Universal R-matrix Of The Super Yangian Double DY(gl(1|1))

Based on Drinfeld realization of super Yangian Double DY(gl(1|1)), its pairing relations and universal R-matrix are given. By taking evaluation representation of universal R-matrix, another realization $L^{\pm}(u)$ of DY(gl(1|1)) is obtained. These two realizations of DY(gl(1|1)) are related by the supersymmetric extension of Ding-Frenkel map.Comment: 6 pages, latex, no figure

The Algebraic Structure of the gl(n∣m)gl(n|m) Color Calogero-Sutherland Models

We extend the study on the algebraic structure of the $su(n)$ color Calogero-Sutherland models to the case of $gl(n|m)$ color CS model and show that the generators of the super-Yangian $Y(gl(n|m))$ can be obtained from two $gl(n|m)$ loop algebras. Also, a super $W_{\infty}$ algebra for the SUSY CS model is constructed.Comment: LaTeX, 13 page

Design and Evaluation of Digital Baseband Converter Sub-channel Delay Compensation Method on Bandwidth Synthesis

The effect of sub-channel delay on bandwidth synthesis is investigated to eliminate the “phase step” phenomenon in bandwidth synthesis during the test of CDBE (Chinese Digital Backend). Through formula derivation, we realize that sub-channel delay may cause phase discontinuity between different sub-channels. Theoretical analysis shows that sub-channel delay can induce bandwidth synthesis error in group delay measurement of the linear system. Furthermore, in the differential delay measurement between two stations, bandwidth synthesis error may occur when the LO (Local Oscillator) frequency differences of corresponding sub-channels are not identical. Error-free conditions are discussed under different applications. The phase errors among different sub-channels can be removed manually. However, the most effective way is the compensation of sub-channel delay. A sub-channel delay calculation method based on Modelsim is proposed. The compensation method is detailed. Simulation and field experiments are presented to verify our approach

A tutorial on the internet of things: from a heterogeneous network integration perspective

The days when the Internet was the only focus of the information society have already gone, and innovative network paradigms such as IoT, cloud computing, smartphone networks, social networks, and industrial networks are gaining popularity and establishing themselves as indispensable ingredients of the future smart universe. Among them, IoT is the most widespread one, envisioned to involve all things in the world. However, its potential will never be fully explored before the complete formation of cyberspace, where humans, computers, and smart objects are pervasively interconnected. Therefore, one of the most important development trends of IoT is its integration with existing network systems. In this tutorial, we provide a detailed analysis of this issue. In particular, the latest achievements, technical solutions, and influential ongoing projects are described, and possible visions and open challenges are also discussed

Discovery and Support of Problem-Solving Knowledge in e-Business

Employees generally need to solve various situations of problems occurred during task-executions in e-business. Such problem-solving behaviors contain rich information helpful to provide effective knowledge support. This work proposes a mining framework to discover problem-solving knowledge by analyzing problem-solving behaviors recorded in an intranet portal log file. The discovered knowledge can provide guidance and support to assist employees handle various situations of problems

A Clustering-Anonymity Approach for Trajectory Data Publishing Considering both Distance and Direction

Trajectory data contains rich spatio-temporal information of moving objects. Directly publishing it for mining and analysis will result in severe privacy disclosure problems. Most existing clustering-anonymity methods cluster trajectories according to either distance- or direction-based similarities, leading to a high information loss. To bridge this gap, in this paper, we present a clustering-anonymity approach considering both these two types of similarities. As trajectories may not be synchronized, we first design a trajectory synchronization algorithm to synchronize them. Then, two similarity metrics between trajectories are quantitatively defined, followed by a comprehensive one. Furthermore, a clustering-anonymity algorithm for trajectory data publishing with privacy-preserving is proposed. It groups trajectories into clusters according to the comprehensive similarity metric. These clusters are finally anonymized. Experimental results show that our algorithm is effective in preserving privacy with low information loss