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 Probabilistic Collaborative Representation Based Approach for Pattern Classification

Abstract: Conventional representation based classifiers, ranging from the classical nearest neighbor classifier and nearest subspace classifier to the recently developed sparse representation based classifier (SRC) and collaborative representation based classifier (CRC), are essentially distance based classifiers. Though SRC and CRC have shown interesting classification results, their intrinsic classification mechanism remains unclear. In this paper we propose a probabilistic collaborative representation framework, where the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and computed. Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes. The final classification is performed by checking which class has the maximum likelihood. The proposed ProCRC has a clear probabilistic interpretation, and it shows superior performance to many popular classifiers, including SRC, CRC and SVM. Coupled with the CNN features, it also leads to state-of-the-art classification results on a variety of challenging visual datasets.

Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections

Abstract: An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.

Panda: Public Auditing for Shared Data with Efficient User Revocation in the Cloud

Abstract: With data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover, our mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.

Achieving Both High Voltage and High Capacity in Aqueous Zinc‐Ion Battery for Record High Energy Density

Abstract: © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1906142 (1 of 10) (1.5–1.7),[11,12] they always deliver a low capacity (<120 mAh g−1 for Zn/PBA batteries; 150 mAh g−1 Zn/MxV3(PO4)2 (M = Li, Na) batteries; and <280 mAh g−1 for Zn/manganese-based oxides batteries). The widely used vanadium-based oxides possess multiple oxidations and could deliver a high capacity >300 mAh g−1 as a cathode for the aqueous zinc-ion battery.[13–15] The V2O5·nH2O with a platelike structure, Zn0.25V2O5·nH2O nanobelts, Ca0.24V2O5·0.83H2O nanobelts, Ag0.4V2O5 nanoblelts,[19] LixV2O5·nH2O blocks,[20] H2V3O8 nanobelts,[21] NaV3O8·1.5H2O nanobelts,[22] and lamellar zinc orthovanadate[23] were intensively studied, which deliver a feasible capacity (>300 mAh g−1). However, their cycling stability is not satisfactory; typically, the Zn/vanadium-based oxide batteries can only run 1000–2000 cycles. What is even more frustrating, is that they all operate at low voltages. Typically, their capacity at above 1.0 V is less than 70 mAh g−1; thus, usually over 80% of the capacity is delivered below 1.0 V, leading to a low energy density (<250 Wh kg−1) in sharp contrast to their large capacity. A high-voltage Zn/vanadium-based oxide battery that maintains a large capacity is highly anticipated, as it may potentially renew the energy density performance of Zn-ion batteries. Herein, for the first time, we report a Zn/ Co0.247V2O5 ⋅ 0.944H2O battery with most of its capacity delivered at a voltage above 1.0 V. Structural characterization and first-principles calculations show that the interlayer cobalt ions Achieving Both High Voltage and High Capacity in Aqueous Zinc-Ion Battery for Record High Energy Density