Enviaments recents

  • Modeling and robust low level control of an omnidirectional mobile robot 

    Comasòlivas Font, Ramon; Quevedo Casín, Joseba Jokin; Escobet Canal, Teresa; Escobet Canal, Antoni; Romera Formiguera, Juli (2017-04-01)
    Article
    Accés restringit per política de l'editorial
    This paper presents the modeling and robust low-level control design of a redundant mobile robot with four omnidirectional wheels, the iSense Robotic (iSRob) platform, that was designed to test safe control algorithms. ...
  • A model for continuous monitoring of patients with major depression in short and long term periods 

    Múgica Álvarez, Francisco; Nebot Castells, M. Àngela; Bagherpour, Solmaz; Baladón, Luisa; Serrano, Antoni (2016-12-19)
    Article
    Accés obert
    BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of ...
  • Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application 

    Jurado, Sergio; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Mihaylov, Mihail (2017-02)
    Article
    Accés restringit per política de l'editorial
    Dealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and BEMS applications, where the ...
  • ECG assessment based on neural networks with pretraining 

    Ribas Ripoll, Vicent; Wojdel, Anna; Romero Merino, Enrique; Ramos, Pablo; Brugada Terradellas, Josep (2016-12-01)
    Article
    Accés restringit per política de l'editorial
    In this paper, we present a new automatic screening method to assess whether a patient from ambulatory care or emergency should be referred to a cardiology service. This method is based on deep neural networks with pretraining ...
  • Glucose oxidase biosensor modeling and predictors optimization by machine learning methods 

    González Navarro, Félix Fernando; Stilianova Stoytcheva, Margarita; Rentería Gutiérrez, Livier; Belanche Muñoz, Luis Antonio; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E. (2016-11-01)
    Article
    Accés obert
    Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides ...
  • Analyzing the amperometric response of a glucose oxidase sensor applying mathematical models 

    González Navarro, Félix Fernando; Stilianova Stoytcheva, Margarita; Belanche Muñoz, Luis Antonio; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E.; Rentería Gutiérrez, Livier; López Morteo, Gabriel A. (2016-12-01)
    Article
    Accés restringit per política de l'editorial
    Background: The biosensors are analytical devices combining a bioreceptor and a physicochemical transducer to translate the signal resulting from the interaction of the analyte with the biological element into an electrical ...
  • Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques 

    González Navarro, Félix Fernando; Belanche Muñoz, Luis Antonio; Gámez Moreno, María G.; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E.; López Morteo, Gabriel A. (2015-12-01)
    Article
    Accés obert
    Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic ...
  • Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian decomposition and Bayesian neural networks 

    Arizmendi Pereira, Carlos Julio; Sierra Bueno, Daniel Alfonso; Vellido Alcacena, Alfredo; Romero Merino, Enrique (2014-09)
    Article
    Accés restringit per política de l'editorial
    Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, ...
  • Sepsis mortality prediction with the Quotient Basis Kernel 

    Ribas Ripoll, Vicent; Vellido Alcacena, Alfredo; Romero Merino, Enrique; Ruiz Rodríguez, Juan Carlos (2014-05)
    Article
    Accés restringit per política de l'editorial
    Objective: This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic ...
  • Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques 

    Jurado Gómez, Sergio; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Avellana, Narcís (2015-06-15)
    Article
    Accés restringit per política de l'editorial
    Scientific community is currently doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. The main ...

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