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Today, airspace control has the challenge of merging information from independent and heterogeneous systems in order to minimize air safety risks and facilitate the decision-making process. One of the main risks for air operations is meteorology because convective formations like Torre cumulus or cumulonimbus could generate several dangerous phenomena such as icing, wind gusts, and thunderstorms, among others, that can affect the air operation safety. Based on previous works that allow the automatic identification of convective phenomena through the fusion of multispectral satellite images and other sources as winds and Meteorological Aerodrome Report (METAR), and establishing a common georeferenced coordinates system like WGS-84, for all sources, it can generate a system that could calculate early alerts about hazardous weather conditions in the aircrafts proximality for air traffic control system. For this, a meteorological analysis system can generate information about convective clouds calculating area, heights, temperatures, risk level and position of the meteorological formation. Parallelly the convective cloud is surrounded by optimal elliptical forms centered on the convective formation, generating a meteorological object. On the other hand, there is a system responsible for monitoring the information of the surveillance sensors. This system fused the air traffic sensors available like primary and secondary radar signals and ADS-B sensors in a unique WGS-84 coordinates system. Finally, in a georeferenced raster-type graphing system or in a Geographic Information System (GIS), the meteorological and surveillance information is correlated projecting the track routes generates by air traffic system and traces generated by meteorological objects in order to establish times and high-risk areas, early. With this information, the Air Traffic Controller (ATC) system users, could minimize risk areas and reorganize the air traffic flow. This methodology then, would contribute to the decision-making process of ATC, facilitating the air flow reorganization and minimizing meteorological risks. For the development of this project a cooperative experimental methodology by subsystems was used. It was based on an operational knowledge and normal operating procedures of the Colombian Air Force, integrated with radar tracking technologies that implement decision trees. These alerts allow the air traffic controller to assess the risk and in accordance with the evaluation, if necessary, reorganize the air traffic flow for a specific area before the aircraft enter areas of bad weather mitigating the risks.

Biotechnological processes represent a challenge into the control field, due to its high non linearity. Particularly, continuous alcoholic fermentation by Zymomonas mobilis (Z.m) presents high ethanol performance, but it shows a highly oscillatory behavior in process variables, because of the influence of inhibition dynamics over Biomass, Substrate and Product concentrations; it is a significant challenge from the control perspective. In this work, a new and significant solution for control of biotechnological variables into the fermentation process, based on numerical methods and linear algebra is proposed. In addition, a state estimator of Biomass into the control loop is presented, using to reach this aim, the Non Linear Filtering based on the recursive application of the Bayes rule and Monte Carlo techniques. Specifically, the recursive Bayesian Filters are used. This methodology allows developing the controller design and a tested state estimator for Biomass in Z.m, without the use of complex calculations.