The new mobile technology, 5G, challenges the current scenario in communications by overcoming the flaws of currently working 4G. Such new technology offers to smart cities and intelligent transportation systems a new way to become fully integrated by allowing massive simultaneous connections and ubiquity of network, even under high mobility situations or dense populated areas. In this way, 5G will become a key enabler for real Internet of Things and its corresponding Internet of Vehicles. This discussion is aimed at providing, in a comprehensive manner, how 5G technology will impact on smart cities, intelligent transportation systems –including autonomous or semi-autonomous vehicles– and vehicular communications, its technical, economic and legal challenges, in the following years.

In medicine, a misdiagnosis or the absence of specialists can affect the patient’s health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage –i.e., diagnosis– using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

Power systems face numerous challenges to address the growing need for sustainable energy worldwide. Several tasks directly focused on power systems operations can be either partially or completely automated by an unmanned or teleoperated robotic system. Robots can improve efficiency, reduce labor costs, and, most importantly, avoid operational risks, since power plants and transmission lines are hostile environments for humans. Although there has been some preliminary development of robotic applications in several tasks related to power systems, the technology is still undergoing research and development; it is highly dependent on the nature of the tasks to be performed, and a unified framework of applications does not yet exist. The aim of this article is to present the main contributions from the robotics field to the inspection and maintenance of power systems through the analysis of state-of-the-art and current advances, thus providing the reader with a comprehensive and insightful analysis of implemented examples and future trends.

In the last few years, scattered experiences of the application of additive manufacturing in the construction of buildings using 3D printing with robots or automated equipment have emerged around the world. These use a variety of procedures and suggest relevant advantages for the construction industry. In order to identify the different processes and features in development in this field and to guide future research and applications, this article presents a review of the literature on the main aspects involved in the use of 3D printing in the construction sector. The review includes state-of-the-art material mixtures, printing technologies, and potential uses, as well as a novel analysis of building strategies, management systems, and benefits stated about this new approach for construction. It reveals progressive experimentation regarding diverse features, with challenges related to the consolidation of procedures and this technology’s readiness to participate in the building market.

Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.