Papers by DANIEL BARRERA PERALTA
El presente libro es el resultado de la docencia e investigación en las áreas de Realidad Virtual... more El presente libro es el resultado de la docencia e investigación en las áreas de Realidad Virtual, Realidad Aumentada e Interfaces basadas en visión, llevadas a cabo en tres instituciones universitarias, dos de ellas de Argentina –Universidad Nacional de La Plata (UNLP) y Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN)– y una española –Universidad de las Islas Baleares (UIB)–. El texto se estructura en dos partes principales: la primera parte relacionada con Realidad Virtual (RV) y Realidad Aumentada (RA) y la segunda parte relacionada con las denominadas Interfaces avanzadas o Basadas en Visión (VBI). La primera parte consta de tres capítulos. El capítulo 1 presenta una introducción a conceptos y tecnología compartidos por las aplicaciones de realidad virtual y realidad aumentada. El capítulo 2 presenta los desafíos actuales para el desarrollo de simuladores de entrenamiento que utilizan realidad virtual, y describe los simuladores desarrollados por el Inst...
Bookmarks Related papers MentionsView impact
Pattern Recognition, 2021
Bookmarks Related papers MentionsView impact
Machine Learning, 2022
Bookmarks Related papers MentionsView impact
Rough Sets, 2020
Bookmarks Related papers MentionsView impact
International Journal of Computational Intelligence Systems, 2021
Time series data are becoming increasingly important due to the interconnectedness of the world. ... more Time series data are becoming increasingly important due to the interconnectedness of the world. Classical problems, which are getting bigger and bigger, require more and more resources for their processing, and Big Data technologies offer many solutions. Although the principal algorithms for traditional vector-based problems are available in Big Data environments, the lack of tools for time series processing in these environments needs to be addressed. In this work, we propose a scalable and distributed time series transformation for Big Data environments based on well-known time series features (SCMFTS), which allows practitioners to apply traditional vector-based algorithms to time series problems. The proposed transformation, along with the algorithms available in Spark, improved the best results in the state-of-the-art on the Wearable Stress and Affect Detection dataset, which is the biggest publicly available multivariate time series dataset in the University of California Irv...
Bookmarks Related papers MentionsView impact
Rough Sets, 2019
Bookmarks Related papers MentionsView impact
Esta tesis aborda el problema de la identificacion mediante huellas dactilares en grandes bases d... more Esta tesis aborda el problema de la identificacion mediante huellas dactilares en grandes bases de datos, utilizando para ello tecnicas de mineria de datos. Los objetivos llevados a cabo fueron: 1. Estudio del estado del arte de las tecnicas locales de matching de huellas dactilares basado en minucias, estableciendo una taxonomia de los tipos de estructuras locales, consolidaciones y otras caracteristicas, y estudiando empiricamente las fortalezas y debilidades de cada uno. 2. Desarrollo de un metodo de preprocesamiento para filtrar minucias espurias, mejorando asi la precision y el tiempo de la identificacion. 3. Propuesta de metodos eficientes, escalables y precisos para la identificacion con huellas. Este punto se lleva a cabo por varias vias. Por una parte, se desarrollan varios sistemas masivamente paralelos basados en computacion de altas prestaciones y plataformas big data. Por otra parte, se propone un sistema de doble huella dactilar y doble algoritmo de matching. 4. Reducc...
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
2019 IEEE International Congress on Big Data (BigDataCongress), 2019
The current explosion of data, which is impacting many different areas, is especially noticeable ... more The current explosion of data, which is impacting many different areas, is especially noticeable in biomedical research thanks to the development of new technologies that are able to capture high-dimensional and high-resolution data at the single-cell scale. Processing such data in an interpretable way often requires the computation of pairwise dissimilarity measures between the multiple features of the data, a task that can be very difficult to tackle when the dataset is large enough, and which is prone to numerical instability. In this paper we propose a distributed framework to efficiently compute dissimilarity matrices in arbitrarily large datasets in a numerically robust way. It implements a combination of the pairwise and two-pass algorithms for computing the variance, in order to maintain the numerical robustness of the former while reducing its overhead. The proposal is parallelizable both across multiple computers and multiple cores, maximizing the performance while maintaining the benefits of memory locality. The proposal is tested on a real use case: a dataset generated from high-content screening images composed by a billion individual cells and 786 features. The results showed linear scalability with respect to the size of the dataset and close to linear speedup.
Bookmarks Related papers MentionsView impact
IEEE Transactions on Fuzzy Systems, 2019
Bookmarks Related papers MentionsView impact
International Journal of Intelligent Systems, 2017
Bookmarks Related papers MentionsView impact
Journal of Computing in Civil Engineering, 2018
Bookmarks Related papers MentionsView impact
Information Sciences, 2017
Bookmarks Related papers MentionsView impact
Knowledge-Based Systems, 2017
Bookmarks Related papers MentionsView impact
International Journal of Computational Intelligence Systems, 2017
Bookmarks Related papers MentionsView impact
Zeledonia, 2007
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Http Www Theses Fr, 1987
Dans de nombreux pays le financement de l'agriculture fait l'objet de mesures particulier... more Dans de nombreux pays le financement de l'agriculture fait l'objet de mesures particulieres. Dans d'autres, il est presque ignore. Les deux exemples que nous presentons, mexicain et francais, demontrent que quelque soit le niveau de maturite d'une politique de credit a l'agriculture et des moyens qui la servent, les contextes nationaux et inter nationaux de la production et des echanges rendent necessaires l'evolu tion et l'adaptation des systemes a une conjoncture en mutation permanente. Ceci explique que, si au cours du dernier demi-siecle, les organismes qui financent l'agriculture et les techniques qu'ils ont mis en place ont su pour une partie accompagner le secteur agricole dans sa moderni sation, ils se doivent aujourd'hui de contribuer a ses efforts de productivite et de rentabilite. Le credit agricole mutuel a aujourd'hui 100 ans, et le systeme de credit rural au mexique en a soixante. C'est a la fois peu et beaucoup mais c'est surtout, pour l'un et l'autre, un heritage et une experience qui se veulent porteurs d'avenir. Dans le domaine agricole, en effet, innovation et modernisme ne signifient pas forcement rejet du passe ni des origines.
Bookmarks Related papers MentionsView impact
Brenesia, 2005
Bookmarks Related papers MentionsView impact
Uploads
Papers by DANIEL BARRERA PERALTA