The inspection of high voltage power transmission lines is mainly carried out by manned aerial vehicles or foot patrol. However, these maintenance methodologies for inspection are somehow inefficient and expensive. Moreover, helicopter assisted inspection endangers the human life. Recently, unmanned aerial vehicles have been under development in several research centers all over the world due to its potential applications. In this paper, we present an unmanned aerial system based on the quadrotor helicopter for high voltage power line inspection. Our interest is to equip the quadrotor helicopter with the necessary payload in order to be able to carry out a qualitative inspection, therefore the hardware architecture of the aerial robotic system is presented. Finally, some experimental results are shown to demonstrate the feasibility of the proposed system.

Humanity is currently experiencing one of the short periods of transition thanks to novel sensing solutions for smart cities that bring the future to today. Overpopulation of cities demands the development of solid strategic plannings that uses infrastructure, innovation, and technology to adapt to rapid changes. To improve mobility in cities with a larger and larger vehicle fleet, a novel sensing solution that is the cornerstone of a smart parking system, the smart vehicular presence sensor (SPIN-V, in its Spanish abbreviation), is presented. The SPIN-V is composed of a small single-board computer, distance sensor, camera, LED indicator, buzzer, and battery and devoted to obtain the status of a parking space. This smart mobility project involves three main elements, namely the SPIN-V, a mobile application, and a monitoring center, working together to monitor, control, process, and display the parking space information in real-time to the drivers. In addition, the design and implementation of the three elements of the complete architecture are presented.

Nowadays, trash has become a problem in the society and the ecosystem due to the way people get rid of it. Most of garbage is buried or burnt or even kept in places to which it does not belong. Big volumes of garbage thrown away and the methods used to store it cause air, water, and soil pollution. Fortunately, people can count on other methods to reduce the quantity of produced litter. An answer is recycling by re-using the materials. Currently, the traditional way to separate waste is to use different containers for each kind of waste separating trash manually, which does not always work. The aim of this paper is to present an Intelligent Waste Separator (IWS) which can replace the traditional way of dealing with waste; the proposed device receives the incoming waste and places it automatically in different containers by using a multimedia embedded processor, image processing, and machine learning in order to select and separate waste.

This paper presents the design and development of an IoT device, called MEIoT weather station, which combines the Educational Mechatronics and IoT to develop the required knowledge and skills for Industry 4.0. MEIoT weather station connects to the internet, measures eight weather variables, and upload the sensed data to the cloud. The MEIoT weather station is the first device working with the IoT architecture of the National Digital Observatory of Intelligent Environments. In addition, an IoT open platform, GUI-MEIoT, serves as a graphic user interface. GUI-MEIoT is used to visualize the real-time data of the weather variables, it also shows the historical data collected, and allows to export them to a csv file. Finally, an OBNiSE architecture application to Engineering Education is presented with a dynamic system case of study that includes the instructional design carried out within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work main contribution to the state of art is the design and integration of the OBNiSE architecture within the EMCF offering the possibility to add more IoT devices for several smart domains such as smart campus, smart cities, smart people and smart industries.