An Innovative Mission Management System for Fixed-Wing UAVs

This paper presents two innovative units linked together to build the main frame of a UAV Mis- sion Management System. The first unit is a Path Planner for small UAVs able to generate optimal paths in a tridimensional environment, generat- ing flyable and safe paths with the lowest com- putational effort. The second unit is the Flight Management System based on Nonlinear Model Predictive Control, that tracks the reference path and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves on-line (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that min- imizes the tracking error with respect to the ref- erence path, driving the aircraft far from sensed obstacles and towards the desired trajectory

Advanced Path Planning and Collision Avoidance Algorithms for UAVs

The thesis aims to investigate and develop innovative tools to provide autonomous flight capability to a fixed-wing unmanned aircraft. Particularly it contributes to research on path optimization, tra jectory tracking and collision avoidance with two algorithms designed respectively for path planning and navigation. The complete system generates the shortest path from start to target avoiding known obstacles represented on a map, then drives the aircraft to track the optimum path avoiding unpredicted ob jects sensed in flight. The path planning algorithm, named Kinematic A*, is developed on the basis of graph search algorithms like A* or Theta* and is meant to bridge the gap between path-search logics of these methods and aircraft kinematic constraints. On the other hand the navigation algorithm faces concurring tasks of tra jectory tracking and collision avoidance with Nonlinear Model Predictive Control. When A* is applied to path planning of unmanned aircrafts any aircraft kinematics is taken into account, then practicability of the path is not guaranteed. Kinematic A* (KA*) generates feasible paths through graph-search logics and basic vehicle characteristics. It includes a simple aircraft kinematic-model to evaluate moving cost between nodes of tridimensional graphs. Movements are constrained with minimum turning radius and maximum rate of climb. Furtermore, separation from obstacles is imposed, defining a volume around the path free from obstacles (tube-type boundaries). Navigation is safe when the tracking error does not exceed this volume. The path-tracking task aims to link kinematic information related to desired aircraft positions with dynamic behaviors to generate commands that minimize the error between reference and real tra jectory. On the other hand avoid obstacles in flight is one of the most challenging tasks for autonomous aircrafts and many elements must be taken into account in order to implement an effective collision avoidance maneuver. Second part of the thesis describes a Nonlinear Model Predictive Control (NMPC) application to cope with collision avoidance and path tracking tasks. First contribution is the development of a navigation system able to match concurring problems: track the optimal path provided with KA* and avoid unpredicted obstacles detected with sensors. Second Contribution is the Sense & Avoid (S&A) technique exploiting spherical camera and visual servoing control logics

NMPC and genetic algorithm based approach for trajectory tracking and collision avoidance of UAVs

Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV Path Planning (PP) system plans the best path to per- form the mission and then it uploads this path on the Flight Management System (FMS) providing reference to the aircraft navigation. Tracking the path is the way to link kine- matic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a Nonlinear Model Predictive Control (NMPC) system that tracks the reference path provided by PP and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control sys- tem solves on-line (i.e., at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that minimises the tracking error with respect to the reference path, driving the aircraft far from sensed obstacles and towards the desired trajectory

Bargaining Coalitions in the Agricultural Negotiations of the Doha Round: Similarity of Interests or Strategic Choices? An Empirical Assessment

The paper aims at understanding the structural features of the bargaining coalitions in the Doha Round of the WTO. We provide an empirical assessment of the preferences of each negotiating actor looking at general economics indicators, development levels, structure of the agricultural sectors, and trade policies for agricultural products. Bargaining coalitions are analyzed by grouping countries through a cluster analysis procedure. The clusters are compared with existing coalitions, in order to assess their degree of internal homogeneity as well as their common interests. Such a comparison allows the detection of possible “defectors”, i.e. countries that according to their economic conditions and policies seem to be relatively less committed to the positions of the coalition they join.Agricultural trade negotiations, Bargaining coalitions, WTO, Cluster analysis

Agricultural Expenditure in the Future European Union Budget

Agricultural and Food Policy, Agricultural Finance,

Impaired release of Vitamin D in dysfunctional adipose tissue: New cues on Vitamin D supplementation in obesity

Context: Vitamin D accumulates in adipose tissue (AT) and vitamin D deficiency is frequent in obesity. Objective: We hypothesize that trafficking of vitamin D is altered in dysfunctional AT. Design, Patients, Settings: 54 normal-weight and 67 obese males were recruited in a prospective study and randomly assigned to supplementation with 50 \ub5g/week 25-hydroxyvitamin-D3 (25(OH)D) or 150 \ub5g/week vitamin D3 for 1 year, raising dosage by 50% if vitamin D-sufficiency (serum 25(OH)D>50 nomol/l), was not achieved at 6 months; 97 subjects completed the study. Methods: Vitamin D3 (D3) and 25(OH)D were quantified by HPLC-MS in control and insulin-resistant (IR) 3T3-L1 cells and subcutaneous AT (SAT) from lean and obese subjects, incubated with or without adrenaline; expression of 25-hydroxylase (CYP27A1), 1\u3b1-hydroxylase (CYP27B1) and vitamin D receptor (VDR) were analysed by real-time PCR. Results: In IR adipocytes the uptake of D3 and 25(OH)D was higher, but after adrenaline stimulation, the decrement in D3 and 25(OH)D was stronger in control cells, which also showed increased expression of CYP27A1 and CYP27B1 and higher levels of 25(OH)D. In SAT from obese subjects, the adrenaline-induced release of D3 and 25(OH)D was blunted; in both IR cells and obese SAT, protein expression of \u3b22-adrenergic receptor was reduced. Supplementation with 25-hydroxyvitamin-D3 was more effective in achieving vitamin D sufficiency in obese, but not in normal weight subjects. Conclusion: Dysfunctional AT shows a reduced catecholamine-induced release of D3 and 25(OH)D, and altered activity of vitamin D-metabolizing enzymes, for these reasons supplementation with 25-hydroxyvitamin-D3 is more effective in obese individuals

Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review

Non-invasive measurements of brain function and structure as neuroimaging in patients with mental illnesses are useful and powerful tools for studying discriminatory biomarkers. To date, functional MRI (fMRI), structural MRI (sMRI) represent the most used techniques to provide multiple perspectives on brain function, structure, and their connectivity. Recently, there has been rising attention in using machine-learning (ML) techniques, pattern recognition methods, applied to neuroimaging data to characterize disease-related alterations in brain structure and function and to identify phenotypes, for example, for translation into clinical and early diagnosis. Our aim was to provide a systematic review according to the PRISMA statement of Support Vector Machine (SVM) techniques in making diagnostic discrimination between SCZ patients from healthy controls using neuroimaging data from functional MRI as input. We included studies using SVM as ML techniques with patients diagnosed with Schizophrenia. From an initial sample of 660 papers, at the end of the screening process, 22 articles were selected, and included in our review. This technique can be a valid, inexpensive, and non-invasive support to recognize and detect patients at an early stage, compared to any currently available assessment or clinical diagnostic methods in order to save crucial time. The higher accuracy of SVM models and the new integrated methods of ML techniques could play a decisive role to detect patients with SCZ or other major psychiatric disorders in the early stages of the disease or to potentially determine their neuroimaging risk factors in the near future