At present, climate change, pollution, and uncontrolled urbanism threaten not only natural ecosystems, but also the urban environment. Approaches to mitigate these challenges and able to provide an alternative for the use of the space are deemed to be multidisciplinary, combining architecture, vegetation integration, circular economy and information and communications technologies (ICT). University campuses are a key scenario to evaluate such solutions as their student and research community is intrinsically willing to support these experiences and provide a wide knowledge on the fields necessary for their design and implementation. However, the creation of areas combining usability and sustainability is commonly lacking a multidisciplinary approach combining all these different perspectives. Hence, the present work aims to overcome this limitation by the development of a novel integrated approach for campus spaces for co-working and leisure, namely a “Smart Tree”, where novel architecture, furniture design, flora integration, environmental sensoring and communications join together. To this end, a survey of the literature is provided, covering related approaches as well as general principles behind them. From this, the general requirements and constraints for the development of the Smart Tree area are identified, establishing the main interactions between the architecture, greening and ICT perspectives. Such requirements guide the proposed system design and implementation, whose impact on the environment is analyzed. Finally, the research challenges and lessons learned for their development are identified in order to support future works.
In the field of rescue robotics, data collection about the environment and efficient communications are fundamental for the success of search and rescue missions. Digitalization provides new ways of detecting and localizing potential victims via the wireless devices carried by the users. Nowadays, the number of personal Bluetooth low energy wearables in use (smartbands, smartwatches, earbuds...) increases constantly, being a yet-to-be-exploited personal radio frequency beacon in the case of an emergency, where the user may not be localized and unconscious. In this paper, the results of experimental tests of a Bluetooth low energy based detection system ported by terrestrial and aerial robots are provided, in order to test the feasibility of such system for the localization of the victims in unknown complex disaster areas. The results show that the tested devices can be reliably detected up to 15 meters away when using transmission power values typical of a smartphone, while being able to detect even lightly burdened devices. These results support the idea of developing an algorithm for the delimitation of areas of interest for the search and rescue groups, influencing the routes followed by the robot with the objective of exploring the detected devices area in the search of victims.
<p>The growing complexity of cellular networks makes it harder for network operators to monitor and manage the system. To ease the management and automatically detect network problems, unsupervised techniques have been put to use. This work proposes a novel method that combines Multi-Resolution Analysis (MRA) by wavelet transforms and unsupervised clustering for the totally unsupervised grouping of cellular network behaviours through different Key-Performance Indicator (KPI)s. The application of multi-resolution decomposition, allows the much simpler clustering technique to take into account temporal information that would require of a much complex method otherwise. The proposed approach has been tested with real network data successfully separating different behaviours.</p>
<p>The growing complexity of cellular networks makes it harder for network operators to monitor and manage the system. To ease the management and automatically detect network problems, unsupervised techniques have been put to use. This work proposes a novel method that combines Multi-Resolution Analysis (MRA) by wavelet transforms and unsupervised clustering for the totally unsupervised grouping of cellular network behaviours through different Key-Performance Indicator (KPI)s. The application of multi-resolution decomposition, allows the much simpler clustering technique to take into account temporal information that would require of a much complex method otherwise. The proposed approach has been tested with real network data successfully separating different behaviours.</p>
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