M
Miguel Ferrer
Researcher at University of Las Palmas de Gran Canaria
Publications - 499
Citations - 13116
Miguel Ferrer is an academic researcher from University of Las Palmas de Gran Canaria. The author has contributed to research in topics: Population & Signature (logic). The author has an hindex of 58, co-authored 478 publications receiving 11560 citations. Previous affiliations of Miguel Ferrer include Spanish National Research Council & Ministry of Science and Innovation.
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Journal ArticleDOI
Pentaammine complexes of chromium(iii) with group v oxoanionic ligands. ii. complexes with ligands hypophosphite and protonated phosphite
Journal ArticleDOI
Apparent natural recolonization of an island by the seychelles kestrel (Falco araea)
TL;DR: The Seychelles Kestrel (Falco araea) is an island species endemic to the SeYchelles Archipelago in the Indian Ocean.
Posted Content
SVC-onGoing: Signature Verification Competition.
Ruben Tolosana,Ruben Vera-Rodriguez,Carlos Gonzalez-Garcia,Julian Fierrez,Aythami Morales,Javier Ortega-Garcia,Juan-Carlos Ruiz-Garcia,Sergio Romero-Tapiador,Santiago Rengifo,Miguel Caruana,Jiajia Jiang,Songxuan Lai,Lianwen Jin,Yecheng Zhu,Javier Galbally,Moisés Díaz Cabrera,Miguel Ferrer,Marta Gomez-Barrero,Ilya Hodashinsky,Konstantin Sarin,Artem Slezkin,Marina Bardamova,Mikhail Svetlakov,Mohammad Saleem,Cintia Lia Szücs,Bence Kovari,Falk Pulsmeyer,Mohamad Wehbi,Dario Zanca,Sumaiya Ahmad,Sarthak Mishra,Suraiya Jabin +31 more
TL;DR: SVC-onGoing as mentioned in this paper is an on-going competition for online signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB, and standard experimental protocols.
Proceedings ArticleDOI
Emotional speech characterization for real time applications in real environments
Jesús B. Alonso,Josue Cabrera,Miguel Ferrer,Jose Miguel Canino,Carlos M. Travieso,Malay Kishore Dutta,Anushikha Singh +6 more
TL;DR: A new strategy based on a few prosodic and paralinguistic features set obtained from a temporal segmentation of the speech signal is proposed, robust to interfering noises that are present in real environments, offering a low computational cost and improving the performance of a segmentation based on linguistic aspects.