The maintenance of road pavements is an essential task to prevent major deterioration and to reduce accident rates. In this task, the detection and classification of different types of cracks on the roads is usually considered. However, in most cases, these tasks are not fully automated and they need to be supervised by an expert to make repair decisions. This work focuses on the automatic classification of the most common types of cracks: longitudinal cracks, transverse cracks, and alligator cracks. Our proposal combines, first, computer vision techniques for crack segmentation and second, an ensemble model (composed of different rule-based algorithms) for the classification. This approach achieves an average precision and recall values greater than 94% for three analyzed data sets improving the results in comparison to other approaches.

Chronotherapy versus conventional statins therapy for the treatment of hyperlipidaemia.

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Microprocessors is a typical subject within the Computer Architecture field of scope. It is quite common to use simulators in practical sessions, due to the complexity of its contents. In this paper a new methodology based on practical sessions with real devices and chips is proposed. Simple designs of microprocessors are exposed to the students at the beginning, rising the complexity gradually toward a final design with a multiprocessor integrated in a single FPGA chip. Finally, assessment results are shown.