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In this paper, we present a full coverage path planning (CPP) algorithm for the marine surveys conducted in the convex polygon shaped search area. The survey is supposed to carry out by torpedo-type AUVs (autonomous underwater vehicles). Due to their nonholonomic mechanical characteristics, these vehicles have nonzero minimum turning radius. For any given polygon shaped search area, it can always be partitioned into one or more convex polygons. With this in mind, this paper proposes a novel search algorithm called CbSPSA (Calculation based Shortest Path Search Algorithm) for full coverage of any given convex polygon shaped search area. By aligning the search inter-tracks alongside the edge with the minimum height, we can guarantee the minimum number of the vehicle's turns. In addition, the proposed method can guarantee the planned path is strictly located inside the polygon area without overlapped or crossed path lines, and also has the total path length as short as possible. Considering the vehicle's nonzero minimum turning radius, we also propose a sort of smoothing algorithm which can smooth the waypoint path searched by CbSPSA so that the vehicle can exactly follow it. The smoothed path is also guaranteed to be strictly located inside the polygon. Numerical simulation analyses are also carried out to verify the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to demonstrate advanced surveying methods and techniques for coastal erosion identification and monitoring in a densely human-populated coastline, the southern coastline of the Gulf of Patras (Greece), which diachronically suffers erosion problems expected to become worse in the forthcoming years due to climate change and human intervention. Its importance lies in the fact that it presents a robust methodology on how all modern scientific knowledge and techniques should be used in coastal erosion problems. The presented methods include the use of satellite and aerial photo imaging, shallow seabed bathymetry and morphology, sediment sampling, geotechnical investigations, as well as hydrodynamic modelling. The results are extensively analyzed in terms of their importance in coastal erosion studies and are cross-validated to define those areas most vulnerable to erosion. Towards this scope, the seabed erosion rate produced by hydrodynamic modelling is compared with the coastal vulnerability index (CVI) calculations performed in the examined area to identify which coastal zones are under a regime of intensive erosion. The results between the CVI and the seabed erosion rate appear to coincide in terms of the erosion potential, especially in zones where the vulnerability regime has been calculated as being high or very high, with the P. oceanica meadows playing an important role in reducing erosion. [ABSTRACT FROM AUTHOR]

Modern studies which dealt with the frequency domain analysis showed that a frequency-domain approach has an essential advantage and mentioned an inner qualitative relationship between the subsurface structure and its frequency spectra. This paper deals with the acoustic spectral response of sand and sandstone sediments at the sea bottom. An acoustic data collection campaign was conducted over two sand sites and two sandstone sites. The analysis of the results shows that reflections of acoustic signals from sand and sandstone sea bottom are characterized by various spectral features in the 2.75–6.75 kHz range. The differences in acoustic response of sand and sandstone can be quantified by examining the maximal normalized reflected power, the mean frequency, and the number of crossings at different power levels. The statistical value distribution of these potential classifiers was calculated and analyzed. These classifiers, and especially the roughness of the spectrum quantified by the number of crossings parameter can give information to assess the probability for sand or sandstone based on the reflected spectra and be used for actual distinction between sand and sandstone in sub bottom profiler data collection campaigns. [ABSTRACT FROM AUTHOR]