2013
DOI: 10.1088/1742-6596/457/1/012007
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Data set reduction for ultrasonic TFM imaging using the effective aperture approach and virtual sources

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Cited by 26 publications
(16 citation statements)
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“…The second drawback is the framerate limit due to the number of transmissions (N) and the storage and processing of the N Â N signals. Techniques exist to reduce the number of signals to be processed, like the Sparse Matrix Capture (SMC) that uses a few elements in transmission, compensating the loss of acoustic power by creating virtual sources [12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…The second drawback is the framerate limit due to the number of transmissions (N) and the storage and processing of the N Â N signals. Techniques exist to reduce the number of signals to be processed, like the Sparse Matrix Capture (SMC) that uses a few elements in transmission, compensating the loss of acoustic power by creating virtual sources [12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…and m is the number of elements in the real phased array that constitutes the v-th virtual source. When 7 m  , the synthesized sound field of the real array is closest to the sound field of single virtual source [20]. ( ′ ) is the virtual source signal obtained by adding a delay vs  to the echo…”
Section: Theory a Virtual Source Sparse Tfmmentioning
confidence: 99%
“…In order to improve the efficiency of full-focus imaging, H R Jin introduced a wavenumber algorithm for the multi-layered medium imaging [11]. S Bannouf proposed an algorithm based on point spread function to enhance the efficiency [12]. H w Hu used sparse matrix for two-layer medium to simplify the total focusing imaging algorithm [13].…”
Section: Introductionmentioning
confidence: 99%