Abstract
Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDI(d: 725; 715; 565)) for the hyperspectral dataset and the modified simple ratio (mSR(c: 740; 705; 865)) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

Abstract
The present work aims at the development of a new heuristic approach named Direct-DRRT . This new algorithm is an improvement of the DRRT* method, which is the fusion between RRT * and DRRT. This improvement aims at the mobile robot autonomous planning considering less memory and computational time for a route design. The results show the efficiency of our approach compared to the other methods, presenting less processing time and a signification reduced number of nodes and paths. © 2018 IEEE.

Abstract
The autonomous robotic system accurate localization is a challenging step in robot navigation field once the mobile device should avoid dangerous situations, such as unsafe conditions and collisions. In this context, the present paper proposes a localization method using the Extended Kalman Filter (EKF) to fuse the information coming from two different sensors (i.e. odometry and computer vision). The localization results present with known and unknown starting points and are tested in a simulated environment. © 2018 IEEE.

Abstract
The applications with Unmanned Aerial Vehicles have increased in the last decades due to their economic and technical feasibility. Moreover, several tasks require online objects tracking as well as the object position knowledge in the real-world with algorithms execution onboard. An example of such task is the video surveillance with human activity recognition. In this paper, we propose a new approach using Extended Kalman Filter to estimate and to predict the object real-world coordinates. This research shows that the results were up to 30% better compared to the results without data processing. © 2018 IEEE.

Abstract
Recent studies have shown that the early detection of neurodegenerative diseases (such as Parkinson) can significantly improve the effectiveness of treatments that increase quality of life, reducing the costs associated with the disease. In this paper, the proposed methodology consists in detecting early signs of Parkinson's disease through speech, with the presence of background noise. The approach uses machine learning algorithms and signal processing techniques to correctly distinguish between healthy controls and Parkinson's disease patients. In order to detect early signs of the disease, a database with patients at different stages of the Parkinson's disease is used. The learning algorithms were optimized for generalization and accuracy. An analysis of the results obtained from the proposed methodology show potential uses of machine learning algorithms in biomedical applications to detect early signs of Parkinson's disease.

Abstract
Foreseen climate change points to shifts in agricultural production patterns worldwide, which may impact ecosystems directly, as well as the economic and cultural contexts of the wine industry. Moreover, the combined effects of environmental threats (light, temperature, and water relations) at different scales are expected to impair natural grapevine mechanisms, decreasing yield and the quality of grapes. Hence, the interaction between several factors, such as climate, terroir features, grapevine stress responses, site-specific spatial-temporal variability, and the management practices applied, which represents and effective challenge for sustainable Mediterranean viticulture, allowed researchers to develop adaptive strategies to cope with environmental stresses. Here, we review the effects of abiotic stresses on Mediterranean-like climate viticulture and the impacts of summer stress on grapevine growth, yield, and quality potential, as well as the subsequent plant responses and the available adaptation strategies for winegrowers and researchers. Our main findings are as follows: (1) environmental stresses can trigger dynamic responses in grapevines, comprising photosynthesis, phenology, hormonal balance, berry composition, and the antioxidant machinery; (2) field research methodologies, laboratory techniques, and precision viticulture are essential tools to evaluate grapevine performance and the potential quality for wine production; and (3) advances in the existing adaptation strategies are vital to maintain sustainability and regional wine identity in a changing climate. Also, these topics suggest that rational and focused management of grapevines may enlighten grapevine summer stress responses and improve the resilience of agro-ecosystems under harsh conditions. Despite the challenge of developing different strategic responses, winegrowers should clearly define their objectives, so applied research can provide rational technical support for the decision making process towards sustainable viticulture.