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.

Optimal Supervisory Control of Flexible Manufacturing Systems by Petri Nets: A Set Classification Approach

Abstract: Supervisory control is usually considered as an external control mechanism to a system by controlling the occurrences of its controllable events. There exist Petri net models whose legal reachability spaces are nonconvex. In this case, they cannot be optimally controlled by the conjunctions of linear constraints. For Petri net models of flexible manufacturing systems, this work proposes a method to classify the legal markings into several subsets. Each subset is associated with a linear constraint that can forbid all first-met bad markings. Then, the disjunctions of the obtained constraints can make all legal markings reachable and forbid all first-met bad markings, i.e., the controlled net is live and maximally permissive. An integer linear programming model is formulated to minimize the number of the constraints. A supervisory structure is also proposed to implement the disjunctions of the constraints. Finally, examples are provided to illustrate the proposed method.

A security and trust framework for virtualized networks and software-defined networking

Abstract: With the great success of the second-generation wireless telephone technology and the third-generation mobile telecommunications technology, and the fast development of the fourth-generation mobile telecommunications technology, the phase of fifth-generation mobile networks or fifth-generation wireless systems 5G is coming. In this article, we indicate the open research issues of 5G security and trust in the context of virtualized networking and software-defined networking. We further propose a framework of security and trust focusing on solving 5G network security issues. The proposed framework applies adaptive trust evaluation and management technologies and sustainable trusted computing technologies to ensure computing platform trust and achieve software-defined network security. It adopts cloud computing to securely deploy various trustworthy security services over the virtualized networks. We analyze that the framework can support and satisfy all security requirements specified in standardization. We also suggest future research work according to the proposed framework and discuss the advantages of our framework in terms of practical deployment. Copyright © 2015 John Wiley & Sons, Ltd.

Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

Abstract: The advent of the cloud computing makes storage outsourcing become a rising trend, which promotes the secure remote data auditing a hot topic that appeared in the research literature. Recently some research consider the problem of secure and efficient public data integrity auditing for shared dynamic data. However, these schemes are still not secure against the collusion of cloud storage server and revoked group users during user revocation in practical cloud storage system. In this paper, we figure out the collusion attack in the exiting scheme and provide an efficient public integrity auditing scheme with secure group user revocation based on vector commitment and verifier-local revocation group signature. We design a concrete scheme based on the our scheme definition. Our scheme supports the public checking and efficient user revocation and also some nice properties, such as confidently, efficiency, countability and traceability of secure group user revocation. Finally, the security and experimental analysis show that, compared with its relevant schemes our scheme is also secure and efficient.

Sparse representation for blind image quality assessment

Abstract: Blind image quality assessment (BIQA) is an important yet difficult task in image processing related applications. Existing algorithms for universal BIQA learn a mapping from features of an image to the corresponding subjective quality or divide the image into different distortions before mapping. Although these algorithms are promising, they face the following problems: 1) they require a large number of samples (pairs of distorted image and its subjective quality) to train a robust mapping; 2) they are sensitive to different datasets; and 3) they have to be retrained when new training samples are available. In this paper, we introduce a simple yet effective algorithm based upon the sparse representation of natural scene statistics (NSS) feature. It consists of three key steps: extracting NSS features in the wavelet domain, representing features via sparse coding, and weighting differential mean opinion scores by the sparse coding coefficients to obtain the final visual quality values. Thorough experiments on standard databases show that the proposed algorithm outperforms representative BIQA algorithms and some full-reference metrics.