Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Panorama phylogenetic diversity and distribution of Type A influenza virus.

Abstract: Background Type A influenza virus is one of important pathogens of various animals, including humans, pigs, horses, marine mammals and birds. Currently, the viral type has been classified into 16 hemagglutinin and 9 neuraminidase subtypes, but the phylogenetic diversity and distribution within the viral type largely remain unclear from the whole view. Methodology/Principal Findings The panorama phylogenetic trees of influenza A viruses were calculated with representative sequences selected from approximately 23000 candidates available in GenBank using web servers in NCBI and the software MEGA 4.0. Lineages and sublineages were classified according to genetic distances, topology of the phylogenetic trees and distributions of the viruses in hosts, regions and time. Conclusions/Significance Here, two panorama phylogenetic trees of type A influenza virus covering all the 16 hemagglutinin subtypes and 9 neuraminidase subtypes, respectively, were generated. The trees provided us whole views and some novel information to recognize influenza A viruses including that some subtypes of avian influenza viruses are more complicated than Eurasian and North American lineages as we thought in the past. They also provide us a framework to generalize the history and explore the future of the viral circulation and evolution in different kinds of hosts. In addition, a simple and comprehensive nomenclature system for the dozens of lineages and sublineages identified within the viral type was proposed, which if universally accepted, will facilitate communications on the viral evolution, ecology and epidemiology.

Output Feedback Fuzzy Controller Design With Local Nonlinear Feedback Laws for Discrete-Time Nonlinear Systems

Abstract: This paper considers the output feedback control problem for nonlinear discrete-time systems, which are represented by a type of fuzzy systems with local nonlinear models. By using the estimations of the states and nonlinear functions in local models, sufficient conditions for designing observer-based controllers are given for discrete-time nonlinear systems. First, a separation property, i.e., the controller and the observer can be independently designed, is proved for the class of fuzzy systems. Second, a two-step procedure with cone complementarity linearization algorithms is also developed for solving the H∞ dynamic output feedback (DOF) control problem. Moreover, for the case where the nonlinear functions in local submodels are measurable, a convex condition for designing H∞ controllers is given by a new DOF control scheme. In contrast to the existing methods, the new methods can design output feedback controllers with fewer fuzzy rules as well as less computational burden, which is helpful for controller designs and implementations. Lastly, numerical examples are given to illustrate the effectiveness of the proposed methods.

Indoor mobile localization based on wi-fi fingerprint’s important access point

Abstract: With the development of the wireless communication technology and intelligent mobile phone, the positioning services based on Wi-Fi and mobile phone are increasingly demanded. In this paper, a Wi-Fi fingerprint localization method is proposed on the basis of important access points (IAP). For the Wi-Fi fingerprint, Wi-Fi access point with the highest received signal strength (RSS) is denoted as the important access point. At the localization stage, the fingerprints are chosen with the same IAP as the estimated fingerprint from the database. Then, the distance and the AP repetition of the fingerprints are used to calculate the similarity degree. The location of the fingerprint which matches the estimated fingerprint well can be regarded as the estimated location. Experimental results show that the proposed algorithm can achieve high accuracy in indoor environment.