Search

This work investigates the use of data-driven approaches for reconstructing rough surfaces from scattered sound. The proposed methods stand as alternatives to matrix inversion, which requires a linearisation of the dependence on the surface parameters. Here, a large dataset was formed from different surface realisations alongside the corresponding scattered acoustic field, estimated through the Kirchhoff Approximation. Limiting this work to the reconstruction of a static surface, K-Nearest Neighbours, Random Forests, and a stochastic approach are compared to recover a parameterisation of surfaces using the scattered acoustical pressure as input. The models are then validated against a laboratory experiment alongside methods highlighted in Dolcetti et. al., JSV, 2021. The models are tested at a frequency that best fits the lab uncertainties, then tested on a broad frequency range. This scheme provides relatively accurate results in comparison to the approaches tested. Estimation errors as well as robustness in the presence of noise are discussed.

Recent studies have demonstrated that acoustic waves can be used to reconstruct the roughness profile of a rigid scattering surface. In particular, the use of multiple microphones placed above a rough surface as well as an analytical model based on the linearised Kirchhoff integral equations provides a sufficient base for the inversion algorithm to estimate surface geometrical properties. Prone to fail in the presence of high noise and measurement uncertainties, the analytical approach may not always be suitable in analysing measured scattered acoustic pressure. With the aim to improve the robustness of the surface reconstruction algorithms, here it is proposed to use a data-driven approach through the application of a random forest regression algorithm to reconstruct specific parameters of one-dimensional sinusoidal surfaces from airborne acoustic phase-removed pressure data. The data for the training set are synthetically generated through the application of the Kirchhoff integral in predicting scattered sound, and they are further verified with data produced from laboratory measurements. The surface parameters from the measurement sample were found to be recovered accurately for various receiver combinations and with a wide range of noise levels ranging from 0.1% to 30% of the average scattered acoustical pressure amplitude. ispartof: Journal Of The Acoustical Society Of America vol:152 issue:2 pages:1045-1057 ispartof: location:United States status: Published online

Background: The lateral extensile approach (LEA) is an operative approach for calcaneal fractures. High rates of wound complications have led to alternative approaches such as the sinus tarsi approach to grow in popularity. The LEA affords substantial visualization of the calcaneus. This visualization has never been compared in a quantitative manner with the sinus tarsi approach (STA). We aim to quantify the calcaneal visualization afforded by STA and LEA. Methods: Seven pair-matched, fresh-frozen, below-knee cadaver extremities were included. For each pair, one side received an LEA and the other side received an STA. Results: There were no statistically significant differences in the articular surfaces accessible between the 2 approaches. The total calcaneal surface area accessible was 3107.08 mm2 for LEA and 1444.19 mm2 for STA ( P = .02). The LEA allowed better exposure to the lateral wall ( P = .01) and the dorsal tuberosity of the calcaneus ( P = .04). Conclusion: The STA allows for equivalent articular surface exposure when compared with the LEA. Although LEA allows for greater exposure of the lateral wall and dorsal tuberosity, direct visualization of these structures may not warrant the higher risk of wound complications. Surgeons should consider these differences when choosing an operative approach in the treatment of calcaneal fractures. : Level III