Shallow Water Sound Speed Estimation with Neural Networks-Based Nonlinear Regression of Space-Time Variability

Author(s):  
Evgeniy M. Zheldak ◽  
Valeriy I. Petukhov ◽  
Kiseon Kim
2004 ◽  
Vol 116 (4) ◽  
pp. 2558-2558
Author(s):  
Bruce D. Cornuelle ◽  
Philippe Roux ◽  
Tuncay Akal ◽  
William S. Hodgkiss ◽  
William A. Kuperman ◽  
...  

2000 ◽  
Vol 27 (17) ◽  
pp. 2709-2712 ◽  
Author(s):  
Alberto Álvarez ◽  
Cristóbal López ◽  
Margalida Riera ◽  
Emilio Hernández-García ◽  
Joaquín Tintoré

2007 ◽  
Vol 46 (6) ◽  
pp. 742-756 ◽  
Author(s):  
Gyu Won Lee ◽  
Alan W. Seed ◽  
Isztar Zawadzki

Abstract The information on the time variability of drop size distributions (DSDs) as seen by a disdrometer is used to illustrate the structure of uncertainty in radar estimates of precipitation. Based on this, a method to generate the space–time variability of the distributions of the size of raindrops is developed. The model generates one moment of DSDs that is conditioned on another moment of DSDs; in particular, radar reflectivity Z is used to obtain rainfall rate R. Based on the fact that two moments of the DSDs are sufficient to capture most of the DSD variability, the model can be used to calculate DSDs and other moments of interest of the DSD. A deterministic component of the precipitation field is obtained from a fixed R–Z relationship. Two different components of DSD variability are added to the deterministic precipitation field. The first represents the systematic departures from the fixed R–Z relationship that are expected from different regimes of precipitation. This is generated using a simple broken-line model. The second represents the fluctuations around the R–Z relationship for a particular regime and uses a space–time multiplicative cascade model. The temporal structure of the stochastic fluctuations is investigated using disdrometer data. Assuming Taylor hypothesis, the spatial structure of the fluctuations is obtained and a stochastic model of the spatial distribution of the DSD variability is constructed. The consistency of the model is validated using concurrent radar and disdrometer data.


Author(s):  
Konstantinos Makantasis ◽  
Athanasios Voulodimos ◽  
Anastasios Doulamis ◽  
Nikolaos Bakalos ◽  
Nikolaos Doulamis

2018 ◽  
Vol 40 (06) ◽  
pp. 722-733 ◽  
Author(s):  
Marco Dioguardi Burgio ◽  
Marion Imbault ◽  
Maxime Ronot ◽  
Alex Faccinetto ◽  
Bernard E. Van Beers ◽  
...  

Abstract Purpose To evaluate the ability of a new ultrasound (US) method based on sound speed estimation (SSE) with respect to the detection, quantification, and grading of hepatic steatosis using magnetic resonance (MR) proton density fat fraction (PDFF) as the reference standard and to calculate one US fat index based on the patient’s SSE. Materials and Methods This study received local IRB approval. Written informed consent was obtained from patients. We consecutively included N = 50 patients as the training cohort and a further N = 50 as the validation cohort who underwent both SSE and abdominal MR. Hepatic steatosis was classified according to MR-PDFF cutoffs as: S0 ≤ 6.5 %, S1 6.5 to 16.5 %, S2 16.5 to 22 %, S3 ≥ 22 %. Receiver operating curve analysis was performed to evaluate the diagnostic performance of SSE in the diagnosis of steatosis (S1–S3). Based on the optimal data fit derived from our study, we proposed a correspondence between the MR-PDFF and a US fat index. Coefficient of determination R2 was used to evaluate fit quality and was considered robust when R2 > 0.6. Results The training and validation cohorts presented mean SSE values of 1.570 ± 0.026 and 1.568 ± 0.023 mm/µs for S0 and 1.521 ± 0.031 and 1.514 ± 0.019 mm/µs for S1–S3 (p < 0.01) patients, respectively. An SSE threshold of ≤ 1.537 mm/µs had a sensitivity of 80 % and a specificity of 85.7 % in the diagnosis of steatosis (S1-S3) in the training cohort. Robust correspondence between MR-PDFF and the US fat index was found both for the training (R2 = 0.73) and the validation cohort (R2 = 0.76). Conclusion SSE can be used to detect, quantify and grade liver steatosis and to calculate a US fat index.


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