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.

A Survey on Security Aspects for LTE and LTE-A Networks

Abstract: High demands for broadband mobile wireless communications and the emergence of new wireless multimedia applications constitute the motivation to the development of broadband wireless access technologies in recent years. The Long Term Evolution/System Architecture Evolution (LTE/SAE) system has been specified by the Third Generation Partnership Project (3GPP) on the way towards fourth-generation (4G) mobile to ensure 3GPP keeping the dominance of the cellular communication technologies. Through the design and optimization of new radio access techniques and a further evolution of the LTE systems, the 3GPP is developing the future LTE-Advanced (LTE-A) wireless networks as the 4G standard of the 3GPP. Since the 3GPP LTE and LTE-A architecture are designed to support flat Internet Protocol (IP) connectivity and full interworking with heterogeneous wireless access networks, the new unique features bring some new challenges in the design of the security mechanisms. This paper makes a number of contributions to the security aspects of the LTE and LTE-A networks. First, we present an overview of the security functionality of the LTE and LTE-A networks. Second, the security vulnerabilities existing in the architecture and the design of the LTE and LTE-A networks are explored. Third, the existing solutions to these problems are classically reviewed. Finally, we show the potential research issues for the future research works.

Consensus of Hybrid Multi-Agent Systems

Abstract: In this brief, we consider the consensus problem of hybrid multiagent systems. First, the hybrid multiagent system is proposed, which is composed of continuous-time and discrete-time dynamic agents. Then, three kinds of consensus protocols are presented for the hybrid multiagent system. The analysis tool developed in this brief is based on the matrix theory and graph theory. With different restrictions of the sampling period, some necessary and sufficient conditions are established for solving the consensus of the hybrid multiagent system. The consensus states are also obtained under different protocols. Finally, simulation examples are provided to demonstrate the effectiveness of our theoretical results.

Verifiable Computation over Large Database with Incremental Updates

Abstract: The notion of verifiable database (VDB) enables a resource-constrained client to securely outsource a very large database to an untrusted server so that it could later retrieve a database record and update a record by assigning a new value. Also, any attempt by the server to tamper with the data will be detected by the client. When the database undergoes frequent while small modifications, the client must re-compute and update the encrypted version (ciphertext) on the server at all times. For very large data, it is extremely expensive for the resources-constrained client to perform both operations from scratch. In this paper, we formalize the notion of verifiable database with incremental updates (Inc-VDB). Besides, we propose a general Inc-VDB framework by incorporating the primitive of vector commitment and the encrypt-then-incremental MAC mode of encryption. We also present a concrete Inc-VDB scheme based on the computational Diffie-Hellman (CDH) assumption. Furthermore, we prove that our construction can achieve the desired security properties.

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Abstract: SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be potentially useful as a predictor for learning and neural rehabilitation.