7 resultados para Underwater archaeology

em SAPIENTIA - Universidade do Algarve - Portugal


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Dissertação de Mestrado, Gestão da Água e da Costa, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2009

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Blind deconvolution is studied in the underwater acoustic channel context, by time-frequency (TF) processing. The acoustic propagation environment is modelled by ray tracing and mathematically described by a multipath propagation channel. Representation of the received signal by means of a signal-dependent TF distribution (radially Gaussian kernel distribution) allowed to visualize the resolved replicas of the emitted signal, while signi cantly attenuating the inherent interferences of classic quadratic TF distributions. The source signal instantaneous frequency estimation was the starting point for both source and channel estimation. Source signature estimation was performed by either TF inversion, based on the Wigner-Ville distribution of the received signal, or a subspace- -based method. The channel estimate was obtained either via a TF formulation of the conventional matched- lter, or via matched- - ltering with the previously obtained source estimate. A shallow water realistic scenario is considered, comprising a 135-m depth water column and an acoustic source located at 90-m depth and 5.6-km range from the receiver. For the corresponding noiseless simulated data, the quality of the best estimates was 0.856 for the source signal, and 0.9664 and 0.9996 for the amplitudes and time-delays of the impulse response, respectively. Application of the proposed deconvolution method to real data of the INTIMATE '96 sea trial conduced to source and channel estimates with the quality of 0.530 and 0.843, respectively. TF processing has proved to remove the typical ill-conditioning of single sensor deterministic deconvolution techniques.

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The analysis of seabed structure is important in a wide variety of scientific and industrial applications. In this paper, underwater acoustic data produced by bottom-penetrating sonar (Topas) are analyzed using unsupervised volumetric segmentation, based on a three dimensional Gibbs-Markov model. The result is a concise and accurate description of the seabed, in which key structures are emphasized. This description is also very well suited to further operations, such as the enhancement and automatic recognition of important structures. Experimental results demonstrating the effectiveness of this approach are shown, using Topas data gathered in the North Sea off Horten, Norway.

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We estimated the detonation depth and net explosive weight for a very shallow underwater explosion using cutoff frequencies and spectral analysis. With detonation depth and a bubble pulse the net explosive weight for a shallow underwater explosion could simply be determined. The ray trace modeling confirms the detonation depth as a source of the hydroacoustic wave propagation in a shallow channel. We found cutoff frequencies of the reflection off the ocean bottom to be 8.5 Hz, 25 Hz, and 43 Hz while the cutoff frequency of the reflection off the free surface to be 45 Hz including 1.01 Hz for the bubble pulse, and also found the cutoff frequency of surface reflection to well fit the ray-trace modeling. We also attempted to corroborate our findings using a 3D bubble shape modeling and boundary element method. Our findings led us to the net explosive weight of the underwater explosion offshore of Baengnyeong-do for the ROKS Cheonan sinking to be approximately 136 kg TNT at a depth of about 8 m within an ocean depth of around 44 m. © 2015 Elsevier B.V.

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This paper describes the results of AcousticRobot'13 - a noise measurement campaign that took place off the Portuguese Coast in May 2013, using two high endurance autonomous vehicles capable of silent operation (an underwater glider and an autonmomous sailing vessel) equipped with hydrophones, and a moored hydrophone that served as reference. We show that the autonomous vehicles used can provide useful measurements of underwater noise, and describe the main advantages and shortcomings that became evident during the campaign.

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The EU-funded project UAN - Underwater Acoustic Network aims at conceiving, developing and testing at sea an innovative and operational concept for integrating in a unique communication system submerged, surface and aerial sensors with the objective of protecting off-shore and coastline critical infrastructures. A crucial aspect of the project consisted in the use of autonomous underwater vehicles (AUVs) as mobile nodes in the underwater acoustic communication network. In particular, AUVs have the role of adapting the network geometry to the variation of the acoustic channel. This paper reports on the project concept and vision as well as on the progress of its various development phases. The recent at-sea successes that have been demonstrated within the UAN framework are detailed and results of the final UAN project demonstration, UAN11, held in the May of 2011, are reported. The UAN network was in operation for five continuous days with up to five nodes, of which three of them were mobile nodes. © IFAC.

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Gettysburg and Ormond are seamounts belonging to the Gorringe Bank, which is located on the Europen-African plate boundary. Given the importance of these oceanic features for understanding marine biodiversity patterns, two surveys were carried out in 1998 (Gettysburg) and 1999 (Ormond) using diving, photography and video for species identification and abundance evaluation. Of the 9 fish species found at Gettysburg and 11 at Ormond, 6 were common to both sites and these included the dominant species: the oceanic and commercially important, Seriola rivoliana, and the coastal, typically Atlantic-Mediterranean Coris julis and Anthias anthias. The strong representation of coastal, demersal and Atlantic-Mediterranean species in the Gorringe Bank region and the presence of Abudefduf luridus, an endemic Macaronesian species, at Gettysburg suggests that the upper part of these seamounts may have acted and still act as “stepping stones” for the dispersal of coastal species.