Leonardo Tomazeli Duarte

Bio: Leonardo Tomazeli Duarte is an academic researcher from State University of Campinas. The author has contributed to research in topics: Blind signal separation & Source separation. The author has an hindex of 14, co-authored 121 publications receiving 753 citations. Previous affiliations of Leonardo Tomazeli Duarte include Applied Science Private University & University of Minho.

Development of a quasi-dry electrode for EEG recording

Abstract: The present work reports on the development of a novel polymer-based electrode prototype for electroencephalography (EEG). The new electrode concept lies between the classic “wet” and “dry” electrodes, allowing addressing most of their drawbacks, while keeping the advantages of both. A localized skin hydration effect at the electrode/scalp contact point is achieved by the release of a small amount of a moisturizing agent (30 μl) from a reservoir inside the electrode, triggered by the electrode/scalp adduction. The electrode's functionality relies on the polymer mechanical properties and on the design of an electrode reservoir that deforms upon the application of a specified adduction force. Numerical modeling tools were used for the definition and analysis of the electrode geometry and the material mechanical properties. The new electrode prototype proved to be able to monitor EEG signals similar to those of the commercial silver/silver chloride electrodes by using a fraction of the hydrating solution necessary for the commercial electrodes, thus avoiding to damage or dirtying the patient's hair. Furthermore, due to the small amount of moisturizing agent employed, the risk of conductive bridges between adjacent electrodes due to gel running is considerably reduced.

An Overview of Blind Source Separation Methods for Linear-Quadratic and Post-nonlinear Mixtures

Abstract: Whereas most blind source separation BSS and blind mixture identification BMI investigations concern linear mixtures instantaneous or not, various recent works extended BSS and BMI to nonlinear mixing models. They especially focused on two types of models, namely linear-quadratic ones including their bilinear and quadratic versions, and some polynomial extensions and post-nonlinear ones. These works are particularly motivated by the associated application fields, which include remote sensing, processing of scanned images show-through effect and design of smart chemical and gas sensor arrays. In this paper, we provide an overview of the above two types of mixing models and of the associated BSS and/or BMI methods and applications.

Unsupervised Processing of Geophysical Signals: A Review of Some Key Aspects of Blind Deconvolution and Blind Source Separation

Abstract: This article reviews some key aspects of two important branches in unsupervised signal processing: blind deconvolution and blind source separation (BSS). It also gives an overview of their potential application in seismic processing, with an emphasis on seismic deconvolution. Finally, it presents illustrative results of the application, on both synthetic and real data, of a method for seismic deconvolution that combines techniques of blind deconvolution and blind source separation. Our implementation of this method contains some improvements overthe original method in the literature described.

A Bayesian Nonlinear Source Separation Method for Smart Ion-Selective Electrode Arrays

Abstract: Potentiometry with ion-selective electrodes (ISEs) provides a simple and cheap approach for estimating ionic activities. However, a well-known shortcoming of ISEs regards their lack of selectivity. Recent works have suggested that smart sensor arrays equipped with a blind source separation (BSS) algorithm offer a promising solution to the interference problem. In fact, the use of blind methods eases the time-demanding calibration stages needed in the typical approaches. In this work, we develop a Bayesian source separation method for processing the outputs of an ISE array. The major benefit brought by the Bayesian framework is the possibility of taking into account some prior information, which can result in more realistic solutions. Concerning the inference stage, it is conducted by means of Markov chain Monte Carlo (MCMC) methods. The validity of our approach is supported by experiments with artificial data and also in a scenario with real data.

Source Separation in Chemical Analysis : Recent achievements and perspectives

Abstract: Since its origins in the mid-1980s, the field of blind source separation (BSS) has attracted considerable attention within the signal processing community. One of the main reasons for such popularity is the existence of many problems that can be addressed in a BSS framework. Two noteworthy examples of applications can be found in audio and biomedical signal processing, for which a number of efficient solutions are now available. There are relevant BSS problems in other domains but on which less effort has been put. In this article, we deal with one of these fields, specifically the field of analytical chemistry (AC), whose goal of is to identify or quantify, or both, chemical components present in a given analyte, i.e., the sample under analysis. As recently discussed in [1], several tasks in AC keep some relationship to the broad classes of detection and estimation problems typically found in signal processing.

Lab-on-Skin: A Review of Flexible and Stretchable Electronics for Wearable Health Monitoring

Abstract: Skin is the largest organ of the human body, and it offers a diagnostic interface rich with vital biological signals from the inner organs, blood vessels, muscles, and dermis/epidermis. Soft, flexible, and stretchable electronic devices provide a novel platform to interface with soft tissues for robotic feedback and control, regenerative medicine, and continuous health monitoring. Here, we introduce the term “lab-on-skin” to describe a set of electronic devices that have physical properties, such as thickness, thermal mass, elastic modulus, and water-vapor permeability, which resemble those of the skin. These devices can conformally laminate on the epidermis to mitigate motion artifacts and mismatches in mechanical properties created by conventional, rigid electronics while simultaneously providing accurate, non-invasive, long-term, and continuous health monitoring. Recent progress in the design and fabrication of soft sensors with more advanced capabilities and enhanced reliability suggest an impending t...

A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid

Abstract: The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed. They concern mainly a bipolar extension of both the Choquet integral and the Sugeno integral, interesting particular submodels, new learning techniques, a better interpretation of the models and a better use of the Choquet integral in multi-criteria decision aid. Parallel to these theoretical works, the Choquet integral has been applied to many new fields, and several softwares and libraries dedicated to this model have been developed.

A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package

Abstract: The application of multi-attribute utility theory whose aggregation process is based on the Choquet integral requires the prior identification of a capacity. The main approaches to capacity identification proposed in the literature are reviewed and their advantages and inconveniences are discussed. All the reviewed methods have been implemented within the Kappalab R package. Their application is illustrated on a detailed example.