Underwater acoustic metamaterials.

Acoustic metamaterials have been widely investigated over the past few decades and have realized acoustic parameters that are not achievable using conventional materials. After demonstrating that locally resonant acoustic metamaterials are capable of acting as subwavelength unit cells, researchers have evaluated the possibility of breaking the classical limitations of the material mass density and bulk modulus. Combined with theoretical analysis, additive manufacturing and engineering applications, acoustic metamaterials have demonstrated extraordinary capabilities, including negative refraction, cloaking, beam formation and super-resolution imaging.

Bioinspired metagel with broadband tunable impedance matching.

To maximize energy transmission from a source through a media, the concept of impedance matching has been established in electrical, acoustic, and optical engineering. However, existing design of acoustic impedance matching, which extends exactly by a quarter wavelength, sets a fundamental limit of narrowband transmission. Here, we report a previously unknown class of bioinspired metagel impedance transformers to overcome this limit.

Physical modeling and validation of porpoises' directional emission via hybrid metamaterials.

In wave physics and engineering, directional emission sets a fundamental limitation on conventional simple sources as their sizes should be sufficiently larger than their wavelength. Artificial metamaterial and animal biosonar both show potential in overcoming this limitation. Existing metamaterials arranged in periodic microstructures face great challenges in realizing complex and multiphase biosonar structures.

Sample training based wildfire segmentation by 2D histogram θ-division with minimum error.

A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram θ-division and minimum error. Based on minimum error principle and 2D color histogram, the θ-division methods were presented recently, but application of prior knowledge on them has not been explored. For the specific problem of wildfire segmentation, we collect sample images with manually labeled fire pixels.