Examinando por Autor "Tellaeche Iglesias, Alberto"

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    Ítem
    Gesture-based human machine interaction using RCNNs in limited computation power devices
    (NLM (Medline), 2021-12-08) Tellaeche Iglesias, Alberto ; Fidalgo Astorquia, Ignacio ; Vazquez, Juan-Ignacio ; Saikia, Surajit
    The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.
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    Ítem
    An innovative framework for supporting content-based authorship identification and analysis in social media networks
    (Oxford University Press, 2024-08) Gaviria de la Puerta, José; Pastor López, Iker; Tellaeche Iglesias, Alberto; Sanz Urquijo, Borja; Sanjurjo González, Hugo; Cuzzocrea, Alfredo; Bringas García, Pablo
    Content-based authorship identification is an emerging research problem in online social media networks, due to a wide collection of issues ranging from security to privacy preservation, from radicalization to defamation detection, and so forth. Indeed, this research has attracted a relevant amount of attention from the research community during the past years. The general problem becomes harder when we consider the additional constraint of identifying the same false profile over different social media networks, under obvious considerations. Inspired by this emerging research challenge, in this paper we propose and experimentally assess an innovative framework for supporting content-based authorship identification and analysis in social media networks.
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    Ítem
    A practical approach on performance assessment of federated learning algorithms for defect detection in industrial applications
    (Institute of Electrical and Electronics Engineers Inc., 2023-09-29) Zuluaga, Eduard; Jaziri, Sondos; Tellaeche Iglesias, Alberto ; Pastor López, Iker
    One of the most common problems to be solved in industrial environments is the detection of defects in the manufacturing quality control of different products. The design of such automatic systems, especially if they are based on image processing, often presents difficulties related to the availability of sufficient well- labeled data for initial training. Usually, it is not easy to have enough industrial samples with defects to have a sufficiently large and balanced dataset. This research work presents a new method of hybridisation of convolutional neural networks used for defect detection in industry by means of image processing, using Federated Learning (FL) techniques. This method is able to overcome the limitations and problems presented by this type of systems stated above. In this research, it is demonstrated with examples of defect detection in textures that it is possible to reach a defect detection effectiveness above 90% on average, in problems where no dataset is available, using federated learning algorithms with classifiers based on Convolutional Neural Networks previously trained in other problems of defect detection in other types of textures. The creation of such systems for error detection on previously untrained data using federated learning and obtaining this effectiveness represents an advance on the state of the art with respect to the existing approaches, and constitutes the main contribution of this research work.