Effect of sparse basis selection on ultrasonic signal representation

Ultrasonics

Volumn 45, Issue 1-4, 2006, Pages 82-91

  Zhang, Guang Ming ^a   Harvey, David M ^a   Braden, Derek R ^b  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^b Delphi Electronics and Safety   (United Kingdom)

Author keywords

Microelectronic package; Scanning acoustic microcopy; Sparse basis selection; Ultrasonic signal representation

Indexed keywords

ACOUSTIC MICROSCOPES; ALGORITHMS; COMPUTER SIMULATION; MICROELECTRONICS; MICROSCOPIC EXAMINATION; WAVEFORM ANALYSIS;

SCANNING ACOUSTIC MICROSCOPY (SAM); SPARSE BASIS SELECTION; ULTRASONIC SIGNALS;

ULTRASONICS;

ACOUSTIC MICROSCOPY; ALGORITHM; ARTICLE; COMPUTER ASSISTED DIAGNOSIS; ECHOGRAPHY; IMAGE ENHANCEMENT; METHODOLOGY; SIGNAL PROCESSING;

ALGORITHMS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY, ACOUSTIC; SIGNAL PROCESSING, COMPUTER-ASSISTED; ULTRASONOGRAPHY;

EID: 33751309368     PISSN: 0041624X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ultras.2006.07.005     Document Type: Article

References (16)

1. R. Dias, R. Goruganthu, D. Goyal et al., Assembly analytical forum analytical tool roadmap white paper, International SEMATECH, 2005.
2. Zhang G.-M., Harvey D.M., and Braden D.R. Resolution improvement of acoustic micro imaging by continuous wavelet transform for semiconductor inspection. Microelectron. Reliab. 46 5-6 (2006) 811-821
3. Zhang G.-M., Harvey D.M., and Braden D.R. Advanced acoustic micro imaging using sparse signal representation for the evaluation of microelectronic packages. IEEE Trans. Adv. Pack. 29 2 (2006) 271-283
4. Zhang G.-M., Harvey D.M., and Braden D.R. An improved acoustic microimaging technique with learning overcomplete representation. J. Acoust. Soc. Am. 118 6 (2005) 3706-3720
5. Lewicki M.S., and Sejnowski T.J. Learning overcomplete representations. Neural Comput. 12 (2000) 337-365
6. Kreurz-Delgado K., Murray J.F., and Rao B.D. Dictionary learning algorithms for sparse representation. Neural Comput. 15 (2003) 349-396
7. Rao B.D., and Kreurz-Delgado K. An affine scaling methodology for best basis selection. IEEE Trans. Signal Process. 47 1 (1999) 187-200
8. Zhang G.-M., Harvey D.M., and Braden D.R. Adaptive sparse representations of ultrasonic signals for acoustic microimaging. J. Acoust. Soc. Am. 120 2 (2006) 862-869
9. Chen S., Donoho D.L., and Saunders M.A. Atomic decomposition by basis pursuit. SJAM J. Sci. Comput. 20 1 (1998) 33-61
10. Tipping M.E. Sparse Bayesian Learning and the Relevance Vector Machine. J. Mach. Learn. Res. 1 (2001) 211-244
11. Wipf D.P., and Rao B.D. Sparse Bayesian learning for basis selection". IEEE Trans. Signal Process. 52 8 (2004) 2153-2164
12. Tang C.W., Chan Y.C., Hung K.C., and Webb D.P. Nondestructive methodology for standoff height measurement of flip chip on flex (FCOF) by SAM. IEEE Trans. Adv. Pack. 24 2 (2001) 163-168
13. Abbate A., Koay J., Das P., et al. Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE Trans. UFFC 44 1 (1997) 14-25
14. Donoho D.L., and Johnstone I.M. Ideal spatial adaption by wavelet shrinkage. Biometrika 81 (1994) 425-455
15. Donoho D.L., and Johnstone I.M. Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90 (1995) 1200-1224
16. Moulin P., and liu J. Analysis of multiresolution image denoising schemes using general gaussian and complexity priors. IEEE Trans. Inform. Theory 45 3 (1999) 909-919