Publications by authors named "B T Balamurali"

2 Publications

  • Page 1 of 1

Acoustic Effect of Face Mask Design and Material Choice.

Acoust Aust 2021 Jun 3:1-8. Epub 2021 Jun 3.

Singapore University of Technology and Design, 8 Somapah Rd, Singapore, Singapore.

The widespread adoption of face masks is now a standard public health response to the 2020 pandemic. Although studies have shown that wearing a face mask interferes with speech and intelligibility, relating the acoustic response of the mask to design parameters such as fabric choice, number of layers and mask geometry is not well understood. Using a dummy head mounted with a loudspeaker at its mouth generating a broadband signal, we report the acoustic response associated with 10 different masks (different material/design) and the effect of material layers; a small number of masks were found to be almost acoustically transparent (minimal losses). While different mask material and design result in different frequency responses, we find that material selection has somewhat greater influence on transmission characteristics than mask design or geometry choices.

Supplementary Information: The online version contains supplementary material available at 10.1007/s40857-021-00245-2.
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http://dx.doi.org/10.1007/s40857-021-00245-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172558PMC
June 2021

Hydrodynamic object identification with artificial neural models.

Sci Rep 2019 08 2;9(1):11242. Epub 2019 Aug 2.

Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore.

The lateral-line system that has evolved in many aquatic animals enables them to navigate murky fluid environments, locate and discriminate obstacles. Here, we present a data-driven model that uses artificial neural networks to process flow data originating from a stationary sensor array located away from an obstacle placed in a potential flow. The ability of neural networks to estimate complex underlying relationships between parameters, in the absence of any explicit mathematical description, is first assessed with two basic potential flow problems: single source/sink identification and doublet detection. Subsequently, we address the inverse problem of identifying an obstacle shape from distant measures of the pressure or velocity field. Using the analytical solution to the forward problem, very large training data sets are generated, allowing us to obtain the synaptic weights by means of a gradient-descent based optimization. The resulting neural network exhibits remarkable effectiveness in predicting unknown obstacle shapes, especially at relatively large distances for which classical linear regression models are completely ineffectual. These results have far-reaching implications for the design and development of artificial passive hydrodynamic sensing technology.
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http://dx.doi.org/10.1038/s41598-019-47747-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677828PMC
August 2019
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