Lateral Drop Rebound on a Hydrophobic and Chemically Heterogeneous Surface.

In this paper, the process of a drop rebounding from a hydrophobic and chemically heterogeneous surface is investigated using the multiphase lattice Boltzmann method. The bounce behavior of drops is dependent on the degree of hydrophobicity and heterogeneity of the surface. When the surface is homogeneous, the drop rebounds vertically and the height increases with the enhancement of the surface hydrophobicity. For the heterogeneous surface with two different hydrophobic parts, the drop rebounds laterally toward the lower hydrophobic side. Because the high hydrophobic side exerts the stronger unbalanced Young's force on the contact line compared with the low hydrophobic side, the difference of the forces results in the asymmetrical rebound. A phase diagram displays the rebound numbers of a drop impacting on the various chemically homogeneous and heterogeneous surfaces. A simply quantitative estimation is made to predict the rebound heights and flying times through the contact angles of the surface. This work promotes the understanding of the rebound mechanism of a drop impacting on a chemically heterogeneous surface and provides a guiding strategy for the precise control of the lateral behavior of rebounding drops by hydrophobic and heterogeneous surfaces.

A Fourier-Based Image Formation Algorithm for Geo-Stationary GNSS-Based Bistatic Forward-Looking Synthetic Aperture Radar.

A Geo-Stationary GNSS-based Bistatic Forward-Looking Synthetic Aperture Radar (GeoSta-GNSS-BFLSAR) system is a particular kind of passive bistatic SAR system. In this system, a geo-stationary GNSS is used as the transmitter, while the receiver is deployed on a moving aircraft, which travels towards a target in a straight line. It is expected that such a radar system has potential for self-landing, self-navigation and battlefield information acquisition applications, etc. Up to now, little information from a research perspective can be found about GeoSta-GNSS-BFLSAR systems. To address this information gap, this paper proposes a preliminary image formation algorithm for GeoSta-GNSS-BFLSAR. The full details of the mathematical derivation are given. It is highlighted that, to overcome the long dwell time and spatial variance of GeoSta-GNSS-BFLSAR, a modified migration correction factor must be designed. In addition, the system performances and technical limitations of GeoSta-GNSS-BFLSAR such as focusing depth and spatial resolution are analytically discussed. In the end, a set of simulations including the image formation algorithm, focusing depth and spatial resolution were conducted for verification. It is demonstrated that the focusing performances of the proposed algorithm have a high level of similarity with the theoretical counterparts. This article thus proves the feasibility of GeoSta-GNSS-BFLSAR systems from a simulation level and establishes a foundation for the real applications of such a radar scheme in the future.

A Fast Image Deformity Correction Algorithm for Underwater Turbulent Image Distortion.

An algorithm correcting distortion based on estimating the pixel shift is proposed for the degradation caused by underwater turbulence. The distorted image is restored and reconstructed by reference frame selection and two-dimensional pixel registration. A support vector machine-based kernel correlation filtering algorithm is proposed and applied to improve the speed and efficiency of the correction algorithm. In order to validate the algorithm, laboratory experiments on a controlled simulation system of turbulent water and field experiments in rivers and oceans are carried out, and the experimental results are compared with traditional, theoretical model-based and particle image velocimetry-based restoration and reconstruction algorithms. Using subjective visual evaluation, image distortion has been effectively suppressed; based on an objective performance statistical analysis, the measured values are better than the traditional and formerly studied restoration and reconstruction algorithms. The method proposed in this paper is also much faster than the other algorithms. It can be concluded that the proposed algorithm can effectively improve the de-distortion effect of the underwater turbulence degraded image, and provide potential techniques for the accurate operation of underwater target detection in real time.

Theoretical model of dynamics and stability of nanobubbles on heterogeneous surfaces.

Surface nanobubbles have revealed a new mechanism of gas-liquid-solid interaction at the nanoscale; however, the nanobubble evolution on real substrates is still veiled, because the experimental observation of contact line motions at the nanoscale is too difficult. HYPOTHESIS: This study proposes a theoretical model to describe the dynamics and stability of nanobubbles on heterogeneous substrates. It simultaneously considers the diffusive equilibrium of the liquid-gas interface and the mechanical equilibrium at the contact line, and introduces a surface energy function to express the substrate's heterogeneity. VALIDATION: The present model unifies the nanoscale stability and the microscale instability of surface bubbles. The theoretical predictions are highly consistent to the nanobubble morphology on heterogeneous surfaces observed in experiments. As the nanobubbles grow, a lower Laplace pressure leads to weaker gas adsorption, and the mechanical equilibrium can eventually revert to the classical Young-Laplace equation above microscale. FINDINGS: The analysis results indicate that both the decrease in substrate surface energy and the increase in gas oversaturation are more conducive to the nucleation and growth of surface nanobubbles, leading to larger stable sizes. The larger surface energy barriers result in the stronger pinning, which is beneficial for achieving stability of the pinned bubbles. The present model is able to reproduce the continual behaviors of the three-phase contact line during the nanobubble evolution, e.g., "pinning, depinning, slipping and jumping" induced by the nanoscale defects.

Learning parallel and hierarchical mechanisms for edge detection.

Edge detection is one of the fundamental components of advanced computer vision tasks, and it is essential to preserve computational resources while ensuring a certain level of performance. In this paper, we propose a lightweight edge detection network called the Parallel and Hierarchical Network (PHNet), which draws inspiration from the parallel processing and hierarchical processing mechanisms of visual information in the visual cortex neurons and is implemented via a convolutional neural network (CNN). Specifically, we designed an encoding network with parallel and hierarchical processing based on the visual information transmission pathway of the "retina-LGN-V1" and meticulously modeled the receptive fields of the cells involved in the pathway. Empirical evaluation demonstrates that, despite a minimal parameter count of only 0.2 M, the proposed model achieves a remarkable ODS score of 0.781 on the BSDS500 dataset and ODS score of 0.863 on the MBDD dataset. These results underscore the efficacy of the proposed network in attaining superior edge detection performance at a low computational cost. Moreover, we believe that this study, which combines computational vision and biological vision, can provide new insights into edge detection model research.

Edge detection networks inspired by neural mechanisms of selective attention in biological visual cortex.

Edge detection is of great importance to the middle and high-level vision task in computer vision, and it is useful to improve its performance. This paper is different from previous edge detection methods designed only for decoding networks. We propose a new edge detection network composed of modulation coding network and decoding network. Among them, modulation coding network is the combination of modulation enhancement network and coding network designed by using the self-attention mechanism in Transformer, which is inspired by the selective attention mechanism of V1, V2, and V4 in biological vision. The modulation enhancement network effectively enhances the feature extraction ability of the encoding network, realizes the selective extraction of the global features of the input image, and improves the performance of the entire model. In addition, we designed a new decoding network based on the function of integrating feature information in the IT layer of the biological vision system. Unlike previous decoding networks, it combines top-down decoding and bottom-up decoding, uses down-sampling decoding to extract more features, and then achieves better performance by fusing up-sampling decoding features. We evaluated the proposed method experimentally on multiple publicly available datasets BSDS500, NYUD-V2, and barcelona images for perceptual edge detection (BIPED). Among them, the best performance is achieved on the NYUD and BIPED datasets, and the second result is achieved on the BSDS500. Experimental results show that this method is highly competitive among all methods.