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• W2905675454 abstract "In this paper, an innovative Compressed Sensing (CS) framework has been proposed with the purpose of providing a sub-Nyquist sampling mechanism to Electromagnetic Acoustic Transducer (EMAT) testing. Particularly, a novel sparse representation method based on dictionary learning was introduced into the CS framework aimed at a more efficient signal sparse representation and more accurate signal reconstruction. The proposed CS framework was examined through a process of thickness measurement of an 813-X70 pipeline. Experimental results indicated that the thickness feature of the specimen could be precisely extracted from the recovered data with 20% of compression ratio compared with conventional sampling method. It demonstrates that the CS theorem can significantly reduce the required sampling rate of EMAT testing system without loss of signal resolution, thus improves the overall testing efficiency." @default.
• W2905675454 created "2019-01-01" @default.
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• W2905675454 date "2017-06-01" @default.
• W2905675454 modified "2023-09-23" @default.
• W2905675454 title "Application of Compressed Sensing on Eletromagetic Transducer Testing" @default.
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• W2905675454 doi "https://doi.org/10.1109/fendt.2017.8584512" @default.
• W2905675454 hasPublicationYear "2017" @default.
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