University of Electronic Science and Technology of China

About: University of Electronic Science and Technology of China is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Antenna (radio) & Dielectric. The organization has 50594 authors who have published 58502 publications receiving 711188 citations. The organization is also known as: UESTC.

Surface Modification of Magnetic Iron Oxide Nanoparticles.

Abstract: Functionalized iron oxide nanoparticles (IONPs) are of great interest due to wide range applications, especially in nanomedicine. However, they face challenges preventing their further applications such as rapid agglomeration, oxidation, etc. Appropriate surface modification of IONPs can conquer these barriers with improved physicochemical properties. This review summarizes recent advances in the surface modification of IONPs with small organic molecules, polymers and inorganic materials. The preparation methods, mechanisms and applications of surface-modified IONPs with different materials are discussed. Finally, the technical barriers of IONPs and their limitations in practical applications are pointed out, and the development trends and prospects are discussed.

Indoor Positioning Systems Based on Visible Light Communication: State of the Art

Abstract: Advances in visible light communication (VLC) technology and the ubiquity of illumination facility have led to a growing interest in VLC-based indoor positioning. Numerous techniques have been proposed to obtain better system performance. In this paper, we survey over 100 papers ranging from pioneering papers to the state-of-the-art in the field to present the positioning technology. Not only the light emitting diode technology, modulation method and types of receivers are compared but also a novel taxonomy method is proposed. In this paper, VLC-based indoor positioning systems (VLC-based-IPSs) are classified based on the methods used: 1) mathematical method; 2) sensor-assisted method; and 3) optimization method. Different from other survey works, we emphasize and analyze the accuracy of VLC-based-IPS in the experiment and simulation environments. Meanwhile, this paper illustrates challenges, countermeasures, and lessons learned.

Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart

Abstract: Large intelligent surface/antennas (LISA), a two-dimensional artificial structure with a large number of reflective-surface/antenna elements, is a promising reflective radio technology to construct programmable wireless environments in a smart way. Specifically, each element of the LISA adjusts the reflection of the incident electromagnetic waves with unnatural properties, such as negative refraction, perfect absorption, and anomalous reflection, thus the wireless environments can be software-defined according to various design objectives. In this paper, we introduce the reflective radio basics, including backscattering principles, backscatter communication, reflective relay, the fundamentals and implementations of LISA technology. Then, we present an overview of the state-of-the-art research on emerging applications of LISA-aided wireless networks. Finally, the limitations, challenges, and open issues associated with LISA for future wireless applications are discussed.

Multivariate classification of social anxiety disorder using whole brain functional connectivity

Abstract: Recent research has shown that social anxiety disorder (SAD) is accompanied by abnormalities in brain functional connections. However, these findings are based on group comparisons, and, therefore, little is known about whether functional connections could be used in the diagnosis of an individual patient with SAD. Here, we explored the potential of the functional connectivity to be used for SAD diagnosis. Twenty patients with SAD and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. The whole brain was divided into 116 regions based on automated anatomical labeling atlas. The functional connectivity between each pair of regions was computed using Pearson’s correlation coefficient and used as classification feature. Multivariate pattern analysis was then used to classify patients from healthy controls. The pattern classifier was designed using linear support vector machine. Experimental results showed a correct classification rate of 82.5 % (p < 0.001) with sensitivity of 85.0 % and specificity of 80.0 %, using a leave-one-out cross-validation method. It was found that the consensus connections used to distinguish SAD were largely located within or across the default mode network, visual network, sensory-motor network, affective network, and cerebellar regions. Specifically, the right orbitofrontal region exhibited the highest weight in classification. The current study demonstrated that functional connectivity had good diagnostic potential for SAD, thus providing evidence for the possible use of whole brain functional connectivity as a complementary tool in clinical diagnosis. In addition, this study confirmed previous work and described novel pathophysiological mechanisms of SAD.