Open Access
Issue |
Acta Acust.
Volume 9, 2025
|
|
---|---|---|
Article Number | 15 | |
Number of page(s) | 12 | |
Section | Ultrasonics | |
DOI | https://doi.org/10.1051/aacus/2025002 | |
Published online | 20 February 2025 |
- N.M. Kalimullah, A. Shelke, A. Habib: A deep learning approach for anomaly identification in PZT sensors using point contact method. Smart Materials and Structures 32, 9 (2023) 095027. [CrossRef] [Google Scholar]
- N.M. Kalimullah, K. Shukla, A. Shelke, A. Habib: Stiffness tensor estimation of anisotropic crystal using point contact method and unscented Kalman filter. Ultrasonics 131 (2023) 106939. [CrossRef] [PubMed] [Google Scholar]
- M. Pluta, M. von Buttlar, A. Habib, E. Twerdowski, R. Wannemacher, W. Grill: Modeling of Coulomb coupling and acoustic wave propagation in LiNbO3. Ultrasonics 48, 6, 7 (2008) 583–586. [CrossRef] [PubMed] [Google Scholar]
- V. Agarwal, A. Shelke, B.S. Ahluwalia, F. Melandsø, T. Kundu, A. Habib: Damage localization in piezo-ceramic using ultrasonic waves excited by dual point contact excitation and detection scheme. Ultrasonics 108 (2020) 106113. [CrossRef] [PubMed] [Google Scholar]
- A. Habib, A. Shelke, M. Pluta, U. Pietsch, T. Kundu, W. Grill: Scattering and attenuation of surface acoustic waves and surface skimming longitudinal polarized bulk waves imaged by Coulomb coupling, in: AIP Conference Proceedings. American Institute of Physics, 2012. [Google Scholar]
- A. Habib, E. Twerdowski, M. von Buttlar, M. Pluta, M. Schmachtl, R. Wannemacher, W. Grill: Acoustic holography of piezoelectric materials by Coulomb excitation, in: Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems V. SPIE, 2006. [Google Scholar]
- H. Singh, A.S. Ahmed, F. Melandsø, A. Habib: Ultrasonic image denoising using machine learning in point contact excitation and detection method. Ultrasonics 127 (2023) 106834. [CrossRef] [PubMed] [Google Scholar]
- S. Jadhav, R. Kuchibhotla, K. Agarwal, A. Habib, D.K. Prasad: Deep learning-based denoising of acoustic images generated with point contact method. Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems 6, 3 (2023) 031002. [CrossRef] [Google Scholar]
- P. Banerjee, P. Saxena, N.M. Kalimullah, A. Shelke, A. Habib: Damage detection and localization by learning deep features of elastic waves in piezoelectric ceramic using point contact method, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024. [Google Scholar]
- Y. Shen, J. Liang, M.C. Lin: Gan-based garment generation using sewing pattern images, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16. Springer, 2020. [Google Scholar]
- F. Garcea, A. Serra, F. Lamberti, L. Morra: Data augmentation for medical imaging: a systematic literature review. Computers in Biology and Medicine 152 (2023) 106391. [CrossRef] [PubMed] [Google Scholar]
- P. Banerjee, et al.: Data set used in this manuscript, in: banerjeepragyan/DataAugmentation. GitHub, 2024. https://github.com/banerjeepragyan/DataAugmentation. [Google Scholar]
- S.J.N. Anita, C.J. Moses: Survey on pixel level image fusion techniques, in: 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN). IEEE, 2013. [Google Scholar]
- S. Liu, J. Zhang, Y. Chen, Y. Liu, Z. Qin, T. Wan: Pixel level data augmentation for semantic image segmentation using generative adversarial networks, in: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. [Google Scholar]
- A. Laugros, A. Caplier, M. Ospici: Addressing neural network robustness with mixup and targeted labeling adversarial training, in: Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16. Springer, 2020. [Google Scholar]
- C. Shorten, T.M. Khoshgoftaar: A survey on image data augmentation for deep learning. Journal of Big Data 6, 1 (2019) 1–48. [CrossRef] [Google Scholar]
- K. Wang, C. Gou, Y. Duan, Y. Lin, X. Zheng, F.Y. Wang: Generative adversarial networks: introduction and outlook. IEEE/CAA Journal of Automatica Sinica 4, 4 (2017) 588–598. [CrossRef] [Google Scholar]
- M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, H. Greenspan: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321 (2018) 321–331. [CrossRef] [Google Scholar]
- A. Radford, L. Metz, S. Chintala: Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint https://arxiv.org/abs/1511.06434, 2015. [Google Scholar]
- E.L. Denton, S. Chintala, R. Fergus: Deep generative image models using aOBJ Laplacian pyramid of adversarial networks. Advances in Neural Information Processing Systems (2015) 28. [Google Scholar]
- M. Mirza, S. Osindero: Conditional generative adversarial nets. Preprint https://arxiv.org/abs/1411.1784, 2014. [Google Scholar]
- A. Odena, C. Olah, J. Shlens: Conditional image synthesis with auxiliary classifier gans, in: International Conference on Machine Learning. PMLR, 2017. [Google Scholar]
- P. Isola, J.Y. Zhu, T. Zhou, A.A. Efros: Image-to-image translation with conditional adversarial networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. [Google Scholar]
- A. Antoniou, A. Storkey, H. Edwards: Data augmentation generative adversarial networks. Preprint https://arxiv.org/abs/1711.04340, 2017. [Google Scholar]
- V. Sandfort, K. Yan, P.J. Pickhardt and R.M. Summers: Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Scientific Reports 9, 1 (2019) 16884. [CrossRef] [PubMed] [Google Scholar]
- O. Ronneberger, P. Fischer, T. Brox: U-net: convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer, 2015. [Google Scholar]
- E. Wu, K. Wu, D. Cox, W. Lotter: Conditional infilling GANs for data augmentation in mammogram classification, in: Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI. Springer, Granada, Spain, 2018. [Google Scholar]
- G. Cohen, S. Afshar, J. Tapson, A. Van Schaik: EMNIST: extending MNIST to handwritten letters, in: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. [Google Scholar]
- Z. Qawaqneh, A.A. Mallouh, B.D. Barkana: Deep convolutional neural network for age estimation based on VGG-face model. Preprint https://arxiv.org/abs/1709.01664, 2017. [Google Scholar]
- S. Fabi, S. Otte, F. Scholz, J. Wührer, M. Karlbauer, M.V. Butz: Extending the omniglot challenge: imitating handwriting styles on a new sequential data set. IEEE Transactions on Cognitive and Developmental Systems 15, 2 (2022) 896–903. [Google Scholar]
- T. Yao, C. Qu, Q. Liu, R. Deng, Y. Tian, J. Xu, A. Jha, S. Bao, M. Zhao, A. Fogo: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. Springer, Cham, Switzerland, 2021. [Google Scholar]
- A. Shelke, A. Habib, U. Amjad, M. Pluta, Tribikram Kundu, U. Pietsch, W. Grill: Metamorphosis of bulk waves to Lamb waves in anisotropic piezoelectric crystals, in: Health Monitoring of Structural and Biological Systems 2011. SPIE, 2011. [Google Scholar]
- A. Habib, U. Amjad, M. Pluta, U. Pietsch, W. Grill: Surface acoustic wave generation and detection by Coulomb excitation, in: Health Monitoring of Structural and Biological Systems 2010. SPIE, 2010. [Google Scholar]
- N.M. Kalimullah, A. Shelke, A. Habib: Multiresolution dynamic mode decomposition (mrDMD) of elastic waves for damage localisation in piezoelectric ceramic. IEEE Access 9 (2021) 120512–120524. [CrossRef] [Google Scholar]
- A. Habib, E. Twerdowski, M. von Buttlar, R. Wannemacher, W. Grill: The influence of the radius of the electrodes employed in Coulomb excitation of acoustic waves in piezoelectric materials, in: Health Monitoring of Structural and Biological Systems 2007. SPIE, 2007. [Google Scholar]
- E. Jacobsen: Sources of sound in piezoelectric crystals. The Journal of the Acoustical Society of America 32, 8 (1960) 949–953. [CrossRef] [Google Scholar]
- R. Pal, N. Ghosh, N.M. Kalimullah, A. Ahmad, F. Melandsø, A. Habib: Subsurface damage identification and localization in PZT ceramics using point contact excitation and detection: an image processing framework. Ultrasonics 147 (2024) 107516. [Google Scholar]
- T. Miyato, T. Kataoka, M. Koyama, Y. Yoshida: Spectral normalization for generative adversarial networks. Preprint https://arxiv.org/abs/1802.05957, 2018. [Google Scholar]
- C. Dewi, R.C. Chen, Y.T. Liu, S.K. Tai: Synthetic Data generation using DCGAN for improved traffic sign recognition. Neural Computing and Applications 34, 24 (2022) 21465–21480. [CrossRef] [Google Scholar]
- T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, T. Aila: Analyzing and improving the image quality of stylegan, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. [Google Scholar]
- V.M. Panaretos, Y. Zemel: Statistical aspects of Wasserstein distances. Annual Review of Statistics and its Application 6, 1 (2019) 405–431. [CrossRef] [Google Scholar]
- M.L. Menéndez, J.A. Pardo, L. Pardo, M.C. Pardo: The Jensen–Shannon divergence. Journal of the Franklin Institute. 334, 2 (1997) 307–318. [CrossRef] [MathSciNet] [Google Scholar]
- H.-Y. Lee, H.Y. Tseng, J.B. Huang, M. Singh, M.H. Yang: Diverse image-to-image translation via disentangled representations, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018. [Google Scholar]
- Y. Freund, R.E. Schapire: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 1 (1997) 119–139. [Google Scholar]
- M. Arjovsky, L. Bottou: Towards principled methods for training generative adversarial networks. Preprint https://arxiv.org/abs/1701.04862, 2017. [Google Scholar]
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