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) & Computer science. 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.

A Conditional Adversarial Network for Change Detection in Heterogeneous Images

Abstract: Due to the distinct statistical properties in cross-sensor images, change detection in heterogeneous images is much more challenging than in homogeneous images. In this letter, we adopt a conditional generative adversarial network (cGAN) to transform the heterogeneous synthetic aperture radar (SAR) and optical images into some space where their information has a more consistent representation, making the direct comparison feasible. Our proposed framework contains a cGAN-based translation network that aims to translate the optical image with the SAR image as a target, and an approximation network that approximates the SAR image to the translated one by reducing their pixelwise difference. The two networks are updated alternately and when they are both trained well, the two translated and approximated images can be considered as homogeneous, from which the final change map can be acquired by direct comparison. Theoretical analysis and experimental results demonstrate the effectiveness and robustness of the proposed framework.

Computation Offloading for Multi-Access Mobile Edge Computing in Ultra-Dense Networks

Abstract: The ultra-dense network (UDN) is envisioned to be an enabling and highly promising technology to enhance spatial multiplexing and network capacity in future 5G networks. Moreover, to address the conflict between computation-intensive applications and resource-constrained IoT mobile devices (MDs), multi-access mobile edge computing (MA-MEC), which provides the IoT MDs with cloud capabilities at the edge of radio access networks, has been proposed. UDN and MA-MEC are regarded as two distinct but complementary enabling technologies for 5G IoT applications. Over the past several years, lots of research on mobile edge computation offloading (MECO) -- the key technique in MA-MEC -- has emerged. However, it is noticed that all these works focused on the single-tier base station scenario and computation offloading between the MD and the MEC server connected to the macro base station, and few works can be found on the problem of computation offloading for MA-MEC in UDN (i.e., a multi-user ultra-dense MEC server scenario). Toward this end, we study in this article the MECO problem in UDN and propose a heuristic greedy offloading scheme as our solution. Extensive numerical results and comparisons demonstrate the necessity for and superior performance of conducting computation offloading over multiple MEC servers.

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Abstract: Wireless sensor network (WSN) is an indispensible part of Internet of Things that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology, and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly toward security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this paper field based on our thorough survey and conclude this paper with possible future research directions.

A Compact Printed Antenna for Triple-Band WLAN/WiMAX Applications

Abstract: In this letter, a novel compact printed antenna for triple-band WLAN/WiMAX applications is presented. The proposed antenna consists of three simple circular-arc-shaped strips, whose whole geometry looks like “ear” type. By adjusting the geometries and the sizes of these three circular-arc-shaped strips, three different resonance modes can be effectively created for three distinct frequency bands, respectively. The overall dimension of the proposed antenna can reach $18\times 37\times 1\ {\hbox{mm}}^{3}$ . Measured results show that the presented antenna can cover three separated impedance bandwidths of 400 MHz (2.38–2.78 GHz), 480 MHz (3.28–3.76 GHz), and 1000 MHz (4.96–5.96 GHz), which are well applied for both 2.4/5.2/5.8-GHz WLAN bands and 2.5/3.5/5.5-GHz WiMAX bands.

An Evolutionary Algorithm for Global Optimization Based on Level-Set Evolution and Latin Squares

Abstract: In this paper, the level-set evolution is exploited in the design of a novel evolutionary algorithm (EA) for global optimization. An application of Latin squares leads to a new and effective crossover operator. This crossover operator can generate a set of uniformly scattered offspring around their parents, has the ability to search locally, and can explore the search space efficiently. To compute a globally optimal solution, the level set of the objective function is successively evolved by crossover and mutation operators so that it gradually approaches the globally optimal solution set. As a result, the level set can be efficiently improved. Based on these skills, a new EA is developed to solve a global optimization problem by successively evolving the level set of the objective function such that it becomes smaller and smaller until all of its points are optimal solutions. Furthermore, we can prove that the proposed algorithm converges to a global optimizer with probability one. Numerical simulations are conducted for 20 standard test functions. The performance of the proposed algorithm is compared with that of eight EAs that have been published recently and the Monte Carlo implementation of the mean-value-level-set method. The results indicate that the proposed algorithm is effective and efficient.