This paper presents a novel home automation system named HASITE (Home Automation System based on Intelligent Transducer Enablers), which has been specifically designed to identify and configure transducers easily and quickly. These features are especially useful in situations where many transducers are deployed, since their setup becomes a cumbersome task that consumes a significant amount of time and human resources. HASITE simplifies the deployment of a home automation system by using wireless networks and both self-configuration and self-registration protocols. Thanks to the application of these three elements, HASITE is able to add new transducers by just powering them up. According to the tests performed in different realistic scenarios, a transducer is ready to be used in less than 13 s. Moreover, all HASITE functionalities can be accessed through an API, which also allows for the integration of third-party systems. As an example, an Android application based on the API is presented. Remote users can use it to interact with transducers by just using a regular smartphone or a tablet.

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

We present several Service-Learning projects developed considering Sustainable Development Goals to provide the students skills for the support of a sustainable society. This project begins with collecting initial impressions from surveys and reflections to know both the students’ expectations and their degree of involvement in the development of a sustainable society. Next, the students design academic projects taking into account the needs of the specific collective to which the Service-Learning activity is oriented. When this design has finished, such projects are developed with the users and the activity is evaluated. During 2019–2020, these projects were done with four entities oriented to people either with Asperger’s syndrome, Down syndrome, Alzheimer’s disease or mental health illnesses. A total of 35 and 10 students, respectively, from the Bachelor’s Degree of Engineering in Industrial Design and Product Development and from the Master’s Degree in Professorship of High School have participated in this experience. We analyze the results from the perspective of the different agents involved, considering both qualitative and quantitative metrics. The results show that both users and staff are satisfied with this collaboration between the university and their entities. Moreover, this experience clearly has contributed to a better personal and professional student’s development.

Service Learning is a methodology in which students achieve academic and transversal competences related to the curriculum of a subject while performing a service for the benefit of the community. With the COVID-19 pandemic, it was necessary to reorganize the Service Learning activities developed in recent years so that they do not lose their pedagogical value and community service. This scenario has been an opportunity to kick-start an e-Service Learning experience. For that purpose, this work shows how different Information and Communication Technology tools are integrated into an online platform to develop both activities and assessment following an e-Service Learning methodology. Since the experience was performed with two collaborating entities serving people with autism and in two schools of the University of A Coruña, the tools are available not only to professors and students, but also to entities. Our experience includes the assessment of both competences and service satisfaction using different resources for virtual collaborative work. The main contribution of our work is that we have greatly simplified our previous project on-site and also the monitoring of the student’s progress, the work of both professors and students, and the analysis of results, providing a virtual service that responds to user needs.

Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms—one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.