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Institution

Xidian University

EducationXi'an, China
About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Synthetic aperture radar. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a broadband planar reflectarray with parasitic dipoles is presented for wireless communication applications, which can effectively extend the reflection phase range beyond 360°, which overcomes the problem of an inadequate phase range when using thicker substrates for a wider operational bandwidth.
Abstract: A novel broadband planar reflectarray with parasitic dipoles is presented for wireless communication applications. A unit cell of the microstrip reflectarray consists of a printed main dipole with a pair of parasitic dipoles. The introduction of parasitic dipoles can effectively extend the reflection phase range beyond 360°, which overcomes the problem of an inadequate phase range when using thicker substrates for a wider operational bandwidth. The parasitic dipole reflectarrray (PDR) is applied to a wideband CDMA (WCDMA) system to eliminate blind spots in communication between the base station and mobile users. A practical link budget analysis demonstrates the effectiveness of the proposed planar reflectarray.

96 citations

Journal ArticleDOI
TL;DR: In this article, a base station antenna with dual-broadband and dual-polarization characteristics is presented, which consists of four parts: a lower-band element, an upper-band elements, arc-shaped baffle plates, and a box-shaped reflector.
Abstract: A base station antenna with dual-broadband and dual-polarization characteristics is presented in this letter. The proposed antenna contains four parts: a lower-band element, an upper-band element, arc-shaped baffle plates, and a box-shaped reflector. The lower-band element consists of two pairs of dipoles with additional branches for bandwidth enhancement. The upper-band element embraces two crossed hollow dipoles and is nested inside the lower-band element. Four arc-shaped baffle plates are symmetrically arranged on the reflector for isolating the lower- and upper-band elements and improving the radiation performance of upper-band element. As a result, the antenna can achieve a bandwidth of 50.6% for the lower band and 48.2% for the upper band when the return loss is larger than 15 dB, fully covering the frequency ranges 704-960 and 1710-2690 MHz for 2G/3G/4G applications. Measured port isolation larger than 27.5 dB in both the lower and upper bands is also obtained. At last, an array that consists of two lower-band elements and five upper-band elements is discussed for giving an insight into the future array design.

96 citations

Journal ArticleDOI
TL;DR: A learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm, which takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space.
Abstract: Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

96 citations

Journal ArticleDOI
TL;DR: This work is the first ensemble of 3D CNNs for suggesting annotations within images, and allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net.

96 citations

Journal ArticleDOI
TL;DR: A novel method, deep memory convolution neural networks (M-Net), to alleviate the problem of overfitting caused by insufficient SAR image samples, and achieves higher accuracy than several other well-known SAR image classification algorithms.
Abstract: Deep learning has obtained state-of-the-art results in a variety of computer vision tasks and has also been used to solve SAR image classification problems. Deep learning algorithms typically require a large amount of training data to achieve high accuracy. In contrast, the size of SAR image datasets is often comparatively limited. Therefore, this paper proposes a novel method, deep memory convolution neural networks (M-Net), to alleviate the problem of overfitting caused by insufficient SAR image samples. Based on the convolutional neural networks (CNN), M-Net adds an information recorder to remember and store samples’ spatial features, and then it uses spatial similarity information of the recorded features to predict unknown sample labels. M-Net's use of this information recorder may cause difficulties for convergence if conventional CNN training methods were directly used to train M-Net. To overcome this problem, we propose a transfer parameter technique to train M-Net in two steps. The first step is to train a CNN, which has the same structure as the part of CNN incorporated in M-Net, to obtain initial training parameters. The second step applies the initialized parameters to M-Net and then trains the entire M-Net. This two-step training approach helps us to overcome the nonconvergence issue, and also reduces training time. We evaluate M-Net using the public benchmark MSTAR dataset, and achieve higher accuracy than several other well-known SAR image classification algorithms.

96 citations


Authors

Showing all 32362 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Jie Zhang1784857221720
Bin Wang126222674364
Huijun Gao12168544399
Hong Wang110163351811
Jian Zhang107306469715
Guozhong Cao10469441625
Lajos Hanzo101204054380
Witold Pedrycz101176658203
Lei Liu98204151163
Qi Tian96103041010
Wei Liu96153842459
MengChu Zhou96112436969
Chunying Chen9450830110
Daniel W. C. Ho8536021429
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023117
2022529
20213,751
20203,816
20194,017
20183,382