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Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied(More)
This paper presents a method of determining the compressional wave attenuation in marine sediment from a short range measurement. The data were collected on a vertical line array at a range of 230 m during the Shallow Water 2006 experiments. The sediment attenuation is extracted from the signal strength ratio of the sea bottom reflection to a sub-bottom(More)
Localizing a source of radial movement at moderate range using a single hydrophone can be achieved in the reliable acoustic path by tracking the time delays between the direct and surface-reflected arrivals (D-SR time delays). The problem is defined as a joint estimation of the depth, initial range, and speed of the source, which are the state parameters(More)
In Bayesian inversion, the solution is characterized by its posterior probability density (PPD). A fast Gibbs sampler (FGS) has been developed to estimate the multi-dimensional integrals of the PPD, which requires solving the forward models many times and leads to intensive computation for multi-frequency or range-dependent inversion cases. This paper(More)
Passive localization of a sound source in the deep ocean is investigated in this study. The source can be localized by taking advantage of a cross-correlation function matching technique. When a two-sensor vertical array is used in the deep ocean, two types of side lobe curves appear in the ambiguity surface of the localization. The side lobe curves are(More)
Two decades ago, it was shown that ambient noise exhibits low dimensional chaotic behavior. Recent new techniques in nonlinear science can effectively detect the underlying dynamics in noisy time series. In this paper, the presence of low dimensional deterministic dynamics in ambient noise is investigated using diverse nonlinear techniques, including(More)
There are numerous studies showing that there is a constant increase in the ocean ambient noise level and the ever-growing demand for developing algorithms for detecting weak signals in ambient noise. In this study, we utilize dynamical and statistical complexity to detect the presence of weak ship noise embedded in ambient noise. The ambient noise and ship(More)
Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from(More)
The arrival signals of the horizontal line array (HLA) in the waveguide are not plane waves. The direction of maximum array output will not be the exact direction of the source. The acoustic pressure field can be expressed in terms of a normal mode expansion. The output of the HLA for a distance source in the waveguide is then constructed by summing up(More)