Abstract

Abstract
The Alto Douro Demarcated Region in Portugal is the oldest and most regulated wine-growing region in the world, formed by an ecosystem of unique value allowing the cultivation of vines on its characteristics terraces vineyards. The detection of vine rows in terrace vineyards constitutes an essential task regarding the achievement of important goals such as multi-Temporal crop evaluation and production estimation. Despite the advances and research in this field, most studies are limited to flat vineyards with straight vine rows. In this study an exploratory approach in the precision agriculture for automatic detection of vine rows in terrace vineyards is presented with remote sensing techniques associated with artificial intelligence such as Machine Learning and Deep learning. At the current stage the preliminary results are encouraging for the detection of vine rows in straight and curved lines considering the complexity of the terrain.

Abstract

Abstract
Autonomous Robots with multiple directional thrusters are normally over-actuated systems that require nonlinear control allocation methods to map the forces that drive the robot's dynamics and act as virtual control variables to the actuators. This process demands computational efforts that, sometimes, are not available in small robotic platforms. The present paper introduces a new control allocation approach with fast convergence, high accuracy, and dealing with complex nonlinear problems, especially in embedded systems. The adopted approach divides the desired nonlinear system into coupled linear problems. For that purpose, the Real Actions (RAs) and Virtual Control Variables (VCVs) are broke in two or more sets each. While the RA subsets are designed to linearize the system according to different input subspaces, the VCV is designed to be partially coupled to overlap the output subspaces. This approach generates smaller linear systems with fast and robust convergence used sequentially to solve nonlinear allocation problems. This methodology is assessed in mathematical tutorial cases and over-actuated UAV simulations.

Abstract
Wireless visual sensor networks have been adopted in different contexts to provide visual information in a more flexible and distributed way, supporting the development of different innovative applications. Although visual data may be central for a considerable set of applications in areas such as Smart Cities, Industry 4.0, and Vehicular Networks, the actual visual data quality may be not easily determined since it may be associated with many factors that depend on the characteristics of the considered application scenario. This entails several aspects from the quality of captured images (sharpness, definition, resolution) to the characteristics of the networks such as employed hardware, power consumption, and networking efficiency. In order to better support quality analysis and performance comparisons among different wireless visual sensor networks, which could be valuable in many monitoring scenarios, this article surveys this area with special concern on assessment mechanisms and quality metrics. In this context, a novel classification approach is proposed to better categorize the diverse applicable metrics for quality assessment of visual monitoring procedures. Hence, this article yields a practical guide for analyzing different visual sensor network implementations, allowing fairer evaluations and comparisons among a variety of research works. Critical analysis are also performed regarding the relevance and usage of the proposed categories and identified quality metrics. Finally, promising open issues and research directions are discussed in order to guide new developments in this research field.

Abstract
This work presents a methodology to segment the MV electric grid into grid zones for which the active power flexibility needs that solve the forecasted voltage and current issues are computed. This methodology enables the Distribution System Operator (DSO) to publish flexibility needs per zones, allowing aggregators to offer flexibility by optimizing their portfolio of resources in each grid zone. A case study is used to support the methodology results and its performance, showing the feasibility of solving grid issues by activating flexibility per grid zones according to the proposed methodology. © 2022 IEEE.