ABSTRACT:In the Galapagos Islands, where 97% of the territory is protected and ecosystem dynamics are highly vulnerable, timely and accurate information is key for decision making. An appropriate monitoring system must meet two key features: on one hand, being able to capture information in a systematic and regular basis, and on the other hand, to quickly gather information on demand for specific purposes. The lack of such a system for geographic information limits the ability of Galapagos Islands' institutions to evaluate and act upon environmental threats such as invasive species spread and vegetation degradation. In this context, the use of UAVs (unmanned aerial vehicles) for capturing georeferenced images is a promising technology for environmental monitoring and management. This paper explores the potential of UAV images for monitoring degradation of littoral vegetation in Puerto Villamil (Isabela Island, Galapagos, Ecuador). Imagery was captured using two camera types: Red Green Blue (RGB) and Infrarred Red Green (NIR). First, vegetation presence was identified through NDVI. Second, object-based classification was carried out for characterization of vegetation vigor. Results demonstrates the feasibility of UAV technology for base-line studies and monitoring on the amount and vigorousness of littoral vegetation in the Galapagos Islands. It is also showed that UAV images are not only useful for visual interpretation and object delineation, but also to timely produce useful thematic information for environmental management.

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Este documento contiene información de prueba. Contáctese con el administrador del Centro para el acceso al documento originar del registro.

No abstract

This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points (Qini and Tend) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60±2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49±1.99 msec.

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Virtual assistants are a growing area of research in academia and industry, with an impact on people’s daily lives. Many disciplines in science are moving towards the incorporation of intelligent virtual assistants in multiple scenarios and application domains, and GIScience is not external to this trend since they may be connected to intelligent spatial decision support systems. This article presents a scoping review to indicate relevant literature pertinent to intelligent virtual assistants and their usage of geospatial information and technologies. In particular, the study was designed to find critical aspects of GIScience and how to contribute to the development of virtual assistants. Moreover, this work explores the most prominent research lines as well as relevant technologies/platforms to determine the main challenges and current limitations regarding the use and implementation of virtual assistants in geospatial‐related fields. As a result, this review shows the current state of geospatial applications regarding the use of intelligent virtual assistants, as well as revealing gaps and limitations in the use of spatial methods, standards, and resources available in spatial data infrastructures to develop intelligent decision systems based on virtual assistants for a wide array of application domains.