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 Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model

Abstract: This paper proposes a unified transmission strategy for multiuser time division duplex (TDD)/frequency division duplex (FDD) massive multiple-input–multiple-output (MIMO) systems, including uplink (UL)/downlink (DL) channel estimation and user scheduling for data transmission. With the aid of antenna array theory and array signal processing, we build a spatial basis expansion model (SBEM) to represent the UL/DL channels with far fewer parameter dimensions. Hence, both the UL and DL channel estimations of multiusers can be carried out with a small amount of training resource, which significantly reduces the training overhead and feedback cost. Meanwhile, the pilot contamination problem in the UL training is immediately relieved by exploiting the spatial information of users. To enhance the spectral efficiency, we also design a greedy user scheduling scheme during the data transmission period. Compared with existing low-rank models, the newly proposed SBEM offers an alternative for channel acquisition without the need for channel statistics and can be applied to both TDD and FDD systems. Various numerical results are provided to corroborate the proposed studies.

A novel genetic algorithm based on immunity

Abstract: A novel algorithm, the immune genetic algorithm (IGA), is proposed based on the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps: 1) a vaccination and 2) an immune selection. IGA proves theoretically convergent with probability 1. Strategies and methods of selecting vaccines and constructing an immune operator are also given. IGA is illustrated to be able to restrain the degenerate phenomenon effectively during the evolutionary process with examples of TSP, and can improve the searching ability and adaptability, greatly increase the convergence rate.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.

Abstract: Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

UAV-Assisted Emergency Networks in Disasters

Abstract: Reliable and flexible emergency communication is a key challenge for search and rescue in the event of disasters, especially for the case when base stations are no longer functioning. Unmanned aerial vehicle (UAV)-assisted networking is emerging as a promising method to establish emergency networks. In this article, a unified framework for a UAV-assisted emergency network is established in disasters. First, the trajectory and scheduling of UAVs are jointly optimized to provide wireless service to ground devices with surviving BSs. Then the transceiver design of UAV and establishment of multihop ground device-to-device communication are studied to extend the wireless coverage of UAV. In addition, multihop UAV relaying is added to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs. Simulation results are presented to show the effectiveness of these three schemes. Finally, open research issues and challenges are discussed.

Fractional-Order Anisotropic Diffusion for Image Denoising

Abstract: This paper introduces a new class of fractional-order anisotropic diffusion equations for noise removal. These equations are Euler-Lagrange equations of a cost functional which is an increasing function of the absolute value of the fractional derivative of the image intensity function, so the proposed equations can be seen as generalizations of second-order and fourth-order anisotropic diffusion equations. We use the discrete Fourier transform to implement the numerical algorithm and give an iterative scheme in the frequency domain. It is one important aspect of the algorithm that it considers the input image as a periodic image. To overcome this problem, we use a folded algorithm by extending the image symmetrically about its borders. Finally, we list various numerical results on denoising real images. Experiments show that the proposed fractional-order anisotropic diffusion equations yield good visual effects and better signal-to-noise ratio.