Xidian University

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.

Compact UWB Bandpass Filter With Dual Notched Bands Based on SCRLH Resonator

Abstract: This letter presents a novel ultra-wideband (UWB) band-pass filter (BPF) with dual sharply rejected notch-bands based on a simplified composite right/left-handed (SCRLH) resonator. The basic UWB BPF is comprised of four folded shunt quarter-wavelength short-circuited stubs separated by connecting lines with length of λg0/4 and λg0/2, respectively. The SCRLH resonator is studied and employed to introduce the dual notched bands. Good insertion/return losses are achieved as demonstrated in both simulation and experiment.

Multi-objective optimization for long tail recommendation

Abstract: Recommender systems are tools to suggest items to target users. Accuracy-focused recommender systems tend to recommend popular items, while suggesting items with few ratings (long tail items) is also of great importance in practice. Recommending long tail items may cause an accuracy loss of recommendation results. Thus, it is necessary to have a recommendation framework that recommends unpopular items meanwhile minimizing the accuracy loss. In this paper, we formulate a multi-objective framework for long tail items recommendation. Under this framework, two contradictory objective functions are designed to describe the abilities of recommender system to recommend accurate and unpopular items, respectively. To optimize these two objective functions, a novel multi-objective evolutionary algorithm is proposed. This multi-objective evolutionary algorithm aims to find a set of tradeoff solutions by optimizing two objective functions simultaneously. Experiments show that the proposed framework is effective to suggest accurate and novel items. The proposed recommendation algorithm could suggest many high-quality recommendation lists for the target user based on the concept of Pareto dominance in one run.

CPW-Fed Planar Printed Monopole Antenna With Impedance Bandwidth Enhanced

Abstract: A coplanar waveguide (CPW)-fed monopole antenna is proposed, which is composed of a rectangular monopole patch notched at the bottom, a T-shaped CPW ground in the notch, and a tapered CPW ground out of the notch. The simulated and experimental results show that the antenna achieves a fractional impedance bandwidth of 164% for S11 ? -10 dB, which is about 2.3 times of the conventional one. The parametric studies and measured radiation characteristics are presented. The results show that the antenna exhibits good characteristics and is suitable for portable mobile ultrawideband (UWB) applications.

An Energy-Efficient Compressive Sensing-Based Clustering Routing Protocol for WSNs

Abstract: A novel algorithm which combined the merits of the clustering strategy and the compressive sensing-based (CS-based) scheme was proposed in this paper. The lemmas for the relationship between any two adjacent layers, the optimal size of clusters, the optimal distribution of the cluster head (CH), and the corresponding proofs were presented first. In addition, to alleviate the “hot spot problem” and reduce the energy consumption resulted from the rotation of the role of CHs, a third role of backup CH (BCH) as well as the corresponding mechanism to rotate the roles between the CH and BCH were proposed. Subsequently, the energy-efficient compressive sensing-based clustering routing (EECSR) protocol was presented in detail. Finally, extensive simulation experiments were conducted to evaluate its energy performance. Comparisons with the existing clustering algorithms and the CS-based algorithm verified the effect of EECSR on improving energy efficiency and extending the lifespan of wireless sensor networks.

SAR Target Detection Based on SSD With Data Augmentation and Transfer Learning

Abstract: In this letter, the single shot multibox detector (SSD), which is a real-time object detection method based on convolutional neural network, is applied to realize target detection for synthetic aperture radar (SAR) images. Since there are no sufficient labeled images for training in SAR target detection, we apply two strategies, data augmentation and transfer learning. For data augmentation, the first approaches to use some image processing methods, i.e., manual-extracting subimages, adding noise, filtering, and flipping, on the original training images to generate some new training images; the second approach is to employ the existing SAR target recognition data set, MSTAR data set, to assist in accomplishing the target detection task. For transfer learning, we first apply subaperture decomposition technique on original SAR images to acquire three-channel subaperture SAR images, and then transfer the three-channel VGGNet model pretrained on the ImageNet data set to the three-channel subaperture SAR images, in order to initialize corresponding parameters of the convolutional layers in the base network in our SSD. The feature extraction network, consisting of the base network and the auxiliary structure, is used to learn multiscale feature maps, and then convolutional predictors are used to acquire the final detection results. The experimental results on the miniSAR real image data set demonstrate that the proposed method can obtain better detection performance than other detection methods.