2021
DOI: 10.1016/j.apacoust.2020.107711
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An improvement of the generalized discrete Fourier series based patch near-field acoustical holography

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Cited by 11 publications
(5 citation statements)
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“…1) Dirichlet condition: š‘“(š‘„) is segmentally monotone on an interval near š‘„ 0 and the number of discontinuity points on the interval is at most a finite number [21].…”
Section: Generalized Fourier Series Algorithmmentioning
confidence: 99%
“…1) Dirichlet condition: š‘“(š‘„) is segmentally monotone on an interval near š‘„ 0 and the number of discontinuity points on the interval is at most a finite number [21].…”
Section: Generalized Fourier Series Algorithmmentioning
confidence: 99%
“…Near-field acoustical holography (NAH) [1][2][3][4][5] has been proven to be a powerful tool for reconstructing the three-dimensional sound field generated by a vibrating structure, using sound pressures measured by a sensor array in the near-field of the structure surface. Implementations of NAH generally require that the sensor array be placed in a source-free region, because they utilize the free-space Green's functions to relate the radiated sound pressures at different field points.…”
Section: Introductionmentioning
confidence: 99%
“…Hu [23] constructed a sparse basis by ESM and the singular value decomposition (SVD). Additionally, plane wave functions [24], cylindrical wave functions and spherical wave functions [25,26] can also be selected as the sparse spatial basis. As for the sparse regularization, various algorithms have been applied to NAH successfully, such as direct l 1 -norm sparse regularization [27], WBH [20], iterative sparse methods [28], Bayesian compressive sensing [29,30], alternating direction method of multipliers [31], block sparse Bayesian learning [32,33] and so on.…”
Section: Introductionmentioning
confidence: 99%