Automatic soliton wave recognition using deep learning algorithms

In this study, deep learning (DL) based wave classification is performed to automatically recognize the soliton waves. Different experiments using non-linear transmission lines (NLTLs) are performed and the signal images obtained from the experiments are recorded. To demonstrate the applicability of the soliton wave in different scenarios, the waves are generated in different devices, under different noise conditions, and in various environments. Based on the images obtained from the experiments, four different classes consisting of sine, square, triangle, and soliton waves are created. 225 different images belonging to each classes are created and thus a total of 900 different image data are obtained. Five popular DL algorithms, namely DenseNet201, VGG16, VGG19, Xception, and ResNet152, are used to train and test the data. The DenseNet201 algorithm showed the best performance with 0.9904 training accuracy, 0.9630 validation accuracy, and 0.9778 test results. Thus, soliton waves are easily separated from other waveforms such as square, triangle, and sine. These results clearly demonstrate the feasibility of using DL algorithms to automatically recognize the soliton waves, which can have significant implications in various fields such as telecommunications, optics, nonlinear electronics, and nonlinear physics.