Analysis of the normalized LMS optimum solution in the context of channel equalization

Albeit being presented as an alternative to the classical least-mean-square (LMS) algorithm, the normalized LMS (NLMS) actually deals with a modified mean squared error (MSE) cost function, so that the expected optimum solution may differ from the Wiener solution. In this work, we perform an investigation concerning the question as to whether such difference may arise in the context of the channel equalization problem by considering a representative set of transmitted signal modulations, channel models and signal-to-noise ratio (SNR) conditions. Additionally, we analyze the influence of the potential deviation from the optimal solution on the performance of the equalizer3312230CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQnão te

Channel equalization based on decision trees

This paper analyzes the application of decision trees to the problem of communication channel equalization. Decision trees are interesting structures because they are nonlinear and relatively simple from a computational standpoint. They are tested for channel models that give rise to classification tasks of different complexity and compared to the Bayesian equalizer and the Wiener linear equalizer. The results are quite encouraging, as they show that the tree-based equalizer reaches, in many cases, a performance similar to that of the Bayesian filter at a lower computational cost351150161CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ305621/2015-7; 134058/2016-

EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPFINANCIADORA DE ESTUDOS E PROJETOS - FINEPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESHands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI); in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that inforniation may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any I near correlation between variations in the synchronization patterns that is, variations in the PSD of mu and beta bands induced by MI and alterations in the corresponding functional networks. Moreover, we (I) explored the feasibility of using functional connectivity parameters as features fora classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 +/- 8)% and (87 +/- 7)% for the mu and beta band, respectively, versus (83 +/- 8)% and (83 +/- 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.5115FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPFINANCIADORA DE ESTUDOS E PROJETOS - FINEPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPFINANCIADORA DE ESTUDOS E PROJETOS - FINEPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES2013/07559-3Sem informaçãoSem informaçãoSem informaçã

COVID-19 detection using chest X-rays: is lung segmentation important for generalization?

We evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as COVID-19, normal or pneumonia, using a relatively small and mixed dataset. We proposed a DNN to perform lung segmentation and classification, stacking a segmentation module (U-Net), an original intermediate module and a classification module (DenseNet201). To evaluate generalization, we tested the DNN with an external dataset (from distinct localities) and used Bayesian inference to estimate probability distributions of performance metrics. Our DNN achieved 0.917 AUC on the external test dataset, and a DenseNet without segmentation, 0.906. Bayesian inference indicated mean accuracy of 76.1% and [0.695, 0.826] 95% HDI (high density interval, which concentrates 95% of the metric's probability mass) with segmentation and, without segmentation, 71.7% and [0.646, 0.786]. We proposed a novel DNN evaluation technique, using Layer-wise Relevance Propagation (LRP) and Brixia scores. LRP heatmaps indicated that areas where radiologists found strong COVID-19 symptoms and attributed high Brixia scores are the most important for the stacked DNN classification. External validation showed smaller accuracies than internal, indicating difficulty in generalization, which segmentation improves. Performance in the external dataset and LRP analysis suggest that DNNs can be trained in small and mixed datasets and detect COVID-19.Comment: This revision mainly changed the text to make explanations clearer and it added better comparisons to related works. Reported results and models did not chang

A Blind Source Separation Method for Chemical Sensor Arrays based on a Second-order mixing model

International audienceIn this paper we propose a blind source separation method to process the data acquired by an array of ion-selective electrodes in order to measure the ionic activity of different ions in an aqueous solution. While this problem has already been studied in the past, the method presented differs from the ones previously analyzed by approximating the mixing function by a second-degree polynomial, and using a method based on the differential of the mutual information to adjust the parameter values. Experimental results, both with synthetic and real data, suggest that the algorithm proposed is more accurate than the other models in the literature

Sobre dinamica caotica e convergencia em algoritmos de equalização autodidata

Orientador : João Marcos Travassos RomanoDissertação (mestrado) - Universidade de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad

A Sparsity-Based Method for Blind Compensation of a Memoryless Nonlinear Distortion: Application to Ion-Selective Electrodes

International audience— In this paper, we propose a method for blind compensation of a memoryless nonlinear distortion. We assume as prior information that the desired signal admits a sparse representation in a transformed domain that should be known in advance. Then, given that a nonlinear distortion tends to generate signals that are less sparse than the desired one, our proposal is to build a compensating function model that gives rise to a maximally sparse signal. The implementation of this proposal has, as central elements, a criterion built upon an approximation of the 0-norm, the use of polynomial functions as compensating structures, and an optimization strategy based on sequential quadratic programming. We provide a theoretic analysis for an 0-norm criterion and results considering synthetic data. We also employ the method in an actual application related to chemical analysis via ion-selective electrode arrays

Blind Source Separation of Overdetermined Linear-Quadratic Mixtures

ISBN 978-3-642-15994-7, SoftcoverInternational audienceThis work deals with the problem of source separation in overdetermined linear-quadratic (LQ) models. Although the mixing model in this situation can be inverted by linear structures, we show that some simple independent component analysis (ICA) strategies that are often employed in the linear case cannot be used with the studied model. Motivated by this fact, we consider the more complex yet more robust ICA framework based on the minimization of the mutual information. Special attention is given to the development of a solution that be as robust as possible to suboptimal convergences. This is achieved by defining a method composed of a global optimization step followed by a local search procedure. Simulations confirm the effectiveness of the proposal