Unity transmittance at an interface between bulk media is quite common for polarized electromagnetic waves incident at the Brewster angle, but it is rarely observed for sound waves at any angle of incidence. In the following, we theoretically and experimentally demonstrate an acoustic metamaterial possessing a Brewster-like angle that is completely transparent to sound waves over an ultra-broadband frequency range with >100% bandwidth. The metamaterial, consisting of a hard metal with subwavelength apertures, provides a surface impedance matching mechanism that can be arbitrarily tailored to specific media. The nonresonant nature of the impedance matching effectively decouples the front and back surfaces of the metamaterial allowing one to independently tailor the acoustic impedance at each interface. On the contrary, traditional methods for acoustic impedance matching, for example in medical imaging, rely on resonant tunneling through a thin antireflection layer, which is inherently narrowband and angle specific.
Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.
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