Cheng Zhu

In this paper, an electromagnetic metasurface is designed, fabricated, and experimentally demonstrated to generate multiple orbital angular momentum (OAM) vortex beams in radio frequency domain. Theoretical formula of compensated phase-shift distribution is deduced and used to design the metasurface to produce multiple vortex radio waves in different directions with different OAM modes. The prototype of a practical configuration of square-patch metasurface is designed, fabricated, and measured to validate the theoretical analysis at 5.8 GHz. The simulated and experimental results verify that multiple OAM vortex waves can be simultaneously generated by using a single electromagnetic metasurface. The proposed method paves an effective way to generate multiple OAM vortex waves in radio and microwave wireless communication applications.

The prevailing of malware variants in ubiquitous Internet of Things (IoT) devices causes enormous losses. Accurate and timely identification of malware variant traffic at an IoT edge gateway can effectively reduce the loss. TransNet, the state-of-the-art technology for malware variant traffic detection, considers only global domain adaptation and ignores the alignment of distributions between different subdomains, which fails to capture the fine-grained information of classification targets. Besides, TransNet converges very slowly, which may use up precious resources in IoT devices. This article proposes a deep subdomain adaptation network with attention mechanism (DSAN-AT) to accurately and efficiently identify malware variant traffic at an IoT edge gateway. DSAN-AT utilizes local maximum mean discrepancy (LMMD) to align the traffic feature distributions of subdomains in the source and target domains. It also exploits channel and spatial attention mechanisms to accelerate learning traffic features between different subdomains to save precious computing resources at the IoT edge gateway. Our experimental study demonstrates that DSAN-AT achieves an average accuracy of 97.15% (96.37% for TransNet) and converges fast without using a large target domain training data set. DSAN-AT has strong practicality for identifying malware variant traffic at an edge IoT gateway.

We describe the fabrication of large areas (4 cm(2)) of metallic structures or aperture elements that have ~100-350-nm linewidths and act as frequency-selective surfaces. These structures are fabricated with a type of soft lithography-near-field contact-mode photolithography-that uses a thin elastomeric mask having topography on its surface and is in conformal contact with a layer of photoresist. The mask acts as an optical element to create minima in the intensity of light delivered to the photoresist. Depending on the type of photoresist used, lines of, or trenches in, photoresist are formed on the substrate by exposure, development, and lift-off. These surfaces act as bandpass or bandgap filters in the infrared.

In this paper, a novel design of dual-band microstrip antenna with complementary split ring resonators (CSRRs) is presented. A simple and successful dual-band antenna can be realized by etching three CSRRs in the ground plane of a conventional patch antenna. The proposed antenna shows good performances at both resonant frequencies. The CSRRs embedded in the ground plane make a major contribution to the flrst operating band, but has minor efiect on the second operating band. It is beneflcial for designing a dual-band antenna as well as a miniaturized antenna ∞exibly. The simulation results are analyzed and compared with measured results in a good agreement.

In single-polarization synthetic aperture radar imagery, coastline extraction is a challenging task due to complex coastal region condition, low incidence angles (<; 30°), VV or HH polarization, speckle and sea wind, which have to be handled by visual inspection and manual operation for coastline extraction. To solve this problem, this article proposes a coastline extraction algorithm by utilizing superpixel as the basic unit. An improved line finder is presented and involved in the simple linear iterative clustering to obtain better results around the dam. Then, Gabor feature is extracted to characterize the superpixels. Finally, the hidden Markov random field model is utilized to classify superpixels. A coarse to fine strategy is utilized to extract coastline for the experimental imagery. Four VV polarization ENVISAT and ERS images with incidence angle (30.9°-36.6°, 17°-42°, 17°-42°, 19°-26°) are used as the experimental imagery. Four sites in each imagery are chosen to evaluate the performance of proposed superpixel-based algorithm under the condition of complex coastal region such as a thin dam, sharp corner, weak boundary, together with low-to-moderate sea wind. By using the manually traced coastlines as the standards in the qualitative evaluations with the extracted results, with mean offset (MO) in pixels, root-mean-square error (RMSE) in pixels, proportion of overlapped pixels (OLP) in percentage, distance within one pixel (WOP) in percentage, and distance within two pixels (WTP) in percentage as performance comparison, the accuracy improvements in detecting coastline along the dam are at least about 0.08, 0.19, 2.07% in MO, RMSE, and OLP. The accuracy improvements of the sharp corner are at least about 0.07, 0.09, 2.44%, 2.69%, and 2.77%. The accuracy improvements of the weak boundary are about 2.63, 2.99, 5.26%, 11.46%, and 13.96%. While the proposed algorithm shows weaknesses with the comparison algorithms in WOP, WTP for the dam, OLP, WOP, and WTP for sea wind. In addition, the processing time of the proposed algorithm is a burden to obtain better accuracy performance. Experimental results show that the proposed coastline extraction algorithm is more effective and suitable for coastline extraction in comparison with other existing algorithms in the dam, weak boundary, and sharp corner.