1. Tegowski, J., Nowak, J., Moskalik, M., & Szefler, K. (2011, June). Seabed classification from multibeam echosounder backscatter data using wavelet transformation and neural network approach. In Proceedings of 4th International Conference and Exhibition on Underwater Acoustic Measurements: Technologies & Results, 20th to 24th June (pp. 1257-1264).
2. Javidan, R., & Eghbali, H. J. (2004, September). Designing and Improving a Receiver of an Active Sonar System Using Fuzzy Logic. In 5th Iranian Conference on Fuzzy Systems (pp. 7-9).
3. Bayesian data fusion of multiview synthetic aperture sonar imagery for seabed classification;Williams;IEEE Transactions on Image Processing,2009
4. Petillot, Y., Reed, S., Coiras, E., & Bell, J. (2006). A framework for evaluating underwater mine detection and classification algorithms using augmented reality. Proc. Undersea Defence Technology.
5. Van Komen, D., Neilsen, T. B., Knobles, D. P., & Badiey, M. (2019, May). A convolutional neural network for source range and ocean seabed classification using pressure time-series. In Proceedings of Meetings on Acoustics 177ASA (Vol. 36, No. 1, p. 070004). Acoustical Society of America.