Perfect Acoustic-Vortexes Constructed by the Fourier Transform of Quasi-Bessel Beams Based on the Simplified Ring Array of Sectorial Planar Transducers.

Benefiting from the independence of the vortex radius on the topological charge (TC), the perfect acoustic-vortex (PAV) with an angular phase gradient exhibits important perspectives in acoustic applications. However, the practical implementation is still restricted by the limited accuracy and flexibility of the phase control for large-scaled source arrays. An applicable scheme of constructing PAVs by the spatial Fourier transform of quasi-Bessel AV (QB-AV) beams is developed using the simplified ring array of sectorial transducers. The principle of PAV construction is derived based on the phase modulation of the Fourier and saw-tooth lenses. Numerical simulations and experimental measurements are carried out for the ring array with the continuous and discrete phase spirals. The construction of PAVs is demonstrated by the annuli at an almost identical peak pressure with the vortex radius independent of the TC. The vortex radius is proved to increase linearly with the increase of the rear focal length and the radial wavenumber, which are determined by the curvature radii and the acoustic refractive index of the Fourier lens and the bottom angle of the saw-tooth lens, respectively. The improved PAV with a more continuous high-pressure annulus and lower concentric disturbances can be constructed by the ring array of more sectorial sources and the Fourier lens of a bigger radius. The favorable results demonstrate the feasibility of constructing PAVs by the Fourier transform of QB-AV beams and provide an implementable technology in the fields of acoustic manipulation and communication.

Phase-Dislocation-Mediated High-Dimensional Fractional Acoustic-Vortex Communication.

With unlimited topological modes in mathematics, the fractional orbital angular momentum (FOAM) demonstrates the potential to infinitely increase the channel capacity in acoustic-vortex (AV) communications. However, the accuracy and stability of FOAM recognition are still limited by the nonorthogonality and poor anti-interference of fractional AV beams. The popular machine learning, widely used in optics based on large datasets of images, does not work in acoustics because of the huge engineering of the 2-dimensional point-by-point measurement. Here, we report a strategy of phase-dislocation-mediated high-dimensional fractional AV communication based on pair-FOAM multiplexing, circular sparse sampling, and machine learning. The unique phase dislocation corresponding to the topological charge provides important physical guidance to recognize FOAMs and reduce sampling points from theory to practice. A straightforward convolutional neural network considering turbulence and misalignment is further constructed to achieve the stable and accurate communication without involving experimental data. We experimentally present that the 32-point dual-ring sampling can realize the 10-bit information transmission in a limited topological charge scope from ±0.6 to ±2.4 with the FOAM resolution of 0.2, which greatly reduce the divergence in AV communications. The infinitely expanded channel capacity is further verified by the improved FOAM resolution of 0.025. Compared with other milestone works, our strategy reaches 3-fold OAM utilization, 4-fold information level, and 5-fold OAM resolution. Because of the extra advantages of high dimension, high speed, and low divergence, this technology may shed light on the next-generation AV communication.