Evaluation of multitrophic biofiltration system with new algae species and the sea cucumber "Holothuria sanctori"

Máster Oficial en Cultivos Marinos. Trabajo presentado como requisito parcial para la obtención del Título de Máster Oficial en Cultivos Marinos, otorgado por la Universidad de Las Palmas de Gran Canaria (ULPGC), el Instituto Canario de Ciencias Marinas (ICCM), y el Centro Internacional de Altos Estudios Agronómicos Mediterráneos de Zaragoza (CIHEAM)

Multi-parameter observations from coastal waters to the deepest trenche: challenges studying the carbón system with optode technology

[EN]This presentation will give examples on how multi-parameter platforms have been used in a variety of applications ranging from shallow coastal on-line observatories down to measuring in the deepest Ocean trenches. Focus will be on projects in which optode technology (primarily for CO2 and O2) has served to study different aspects of the carbon system including primary production/consumption, air-sea exchange, leakage detection from underwater storage of CO2 and measurements from moving platforms like gliders and ferries. The performance of recently developed pH optodes will als

A Virtual Programming Lab for Moodle with automatic assessment and anti-plagiarism features

[EN] This paper describes VPL, a Virtual Programming Lab module for Moodle, developed at the University of Las Palmas of Gran Canaria (ULPGC) and released for free uses under GNU/GPL license. For the students, it is a simple development environment with auto evaluation capabilities. For the instructors, it is a students' work management system, with features to facilitate the preparation of assignments, manage the submissions, check for plagiarism, and do assessments with the aid of powerful and flexible assessment tools based on program testing, all of that being independent of the programming language used for the assignments and taken into account critical security issues.

A low complexity system based on multiple weighted decision trees for indoor localization

[EN] Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity.