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.

Design Bandpass Frequency Selective Surface Absorber Using LC Resonators

Abstract: A novel method of designing a frequency selective surface absorber (FSSA) with bandpass characteristic between two neighboring absorption bands is proposed in this letter. This method is implemented by loading lumped resistors and LC resonators in the FSSA structure. An equivalent circuit model together with an analytical design method is developed for further illustration. The performance of the FSSA structure is tested using a free-space measurement system. Experiments show a bandpass response at the center frequency of 6.74 GHz with a fractional bandwidth of 5% and two absorption bands over 3.33–6.26 and 7.09–10.36 GHz with an absorption rate of more than 80%, which indicates a good agreement with the simulation results.

Image deblocking via sparse representation

Abstract: Image compression based on block-based Discrete Cosine Transform (BDCT) inevitably produces annoying blocking artifacts because each block is transformed and quantized independently. This paper proposes a new deblocking method for BDCT compressed images based on sparse representation. To remove blocking artifacts, we obtain a general dictionary from a set of training images using the K-singular value decomposition (K-SVD) algorithm, which can effectively describe the content of an image. Then, an error threshold for orthogonal matching pursuit (OMP) is automatically estimated to use the dictionary for image deblocking by the compression factor of compressed image. Consequently, blocking artifacts are significantly reduced by the obtained dictionary and the estimated error threshold. Experimental results indicate that the proposed method is very effective in dealing with the image deblocking problem from compressed images.

A Privacy Enhanced Authentication Scheme for Telecare Medical Information Systems

Abstract: The telecare medical information system (TMIS) aims to establish telecare services and enable the public to access medical services or medical information at remote sites. Authentication and key agreement is essential to ensure data integrity, confidentiality, and availability for TMIS. Most recently, Chen et al. proposed an efficient and secure dynamic ID-based authentication scheme for TMIS, and claimed that their scheme achieves user anonymity. However, we observe that Chen et al.'s scheme achieves neither anonymity nor untraceability, and is subject to the identity guessing attack and tracking attack. In order to protect user privacy, we propose an enhanced authentication scheme which achieves user anonymity and untraceablity. It is a secure and efficient authentication scheme with user privacy preservation which is practical for TMIS.

Classification of Hyperspectral Images Based on Multiclass Spatial–Spectral Generative Adversarial Networks

Abstract: Generative adversarial networks (GANs) are famous for generating samples by training a generator and a discriminator via an adversarial procedure. For hyperspectral image classification, the collection of samples is always difficult. However, directly applying GAN to hyperspectral image classification exists two problems. One is that the generated samples lack discriminative information. Meanwhile, the discriminator has no discriminative ability for multiclassification. Another is that spatial and spectral information requires to be considered in hyperspectral image classification simultaneously. To address these problems, a novel multiclass spatial–spectral GAN (MSGAN) method is proposed. In MSGAN, two generators are devised to generate the samples containing spatial and spectral information, respectively, and the discriminator is devised to extract joint spatial–spectral features and output multiclass probabilities. Moreover, novel adversarial objectives for multiclass are defined. The discriminator is devised to predict training samples belonging to true classes and generated samples belonging to all the classes with the same probability. The generators are devised to make the discriminator mistake. By adversarial learning between the discriminator and generators, the classification performance of the discriminator is promoted with the assistance of discriminative generated samples. Experimental results on hyperspectral images demonstrate that the proposed method achieves encouraging classification performance compared with several state-of-the-art methods, especially with the limited training samples.

Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking

Abstract: A colocated multiple-input multiple-output (MIMO) radar system has the ability to address multiple beam information. However, the simultaneous multibeam working mode has two finite working resources: the number of beams and the total transmit power of the multiple beams. In this scenario, a resource allocation strategy for the multibeam working mode with the task of tracking multiple targets is developed in this paper. The basis of our technique is to adjust the number of beams and their directions and the transmit power of each beam through feedback, with the purpose of improving the worst tracking performance among the multiple targets. The Bayesian Cramer–Rao lower bound (BCRLB) provides us with a lower bound on the estimated mean square error (MSE) of the target state. Hence, it is derived and utilized as an optimization criterion for the resource allocation scheme. We prove that the resulting resource optimization problem is nonconvex but can be reformulated as a set of convex problems. Therefore, optimal solutions can be obtained easily, which greatly aids real-time resource management. Numerical results show that the worst case tracking accuracy can be efficiently improved by the proposed simultaneous multibeam resource allocation (SMRA) algorithm.