This paper presents a wide-angle scanning phased array antenna using high gain pattern reconfigurable antenna (PRA) elements. Using PRA elements is an attractive solution for wide-angle scanning phased array antennas because the scanning range can be divided into several subspaces. To achieve the desired scanning performance, some characteristics of the PRA element such as the number of switching modes, tilt angle, and maximum half-power beamwidth (HPBW) are required. We analyzed the required characteristics of the PRA element according to the target scanning range and element spacing, and presented a PRA element design guideline for phased array antennas. In accordance with the guideline, the scanning range was set as ±70° and a high gain PRA element with three reconfigurable patterns was used to compose an 8x1 array antenna with 0.9 λ0 spacing. After analyzing whether the active element patterns meet the guideline, the array antenna was fabricated and measured to demonstrate the scanning performance. The fabricated array can scan its beam from -70° to 70° by dividing the scanning range into three subspaces. It shows that even if the array antenna has large element spacing, the desired scanning performance can be obtained using the elements designed under the guideline.
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these two paradigms in a joined fashion. In this paper, we propose Label Propagation Adaptive Resonance Theory (LPART) for semi-supervised continuous learning. LPART uses an online label propagation mechanism to perform classification and gradually improves its accuracy as the observed data accumulates. We evaluated the proposed model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets by adjusting the ratio of the labeled and unlabeled data. The accuracies are much higher when both labeled and unlabeled data are used, demonstrating the significant advantage of LPART in environments where the data labels are scarce.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.