Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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The efficiency and profitability of photovoltaic (PV) plants are highly controlled by their operation and maintenance (O&M) procedures. Today, the effective diagnosis of any possible fault of PV plants remains a technical and economic challenge, especially when dealing with large-scale PV plants. Currently, PV plant monitoring is carried out by either electrical performance measurements or image processing. The first approach presents limited fault detection ability, it is costly and time-consuming, and it is incapable of fast identification of the physical location of the fault. In the second approach, Infrared Thermography (IRT) imaging has been used for the characterization of PV module failures, but their setup and processing are rather complex and an experienced technician is required. The use of Unmanned Aerial Vehicles (UAVs) for IRT imaging of PV plants for health status monitoring of PV modules has been identified as a cost-effective approach that offers 10–-15 fold lower inspection times than conventional techniques. However, previous works have not performed a comprehensive approach in the context of automated UAV inspection using IRT. This work provides a fully automated approach for the: (a) detection, (b) classification, and (c) geopositioning of the thermal defects in the PV modules. The system has been tested on a real PV plant in Spain. The obtained results indicate that an autonomous solution can be implemented for a full characterization of the thermal defects.

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New technological, societal and legislative developments are necessary to support transitions to low-carbon energy systems. The building sector is responsible for almost 36% of the global final energy and 40% of CO2 emissions, so this sector has high potential to contribute to the expansion of positive energy districts. With this aim, a new digital Geographic Information System (GIS) platform has been developed to quantify the energy savings obtained through the implementation of refurbishment measures in residential buildings, including solar thermal collectors and geothermal technologies and assuming the postal district as the representative unit for the territory. Solar resources have been estimated from recently updated solar irradiation maps, whereas geothermal resources have been estimated from geological maps. Urbanistic data have been estimated from official cadastre databases. For representative buildings, the annual energy demand and savings are obtained and compared with reference buildings, both for heating and cooling. The GIS platform provides information on average results for each postal district, as well as estimates for buildings with particular parameters. The methodology has been applied to the Asturian region, an area of about 10,600 km2 on the Cantabrian coast of Spain, with complex orography and scattered population, qualified as a region in energy transition. High rehabilitation potentials have been achieved for buildings constructed before the implementation of the Spanish Technical Building Code of 2006, being higher for isolated houses than for collective buildings. Some examples of results are introduced in specific localities of different climatic zones.