarzoso, Vicente; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Economía y Competitividad (MINECO). España; Universidad de Sevilla. TIC203: Ingeniería Biomédica
Principal component analysis (PCA) and Fisher's linear discriminant analysis (LDA) are widespread techniques in data analysis and pattern recognition. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA, drawing considerable research interest on account of its increased robustness to outliers. The present work proves that, combined with a whitening preprocessing step, L1-PCA can perform LDA in an unsupervised manner, i.e., sparing the need for labelled data. Rigorous proof is given in the case of data drawn from a mixture of Gaussians. A number of numerical experiments on synthetic as well as real data confirm the theoretical findings.
A full-head model to investigate intra and extracochlear electric fields in cochlear implant stimulation
(Institute of Physics, 2024-08-07) Callejón Leblic, María Amparo; Lazo Maestre, Manuel; Fratter, A.; Ropero Romero, Francisco; Sánchez Gómez, Serafín; Reina Tosina, Luis Javier; Universidad de Sevilla. Departamento de Cirugía; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Junta de Andalucía
Objective. Despite the widespread use and technical improvement of cochlear implant (CI) devices over past decades, further research into the bioelectric bases of CI stimulation is still needed. Various stimulation modes implemented by different CI manufacturers coexist, but their true clinical benefit remains unclear, probably due to the high inter-subject variability reported, which makes the prediction of CI outcomes and the optimal fitting of stimulation parameters challenging. A highly detailed full-head model that includes a cochlea and an electrode array is developed in this study to emulate intracochlear voltages and extracochlear current pathways through the head in CI stimulation. Approach. Simulations based on the finite element method were conducted under monopolar, bipolar, tripolar (TP), and partial TP modes, as well as for apical, medial, and basal electrodes. Variables simulated included: intracochlear voltages, electric field (EF) decay, electric potentials at the scalp and extracochlear currents through the head. To better understand CI side effects such as facial nerve stimulation, caused by spurious current leakage out from the cochlea, special emphasis is given to the analysis of the EF over the facial nerve. Main results. The model reasonably predicts EF magnitudes and trends previously reported in CI users. New relevant extracochlear current pathways through the head and brain tissues have been identified. Simulated results also show differences in the magnitude and distribution of the EF through different segments of the facial nerve upon different stimulation modes and electrodes, dependent on nerve and bone tissue conductivities. Significance. Full-head models prove useful tools to model intra and extracochlear EFs in CI stimulation. Our findings could prove useful in the design of future experimental studies to contrast FNS mechanisms upon stimulation of different electrodes and CI modes. The full-head model developed is freely available for the CI community for further research and use.
Blind Low Complexity Time-Of-Arrival Estimation Algorithm for UWB Signals
(Institute of Electrical and Electronics Engineers (IEEE), 2005-06) Boloix Tortosa, Rafael; Arias de Reyna Domínguez, Eva María; Murillo Fuentes, Juan José; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Educación y Ciencia (MEC). España; Junta de Andalucía; Universidad de Sevilla. TIC155: Tratamiento de Señales y Comunicaciones
This letter presents a novel time-of-arrival (TOA) blind estimation technique for ultra wideband energy detection receivers with reduced complexity. The proposed method is blind in the sense that it does not exploit any information about channel or noise power. This new approach is based on a set of approximations of the exact likelihood function (ELF) of the observed energy. Even though these approximations achieve an important reduction of complexity, the shape of the new approximated function is accurate enough compared to the ELF. Application of a threshold to the differential of the approximated log-likelihood function completes the procedure. Simulations show that the performance of the proposed method in terms of the cumulative distribution function of the estimation error approaches that of a method based on the ELF and a genie-aided algorithm with perfect knowledge of the optimal threshold.
De-Randomization of MAC Addresses Using Fingerprints and RSSI with ML for Wi-Fi Analytics
(IEEE, 2024) Pérez Hernández, Abraham; Barreras Martín, Maydelis N.; Becerra González, Juan Antonio; Madero Ayora, María José; Aguilera Bonet, Pablo; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Universidad de Sevilla. TIC158: Sistemas de Radiocomunicación
Media Access Control (MAC) address randomization causes significant distortion and data loss in Wi-Fi analytics systems, becoming a real challenge for building services based on tracking, location, and presence data. This study aims to mitigate this problem by combining two key points: the construction of a quasi-unique, stable, reliable, and anonymous identifier for non-connected Wi-Fi devices, and the inability of Wi-Fi devices to deliberately change the physical conditions of the connection. We propose a new system that builds identifiers based on the capabilities and information elements announced within the probe request management frames, and consequently applies unsupervised machine learning techniques in the multidimensional Received Signal Strength Indicator (RSSI) space. Experimental tests in a real-world environment were conducted, and the results of this extensive field study demonstrated that the proposed system achieves high accuracy in identifying and tracking non-connected Wi-Fi devices in these challenging scenarios, even in the presence of MAC randomization. Our findings suggest that the proposed system has a significant potential for enhancing building services that rely on Wi-Fi data.
Hybrid higher-order statistics learning in multiuser detection
(Institute of Electrical and Electronics Engineers (IEEE), 2004-11) Caamano, Antonio J.; Boloix Tortosa, Rafael; Ramos, Javier; Murillo Fuentes, Juan José; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia Y Tecnología (MCYT). España; Universidad de Sevilla. TIC-155: Tratamiento de señales y comunicaciones
In this paper, we explore the significance of second- and higher-order statistics learning in communication systems. The final goal in spread-spectrum communication systems is to receive a signal of interest completely free from interference caused by other concurrent signals. To achieve this end, we exploit the structure of the interference by designing second-order statistics detectors, such as the minimum square error, in conjunction with higher-order statistics (HOS) techniques, such as the blind source separation (BSS). This hybrid higher-order statistics (HyHOS) approach enables us to alleviate BSS algorithms of one of their main problems, that is, their sensitiveness to high levels of noise. In addition, we benefit from remarkable properties of BSS in learning such as fast learning (superefficiency) and independence of the initial settings of the problem (equivariance). We successfully applied the results of this approach to the design of multiuser detectors in code-division multiple access channels. © 2004 IEEE.
Strict separability and identifiability of a class of ICA models
(2010-03) Murillo Fuentes, Juan José; Boloix Tortosa, Rafael; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TIC-155: Tratamiento de señales y comunicaciones
In this letter we focus on the application of independent component analysis (ICA) to a class of overdetermined blind source separation (BSS) problems. The mixing matrix in the BSS model is the product of an unknown square diagonal matrix and a projection matrix. The last matrix performs a known projection to the same or larger dimensional space. We demonstrate the conditions for the model to be strictly separable and identifiable under the statistical independence condition, paying attention to permutations and relative scalings. These results find application, e.g., in the channel estimation of ZP-OFDM and Precoded-OFDM systems
Near the Cramér-Rao bound precoding algorithms for OFDM blind channel estimation
(Institute of Electrical and Electronics Engineers (IEEE), 2012-02) Simois Tirado, Francisco José; Murillo Fuentes, Juan José; Boloix Tortosa, Rafael; Salamanca, Luis; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Educación y Ciencia (MEC). España; Ministerio de Educación y Ciencia (MEC). España; Universidad de Sevilla. TIC-155: Tratamiento de señales y comunicaciones
The authors present a blind channel estimation of cyclic prefix (CP) orthogonal frequency-division multiplexing (OFDM) systems with nonredundant precoding based on secondorder statistics. The study analyzes first the mean square error for the estimation of the covariance matrix of the received symbols. We prove that, for high and medium signal-to-noise ratios (SNRs), the estimation error in the diagonal entries of the covariance matrix exhibits a lower error than that in the off-diagonal elements. This behavior holds for SNR values in digital communication. Contrary to general belief, we prove that the diagonal of this matrix can be used for channel estimation. Hence, we develop a novel algorithm that utilizes this result. We also develop a low-complexity version that provides acceptable results with reduced computational requirements. Finally, we analyze the covariance matrix and propose another new algorithm with noise suppression capabilities. Some experimental results for Rayleigh channels are included to support these conclusions. In addition, they illustrate better performance of the new methods, compared with previous proposals and with the Cramér-Rao bound (CRB).
Complex-Valued Kernel Methods for Regression
(Institute of Electrical and Electronics Engineers, 2017-10) Boloix Tortosa, Rafael; Murillo Fuentes, Juan José; Santos, Irene; Pérez Cruz, Fernando; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Educación y Ciencia (MEC). España; Universidad de Sevilla: TIC-155: Tratamiento de señales y comunicaciones
In this paper, we propose a widely linear reproducing kernel Hilbert space (WL-RKHS) for nonlinear regression with complex-valued signals. Our approach is a nonlinear extension of WL signal processing that has been proven to be more versatile than linear systems for dealing with complex-value signals. To be able to use the WL concept in kernel methods, we need to introduce a pseudo-kernel to complement the standard kernel in RKHS, which is not defined in previous RKHS approaches in the existing literature. In this paper, we present WL-RKHS, its properties, and the kernel and pseudo-kernel designs. We illustrate the need of the pseudo-kernel with simply verifiable examples that allow understanding the intuitions behind this kernel. We conclude this paper, showing that in the all-relevant nonlinear equalization problem the pseudo-kernel plays a significant role and previous approaches that do not rely on this kernel clearly underperform.
Expectation Propagation as Turbo Equalizer in ISI Channels
(Institute of Electrical and Electronics Engineers (IEEE), 2017-01) Santos, Irene; Murillo Fuentes, Juan José; Boloix Tortosa, Rafael; Arias de Reyna Domínguez, Eva María; Olmos, Pablo M.; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Universidad de Sevilla. TIC155: Tratamiento de Señales y Comunicaciones
In probabilistic equalization of channels with inter-symbol interference, the BCJR algorithm and its approximations become intractable for high-order modulations, even for moderate channel dispersions. In this paper, we introduce a novel soft equalizer to approximate the symbol a posteriori probabilities (APP), where the expectation propagation (EP) algorithm is used to provide an accurate estimation. This new soft equalizer is presented as a block solution, denoted as block-EP (BEP), where the structure of the matrices involved is exploited to reduce the complexity order to O(L N2), i.e., linear in the length of the channel, L, and quadratic in the frame length, N. The solution is presented in complex-valued formulation within a turbo equalization scheme. This algorithm can be cast as a linear minimum-mean-squared-error (LMMSE) turbo equalization with double feedback architecture, where constellations being discrete is a restriction exploited by the EP that provides a first refinement of the APP. In the experiments included, the BEP exhibits a robust performance, regardless of the channel response, with gains in the range 1.5-5 dB compared with the LMMSE equalization. © 2016 IEEE.
Complex Gaussian Processes for Regression
(Institute of Electrical and Electronics Engineers, 2018-11) Boloix Tortosa, Rafael; Murillo Fuentes, Juan José; Payan-Somet, Francisco Javier; Pérez Cruz, Fernando; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Universidad de Sevilla. TIC-155: Tratamiento de señales y comunicaciones
In this paper, we propose a novel Bayesian solution for nonlinear regression in complex fields. Previous solutions for kernels methods usually assume a complexification approach, where the real-valued kernel is replaced by a complex-valued one. This approach is limited. Based on the results in complex-valued linear theory and Gaussian random processes, we show that a pseudo-kernel must be included. This is the starting point to develop the new complex-valued formulation for Gaussian process for regression (CGPR). We face the design of the covariance and pseudo-covariance based on a convolution approach and for several scenarios. Just in the particular case where the outputs are proper, the pseudo-kernel cancels. Also, the hyperparameters of the covariance can be learned maximizing the marginal likelihood using Wirtinger's calculus and patterned complex-valued matrix derivatives. In the experiments included, we show how CGPR successfully solves systems where the real and imaginary parts are correlated. Besides, we successfully solve the nonlinear channel equalization problem by developing a recursive solution with basis removal. We report remarkable improvements compared to previous solutions: a 2-4-dB reduction of the mean squared error with just a quarter of the training samples used by previous approaches. © 2012 IEEE.
The Generalized Complex Kernel Least-Mean-Square Algorithm
(Institute of Electrical and Electronics Engineers (IEEE), 2019-08) Boloix Tortosa, Rafael; Murillo Fuentes, Juan José; Baskoutas, Sotirios; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Economía y Competitividad (MINECO). España; Ministerio de Educación, Cultura y Deporte (MECD). España; Universidad de Sevilla: TIC-155: Tratamiento de señales y comunicaciones
We propose a novel adaptive kernel-based regression method for complex-valued signals: the generalized complex-valued kernel least-mean-square (gCKLMS). We borrow from the new results on widely linear reproducing kernel Hilbert space (WL-RKHS) for nonlinear regression and complex-valued signals, recently proposed by the authors. This paper shows that in the adaptive version of the kernel regression for complex-valued signals we need to include another kernel term, the so-called pseudo-kernel. This new solution is endowed with better representation capabilities in complex-valued fields since it can efficiently decouple the learning of the real and the imaginary part. Also, we review previous realizations of the complex KLMS algorithm and its augmented version to prove that they can be rewritten as particular cases of the gCKLMS. Furthermore, important conclusions on the design of the kernels are drawn that help to greatly improve the convergence of the algorithms. In the experiments, we revisit the nonlinear channel equalization problem to highlight the better convergence of the gCKLMS compared to previous solutions. Also, the flexibility of the proposed generalized approach is tested in a second experiment with non-independent real and imaginary parts. The results illustrate the significant performance improvements of the gCKLMS approach when the complex-valued signals have different properties for the real and imaginary parts.
A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining
(MDPI, 2024-02) Durán Díaz, Iván; Sarmiento Vega, María Auxiliadora; Fondón García, Irene; Bodineau, Clément; Tomé, Mercedes; Durán, Raúl V.; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia, Innovación y Universidades. España; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Junta de Andalucía; Universidad de Sevilla. TIC246: Tecnologías de aprendizaje automático y procesado digital de la información
Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.
EEG Signal Processing in MI-BCI Applications With Improved Covariance Matrix Estimators
(IEEE, 2019) Olías, Javier; Martín Clemente, Rubén; Sarmiento Vega, María Auxiliadora; Cruces Álvarez, Sergio Antonio; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Economía y Competitividad (MINECO). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TIC246: Tecnologías de Aprendizaje Automático y Procesado Digital de la Información
In brain-computer interfaces (BCIs), the typical models of the EEG observations usually lead to a poor estimation of the trial covariance matrices, given the high non-stationarity of the EEG sources. We propose the application of two techniques that significantly improve the accuracy of these estimations and can be combined with a wide range of motor imagery BCI (MI-BCI) methods. The first one scales the observations in such a way that implicitly normalizes the common temporal strength of the source activities. When the scaling applies independently to the trials of the observations, the procedure justifies and improves the classical preprocessing for the EEG data. In addition, when the scaling is instantaneous and independent for each sample, the procedure particularizes to Tyler's method in statistics for obtaining a distribution-free estimate of scattering. In this case, the proposal provides an original interpretation of this existing method as a technique that pursuits an implicit instantaneous power-normalization of the underlying source processes. The second technique applies to the classifier and improves its performance through a convenient regularization of the features covariance matrix. Experimental tests reveal that a combination of the proposed techniques with the state-of-the-art algorithms for motor-imagery classification provides a significant improvement in the classification results.
Automatic classification of tissue malignancy for breast carcinoma diagnosis
(Elsevier, 2018-05-05) Fondón García, Irene; Sarmiento Vega, María Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo; Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Universidad de Sevilla. TIC246: Tecnologías de Aprendizaje Automático y Procesado Digital de la Información
Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer.

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