A low profile circularly polarized (CP) antenna with reconfigurable polarization is designed and presented, which can radiate omnidirectional patterns that can be switched between left‐hand circularly polarized (LHCP) and right‐hand circularly polarized (RHCP). A pair of arc‐shaped complementary dipoles is acted as reconfigurable elements by bridging four pin diodes at the dipole arced arms. A meander phase shift line is employed to connected the arc dipole arms and plate cavity to adjust the phase relationship of two sources. The proposed antenna exhibits the omnidirectional radiation pattern by combining six identical elements placed in a circular array configuration. 24 p‐i‐n diodes are exploited to six elements, by manipulating the dc bias voltage across the diodes, the polarization state of the antenna can be switched. The patterns of the antenna are similar to that of a dipole, but its size is only about Φ0.87 × 0.029λ0 at 5.8 GHz. The overlapped bandwidth of measured 3‐dB axial ratio (AR) and 10‐dB return loss is 5.724‐5.87 and 5.738‐5.91 GHz for two polarization states, which are right on the target of ISM band. It can be well adapted to medical diagnosis systems. 相似文献
3D movies/videos have become increasingly popular in the market; however, they are usually produced by professionals. This paper presents a new technique for the automatic conversion of 2D to 3D video based on RGB-D sensors, which can be easily conducted by ordinary users. To generate a 3D image, one approach is to combine the original 2D color image and its corresponding depth map together to perform depth image-based rendering (DIBR). An RGB-D sensor is one of the inexpensive ways to capture an image and its corresponding depth map. The quality of the depth map and the DIBR algorithm are crucial to this process. Our approach is twofold. First, the depth maps captured directly by RGB-D sensors are generally of poor quality because there are many regions missing depth information, especially near the edges of objects. This paper proposes a new RGB-D sensor based depth map inpainting method that divides the regions with missing depths into interior holes and border holes. Different schemes are used to inpaint the different types of holes. Second, an improved hole filling approach for DIBR is proposed to synthesize the 3D images by using the corresponding color images and the inpainted depth maps. Extensive experiments were conducted on different evaluation datasets. The results show the effectiveness of our method.
Person re-identification (re-id) aims to identity the same person over multiple cameras; it has been successfully applied to various computer vision applications as a fundamental method. Owing to the development of deep learning, person re-id methods, which typically use triplet networks based on triplet loss, have demonstrated great success. However, the appearances of people are similar and hence difficult to distinguish in many cases. Therefore, we present a novel graph convolution network and enhances traditional triplet loss functions. Our method defines reference, positive, and negative features for triplet loss as three vertices of a graph, respectively, and adjusts their mutual distance through learning. The method adopts graph convolutions efficiently, thereby affording low computational costs. Experimental results demonstrate that our method is superior to the baseline on the Market-1501 dataset. The proposed GCN-based triplet loss considerably contributes to improve re-identification methods quantitatively and qualitatively.
World Wide Web - User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise... 相似文献