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.

High resolution ISAR imaging of high speed moving targets

Abstract: Range-Doppler (RD) algorithms are idely used i inverse synthetic aperture radar (ISAR) imaging. I the standard RD algorithm, envelope alignment and autofocus are first applied to transform the original data into equivalent turntable target data, and the FFT is used for the image formation. Usually, the migratio through resolution cells (MTRCs) due to target rotatio is ignored. ith the improvement of resolutio or the increase of target size, MTRCs cannot be ignored and must be mitigated. For high speed moving targets, the most idely used 'stop-and-go' data model is violated. I this case, the mitigation of MTRCs becomes eve more complicated. I the paper, technical issues associated ith high resolutio ISAR imaging of high speed moving targets are addressed. A preprocessing algorithm is first proposed to convert the non-coherent ra ISAR data into coherent data, the a keystone formatting algorithm developed for the imaging of slo ground moving targets i SAR is applied to mitigate the MTRCs o ing to rotational motion, and, finally, a high resolutio time-frequency analysis based range-instantaneous Doppler (RID) algorithm is used to produce the focused ISAR image. Numerical examples are provided to illustrate the performance of the proposed approach.

A Survey on Secure Data Analytics in Edge Computing

Abstract: Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications, such as environmental monitoring, smart navigation, and smart healthcare come with new requirements, such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

Collaborative Hashing

Abstract: Hashing technique has become a promising approach for fast similarity search. Most of existing hashing research pursue the binary codes for the same type of entities by preserving their similarities. In practice, there are many scenarios involving nearest neighbor search on the data given in matrix form, where two different types of, yet naturally associated entities respectively correspond to its two dimensions or views. To fully explore the duality between the two views, we propose a collaborative hashing scheme for the data in matrix form to enable fast search in various applications such as image search using bag of words and recommendation using user-item ratings. By simultaneously preserving both the entity similarities in each view and the interrelationship between views, our collaborative hashing effectively learns the compact binary codes and the explicit hash functions for out-of-sample extension in an alternating optimization way. Extensive evaluations are conducted on three well-known datasets for search inside a single view and search across different views, demonstrating that our proposed method outperforms state-of-the-art baselines, with significant accuracy gains ranging from 7.67% to 45.87% relatively.