Supervised Classification of Sound Speed Profiles via Dictionary Learning

Castro-Correa, Jhon A.; Arnett, Stephanie A.; Neilsen, Tracianne B.; Wan, Lin; Badiey, Mohsen

doi:10.1175/jtech-d-21-0090.1

Cited by 5 publications

(3 citation statements)

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“…The parameter λ in (18) controls the trade-off between the Sobolev smoothness term and the MSE loss. Figure 1 visually illustrates the pipeline of GegenGNN applied to a graph representing the sensor network deployed on the New Jersey coast during the Shallow Water experiment 2006 (SW06) [32], [64].…”

Section: Graph Neural Network Architecturementioning

confidence: 99%

Time-Varying Signals Recovery Via Graph Neural Networks

Castro-Correa

Giraldo

Mondal

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques have inherent limitations. To address these challenges, we propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. To this end, we introduce the Gegenbauer-based graph convolutional (GegenConv) operator, which is a generalization of the conventional Chebyshev graph convolution by leveraging the theory of Gegenbauer polynomials. By deviating from traditional convex problems, we expand the complexity of the model and offer a more accurate solution for recovering time-varying graph signals. Building upon GegenConv, we design the Gegenbauerbased time Graph Neural Network (GegenGNN) architecture, which adopts an encoder-decoder structure. Likewise, our approach also utilizes a dedicated loss function that incorporates a mean squared error component alongside Sobolev smoothness regularization. This combination enables GegenGNN to capture both the fidelity to ground truth and the underlying smoothness properties of the signals, enhancing the reconstruction performance. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that GegenGNN outperforms state-of-the-art methods, showcasing its superior capability in recovering time-varying graph signals.

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Section: Graph Neural Network Architecturementioning

confidence: 99%

Time-Varying Signals Recovery Via Graph Neural Networks

Castro-Correa

Giraldo

Mondal

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Furthermore, the temporal-spatial variations in underwater conditions easily make the measured SSP outdated. To avoid these errors from mismatches between the measured sound speed with the real one, the inversion of SSPs has attracted the researchers' attention and become one necessary approach to improve the performance of underwater signal processing [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Yan utilized dictionary learning for the sparse encoding and compressing storage of SSPs [17]. The paper [4] proposed a learned dictionary-based supervised framework for SSP classifications. The paper [5] proposed an interpretable deep-K singular-value decomposition model to analyze the SSP data with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning

Feng,

Tian,

Zhou

et al. 2024

Remote Sensing

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Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

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