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 Cluster-Based Differential Evolution With Self-Adaptive Strategy for Multimodal Optimization

Abstract: Multimodal optimization is one of the most challenging tasks for optimization. It requires an algorithm to effectively locate multiple global and local optima, not just single optimum as in a single objective global optimization problem. To address this objective, this paper first investigates a cluster-based differential evolution (DE) for multimodal optimization problems. The clustering partition is used to divide the whole population into subpopulations so that different subpopulations can locate different optima. Furthermore, the self-adaptive parameter control is employed to enhance the search ability of DE. In this paper, the proposed multipopulation strategy and the self-adaptive parameter control technique are applied to two versions of DE, crowding DE (CDE) and species-based DE (SDE), which yield self-CCDE and self-CSDE, respectively. The new algorithms are tested on two different sets of benchmark functions and are compared with several state-of-the-art designs. The experiment results demonstrate the effectiveness and efficiency of the proposed multipopulation strategy and the self-adaptive parameter control technique. The proposed algorithms consistently rank top among all the competing state-of-the-art algorithms.

Unified Biometric Privacy Preserving Three-Factor Authentication and Key Agreement for Cloud-Assisted Autonomous Vehicles

Abstract: Autonomous vehicles (AVs) are increasingly common, although there remain a number of limitations that need to be addressed in order for their deployment to be more widespread. For example, to mitigate the failure of self-driving functions in AVs, introducing the remote control capability (which allows a human driver to operate the vehicle remotely in certain circumferences) is one of several countermeasures proposed. However, the remote control capability breaks the isolation of onboard driving systems and can be potentially exploited by malicious actors to take over control of the AVs; thus, risking the safety of the passengers and pedestrians (e.g., AVs are remotely taken over by terrorist groups to carry out coordinated attacks in places of mass gatherings). Therefore, security is a key, mandatory feature in the design of AVs. In this paper, we propose a cloud-centric three-factor authentication and key agreement protocol (CT-AKA) integrating passwords, biometrics and smart cards to ensure secure access to both cloud and AVs. Three typical biometric encryption approaches, including fuzzy vault, fuzzy commitment, and fuzzy extractor, are unified to achieve three-factor authentication without leaking the biometric privacy of users. Moreover, two session keys are negotiated in our protocol, namely: one between the user and AV to support secure remote control of the AV, and the other is negotiated between the mobile device and the cloud to introduce resilience to the compromise of ephemeral security parameters to ensure cloud data access security with a high security guarantee. Finally, we formally verify the security properties and evaluate the efficiency of CT-AKA, whose findings demonstrate that the protocol achieves high security strength with reasonable computation and communication costs.

Deadlock Prevention Based on Structure Reuse of Petri Net Supervisors for Flexible Manufacturing Systems

Abstract: Deadlocks are an undesirable situation in automated flexible manufacturing systems (FMS) Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results Finding an optimal supervisor is NP-hard A computationally efficient method often ends up with a suboptimal one This paper develops a deadlock prevention method that makes a good tradeoff between optimality and computational tractability for a class of Petri nets, which can model many FMS The theory of regions guides our efforts toward the development of near-optimal solutions for deadlock prevention Given a plant net, a minimal initial marking is first decided by structural analysis, and an optimal live controlled system is computed Then, a set of inequality constraints is derived with respect to the markings of monitors and the places in the model such that no siphon can be insufficiently marked A method is proposed to identify the redundancy condition for constraints For a new initial marking of the plant net, a deadlock-free controlled system can be obtained by regulating the markings of the monitors such that the inequality constraints are satisfied, without changing the structure of the controlled system The near-optimal performance of a controlled net system via the proposed method is shown through several examples

Sparse Bayesian Learning for the Time-Varying Massive MIMO Channels: Acquisition and Tracking

Abstract: The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch–Tung–Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

L-EncDB: A lightweight framework for privacy-preserving data queries in cloud computing

Abstract: With the advent of cloud computing, individuals and organizations have become interested in moving their databases from local to remote cloud servers However, data owners and cloud service providers are not in the same trusted domain in practice For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective database utilization a very challenging task To address this challenge, in this paper, we propose L-EncDB, a novel lightweight encryption mechanism for database, which (i) keeps the database structure and (ii) supports efficient SQL-based queries To achieve this goal, a new format-preserving encryption (FPE) scheme is constructed in this paper, which can be used to encrypt all types of character strings stored in database Extensive analysis demonstrates that the proposed L-EncDB scheme is highly efficient and provably secure under existing security model