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.

BPDS: A Blockchain Based Privacy-Preserving Data Sharing for Electronic Medical Records

Abstract: Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.

Numerical Analysis on Transmission Efficiency of Evanescent Resonant Coupling Wireless Power Transfer System

Abstract: A practical wireless power transmission system consisting of a large rectangular wire loop and a small square wire loop with a parasitic square helical coil is proposed for use as an efficient evanescent resonant coupling wireless power transmission system in an indoor environment. In addition, a full wave-based numerical analysis on the resonant frequency of and the power transmission efficiency of wireless power transmission system are performed in this paper. The effects of the following on power transmission efficiency and the resonant frequency are numerically investigated: The load of the receiving element and the presence of non-resonant objects such as a conducting box or human body. The numerical results show that a power transmission efficiency of nearly 50% can be achieved when the proposed system is used to charge only one user with the optimized load. The results also show that power transmission efficiency is reduced significantly when a human body is in very close proximity to the receiving element. This reduction in efficiency can be alleviated significantly, however, if the relative distance between the receiving element and the human body is greater than 0.5 m or 0.03? at a resonant frequency of 19.22 MHz.

Multi-Task Collaborative Network for Joint Referring Expression Comprehension and Segmentation

Abstract: Referring expression comprehension (REC) and segmentation (RES) are two highly-related tasks, which both aim at identifying the referent according to a natural language expression. In this paper, we propose a novel Multi-task Collaborative Network (MCN) to achieve a joint learning of REC and RES for the first time. In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent. In addition, we address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS). Specifically, CEM enables REC and RES to focus on similar visual regions by maximizing the consistency energy between two tasks. ASNLS supresses the response of unrelated regions in RES based on the prediction of REC. To validate our model, we conduct extensive experiments on three benchmark datasets of REC and RES, i.e., RefCOCO, RefCOCO+ and RefCOCOg. The experimental results report the significant performance gains of MCN over all existing methods, i.e., up to +7.13% for REC and +11.50% for RES over SOTA, which well confirm the validity of our model for joint REC and RES learning.