Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification. Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks (logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter). After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. This work obtained an average of 88% of success detecting cracks and an 80% of success detecting the type of the crack. It could be implemented in a vehicle traveling as fast as 130 kmh or 81 mph.
This work analyzes several drift compensation mechanisms in wireless sensor networks (WSN). Temperature is an environmental factor that greatly affects oscillators shipped in every WSN mote. This behavior creates the need of improving drift compensation mechanisms in synchronization protocols. Using the Flooding Time Synchronization Protocol (FTSP), this work demonstrates that crystal oscillators are affected by temperature variations. Thus, the influence of temperature provokes a low performance of FTSP in changing conditions of temperature. This article proposes an innovative correction factor that minimizes the impact of temperature in the clock skew. By means of this factor, two new mechanisms are proposed in this paper: the Adjusted Temperature (AT) and the Advanced Adjusted Temperature (A2T). These mechanisms have been combined with FTSP to produce AT-FTSP and A2T-FTSP Both have been tested in a network of TelosB motes running TinyOS. Results show that both AT-FTSP and A2T-FTSP improve the average synchronization errors compared to FTSP and other temperature-compensated protocols (Environment-Aware Clock Skew Estimation and Synchronization for WSN (EACS) and Temperature Compensated Time Synchronization (TCTS)).
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.
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