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A single-layer ±45° dual-polarized directional array antenna for millimeter wave (mm-wave) applications is designed in this communication. Based on the theory of orthogonal circularly polarized (CP) wave multiplexing, two ports of a series-fed dual CP array are fed with equal amplitudes, and the array can radiate a linearly polarized wave with ±45° polarization orientations through the adjustment of the feeding phase difference. As the two ports of the series-fed array are simultaneously excited, the antenna can achieve directional radiation. In addition, the cross-polarization level of the array can be effectively suppressed by placing two series-fed arrays side by side. A prototype of the designed array antenna operating at 30 GHz is fabricated and measured; the working bandwidth of the proposed antenna is approximately 3.5%. Owing to its simple structure and directional radiation, the proposed antenna array is a competitive candidate for mm-wave applications.
Qinyi Lv; Yu-Hang Yang; Shi-Gang Zhou; Chan Shao; Deyun Zhou; Chow-Yen-Desmond Sim. Design of a Single-Layer ±45° Dual-Polarized Directional Array Antenna for Millimeter Wave Applications. Sensors 2021, 21, 4326 .
AMA StyleQinyi Lv, Yu-Hang Yang, Shi-Gang Zhou, Chan Shao, Deyun Zhou, Chow-Yen-Desmond Sim. Design of a Single-Layer ±45° Dual-Polarized Directional Array Antenna for Millimeter Wave Applications. Sensors. 2021; 21 (13):4326.
Chicago/Turabian StyleQinyi Lv; Yu-Hang Yang; Shi-Gang Zhou; Chan Shao; Deyun Zhou; Chow-Yen-Desmond Sim. 2021. "Design of a Single-Layer ±45° Dual-Polarized Directional Array Antenna for Millimeter Wave Applications." Sensors 21, no. 13: 4326.
Distribution mismatch can be easily found in multi-sensor systems, which may be caused by different shoot angles, weather conditions and so on. Domain adaptation aims to build robust classifiers using the knowledge from a well-labeled source domain, while applied on a related but different target domain. Pseudo labeling is a prevalent technique for class-wise distribution alignment. Therefore, numerous efforts have been spent on alleviating the issue of mislabeling. In this paper, unlike existing selective hard labeling works, we propose a fuzzy labeling based graph learning framework for matching conditional distribution. Specifically, we construct the cross-domain affinity graph by considering the fuzzy label matrix of target samples. In order to solve the problem of representation shrinkage, the paradigm of sparse filtering is introduced. Finally, a unified optimization method based on gradient descent is proposed. Extensive experiments show that our method achieves comparable or superior performance when compared to state-of-the-art works.
Lingtong Min; Deyun Zhou; Xiaoyang Li; Qinyi Lv; Yuanjie Zhi. Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation. Applied Sciences 2021, 11, 4503 .
AMA StyleLingtong Min, Deyun Zhou, Xiaoyang Li, Qinyi Lv, Yuanjie Zhi. Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation. Applied Sciences. 2021; 11 (10):4503.
Chicago/Turabian StyleLingtong Min; Deyun Zhou; Xiaoyang Li; Qinyi Lv; Yuanjie Zhi. 2021. "Fuzzy Graph Learning Regularized Sparse Filtering for Visual Domain Adaptation." Applied Sciences 11, no. 10: 4503.