Abstract

Abstract
The increasing availability and use of autonomous vehicles for real operational scenarios has led to the need for tools that allow human operators to interact with multiple systems effectively, taking into account their capabilities, limitations and environmental constraints. Multiple vehicles, deployed together in order to accomplish a common goal, impose a high burden on a human operator for specifying and executing coordinated behavior, particularly in mixed-initiative systems where humans are part of the control loop. In this paper, we describe experimental field tests for Dolphin, a domain-specific language that allows a single program to define the joint behaviour of multiple vehicles over a network. Using the language, it is possible to accomplish an orchestrated execution of single-vehicle tasks according to several patterns such as sequential, concurrent, or event-based program flow. With this aim, Dolphin has been integrated modularly with a software toolchain for autonomous vehicles developed by Laboratorio de Sistemas e Tecnologia Subaquatica (LSTS). The tests we describe made use of LSTS unmanned underwater vehicles (UUVs) at open sea during the 2017 edition of Rapid Environment Picture (REP), an annual exercise jointly organised by LSTS and the Portuguese Navy.

Abstract

Abstract
Phenology is one of the most reliable indicators of vegetation dynamics. Assessing and monitoring the dynamics of phenology is relevant to support several decisions in order to improve the efficiency of several farming practices. An open source application QPhenoMetrics - implemented in QGIS software that estimates vegetation phenology metrics is presented, using Earth Observation Systems (EOS) based time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology. QPhenoMetrics is characterized by freely-usable and updatable code, acceptance of satellite images or text formats, time-series analysis toolbox allowing the selection of region of interest with statistical quality assessment for Vegetation Indices (VI), and estimation of ensemble metrics. The application is structured in three components: (i) input data; (ii) pre-processing of the VI time-series and several fitting methods and (iii) computation of the phenological metrics. QPhenoMetrics produces a plot with the VI time-series and corresponding phenology metrics, and a spreadsheet is created with a list of NDVI or EVI values estimated using the selected fitting method. To evaluate the application, two main Portuguese crops, vineyards and maize, and MOD13 data from MODIS sensor during 2011-2012 were considered. QPhenoMetrics was validated with vineyard phenology observations (2007-2011). A comparative analysis with software products TimeSat and Spirits was also performed. It was concluded that QPhenoMetrics can be very useful for common users to extract phenology information for 16 daily MODIS data in HDF format, text files with NDVI/EVI data and ASCII files, through a simple and intuitive graphic interface. Furthermore, the user can evaluate the quality assessment of VI of the images used. QPhenoMetrics is an effective open source tool that in addition to being free, is readily modifiable by user according to the study requirements.

Abstract
An holistic approach to decision support systems for intelligent public lighting control, must address both energy efficiency and maintenance. Currently, it is possible to remotely control and adjust luminaries behaviour, which poses new challenges at the maintenance level. The luminary efficiency depends on several efficiency factors, either related to the luminaries or the surrounding conditions. Those factors are hard to measure without understanding the luminary operating boundaries in a real context. For this early stage on preventive maintenance design, we propose an approach based on the combination of two models of the network, wherein each is representing a different but complementary perspective on the classifying of the operating conditions of the luminary as normal or abnormal. The results show that, despite the expected and normal differences, both models have a high degree of concordance in their predictions. © 2020, Springer Nature Switzerland AG.

Abstract