Gradient index phononic crystals and metamaterials

Phononic crystals and acoustic metamaterials are periodic structures whose effective properties can be tailored at will to achieve extreme control on wave propagation. Their refractive index is obtained from the homogenization of the infinite periodic system, but it is possible to locally change the properties of a finite crystal in such a way that it results in an effective gradient of the refractive index. In such case the propagation of waves can be accurately described by means of ray theory, and different refractive devices can be designed in the framework of wave propagation in inhomogeneous media. In this paper we review the different devices that have been studied for the control of both bulk and guided acoustic waves based on graded phononic crystals

Effects of beer, wine, and baijiu consumption on non-alcoholic fatty liver disease: Potential implications of the flavor compounds in the alcoholic beverages

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease and its global incidence is estimated to be 24%. Beer, wine, and Chinese baijiu have been consumed worldwide including by the NAFLD population. A better understanding of the effects of these alcoholic beverages on NAFLD would potentially improve management of patients with NAFLD and reduce the risks for progression to fibrosis, cirrhosis, and hepatocellular carcinoma. There is evidence suggesting some positive effects, such as the antioxidative effects of bioactive flavor compounds in beer, wine, and baijiu. These effects could potentially counteract the oxidative stress caused by the metabolism of ethanol contained in the beverages. In the current review, the aim is to evaluate and discuss the current human-based and laboratory-based study evidence of effects on hepatic lipid metabolism and NAFLD from ingested ethanol, the polyphenols in beer and wine, and the bioactive flavor compounds in baijiu, and their potential mechanism. It is concluded that for the potential beneficial effects of wine and beer on NAFLD, inconsistence and contrasting data exist suggesting the need for further studies. There is insufficient baijiu specific human-based study for the effects on NAFLD. Although laboratory-based studies on baijiu showed the antioxidative effects of the bioactive flavor compounds on the liver, it remains elusive whether the antioxidative effect from the relatively low abundance of the bioactivate compounds could outweigh the oxidative stress and toxic effects from the ethanol component of the beverages

Engineered Diffraction Gratings for Acoustic Cloaking

We show that engineered diffraction gratings can considerably simplify the design of acoustic ground cloaking devices. Acoustic reflecting gratings are designed in such a way that all the incident energy is channeled toward the diffracted mode traveling in the direction opposite the direction of the incident field (retroreflection effect), and this effect is used to cloak an object placed over an acoustically rigid surface. Axisymmetric gratings consisting of rigid surfaces with just one groove per unit cell are used to design thin acoustic carpet cloaks. Finally, full-wave numerical simulations are performed and a conical carpet cloak is experimentally tested, showing an excellent scattering-cancellation effect

Learning Target-oriented Dual Attention for Robust RGB-T Tracking

RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multi-modal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.Comment: Accepted by IEEE ICIP 201

RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets.Comment: 13 pages, 9 figure

Topological States in Twisted Pillared Phononic Plates

In recent years, the advances in topological insulator in the fields of condensed matter have been extended to classical wave systems such as acoustic and elastic waves. However, the quantitative robustness study of topological states which is indispensable in practical realization is rarely reported. In this work, we proposed topologically protected edge states with zigzag, bridge and armchair interfaces in a new twisted phononic plate. The robustness of non-trivial band gap in bulk structure is clearly presented versus twisted angles, revealing a threshold of 5 degrees which is the key fundamental information for the robustness of topological edge states. We further defined a localized displacement ratio as an efficient parameter to characterize edge states. Due to the different orientation of the three interfaces, zigzag and bridge edge states show higher quantitative robustness in their localized displacement ratio. A map of robustness as a function of both frequency and twisted angle highlights the better performance of the topological zigzag edge state. Robustness is evaluated for twisted angle and for all possible types of interfaces for the first time, which benefits for the design and fabrication of solid functional devices with great potential applications

Inverse design of topological metaplates for flexural waves with machine learning

The mechanical analog to the topological insulators brings anomalous elastic wave properties which diversifies classic wave functions for potential broad applications. To obtain topological mechanical wave states with good quality at desired frequency ranges, it needs repetitive trials of different geometric parameters with traditional forward designs. In this work, we develop an inverse design of topological edge states for flexural wave using machine learning method which is promising for instantaneous design. Nonlinear mapping function from input targets to output desired parameters are adopted in artificial neural networks where the data sets for training are generated by the plane wave expansion method. Topological edge states are then realized and compared for different bandgap width conditions with such inverse designs, proving that wide bandgap can promote the confinement of the topological edge states. Finally, direction selective propagations with sharp turns are further demonstrated as anomalous wave behaviors. The machine learning inverse design of topological states for flexural wave provides an efficient way to design practical devices with targeted needs for potential applications such as signal processing, sensing and energy harvesting

Pillar-type acoustic metasurface

International audienceWe theoretically investigate acoustic metasurfaces consisting of either a single pillar or a line of identical pillars on a thin plate, and we report on the dependence on the geometrical parameters of both the monopolar compressional and dipolar bending modes. We show that for specific dimensions of the resonators, a bending and a compressional modes may be simultaneously excited. We study their interaction with an anti-symmetric Lamb wave, whether or not they occur at the same frequency, with particular consideration for the amplitude and phase of waves emitted by the pillars at resonance. Especially, the analysis of both the amplitude and the phase of the wave at the common resonant frequency downstream a line of pillars, demonstrates that the reemitted waves allow for the transmission with phase shift of π