Hybrid Metasurfaces for Perfect Transmission and Customized Manipulation of Sound Across Water-Air Interface.

Extreme impedance mismatch causes sound insulation at water-air interfaces, limiting numerous cross-media applications such as ocean-air wireless acoustic communication. Although quarter-wave impedance transformers can improve transmission, they are not readily available for acoustics and are restricted by the fixed phase shift at full transmission. Here, this limitation is broken through impedance-matched hybrid metasurfaces assisted by topology optimization. Sound transmission enhancement and phase modulation across the water-air interface are achieved independently. Compared to the bare water-air interface, it is experimentally observed that the average transmitted amplitude through an impedance-matched metasurface at the peak frequency is enhanced by ≈25.9 dB, close to the limit of the perfect transmission 30 dB. And nearly 42 dB amplitude enhancement is measured by the hybrid metasurfaces with axial focusing function. Various customized vortex beams are experimentally realized to promote applications in ocean-air communication. The physical mechanisms of sound transmission enhancement for broadband and wide-angle incidences are revealed. The proposed concept has potential applications in efficient transmission and free communication across dissimilar media.

Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete.

Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water-cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC's strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models' decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

Machine-learning-assisted inverse design of scattering enhanced metasurface.

The scattering enhancement technique has shown prominent potential in various regimes such as satellite communication, Radar Cross Section (RCS) camouflage, and remote sensing. Currently, the scattering enhancement devices based on the metasurface have shown advantages in light weight and better performance. These metasurfaces always possess complex structure, it is hard to achieve through the tradition trial-and-error method which relies on the full-wave numerical simulation. In this paper, a new method combining the machine learning and the evolution optimization algorithm is proposed to design the metasurface retroreflector (MRF) for arbitrary direction incident wave. In this method, a predicting model and a generative inverse design model are constructed and trained, the predicting model is used to evaluate the fitness of each offspring in the genetic algorithm (GA), the generative model is used to initialize the first offspring of the GA by inverse generate the MRF based on the requirements of the designer. With the assistance of these two machine learning models, the evolution optimization algorithm is employed to find the optimal design of the MRF. This approach enables automatic solution of electromagnetic inverse design problems and opens the way to facilitate the optimization of other metadevices.

Mid-wave infrared polarization imaging system for detecting moving scene.

In this Letter, a polarimetric analyzer is designed for a mid-wave infrared camera. This kind of infrared camera transforms into the mid-wave infrared polarization imaging system to measure the infrared polarization characteristics of the object in the moving scene. The polarimetric analyzer is designed by using the ultra-high-speed and high-position method to drive the polarizer to rotate uniformly at the speed of 900 rpm. The polarization state of the object scene is changed, and the mid-wave infrared camera synchronously acquires the infrared intensity image in different polarized directions, those of 0°, 120°, and 240°. Then, a Stokes vector model is established with the basic rotation angles, and a sort-iteration method is proposed to process the original infrared intensity image. Three continuously neighboring infrared intensity images are used to calculate the degree of linear polarization (DoLP) and the angle of polarization (AoP), which make the infrared polarization image the same imaging frame as the infrared intensity image. Test results show that the mid-wave infrared polarization imaging system can complete the acquisition of the DoLP and the AoP images well with the frame frequency of 45 fps, which is suitable for the infrared polarization detection of the moving scenes. The study has great potential for polarization remote sensing and marine object detection.