{"id":1216,"date":"2025-06-03T02:39:51","date_gmt":"2025-06-03T02:39:51","guid":{"rendered":"https:\/\/smartdata.ece.ufl.edu\/?p=1216"},"modified":"2026-04-07T13:09:38","modified_gmt":"2026-04-07T13:09:38","slug":"temporal-sparse-wavenumber-analysis-repository","status":"publish","type":"post","link":"https:\/\/smartdata.ece.ufl.edu\/index.php\/2025\/06\/03\/temporal-sparse-wavenumber-analysis-repository\/","title":{"rendered":"Temporal Sparse Wavenumber Analysis Repository"},"content":{"rendered":"\n
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Get the Code (Code Ocean)<\/a><\/div>\n<\/div>\n\n\n\n

Summary<\/h2>\n\n\n\n

Temporal Sparse Wavenumber Analysis (TSWA)<\/strong> is a novel technique that reconstructs high-resolution spatiotemporal wavefields using fewer temporal samples<\/strong> than traditional methods typically require. Developed by Soroosh Sabeti<\/strong> and Dr. Joel B. Harley<\/strong>, TSWA makes it possible to retrieve accurate guided wave information from temporally undersampled data, which is crucial in applications where high-speed sensing<\/strong>, data storage<\/strong>, or power consumption<\/strong> are limiting factors.<\/p>\n\n\n\n

This CodeOcean capsule<\/a> contains a full implementation of TSWA, as described in:<\/p>\n\n\n\n

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Sabeti, S., & Harley, J. B. (2017).<\/strong> Guided wave retrieval from temporally undersampled data<\/em>. 2017 IEEE International Ultrasonics Symposium (IUS), 1\u20134. https:\/\/doi.org\/10.1109\/ULTSYM.2017.8091996<\/a><\/p>\n<\/blockquote>\n\n\n\n

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? Why TSWA?<\/h3>\n\n\n\n

Conventional methods of wavefield reconstruction require Nyquist-rate temporal sampling<\/strong>, leading to high data acquisition and storage burdens. In practice, this restricts sensing systems on resource-constrained platforms<\/strong> like embedded SHM systems or UAVs. TSWA overcomes this by using compressive sensing<\/strong> to reconstruct wavefields from fewer temporal samples<\/strong>, enabling efficient, high-resolution guided wave analysis<\/strong> without compromising signal integrity.<\/p>\n\n\n\n

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? How It Works<\/h3>\n\n\n\n

The core idea of TSWA is to exploit the sparsity<\/strong> of a wavefield in the wavenumber domain<\/strong> and recover full-resolution temporal signals from sub-sampled data using sparse optimization.<\/p>\n\n\n\n

Let:<\/p>\n\n\n\n