This work introduces the source localization application using a phononic crystal (PC) array. The PC band structure and the eigen-modes are analyzed and utilized for detecting the angle of arrival. The eigen-modes, as the basis functions of the scattering wave, possess strong angle-dependent features, naturally suitable for developing source localization algorithms. An artificial neural network is trained with randomly weighted eigen-modes to achieve deep learning of the modal features and angle dependence. The trained neural network can then accurately identify the incident angle of an unknown scattering signal, with minimal side lobe levels and suppressed main lobe width.
An exceptional point (EP) is a branch singularity where eigen-modes coalesce. Using a discrete metamaterial model, this work studies the eigenfrequency band structure and the scattering response in the vicinity of an EP. Specific phenomena associated with EPs in the eigenfrequency band structure, including level repulsion, mode switching, and self-orthogonality are presented. The effects of reciprocity and fundamental symmetries are addressed in the 1D scattering analysis. By enabling complex stiffness in frequency domain, a MM specimen may be tuned to become completely undetectable from both directions at the EP frequency, thus having potential for novel wave filtering and cloaking applications.
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