Over the last years, existing technologies have been applied to agricultural environments, resulting in new precision agriculture systems. Some of the multiple profits of developing new agricultural technologies and applications include the cost reduction around the building and deployment of them, together with more energy-efficient consumption. Therefore, agricultural precision systems focus on developing better, easier, cheaper, and overall more efficient ways of handling agricultural monitoring and actuation. To achieve this vision, we use a set of technologies such as Wireless Sensor Networks, Sensors devices, Internet of Things, or data analysis. More specifically, in this study, we proposed a combination of all these technologies to design and develop a prototype of a precision agriculture system for medium and small agriculture plantations that highlights two major advantages: efficient energy management with self-charging capabilities and a low-cost policy. For the development of the project, several prototype nodes were built and deployed within a sensor network connected to the cloud as a self-powered system. The final target of this system is, therefore, to gather environment data, analyze it, and actuate by activating the watering installation. An analysis of the exposed agriculture monitoring system, in addition to results, is exposed in the paper.

The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training.

This paper describes the architecture of an Arduino remote lab that supports the deployment of many Arduino-based experiments, such as a sensors remote lab consisting on eleven sensors and a LCD display connected to an Arduino MEGA, or a 3D RGB LED cube remote lab consisting on 16 RGB LEDs connected to an Arduino UNO-compatible board. The proposed on-line system allows students to write code on a website to be executed on these experiments. The execution results can be observed in real time through an IP camera. The use of this kind of on-line didactic tools is very important to provide high quality on-line education programs on technical fields.

Today, there are a multitude of initiatives that favor the learning of Science, Technology, Engineering, and Mathematics (STEM) and educational robotics (ER). Thanks to the available means, it is very easy to access a wide variety of tools based on programmable hardware. Although there are many options, most of them either require a high economic investment or require a great deal of time for the development of educational activities. Through the results shown in this paper, the great potential of Scratch as a solution for students who are just starting out in the knowledge related to STEM and ER is shown. To show the usefulness of Scratch a course has been designed, which dedicates three modules to the use of Scratch. The three modules have different levels of difficulty for the students, a basic level of difficulty, another intermediate level of difficulty and a last module with an advanced degree of difficulty.