Color measurements have traditionally been linked to expensive and difficult to handle equipment. The set of mathematical transformations that are needed to transfer a color that we observe in any object that doesn’t emit its own light (which is usually called a color-object) so that it can be displayed on a computer screen or printed on paper is not at all trivial. This usually requires a thorough knowledge of color spaces, colorimetric transformations and color management systems. The TCS3414CS color sensor (I2C Sensor Color Grove), a system for capturing, processing and color management that allows the colors of any non-self-luminous object using a low-cost hardware based on Arduino, is presented in this paper. Specific software has been developed in Matlab and a study of the linearity of chromatic channels and accuracy of color measurements for this device has been undertaken. All used scripts (Arduino and Matlab) are attached as supplementary material. The results show acceptable accuracy values that, although obviously do not reach the levels obtained with the other scientific instruments, for the price difference they present a good low cost option.

The Internet of Things is one of the ideas that has become increasingly relevant in recent years. It involves connecting things to the Internet in order to retrieve information from them at any time and from anywhere. In the Internet of Things, sensor networks that exchange information wirelessly via Wi-Fi, Bluetooth, Zigbee or RF are common. In this sense, our paper presents a way in which each classroom control is accessed through Near Field Communication (NFC) and the information is shared via radio frequency. These data are published on the Web and could easily be used for building applications from the data collected. As a result, our application collects information from the classroom to create a control classroom tool that displays access to and the status of all the classrooms graphically and also connects this data with social networks.

In this paper we propose an object detector based on deep learning for scanning samples of table olives. For the construction of the system we have used a Mask R-CNN neural network. This network is able to segment the image providing a mask for each of the olives in the sample from which we can obtain the calibre of the object. In addition, the system is able to measure the degree of ripeness of the olives classifying them as green, semi-ripe and ripe, and identifying those fruits that are defective due to disease or damage caused by the harvesting process. The proposed system achieves success rates of 99.8% in the detection of olive fruits in photograms, 93.5% in the classification of fruit by ripeness and close to 80% in the detection of defects.

Internet and, in particular, computer networks have become a key issue in our daily lives, due to the birth of wireless technologies. Internet of Things (IoT) aims to integrate sensors and actuators in daily objects, maximizing miniaturization and minimizing the economic cost of these hardware components. The purpose is to connect these components to the Internet through wireless and fixed networks, and thereby produce information in real time which is then stored for later processing. On the other hand, Interactive TV (iTV) combines traditional TV with interactivity similar to that of the Internet and the personal computer. The evolution of TV technology has brought computing power to this device, offering services apart from the traditional ones, making it a device capable of running applications and maximizing its networking potential. This work presents a framework that includes and integrates a wireless sensor network, an IoT platform, and a real interactive TV application. It covers the deployment and communication of the wireless sensor network through the interoperability of data, to final consumption, through a real interactive television application. It has been tested within a residential community to provide real-time information, in order to improve the quality of life of its inhabitants. In addition, it incorporates the possibility of analyzing this information to establish processes with the objective of reducing energy consumption, thus improving sustainability and contributing to the efficient use of existing resources. The proposed framework serves as the basis for any deployment of similar characteristics.

Aplicar estrategias de gamificación implementadas en una plataforma virtual de aprendizaje como apoyo a clases presenciales, es una propuesta educativa que incrementa la motivación y compromiso en la realización de actividades y tareas autónomas haciendo divertido el aprendizaje. Esta propuesta se aplicó en la asignatura Programación I (P-I) de la Facultad de Ingeniería y Ciencias Aplicadas (FICA) de la Universidad Central de Ecuador (UCE), donde se evaluó el rendimiento académico de los estudiantes comparando grupos: experimental (metodología gamificada) y de control (metodología tradicional). Mediante encuestas también se valoró el curso gamificado y las estrategias empleadas que más aportaron a hacer divertido el aprendizaje. Como conclusión de la investigación se tiene que una plataforma virtual de aprendizaje gamificada hizo que los alumnos se diviertan aprendiendo a programar influyendo positivamente en su motivación por el desarrollo de actividades autónomas de aprendizaje, además que también permitió mejorar su rendimiento académico.

Se realizó una investigación con estudiantes del Grado en Ingeniería en Diseño Industrial y Desarrollo de Productos de la Universidad de Extremadura en España durante dos años consecutivos para evaluar la adquisición de competencias transversales. El estudio se enmarca en el contexto del nuevo sistema de enseñanza basado en competencias que reta a los docentes a buscar estrategias para enseñar y evaluar dichas competencias. Los datos fueron analizados estadísticamente tras un proceso de remuestreo, Bootstrap. En general, el alumnado de segundo curso presenta una mejor percepción del desarrollo de las competencias transversales que el de primero, especialmente en las competencias de trabajo en equipo, conocimiento de las características personales y aptitudes profesionales y capacidad para conseguir los objetivos programados. La principal aportación de este estudio consiste en emplear herramientas para evaluar al alumnado universitario de forma integral, considerando las competencias transversales como parte fundamental del proceso de formación.