HispaVeg: a new online vegetation plot database for Spain

We describe a new online database, named HispaVeg, which currently holds data from 2663 vegetation plots of Spanish woodlands, scrublands and grasslands. Unlike other similar databases, a detailed description of the structure is stored with the floristic data of each plot (i.e., number and physiognomy of the vertical layers, cover values for each layer).Most of the vegetation plots are large rectangles (400 to 2000 square meters) with an average of 34 species per plot. The survey dates range from 1956 to present, with most of the records between 1964 and 1994. The elevation of the plots ranges from 0 to 2880, with most of the plots between 300 and 1500 m. HispaVeg is freely available to the scientific community. Users can query the online database, view printable reports for each plot and download spreadsheet-like raw data for subsets of vegetation plots.

Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears

Background: There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist’s time. Objective: This research tests the feasibility of a crowdsourced approach to malaria image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. Methods: The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. Data were collected through the MalariaSpot website. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. In the game, players had to find and tag as many parasites as possible in 1 minute. In the event that players found all the parasites present in the image, they were presented with a new image. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. Results: Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. Results revealed that combining 22 games from nonexpert players achieved a parasite counting accuracy higher than 99%. This performance could be obtained also by combining 13 games from players trained for 1 minute. Exhaustive computations measured the parasite counting accuracy for all players as a function of the number of games considered and the experience of the players. In addition, we propose a mathematical equation that accurately models the collective parasite counting performance. Conclusions: This research validates the online gaming approach for crowdsourced counting of malaria parasites in images of thick blood films. The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists. This experiment illustrates the potential of the crowdsourced gaming approach for performing routine malaria parasite quantification, and more generally for solving biomedical image analysis problems, with future potential for telediagnosis related to global health challenges.

e-MIR2: a public online inventory of medical informatics resources

Background. Over the last years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for Medical Informatics (MI) field, so that locating and accessing them currently remains a hard and time-consuming task. Description. We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources? names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different taxonomies by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the taxonomies. The classification algorithm identifies the categories associated to resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. Conclusions. We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contained 282 resources at the time of writing. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers.

Actividades online de vocabulario y pronunciación para el nivel de la lengua inglesa A2

El objetivo del presente proyecto es proporcionar una actividad de la pronunciación y repaso de vocabulario en lengua inglesa para la plataforma Moodle alojada en la página web de Integrated Language Learning Lab (ILLLab). La página web ILLLab tiene el objetivo de que los alumnos de la EUIT de Telecomunicación de la UPM con un nivel de inglés A2 según el Marco Común Europeo de Referencia para las Lenguas (MCERL), puedan trabajar de manera autónoma para avanzar hacia el nivel B2 en inglés. La UPM exige estos conocimientos de nivel de inglés para cursar la asignatura English for Professional and Academic Communication (EPAC) de carácter obligatorio e impartida en el séptimo semestre del Grado en Ingeniería de Telecomunicaciones. Asimismo, se persigue abordar el problema de las escasas actividades de expresión oral de las plataformas de autoaprendizaje se dedican a la formación en idiomas y, más concretamente, al inglés. Con ese fin, se proporciona una herramienta basada en sistemas de reconocimiento de voz para que el usuario practique la pronunciación de las palabras inglesas. En el primer capítulo del trabajo se introduce la aplicación Traffic Lights, explicando sus orígenes y en qué consiste. En el segundo capítulo se abordan aspectos teóricos relacionados con el reconocimiento de voz y se comenta sus funciones principales y las aplicaciones actuales para las que se usa. El tercer capítulo ofrece una explicación detallada de los diferentes lenguajes utilizados para la realización del proyecto, así como de su código desarrollado. En el cuarto capítulo se plantea un manual de usuario de la aplicación, exponiendo al usuario cómo funciona la aplicación y un ejemplo de uso. Además, se añade varias secciones para el administrador de la aplicación, en las que se especifica cómo agregar nuevas palabras en la base de datos y hacer cambios en el tiempo estimado que el usuario tiene para acabar una partida del juego. ABSTRACT: The objective of the present project is to provide an activity of pronunciation and vocabulary review in English language within the platform Moodle hosted at the Integrated Language Learning Lab (ILLLab) website. The ILLLab website has the aim to provide students at the EUIT of Telecommunication in the UPM with activities to develop their A2 level according to the Common European Framework of Reference for Languages (CEFR). In the platform, students can work independently to advance towards a B2 level in English. The UPM requires this level of English proficiency for enrolling in the compulsory subject English for Professional and Academic Communication (EPAC) taught in the seventh semester of the Degree in Telecommunications Engineering. Likewise, this project tries to provide alternatives to solve the problem of scarce speaking activities included in the learning platforms that offer language courses, and specifically, English language courses. For this purpose, it provides a tool based on speech recognition systems so that the user can practice the pronunciation of English words. The first chapter of the project introduces the application Traffic Lights, explaining its origins and what it is. The second chapter deals with theoretical aspects related with speech recognition and comments their main features and current applications for which it is generally used. The third chapter provides a detailed explanation of the different programming languages used for the implementation of the project and reviews its code development. The fourth chapter presents an application user manual, exposing to the user how the application works and an example of use. Also, several sections are added addressed to the application administrator, which specify how to add new words to the database and how to make changes in the original stings as could be the estimated time that the user has to finish the game.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.