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.

iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks

Abstract: Recently, as the development of artificial intelligence (AI), data-driven AI methods have shown amazing performance in solving complex problems to support the Internet of Things (IoT) world with massive resource-consuming and delay-sensitive services. In this paper, we propose an intelligent resource allocation framework (iRAF) to solve the complex resource allocation problem for the collaborative mobile edge computing (CoMEC) network. The core of iRAF is a multitask deep reinforcement learning algorithm for making resource allocation decisions based on network states and task characteristics, such as the computing capability of edge servers and devices, communication channel quality, resource utilization, and latency requirement of the services, etc. The proposed iRAF can automatically learn the network environment and generate resource allocation decision to maximize the performance over latency and power consumption with self-play training. iRAF becomes its own teacher: a deep neural network (DNN) is trained to predict iRAF’s resource allocation action in a self-supervised learning manner, where the training data is generated from the searching process of Monte Carlo tree search (MCTS) algorithm. A major advantage of MCTS is that it will simulate trajectories into the future, starting from a root state, to obtain a best action by evaluating the reward value. Numerical results show that our proposed iRAF achieves 59.27% and 51.71% improvement on service latency performance compared with the greedy-search and the deep $Q$ -learning-based methods, respectively.

2D Material Optoelectronics for Information Functional Device Applications: Status and Challenges

Abstract: Graphene and the following derivative 2D materials have been demonstrated to exhibit rich distinct optoelectronic properties, such as broadband optical response, strong and tunable light-mater interactions, and fast relaxations in the flexible nanoscale. Combining with optical platforms like fibers, waveguides, grating, and resonators, these materials has spurred a variety of active and passive applications recently. Herein, the optical and electrical properties of graphene, transition metal dichalcogenides, black phosphorus, MXene, and their derivative van der Waals heterostructures are comprehensively reviewed, followed by the design and fabrication of these 2D material-based optical structures in implementation. Next, distinct devices, ranging from lasers to light emitters, frequency convertors, modulators, detectors, plasmonic generators, and sensors, are introduced. Finally, the state-of-art investigation progress of 2D material-based optoelectronics offers a promising way to realize new conceptual and high-performance applications for information science and nanotechnology. The outlook on the development trends and important research directions are also put forward.

Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity--A multi-center study.

Abstract: Background Resting-state functional magnetic resonance imaging studies examining low frequency fluctuations (0.01–0.08 Hz) have revealed atypical whole brain functional connectivity patterns in adolescents with autism spectrum disorder (ASD), and these atypical patterns can be used to discriminate individuals with ASD from controls. However, at present it is unknown whether functional connectivity at specific frequency bands can be used to discriminate individuals with ASD from controls, and whether relationships with symptom severity are stronger in specific frequency bands. Methods We selected 240 adolescent subjects (12–18 years old, 112 with autism spectrum disorder (101/11, males/females) and 128 healthy controls (104/24, males/females)) from 6 separate international sites in the Autism Brain Imaging Data Exchange database. Whole brain functional connectivity networks were constructed in the Slow-5 (0.01–0.027 Hz) and Slow-4 (0.027–0.073 Hz) frequency bands, which were then used as classification features. Results An accuracy of 79.17% (p Conclusions Our findings provide preliminary evidence for frequency-specific whole brain functional connectivity indices that may eventually be used to aid detection of ASD.

Dependent Evidence Combination Based on Shearman Coefficient and Pearson Coefficient

Abstract: Dempster–Shafer evidence theory is efficient to deal with uncertain information. One assumption of evidence theory is that the source of information should be independent when combined by Dempster’s rule for evidence combination. However, the assumption does not coincide with the reality. A lot of works are done to solve the problem about the independence. The existing method based on the statistical parameter Pearson correlation coefficient discount is one of the feasible methods. However, the Pearson correlation coefficient is only used to characterize the linear correlation between the attributes of the normal distribution. In this paper, a new method is proposed, the Pearson correlation coefficient and Shearman correlation coefficient to generate the discounting factor. Taking the parametric statistic and nonparametric statistic into consideration, the proposed method is more efficient. The experiments on wine data set are illustrated to show the efficiency of our proposed method.

A Novel Risk Decision Making Based on Decision-Theoretic Rough Sets Under Hesitant Fuzzy Information

Abstract: Decision-theoretic rough sets (DTRSs) play a crucial role in risk decision-making problems. With respect to the minimum expected risk, DTRSs deduce the rules of three-way decisions. Considering the new expression of evaluation information with hesitant fuzzy sets (HFSs), we introduce HFSs into DTRSs and explore their decision mechanisms. More specifically, we take into account the losses of DTRSs with hesitant fuzzy elements and propose a new model of hesitant fuzzy decision-theoretic rough sets (HFDTRSs). Some properties of the expected losses and their corresponding scores are carefully investigated under the hesitant fuzzy information. Three-way decisions and the associated cost of each object are further derived. With the above analysis, a novel risk decision-making method with the aid of HFDTRSs is developed. Besides the three-way decisions with DTRSs, the method investigates the ranking and resource allocation by utilizing the associated costs of alternatives and multiobjective 0–1 integer programming. Our study also offers a solution in the aspect of determining losses of DTRS and extends the range of applications.