Open Access
| Issue |
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|
|
|---|---|---|
| Article Number | 01039 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/e3sconf/202235101039 | |
| Published online | 24 May 2022 | |
- S. K. Pedram, S. Fateri, L. Gan, A. Haig, and K. Thornicroft, “Split-spectrum processing technique for SNR enhancement of ultrasonic guided wave,” Ultrasonics, 83, pp. 48–59, (2018) [Google Scholar]
- P. M. Shankar, V. L. Newhouse, P. Karpur, and J. L. Rose, “Split-Spectrum Processing: Analysis of Polarity Thresholding Algorithm for Improvement of Signal-to-Noise Ratio and Detectability in Ultrasonic Signals,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 36, pp. 101–108, (1989) [CrossRef] [PubMed] [Google Scholar]
- E. Pardo, J. L. San Emeterio, M. A. Rodriguez, and A. Ramos, “Noise reduction in ultrasonic NDT using undecimated wavelet transforms,” Ultrasonics, 44, pp. 1063–1067, (2006) [Google Scholar]
- A. Abbate, J. Koay, J. Frankel, S. C. Schroeder, and P. Das, “Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 44, pp. 14–26, (1997) [CrossRef] [PubMed] [Google Scholar]
- R. Vicen, R. Gil, P. Jarabo, M. Rosa, F. Lopez, and D. Martinez, “Non-linear filtering of ultrasonic signals using neural networks,” Ultrasonics, 42, pp. 355–360, (2004) [Google Scholar]
- A. Chapon, D. Pereira, M. Toews, and P. Belanger, “Deconvolution of ultrasonic signals using a convolutional neural network,” Ultrasonics, 111, p. 106312, (2021) [Google Scholar]
- F. Gao, B. Li, L. Chen, X. Wei, Z. Shang, and C. He, “Ultrasonic signal denoising based on autoencoder,” Review of Scientific Instruments, 91, (2020) [Google Scholar]
- W. Xu, X. Li, J. Zhang, Z. Xue, and J. Cao, “Ultrasonic signal enhancement for coarse grain materials by machine learning analysis,” Ultrasonics, 117, p. 106550, (2021) [Google Scholar]
- Y. E. Wang, G.-Y. Wei, and D. Brooks, “Benchmarking TPU, GPU, and CPU Platforms for Deep Learning,” (July 2019) [Google Scholar]
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, 15, pp. 1929–1958, (January 2014) [Google Scholar]
- D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–15, (2015) [Google Scholar]
- S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Transactions on Image Processing, 9, pp. 1532–1546, (2000) [CrossRef] [PubMed] [Google Scholar]
- D. L. Donoho and J. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, 81, pp. 425–455, (1994) [CrossRef] [Google Scholar]
- S. Panigrahi, A. Nanda, and T. Swarnkar, “A Survey on Transfer Learning,” Smart Innovation, Systems and Technologies, 194, pp. 781–789, (2021) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.