Now showing items 21-40 of 1003

    • A lightweight perception module for planning purposes 

      Ud Din, Muhayy; Rosell Gratacòs, Jan; Bukhari, Sohail; Ahmad, Mansoor; Qazi, Wajahat Mahmood (Institute of Electrical and Electronics Engineers (IEEE), 2020)
      Conference lecture
      Open Access
      Sensing is an essential component for robots to perform the manipulation tasks in real environments. This study proposes a lightweight deep-learning-based sensing modules which allows the robots to automatically model the ...
    • A lower bound for learning distributions generated by probabilistic automata 

      Balle Pigem, Borja de; Castro Rabal, Jorge; Gavaldà Mestre, Ricard (Springer, 2010)
      Conference report
      Open Access
      Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. ...
    • A machine learning approach for layout inference in spreadsheets 

      Koci, Elvis; Thiele, Maik; Romero Moral, Óscar; Lehner, Wolfgang (SciTePress, 2016)
      Conference report
      Open Access
      Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and ...
    • A machine learning approach to calculate the dropout risk factor in a children's programming school 

      Horta Bartomeu, Raquel (Universitat Politècnica de Catalunya, 2019-01-29)
      Bachelor thesis
      Restricted access - confidentiality agreement
    • A machine learning approach to stock screening with fundamental analysis 

      Alvarez Vecino, Pol (Universitat Politècnica de Catalunya, 2019-04-15)
      Master thesis
      Open Access
      We present HPC.FASSR, a High-Performance Computation Fundamental Analysis Stock Screener and Ranker to compare many ML models and the criteria of famous Benjamin Graham for stock investing using fundamental data. FASSR is ...
    • A machine learning approach to the identification of encrypted web traffic 

      Marías Pérez, Rubén (Universitat Politècnica de Catalunya, 2017-06-29)
      Bachelor thesis
      Restricted access - confidentiality agreement
    • A machine learning enabled network planning tool 

      Moysen Cortes, Jessica; Giupponi, Lorenza; Mangues Bafalluy, Josep (Institute of Electrical and Electronics Engineers (IEEE), 2016)
      Conference report
      Restricted access - publisher's policy
      In the coming years, planning future mobile networks will be infinitely more complex than nowadays. Future networks are expected to present multiple Network Management (NM) challenges to operators, such as managing network ...
    • A machine learning methodology for structural damage classification in structural health monitoring 

      Pozo Montero, Francesc; Tibaduiza Burgos, Diego Alexander; Anaya Vejar, Maribel; Vitola Oyaga, Jaime (2017)
      Conference report
      Restricted access - publisher's policy
      One of the goals of structural health monitoring (SHM) applications is to determine the presence and the severity of a damage. In some cases, this is an element to forecast the behaviour and take decisions to allocate ...
    • A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases 

      Mocioiu, Victor; de Barros, Nuno M. Pedrosa; Ortega Martorell, Sandra; Slotboom, Johannes; Knecht, Urspeter; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (I6doc.com, 2016)
      Conference report
      Open Access
      Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to ...
    • A machine learning workflow for hurricane prediction 

      Kahira, Albert; Bautista Gomez, Leonardo; Badia Sala, Rosa Maria (Barcelona Supercomputing Center, 2018-04-24)
      Conference report
      Open Access
    • A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration 

      Paz Ortiz, Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
      Conference lecture
      Open Access
      Algorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. ...
    • A mobile network planning tool based on data analytics 

      Moysen Cortes, Jessica; Giupponi, Lorenza; Mangues Bafalluy, Josep (HINDAWI, 2017-02-05)
      Article
      Open Access
      Planning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity ...
    • A multi-scale smoothing kernel for measuring time-series similarity 

      Troncoso, Alicia; Arias Vicente, Marta; Riquelme Santos, José Cristóbal (2015-11-01)
      Article
      Open Access
      In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly ...
    • A multiscale method for periodic structures using domain decomposition and ECM-hyperreduction 

      Hernández Ortega, Joaquín Alberto (2020-08-15)
      Article
      Restricted access - publisher's policy
      This paper presents a nonlinear multiscale approach for periodic structures in the quasi-static, small strain regime. The approach consists in combining a domain decomposition method in which interface conditions are ...
    • A new kernelized associative memory and some of its applications 

      Saltz, Matthew; Belanche Muñoz, Luis Antonio (IOS Press, 2016)
      Conference report
      Open Access
      The classical Bidirectional Associative Memory (BAM) allows for the storage of pairs of vectors, such that when either member of the pair is presented to the BAM, the other member may be successfully recalled. This work ...
    • A novel approach to real-time range estimation of underwater acoustic sources using supervised machine learning 

      Houégnigan, Ludwig; Safari, Pooyan; Nadeu Camprubí, Climent; Van der Schaar, Mike Connor Roger Malcolm; André, Michel (Institute of Electrical and Electronics Engineers (IEEE), 2017)
      Conference lecture
      Open Access
      The proposed paper introduces a novel method for range estimation of acoustic sources, both cetaceans and industrial sources, in deep sea environments using supervised learning with neural networks in the contex of a single ...
    • A novel methodology to predict regulations using deep learning 

      Mas Pujol, Sergi; Salamí San Juan, Esther; Pastor Llorens, Enric (Single European Sky ATM Research (SESAR), 2020)
      Conference report
      Open Access
      The current air traffic control system tries to allocate as many flights as possible in a scenario that is expected to be time-efficient, cost-efficient, and safe. To guaranty these safety conditions, it is performed a ...
    • A probabilistic approach to the visual exploration of G protein-coupled receptor sequences 

      Vellido Alcacena, Alfredo; Cárdenas, Martha Ivón; Olier Caparroso, Iván; Rovira, Xavier; Giraldo, Jesús (2011)
      Conference report
      Open Access
      The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and ...
    • A probabilistic tri-class Support Vector Machine 

      González Abril, Luis; Angulo Bahón, Cecilio; Velasco Morente, Francisco; Ortega Ramírez, Juan Antonio (2010-07)
      Article
      Open Access
      A probabilistic interpretation for the output obtained from a tri-class Support Vector Machine into a multi-classification problem is presented in this paper. Probabilistic outputs are defined when solving a multi-class ...
    • A proportional controller based on clustering theory: an academic example of a machine learning discipline 

      Acho Zuppa, Leonardo; Buenestado Caballero, Pablo (EDP Sciences, 2018)
      Conference report
      Open Access
      The main objective of this paper is to present a controller design based on the K-means clustering theory. The controller is realized in such way that when the plant output is located outside of the designed clustering ...