Effects of Surface Defects on Adsorption of CO and Methyl Groups on Rutile TiO$_\text{2}$(110)

The interaction of reactants with catalysts has always been an important subject for catalytic reactions. As a promising catalyst with versatile applications, titania has been intensively studied for decades. In this work we have investigated the role of bridge bonded oxygen vacancy (O\begin{document}$_\textrm{v}$\end{document}) in methyl groups and carbon monoxide (CO) adsorption on rutile TiO\begin{document}$_2$\end{document}(110) (R-TiO\begin{document}$_2$\end{document}(110)) with the temperature programmed desorption technique. The results show a clear different tendency of the desorption of methyl groups adsorbed on bridge bonded oxygen (O\begin{document}$_\textrm{b}$\end{document}), and CO molecules on the five coordinate Ti\begin{document}$^{4+}$\end{document} sites (Ti\begin{document}$_{5\textrm{c}}$\end{document}) as the O\begin{document}$_\textrm{v}$\end{document} concentration changes, suggesting that the surface defects may have crucial influence on the absorption of species on different sites of R-TiO\begin{document}$_2$\end{document}(110).     

Deep learning-based channel estimation using Gaussian mixture distribution and expectation maximum algorithm

In massive multiple-input multiple-output (MIMO), it is much challenging to obtain accurate channel state information (CSI) after radio frequency (RF) chain reduction due to the high dimensions. With the fast development of machine learning(ML), it is widely acknowledged that ML is an effective method to deal with channel models which are typically unknown and hard to approximate. In this paper, we use the low complexity vector approximate messaging passing (VAMP) algorithm for channel estimation, combined with a deep learning framework for soft threshold shrinkage function training. Furthermore, in order to improve the estimation accuracy of the algorithm for massive MIMO channels, an optimized threshold function is proposed. This function is based on Gaussian mixture (GM) distribution modeling, and the expectation maximum Algorithm (EM Algorithm) is used to recover the channel information in beamspace. This contraction function and deep neural network are improved on the vector approximate messaging algorithm to form a high-precision channel estimation algorithm. Simulation results validate the effectiveness of the proposed network.     

Message Passing-Based Inference for Time-Varying Autoregressive Models

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by a factor graph and solve the inference problem by automated message passing-based inference for states and parameters. We derive structured variational update rules for a composite “AR node” with probabilistic observations that can be used as a plug-in module in hierarchical models, for example, to model the time-varying behavior of the hyper-parameters of a time-varying AR model. Our method includes tracking of variational free energy (FE) as a Bayesian measure of TVAR model performance. The proposed methods are verified on a synthetic data set and validated on real-world data from temperature modeling and speech enhancement tasks.     

Real-space parallel density matrix renormalization group with adaptive boundaries

We propose an improved real-space parallel strategy for the density matrix renormalization group (DMRG) method, where boundaries of separate regions are adaptively distributed during DMRG sweeps. Our scheme greatly improves the parallel efficiency with shorter waiting time between two adjacent tasks, compared with the original real-space parallel DMRG with fixed boundaries. We implement our new strategy based on the message passing interface (MPI), and dynamically control the number of kept states according to the truncation error in each DMRG step. We study the performance of the new parallel strategy by calculating the ground state of a spin-cluster chain and a quantum chemical Hamiltonian of the water molecule. The maximum parallel efficiencies for these two models are 91% and 76% in 4 nodes, which are much higher than the real-space parallel DMRG with fixed boundaries.     

开尔文电桥测电阻及用Visual Basic处理测量数据

用开尔文电桥测量金属棒的电阻率时,电阻率的不确定度是由测量金属棒直径、长度和电阻的不确定度来决定。为了又快又准确地处理实验的测量数据,设计编制了Visual Basic程序,从而方便快捷的计算出金属棒电阻率的不确定度。     

大型综合电子信息系统软件体系结构设计

阐述了体系结构在综合电子信息系统(C4ISR)系统的设计、研制和建设过程中所起的关键作用。分析了大型综合电子信息系统的特点,阐述了系统体系结构的总体设计和软件体系结构设计,介绍了系统采用的基于核心服务引擎的分层与SOA相结合的体系结构,以及大型信息系统体系结构设计所采用的关键技术。探讨了工作流管理、持久化访问、信息订制/推送关键技术的实现途径,并总结了该系统软件体系结构的应用效果。     

基于DSP系统的1553B总线RT设计

1553B总线在航天领域应用越来越广泛,已成为星载有效载荷数据传输的主要途径。为了实现星载有效载荷通过1553B总线传输指令和科学实验数据,选用了以TI公司DSP芯片SMJ320VC33为控制器、DDC公司BU65170为通讯芯片的设计方案。采用本设计方案的有效载荷具有接口简单、通讯速度快、可靠性高的特点,并已在某星载有效载荷中成功应用,效果良好,对星载1553B总线RT（远置终端）设计具有重要的指导和参考意义。     

GNSS导航电文对接收机用户的性能影响分析

导航电文是全球卫星导航系统(GNSS)的关键要素,依靠由系统广播的导航电文所提供的信息数据,接收机用户得以正确计算出其位置并获得精确的时间信息。导航电文的数据内容取决于系统控制段,系统确保电文数据的精度和时效性至关重要,而控制段误差则通过电文传递给用户。从接收机用户性能的角度分析了GNSS导航电文的设计依据,所涉及的用户性能主要包括解调门限、首次定位时间、精度和完好性。阐述了支持上述电文分析的软件工具设计与实现。     

一种短信查缴水电费的实现方法与流程优化

为使短信缴费给公众带来更多的便利,介绍了一种通过SMS（手机短信）查询缴纳水电费的整体实现方法,包括系统结构、查缴及对账流程等,并提出了短信支付的关键点与短信交互流程的优化方法。通过优化,用户只需发送2条短信即可完成查询缴费过程,使短信成为了一种实用、便捷的缴费方法。     

cdma2000网络监测系统L1接口协议监测方案研究

以cdma20001x EV-DO网络分组域监测系统的开发为研究背景,在学习研究CDMA数字蜂窝移动通信网定位业务L1接口协议的基础上,提出采用哈希（Hash）算法和方法封装等技术实现L1接口协议的监测模块。本监测方案对L1接口协议的解码、呼叫详细记录（CDR）合成等技术进行了详细的描述,并通过现网采集的数据对此监测方案进行测试,达到了预期的效果。