Gas bubbles are a powerful tool with applications in particle visualization, spacers, actuation pistons, and pressure sensors. Controlling the transportation of bubbles in the liquid phase is a challenge that needs to be solved in many industrial processes, such as in the pipe transportation of fluids, the corrosion of ocean vessels, and the control of foaming processes. There are few existing materials capable of the antibuoyancy unidirectional transportation of bubbles. Here, a Janus superwetting mesh is fabricated by integrating aerophilic (AL) and superaerophobic (SAB) surfaces. The resulting composite mesh achieves underwater bubble antibuoyancy unidirectional penetration. In aqueous solution, bubbles pass through the mesh from the SAB side to the AL side, but are blocked from passing through in the opposite direction. This Janus mesh can be considered to be a bubble diode, so is convenient for use in underwater bubble unidirectional transportation. This work may promote the development of advanced materials for gas bubble directional transportation and separation in aqueous media.

In a conventional synthetic aperture radar (SAR) image, a moving target may be smeared and displaced. Taking direct action on the defocused region of interest (ROI) data from the result of a conventional imaging algorithm, this paper presents an imaging method of the ground moving target in high-resolution SAR. A 2-D equivalent velocity parameter space is built along the azimuth and range directions with the derivation of an exact analytic expression of the ROI. In each pair of equivalent velocity parameters, the Stolt interpolation is used herein to remove the residual phase error. After that, a graph of the ROI complex subimage contrast is produced with respect to the equivalent velocity parameter space. Based on the maximum contrast principle, the desired equivalent velocity is then estimated and applied for deblurring the ROI. Finally, we can achieve the refocused SAR image of the moving target. Different from the conventional approach of moving target autofocusing that requires resynthesizing back to the full data from the cropped ROI data, the proposed method directly operates on the small-sized defocused ROI subimage without any resynthesizing operations. It is helpful for the computational burden reduction, procedure simplification, and clutter interference suppression. The experiments on synthetic and real data are carried out to validate the effectiveness of the proposed method. Index Terms-Ground moving target, region of interest (ROI), synthetic aperture radar (SAR). 0196-2892

The terahertz (THz) spectra in the range of 0.2-1.6 THz (6.6-52.8 cm^-1) of wheat grains with various degrees of deterioration (normal, worm-eaten, moldy, and sprouting wheat grains) were investigated by terahertz time domain spectroscopy. Principal component analysis (PCA) was employed to extract feature data according to the cumulative contribution rates; the top four principal components were selected, and then a support vector machine (SVM) method was applied. Several selection kernels (linear, polynomial, and radial basis functions) were applied to identify the four types of wheat grain. The results showed that the materials were identified with an accuracy of nearly 95%. Furthermore, this approach was compared with others (principal component regression, partial least squares regression, and back-propagation neural networks). The comparisons showed that PCA-SVM outperformed the others and also indicated that the proposed method of THz technology combined with PCA-SVM is efficient and feasible for identifying wheat of different qualities.

Cloud-assisted Industrial Internet of Things (IIoT) relies on cloud computing to provide massive data storage services. To ensure the confidentiality, sensitive industrial data need to be encrypted before being outsourced to cloud storage server. Public-key encryption with keyword search (PEKS) enables users to search target encrypted data by keywords. However, most existing PEKS schemes are based on conventional hardness assumptions, which are vulnerable to adversaries equipped with quantum computers in the near future. Moreover, they suffer from key exposure, and thus the security would be broken once the keys are compromised. In this paper, we propose a forward secure PEKS scheme (FS-PEKS) based on lattice assumptions for cloud-assisted IIoT, which is post-quantum secure. We integrate a lattice-based delegation mechanism into FS-PEKS to achieve forward security, such that the security of the system is still guaranteed even the keys are compromised by the adversaries. We define the first formal security model on forward security of PEKS, and prove the security of FS-PEKS under the model. As the keywords of industrial data are with inherently low entropy, we further extend FS-PEKS to resist insider keyword guessing attacks (IKGA). The comprehensive performance evaluation demonstrates that FS-PEKS is practical for cloud-assisted IIoT.