volume | PIER Journals

Abstract

An inverse method for parameters estimation of dielectric cylinders (dielectric properties, location, and radius) from amplitude-only microwave information is presented. To this end two different Artificial Neural Networks (ANN) topologies were compared; a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN). Several two-dimensional (2D) simulations, with different sizes and locations of homogeneous dielectric cylinders employing the Finite Differences Time Domain (FDTD) method, were performed to generate training, validation, and test sets for both ANN models. The prediction errors were lower for the CNN in high Signal-to-Noise Ratio (SNR) scenarios, although the MLP was more robust in low SNR situations. The CNN model performance was also tested for 2D simulations of dielectrically homogeneous and heterogeneous cylinders placed in acrylic holders showing potential experimental applications. Moreover, the CNN was also tested for a three-dimensional model simulated as realistic as possible, showing good results in predicting all parameters directly from the S-parameters.

1. Pastorino, M., Microwave Imaging, John Wiley & Sons, 2010.
doi:10.1002/9780470602492

2. Costanzo, S., G. Di Massa, M. Pastorino, and A. Randazzo, "Hybrid microwave approach for phaseless imaging of dielectric targets," IEEE Geoscience and Remote Sensing Letters, Vol. 12, No. 4, 851-854, 2015.
doi:10.1109/LGRS.2014.2364077 Google Scholar

3. Caorsi, S., A. Massa, M. Pastorino, and A. Randazzo, "Electromagnetic detection of dielectric scatterers using phaseless synthetic and real data and the memetic algorithm," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 12, 2745-2753, 2003.
doi:10.1109/TGRS.2003.815676 Google Scholar

4. Li, L., W. Zhang, and F. Li, "Tomographic reconstruction using the distorted Rytov iterative method with phaseless data," IEEE Geoscience and Remote Sensing Letters, Vol. 5, No. 3, 479-483, 2008.
doi:10.1109/LGRS.2008.919818 Google Scholar

5. Li, L., H. Zheng, and F. Li, "Two-dimensional contrast source inversion method with phaseless data: TM case," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 6, 1719-1736, 2008. Google Scholar

6. Bermani, E., S. Caorsi, and M. Raffetto, "Microwave detection and dielectric characterization of cylindrical objects from amplitude-only data by means of neural networks," IEEE Transactions on Antennas and Propagation, Vol. 50, No. 9, 1309-1314, 2002.
doi:10.1109/TAP.2002.801274 Google Scholar

7. Alvarez, Y., M. Garcia-Fernandez, L. Poli, C. Garcıa-Gonzalez, P. Rocca, A. Massa, and F. Las- Heras, "Inverse scattering for monochromatic phaseless measurements," IEEE Transactions on Instrumentation and Measurement, Vol. 66, No. 1, 45-60, 2016.
doi:10.1109/TIM.2016.2615478 Google Scholar

8. Li, L., L. G. Wang, F. L. Teixeira, C. Liu, A. Nehorai, and T. J. Cui, "Deepnis: Deep neural network for nonlinear electromagnetic inverse scattering," IEEE Transactions on Antennas and Propagation, 2018.
doi:10.1109/TAP.2017.2768562 Google Scholar

9. Kamilov, U. S., D. Liu, H. Mansour, and P. T. Boufounos, "A recursive born approach to nonlinear inverse scattering," IEEE Signal Processing Letters, Vol. 23, No. 8, 1052-1056, 2016.
doi:10.1109/LSP.2016.2579647 Google Scholar

10. Goodfellow, I., Y. Bengio, and A. Courville, Deep Learning, MIT press, 2016.

11. Wei, Z. and X. Chen, "Deep-learning schemes for full-wave nonlinear inverse scattering problems," IEEE Transactions on Geoscience and Remote Sensing, 2018. Google Scholar

12. Jin, K. H., M. T. McCann, E. Froustey, and M. Unser, "Deep convolutional neural network for inverse problems in imaging," IEEE Transactions on Image Processing, Vol. 26, No. 9, 4509-4522, 2017.
doi:10.1109/TIP.2017.2713099 Google Scholar

13. Ronneberger, O., P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical Image Computing and Computer-assisted Intervention, 234-241, Springer, 2015. Google Scholar

14. Meaney, P. M., T. Zhou, D. Goodwin, A. Golnabi, E. A. Attardo, and K. D. Paulsen, "Bone dielectric property variation as a function of mineralization at microwave frequencies," Journal of Biomedical Imaging, Vol. 7, 2012. Google Scholar

15. Meaney, P. M., D. Goodwin, A. Golnabi, T. Zhou, M. Pallone, S. Geimer, G. Burke, and K. D. Paulsen, "Clinical microwave tomographic imaging of the calcaneus: A first-in-human case study of two subjects," IEEE transactions on biomedical engineering, Vol. 59, No. 12, 3304-3313, 2012.
doi:10.1109/TBME.2012.2209202 Google Scholar

16. Oskooi, A. F., D. Roundy, M. Ibanescu, P. Bermel, J. D. Joannopoulos, and S. G. Johnson, "MEEP: A flexible free-software package for electromagnetic simulations by the FDTD method," Computer Physics Communications, Vol. 181, 687-702, January 2010.
doi:10.1016/j.cpc.2009.11.008 Google Scholar

17. Harrington, R. F., Time-harmonic Electromagnetic Fields, McGraw-Hill, 1961.

18. Arslanagic, S. and O. Breinbjerg, "Electric-line-source illumination of a circular cylinder of lossless double-negative material: An investigation of near field, directivity, and radiation resistance," IEEE Antennas and Propagation Magazine, Vol. 48, No. 3, 38-54, 2006.
doi:10.1109/MAP.2006.1703397 Google Scholar

19. Attardo, E. A., A. Borsic, G. Vecchi, and P. M. Meaney, "Whole-system electromagnetic modeling for microwave tomography," IEEE Antennas and Wireless Propagation Letters, Vol. 11, 1618-1621, 2012.
doi:10.1109/LAWP.2013.2237745 Google Scholar

20. Chollet, F., et al. "Keras,", https://github.com/fchollet/keras, 2015. Google Scholar

21. Abadi, M., et al. "TensorFlow: Large-scale machine learning on heterogeneous systems,", 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/. Google Scholar

22. Ruder, S., "An overview of gradient descent optimization algorithms,", arXiv preprint arXiv:1609.04747, 2016. Google Scholar

23. Nair, V. and G. R. Hinton, "Rectified linear units improve restricted boltzmann machines," Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807-814, 2010. Google Scholar

24. Kingma, D. P. and J. Ba, "Adam: A method for stochastic optimization,", arXiv preprint arXiv:1412.6980, 2014. Google Scholar

25. Sihvola, A., "Electromagnetic mixing formulas and applications," IET Electromagnetic Waves Series, Vol. 47, 1999. Google Scholar

Dr. Jesús E. Fajardo

Dr. Julián Galván

Dr. Fernando Vericat

Dr. Carlos Manuel Carlevaro

Dr. Ramiro Miguel Irastorza