The upgrade and enhancement of sea level networks worldwide for integration in sea level hazard warning systems have significantly increased the possibilities for measuring and analyzing high frequency sea level oscillations, with typical periods ranging from a few minutes to a few hours. Many tide gauges now afford 1 min or more frequent sampling and have shown such events to be a common occurrence. Their origins and spatial distribution are diverse and must be well understood in order to correctly design and interpret, for example, the automatic detection algorithms used by tsunami warning centers. Two events recorded recently in European Atlantic waters are analyzed here: possible wave-induced "seiches" that occurred along the North coast of Spain during the storms of January and February of 2014, and small sea level oscillations detected after an earthquake in the mid-Atlantic the 13th of February of 2015. The former caused significant flooding in towns and villages and a huge increase in wave-induced coastal damage that was reported in the media for weeks. The latter was a smaller signal present in several tide gauges along the Atlantic coast that coincided with the occurrence of this earthquake, leading to a debate on the potential detection of a very small tsunami and how it might yield significant information for tsunami wave modelers and for the development of tsunami detection software. These kind of events inform us about the limitations of automatic algorithms for tsunami warning and help to improve the information provided to tsunami warning centers, whilst also emphasizing the importance of other forcings in generating extreme sea levels and their associated potential for causing damage to coastal infrastructure and flooding.
Skeletal maturity estimation is an important medical procedure in the early diagnosis of growth disorders. Traditionally, it is performed by an expert physician or radiologist who determines it based on a visual subjective inspection, the approximated bone age of the child. This task is time consuming and is usually dependent on the judgment of each particular physician. Therefore, automated methods are extremely valuable and desirable. In this paper, we propose and describe an automatic method to estimate skeletal maturity through a supervised and incremental learning approach. Our method determines bone age by comparing aligned images with a [Formula: see text]–[Formula: see text] regression classifier. Here, we have solved the difficult task of image alignment by designing a radiological-hand specific Active Appearance Model, which was developed from a varied set of hand-labeled radiological images. By using this active model, our system constructs its own learned database by increasing a set of shape-aligned training images which are incrementally stored. Thus, when a test image arrives at the system, the alignment process is performed before the classification task takes place. For that purpose, we designed an original layout of landmarks to be located in representative regions of the radiographical image of the hand. Our results show that it is possible to use pixels directly as classification features as long as training and testing images have been previously aligned in shape and pose.
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