Lack of diversity, and specifically, gender diversity, is one of the key problems that both technological companies and academia are facing these days. Moreover, recent studies show that the number of female students enrolled in science, technology, engineering and mathematics (STEM) related disciplines have been decreasing in the last twenty years, while the number of women resigning from technological job positions remains unacceptably high. As members of a higher education institution, we foresee that working towards increasing and retaining the number of female students enrolled in STEM disciplines can help to alleviate part of the challenges faced by women in STEM fields. In this paper, we first review the main barriers and challenges that women encounter in their professional STEM careers through different age stages. Next, we focus on the special case of the information theory field, discussing the potential of gendered innovation, and whether it can be applied in the Information Theory case. The working program developed by the School of Engineering at the University of Valencia (ETSE-UV), Spain, which aims at decreasing the gender diversity gap, is then presented and recommendations for practice are given. This program started in 2011 and it encompasses Bachelor, Master and PhD levels. Four main actions are implemented: Providing institutional encouragement and support, increasing the professional support network, promoting and supporting the leadership, and increasing the visibility of female role models. To assess the impact of these actions, a chi-square test of independence is included to evaluate whether there is a significant effect on the percentage of enrolled female students. The percentage of graduated female students in the information and Communications Technology Field is also positioned with respect to other universities and the Spanish reference value. This analysis establishes that, in part, this program has helped to achieve higher female graduation rates, especially among Bachelor students, as well as increasing the number of top-decision positions held by faculty women.

This letter reports the experimental demonstration of an indoor visible light nonimaging multiple-input multiple-output system with an aggregate error free bit rate of 50 Mb/s over a distance of 2 m. The system uses four independent white LED transmitters, each transmitting 12.5 Mb/s of data in the ON-OFF keying nonreturn zero format, and four independent nonimaging optical receivers. The performance of four detection methods ranging from the basic channel inversion to the more advanced space time techniques is compared experimentally. The results gathered demonstrate that the simplest technique is capable of the same bit error rate as the most complex scheme. The system also provides full illumination with a mean level of 350 Lux satisfying the ISO lighting standards for home and office environments.

Science, Technology, Engineering, and Mathematics (STEM) are key disciplines towards tackling the challenges related to the Sustainable Development Goals. However, evidence shows that women are enrolling in these disciplines in a smaller percentage than men, especially in Engineering related fields. As stated by the United Nations Women section, increasing the number of women studying and working in STEM fields is fundamental towards achieving better solutions to the global challenges, since the potential for innovation is larger. In this paper, we present the Girls4STEM project, which started in 2019 at the Escola Tècnica Superior d’Enginyeria de la Universitat de València, Spain. This project works towards breaking the stereotypes linked to STEM fields, addressing both boys and girls aged from 6 to 18, but especially trying to open the range of career options for young girls through interaction with female STEM experts. The goal is to spark girls’ interest in STEM disciplines from childhood, so that they become more self-confident in these areas. To achieve this goal, the project is built over three main actions: the Girls4STEM Family Talks, where students, families, and teachers participate; the Girls4STEM Professional Talks, where the target is a general audience; and the Initial Training Seminars for teachers. Short-term results are here presented, showing that aspects related to self-perception and perception from others (family, teachers) play a significant role. Moreover, these results also indicate that there may not be a general understanding of which disciplines are included in STEM.

Radio maps are expected to be an essential tool for the resource optimization and management of 5G automotive. In this paper, we consider the problem of radio map reconstruction using a wireless sensor network formed by sensor nodes in vehicles, access nodes from a smart city infrastructure, etc. Due to limited resource constraints in sensor networks, it is crucial to select a small number of sensor measurements for field reconstruction. In this context, we present a novel distributed incremental clustering algorithm based on the regression Kriging method for radio map reconstruction in terms of average received power at locations where no sensor measurements are available. The path-loss and shadowing components of the wireless channel are separately estimated. For shadowing estimation, clusters of sensors are adaptively formed and their size is optimized in terms of the least number of sensors by minimizing the ordinary Kriging variance. The complexity of the proposed algorithm is analyzed and simulation results are presented to showcase the algorithm efficacy to field reconstruction.

The gender gap in Science, Technology, Engineering and Mathematics (STEM) has drawn the attention of research and academic communities due to its impact in the Digital Society, targeting the fourth and fifth 2030 sustainable development goals of achieving quality education and gender equality. Recent studies show that women are enrolling STEM studies in smaller proportion than men and that they have a larger probability to renounce to their jobs or to take leaves. In this scenario, the involvement of educational institutions is seminal to change this trend. The School of Engineering of the University of Valencia (ETSE-UV), Spain, launched in 2011 a pilot program to promote STEM careers, focusing on increasing and retaining the number of female students choosing these studies. Building from this experience, the Girls4STEM project has been launched in 2019 with the aim of reaching female students from 6 to 18 years old, their families and teachers. In this paper, we present and motivate the project’s objectives and main activities, framing them in current state of the art’s literature. Preliminary results of the pilot program actions are presented, demonstrating the statistically significant impact on the percentage of enrolled female students and motivating the subsequent Girls4STEM project.

Radio environment map (REM) reconstruction based on large-scale channel measurements is a promising technology for future mobility services involving vehicle-to-everything (V2X) communications. REMs provide contextual information which can be exploited to reduce V2X communication latency and control signaling, for instance, through a fast access to channel state information. However, the accuracy of radio mapping techniques is limited by the availability of measurements, which require for collection significant signaling overhead. Moreover, mobility scenarios impose strict latency constraints that render fast channel acquisition a challenging problem. This paper presents a low-complexity deep-learning-based approach based on long-short term memory (LSTM) cells for REM reconstruction on roads, addressed as a data-filling problem. To improve model generalization, the network is trained on a virtually infinite dataset generated according to a 3GPP-compliant freeway scenario, considering different correlation properties and missing point configurations. The results show that the proposed approach provides a performance closer to the theoretical lower bound than the classical Ordinary Kriging spatial interpolation method, without increasing the complexity order. Experiments performed in realistic scenarios using a 3D city model confirm the generalization capability of the proposed solution.