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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

Revising the Impact and Prospects of Activity and Ventilation Rate Bio-Loggers for Tracking Welfare and Fish-Environment Interactions in Salmonids and Mediterranean Farmed Fish

TL;DR: AEFishBIT as mentioned in this paper is a tri-axial accelerometer with a frequency sampling of 50-100 Hz that is able to provide proxy measurements of physical and metabolic activities validated by video recording, exercise tests in swim tunnel respirometers, and differential operculum and body tail movements across fish species with differences in swimming capabilities.
Journal ArticleDOI

The effects of direct hemoperfusion using a polymyxin B-immobilized column in a pig model of severe Pseudomonas aeruginosa pneumonia

TL;DR: In mechanically ventilated pigs with severe P. aeruginosa pneumonia, PMX-HP does not have any valuable clinical benefit, and studies are warranted to fully evaluate a potential role of PMx-HP in septic shock associated with severe pulmonary infections.
Journal ArticleDOI

Rare cases of carnivore mortality due to electric power distribution lines in Iran

TL;DR: In this paper, power lines are one of the mammals' mortality factors and there are some records of electrocuted large mammals such as elephants and leopards in India, deer and cougars in the USA.
Book ChapterDOI

Improving a leaves automatic recognition process using PCA

TL;DR: In this paper, a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter is presented, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a SVM system.