Generation of Sound Bullets with a Nonlinear Acoustic Lens

Acoustic lenses are employed in a variety of applications, from biomedical imaging and surgery, to defense systems, but their performance is limited by their linear operational envelope and complexity. Here we show a dramatic focusing effect and the generation of large amplitude, compact acoustic pulses (sound bullets) in solid and fluid media, enabled by a tunable, highly nonlinear acoustic lens. The lens consists of ordered arrays of granular chains. The amplitude, size and location of the sound bullets can be controlled by varying static pre-compression on the chains. We support our findings with theory, numerical simulations, and corroborate the results experimentally with photoelasticity measurements. Our nonlinear lens makes possible a qualitatively new way of generating high-energy acoustic pulses, enabling, for example, surgical control of acoustic energy.Comment: 19 pages, 7 figures, includes supplementary informatio

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. Specifically, we propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $\ell_1$ norm CS reconstruction model. To cast ISTA into deep network form, we develop an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms, shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than being hand-crafted. Moreover, considering that the residuals of natural images are more compressible, an enhanced version of ISTA-Net in the residual domain, dubbed {ISTA-Net}$^+$, is derived to further improve CS reconstruction. Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform existing state-of-the-art optimization-based and network-based CS methods by large margins, while maintaining fast computational speed. Our source codes are available: \textsl{http://jianzhang.tech/projects/ISTA-Net}.Comment: 10 pages, 6 figures, 4 Tables. To appear in CVPR 201