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.

Lightweight RFID Protocol for Medical Privacy Protection in IoT

Abstract: Traditional medical privacy data are at a serious risk of disclosure, and many related cases have occurred over the years. For example, personal medical privacy data can be easily leaked to insurance companies, which not only compromises the privacy of individuals, but also hinders the healthy development of the medical industry. With the continuous improvement of cloud computing and big data technologies, the Internet of Things technology has been rapidly developed. Radio frequency identification (RFID) is one of the core technologies of the Internet of Things. The application of the RFID system to the medical system can effectively solve this problem of medical privacy. RFID tags in the system can collect useful information and conduct data exchange and processing with a back-end server through the reader. The whole process of information interaction is mainly in the form of ciphertext. In the context of the Internet of Things, the paper presents a lightweight RFID medical privacy protection scheme. The scheme ensures security privacy of the collected data via secure authentication. The security analysis and evaluation of the scheme indicate that the protocol can effectively prevent the risk of medical privacy data being easily leaked.

Obesity: pathophysiology and intervention.

Abstract: Obesity presents a major health hazard of the 21st century. It promotes co-morbid diseases such as heart disease, type 2 diabetes, obstructive sleep apnea, certain types of cancer, and osteoarthritis. Excessive energy intake, physical inactivity, and genetic susceptibility are main causal factors for obesity, while gene mutations, endocrine disorders, medication, or psychiatric illnesses may be underlying causes in some cases. The development and maintenance of obesity may involve central pathophysiological mechanisms such as impaired brain circuit regulation and neuroendocrine hormone dysfunction. Dieting and physical exercise offer the mainstays of obesity treatment, and anti-obesity drugs may be taken in conjunction to reduce appetite or fat absorption. Bariatric surgeries may be performed in overtly obese patients to lessen stomach volume and nutrient absorption, and induce faster satiety. This review provides a summary of literature on the pathophysiological studies of obesity and discusses relevant therapeutic strategies for managing obesity.

Joint Beamforming and Power Splitting for MISO Interference Channel With SWIPT: An SOCP Relaxation and Decentralized Algorithm

Abstract: This paper considers a power splitting-based MISO interference channel for simultaneous wireless information and power transfer (SWIPT), where each single antenna receiver splits the received signal into two streams of different power for decoding information and harvesting energy separately. We aim to minimize the total transmission power by joint beamforming and power splitting (JBPS) under both the signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting (EH) constraints. The JBPS problem is nonconvex and has not yet been well addressed in the literature. Moreover, decentralized algorithm design for JBPS based on local channel state information (CSI) and limited information exchange remains open. In this paper, we first propose a novel relaxation method named second-order cone programming (SOCP) relaxation to address the JBPS problem. We formulate the relaxed problem as an SOCP and present two sufficient conditions under which the SOCP relaxation is tight. For the case when the SOCP solution is not necessarily optimal to the JBPS problem, a closed-form feasible-solution-recovery method is provided. Then, we develop a distributed algorithm for the JBPS problem based on primal-decomposition (PD) method. The PD-based distributed algorithm consists of a master problem and a set of subproblems. The former is solved by using subgradient method while the latter are solved using coordinate descent method. Finally, numerical results validates the efficiency of the proposed algorithms.

Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection

Abstract: Multitemporal synthetic aperture radar (SAR) images have been successfully used for the detection of different types of terrain changes. SAR image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR images based on image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a difference image. In order to obtain a better difference map, image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio difference image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused difference image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR image change detection in terms of shape preservation of the detected change portion and the numerical results.