Urban cycling is a sustainable mode of transportation in large cities, and it offers many advantages. It is an eco-friendly means of transport that is accessible to the population and easy to use. Additionally, it is more economical than other means of transportation. Urban cycling is beneficial for physical health and mental well-being. Achieving sustainable mobility and the evolution towards smart cities demands a comprehensive analysis of all the essential aspects that enable their inclusion. Road safety is particularly important, which must be prioritized to ensure safe transportation and reduce the incidence of road accidents. In order to help reduce the number of accidents that urban cyclists are involved in, this work proposes an alternative solution in the form of an intelligent computational assistant that utilizes simplified machine learning (SML) to detect potential risks of unexpected collisions. This technological approach serves as a helpful alternative to the current problem. Through our methodology, we were able to identify the problem involved in the research, design, and development of the solution proposal; collect and analyze data; and obtain preliminary results. These results experimentally demonstrate how the proposed model outperforms most state-of-the-art models that use a metric learning layer for small image sets.

Urban cycling is a sustainable mode of transportation in large cities, and it offers many advantages. It is an eco-friendly means of transport that is accessible to the population and easy to use. Additionally, it is more economical than other means of transportation. Urban cycling is beneficial for physical health and mental well-being. Achieving sustainable mobility and the evolution towards smart cities demands a comprehensive analysis of all the essential aspects that enable their inclusion. Road safety is particularly important, which must be prioritized to ensure safe transportation and reduce the incidence of road accidents. In order to help reduce the number of accidents that urban cyclists are involved in, this work proposes an alternative solution in the form of an intelligent computational assistant that utilizes Simplified Machine Learning (SML) to detect potential risks of unexpected collisions. This technological approach serves as a helpful alternative to the current problem. Through our methodology, we were able to identify the problem involved in the research, design and development of the solution proposal, collect and analyze data, and obtain preliminary results. These results experimentally demonstrate how the proposed model outperforms most state-of-the-art models that use a metric learning layer for small image sets.

Passive LEO (Low Earth Orbit) satellites are a telecom premier option. However, LEO satellites impose not only stringent specifications on the resolution, precision and repeatability but also requiring advanced antenna technology for signal downlinking. To efficiently downlink LEO signals at each passage onto a given Earth region, we explored large size passive Earth antenna and an array of smaller size active Earth antennas to minimize the trajectory loss. To guarantee design specifications, the dynamics cannot be neglected given the size and inertia of base and antenna. In this paper, it is proposed the design, path planning and control of a six DoF robotic antenna maneuvering the antenna subject to aerodynamic wind disturbance. The system maneuvers to point at the LEO satellite over the whole envelope with the required precision to guarantee robust point-to-point tracking. Representative simulation results for three geolocations shows practical tracking with off-the-shelf-component actuators, without requiring any knowledge of the dynamics while withstanding state-dependent persistent disturbances.