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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5-7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.An approach of broadband mode separation in shallow water is proposed using phase speed extracted from one hydrophone and solved with sparse Bayesian learning (SBL). The approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, is used to build the dictionary matrix for SBL. Given a multi-frequency pressure vector on one hydrophone, SBL estimates a set of sparse coefficients for a large number of atoms in the dictionary. With the estimated coefficients and corresponding atoms, the separated normal modes are retrieved. The presented method can be used for impulsive or known-form signals in a shallow-water environment while no bottom information is required. The simulation results demonstrate that the proposed approach is adapted to the environment where both the reflected and refracted modes coexist, whereas the performance of the time warping transformation degrades significantly in this scenario.Available data suggests that granulated aerogels can be of interest in terms of their sound absorption performance in the audio frequency range. However, there is still no thorough understanding of the complex physical phenomena which are responsible for their observed acoustical properties. This work is an attempt to address this gap through advanced material characterization methods and mathematical modelling. Aerogel samples are produced through a two-step, acid-base sol-gel process, with sol silica concentration and density being the main variables. Their pore structure is carefully characterized by nitrogen sorption analysis and scanning electron microscopy. The acoustical properties of hard-backed granular silica aerogels are measured in an impedance tube and the results predicted accurately with the adopted theoretical model. Although silica aerogels have over 90% of open interconnected pores, this was neither reflected in the measured acoustical properties nor the parameter values predicted with the model. Novel results show that only a proportion of the micro and mesopores in the direct vicinity of the grain surface influenced the acoustical properties of aerogels. Further work in the hierarchical pore structure of aerogels is required to better understand the roles of different pore scales on the measured acoustical properties of a granulated aerogel.Circular synthetic aperture sonar (CSAS) is a method for improving the resolution and target detection capabilities of a synthetic aperture sonar system. BI-1347 manufacturer CSAS data are difficult to focus because of their large aperture sizes and elevation sensitivity. This difficulty has sometimes been addressed by using transponders or distributing isotropic scatterers in the field of view of the system; however, this comes at the cost of reduced practicality. As an alternative, map-drift based multipoint autofocus (“multilateration”) was proposed by Cantalloube and Nahum [IEEE Trans. Geosci. Remote Sens. 49, 3730-37 (2011)] for autofocusing analogous circular synthetic aperture radar data. Multilateration also solves the problem of aberration spatial variance by providing a three-dimensional navigation correction. In circular synthetic aperture focusing problems, though, correcting aberrations is a joint navigation and elevation estimation problem, and the present work extends the multilateration approach to simultaneously solve both a navigation solution and coordinate corrections for the multilateration control patches. Additionally, methods for addressing the stability and behavior of the inverse problem are addressed, and an adaptive weighting scheme for reducing the influence of outliers is presented. The field results demonstrate near optimal point-spread functions on distributions of natural isotropic scatterers and robustness in regions with bathymetric variability.Glottal resistance plays an important role in airflow conservation, especially in the context of high vocal demands. However, it remains unclear if laryngeal strategies most effective in controlling airflow during phonation are consistent with clinical manifestations of vocal hyperfunction. This study used a previously validated three-dimensional computational model of the vocal folds coupled with a respiratory model to investigate which laryngeal strategies were the best predictors of lung volume termination (LVT) and how these strategies’ effects were modulated by respiratory parameters. Results indicated that the initial glottal angle and vertical thickness of the vocal folds were the best predictors of LVT regardless of subglottal pressure, lung volume initiation, and breath group duration. The effect of vertical thickness on LVT increased with the subglottal pressure-highlighting the importance of monitoring loudness during voice therapy to avoid laryngeal compensation-and decreased with increasing vocal fold stiffness.