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This work performs an extensive investigation about the application of unorganized machines – extreme learning machines and echo state networks – to predict monthly seasonal streamflow series, associated to three important Brazilian hydroelectric plants, for many forecasting horizons. The aforementioned models are neural network architectures which present efficient and simple training processes. Moreover, the selection of the best inputs of each model is carried out by the wrapper method, using three different evaluation criteria, and three filters, viz., those based on the partial autocorrelation function, the mutual information and the normalization of maximum relevance and minimum common redundancy method. This study also establishes a comparison between the unorganized machines and two classical models: the partial autoregressive model and the multilayer perceptron. The computational results demonstrate that the unorganized machines, especially the echo state networks, represent efficient alternatives to solve the task.

Independent Component Analysis over finite fields is an unsupervised signal processing problem that poses a challenging combinatorial optimization task. In this context, a solution based on Simulated Annealing, with an entropy-based objective function, is proposed. The empirical results demonstrate the effectiveness of the method, with a performance competitive or superior to that of the reference techniques, at an inferior asymptotic computational cost.

In supervised infinite impulse response adaptive filtering, approximate gradient-based approaches are the usual option among optimisation methods. When based on the mean squared error (MSE) criterion, however, these approaches may present biased solutions in noisy scenarios. In that sense, instead of the MSE, the authors propose the use of Shannon's error entropy, an information theoretic learning criterion, which is able to extract higher order statistics from the underlying signals. In particular, a multivariate entropy definition is considered, which is applied to derive a Recursive Prediction Error-based algorithm. The performance analyses are carried out in the context of the supervised channel equalisation problem, with results very favourable to the proposal, in high and low noise level environments.

Agradecimentos: This work was supported by the São Paulo Research Foundation (FAPESP, Brazil – Grant 2013/07559-3), the Studies and Projects Funding Agency(FINEP, Brazil), the National Council of Scientific and Technological Development (CNPq, Brazil), and the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil) Abstract: The study of motor imagery (MI) has been a subject of great interest within the brain-computer interface (BCI) community. Several approaches have been proposed to solve the problem of classifying cerebral responses due to MI, mostly based on the power spectral density of the mu and beta bands; however, no optimum manner of proceeding through the fundamental steps of a MI-BCI has yet been established. In this work, we explored a relatively novel approach regarding feature generation for a MI-BCI by assuming that functional connectivity patterns of the brain are altered during hand MI. We modelled interactions among EEG electrodes by a graph, extracted metrics from it during left and right hand MI from eight subjects and classified the signals using commonly employed techniques in the BCI community (LDA and SVM). We also compared this approach to the more established method of using the signal power spectral density as the classifier features. With the graph method, we confirmed that only specific electrodes provide relevant information for data classification. A first approach provided maximum average classification rates across all subjects for the graph method of 86% for the mu band and 87% for the beta band. For the PSD method, average rates were of 98% and 99% for the mu and beta bands, respectively. However, a much larger number of features was needed: (130 44) and (273 89) for the mu and beta bands, respectively. Aiming to reproduce these rates using the graph method, pairwise inputs combinations of graph metrics were tested. They proved to be sufficient to obtain essentially the same classification accuracy rates, but with a considerably smaller number of features - about 60 features, for both bands. We thus conclude that the graph method is a feasible option for classification of hand MI signals FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP FINANCIADORA DE ESTUDOS E PROJETOS - FINEP CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES Fechado

This paper presents a systematic analysis of a game controlled by a Brain-Computer Interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEP). The objective is to understand BCI systems from the Human-Computer Interface (HCI) point of view, by observing how the users interact with the game and evaluating how the interface elements influence the system performance. The interactions of 30 volunteers with our computer game, named “Get Coins,” through a BCI based on SSVEP, have generated a database of brain signals and the corresponding responses to a questionnaire about various perceptual parameters, such as visual stimulation, acoustic feedback, background music, visual contrast, and visual fatigue. Each one of the volunteers played one match using the keyboard and four matches using the BCI, for comparison. In all matches using the BCI, the volunteers achieved the goals of the game. Eight of them achieved a perfect score in at least one of the four matches, showing the feasibility of the direct communication between the brain and the computer. Despite this successful experiment, adaptations and improvements should be implemented to make this innovative technology accessible to the end user.

Non-negative matrix factorization (NMF) is a traditionalsignal processing technique that allows approximating a measured spectrum as a weighted sum of known spectra. It may be applied to automatic transcription of piano music by assuming that each note is related to a known spectral template. In this model, active notes are related to peaks that can be identified by thresholding. We show that the optimal threshold is highly dependent on the timbre of the particular instrument used to play the analysed audio file. This can cause difficulties to the transcription. We propose an unsupervised technique that allows finding a threshold based on known properties of the audio signal. We show that the proposed technique’s performance compares favourably with manual threshold optimization. Moreover, we provide instructions and source codes that allow reproducing all described experiments.

This work proposes a new approach to the blind inversion of Wiener systems. A Wiener system is composed of a linear time-invariant (LTI) sub-system followed by a memoryless nonlinear function. The goal is to recover the input signal by knowing just the output of the Wiener system, and the straightforward scheme to achieve this is called the Hammerstein system - apply a memoryless nonlinear mapping followed by a LTI sub-system to the output signal of the Wiener system. If the input of the Wiener system is originally iid and some mild conditions are satisfied, the inversion is possible. Based on this statement and the limitations of relevant previous works, a solution is proposed combining (i) immune-inspired optimization algorithms, (ii) information theory and (iii) IIR filters that yield a robust scheme with a relatively reduced risk of local convergence. Experimental results indicated a similar or superior performance of the new approach, in comparison with two other blind methodologies. HighlightsThis work proposes an immune-inspired algorithm to perform blind inversion of Wiener systems.The method employs mutual information-based criteria and IIR filters to adapt the inverse structure.The experimental results indicate a superior or equivalent performance of the new technique, in comparison with gradient-based search or kurtosis-based criterion.

In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem - associated with a minimal entropy configuration - adopting a Michigan-like population structure. The simulation results reveal that the strategy is capable of reaching a performance similar to that of standard methods for lower-dimensional instances with the advantage of also handling scenarios with an elevated number of sources.

In 2007, a theory of ICA over finite fields emerged and an algorithm based on pairwise comparison of mixtures, called MEXICO, was developed to deal with this new problem. In this letter, we propose improvements in the method that, according to simulations in GF(2) and GF(3) scenarios, lead to a faster convergence and better separation results, increasing the application possibilities of the new theory in the context of large databases.

An automatic music transcriber is a device that detects, without human interference, the musical gestures required to play a particular piece. Many techniques have been proposed to solve the problem of automatic music transcription. This paper presents an overview on the theme, discussing digital signal processing techniques, pattern classification techniques and heuristic assumptions derived from music knowledge that were used to build some of the main systems found in the literature. The paper is focused on the motivations behind each technique, aiming to serve both as an introduction to the theme and as resource for the development of new solutions for automatic transcription.

This paper addresses the problem of direction-of-arrival (DOA) parameter estimation in array processing when the signals are inherently discrete, which is the case mainly in the digital communication context. Based on the particular structure of the signal space in the data model, a maximum likelihood-based approach is introduced. The strategy consists in transforming the parameter estimation problem into a decision task. It is shown through numerical simulations that the proposed solution closely follows the performance limit given by the Cramer–Rao bound. Some important features of the technique are as follows: (i) it is capable of handling any number of sources, provided that the number of sensors is greater than or equal to two and the number of snapshots is sufficiently greater than the cardinality of the signal space; (ii) the estimation quality is not affected by the angle and phase separation; and (iii) it offers the possibility to deal with uncalibrated arrays.

International audience; In this paper, we address the problem of blind compensation of nonlinear distortions. Our approach relies on the assumption that the input signals are bandlimited. We then make use of the classical result that the output of a nonlinear memoryless system has a wider spectrum than the one of the in- put signal. However, different from previous works, our approach does not assume the knowledge of the input signal's bandwidth. The proposed approached is considered in the development of a two-stage method for blind source separation (BSS) in post- nonlinear (PNL) models. Indeed, once the functions present in the nonlinear stage of a PNL model are compensated, one can apply the well-established linear BSS algorithms to complete the task of separating the sources. Numerical experiments performed in different scenarios attest the viability of the proposal. Moreover, the proposed method is tested in a real situation where the data are acquired by smart chemical sensor arrays.

This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application.

This work proposes a framework to determine the optimal Wiener equalizer by using an artificial immune network model together with the constant modulus (CM) cost function. This study was primarily motivated by recent theoretical results concerning the CM criterion and its relation to the Wiener approach. The proposed immune-based technique was tested under different channel models and filter orders, and benchmarked against a procedure using a genetic algorithm with niching. The results demonstrated that the proposed strategy has a clear superiority when compared with the more traditional technique. The proposed algorithm presents interesting features from the perspective of multimodal search, being capable of determining the optimal Wiener equalizer in most runs for all tested channels.

A pragmatic approach for entropy estimation is presented. It is based on coincidence detection and its application leads to a simple algorithm with two attractive features: the method can be used without any a priori knowledge concerning the source alphabet cardinality ( K ) and it can provide useful, though biased, estimates even when the number of samples is less than K .

We investigate the application of cost functions based on the l 0 -norm to the problem of blind source extraction (BSE). We show that if the sources have different levels of sparsity, then the minimization of the l 0 -norm leads to the extraction of the sparsest component even when the sources are statistically dependent. We also study the conditions guaranteeing BSE when an approximation of the l 0 -norm is considered. Finally, we provide a numerical example to illustrate the applicability of our proposal.

The present work aims to apply a recently proposed method for estimating Lyapunov exponents to characterize-with the aid of the metric entropy and the fractal dimension-the degree of information and the topological structure associated with multiscroll attractors. In particular, the employed methodology offers the possibility of obtaining the whole Lyapunov spectrum directly from the state equations without employing any linearization procedure or time series-based analysis. As a main result, the predictability and the complexity associated with the phase trajectory were quantified as the number of scrolls are progressively increased for a particular piecewise linear model. In general, it is shown here that the trajectory tends to increase its complexity and unpredictability following an exponential behaviour with the addition of scrolls towards to an upper bound limit, except for some degenerated situations where a non-uniform grid of scrolls is attained. Moreover, the approach employed here also provides an easy way for estimating the finite time Lyapunov exponents of the dynamics and, consequently, the Lagrangian coherent structures for the vector field. These structures are particularly important to understand the stretching/folding behaviour underlying the chaotic multiscroll structure and can provide a better insight of phase space partition and exploration as new scrolls are progressively added to the attractor.

This work has a twofold aim: to present a numerical analysis of the Hodgkin–Huxley model in a nonsmooth excitation scenario — which is both challenging and theoretically relevant — and to use the established framework as a basis for testing a method to search for specific oscillating patterns in dynamical systems. The analysis is founded on classical qualitative methods — bifurcation diagrams, phase space and spectral analysis — and on the calculation of the system Lyapunov spectrum. This calculation is carried out by means of an algorithm particularly suited to deal with nonsmooth excitation and the complexity of the state equations. The obtained Lyapunov exponents are then used to build a robust cost function (invariant with respect to the initial conditions or specific trajectories in a given basin of attraction) for seeking predefined dynamical patterns that are optimized using the particle swarm optimization algorithm. This bioinspired method possesses two desirable features: it has a significant global search potential and does not demand cost function manipulation. The proposed approach, which was tested here in different representative scenarios for the Hodgkin–Huxley model, has a promising application potential in general dynamical contexts and can also be a valuable tool in the planning of drug administration and electrical stimulation of neuronal and cardiac cells.