面向对象的软件原型化可操作模型OPM

基于OMT(Object Modeling Technique),面向软件原型化领域,提出了可操作模型OPM(Object-Oriented Prototyping Modeling)。OPM是在对象模型OM(Object-Model)之上的一种二维的模型化方法,纵向,功能对象的APO抽象与继承的自顶向下模型化机制;横向,功能对象的有穷状态机中,状态对象的AKO抽象与继承的逐步细化的模型化机制。OPM通过当前状态对象的转换、消息授受来描述系统行为,原型化深度通过上述二维的对象模型化粒度来刻划,并给出了OPM应用的具体例子。     

An improved signature scheme without using one-way Hash functions

Recently, Chang et al. give a digital signature scheme, where neither one-way hash function nor message redundancy schemes are used, but Zhang et al. has shown that the scheme was forgeable, namely, any one can forge a new signature by the signer’s signature, and give two forgery attacks. To the above attacks, we give an improved signature scheme based on Chang signature scheme and analyze the security of the improved scheme.     

基于PCIe总线的双路1394B接口模块设计与实现

为了满足航电系统中前端传感器捕获的图形图像数据实时传输的需求,设计并实现了基于PCIe总线的双路1394B接口模块。该模块通过PCIe接口与主机交互,采用S400β传输模式,软件设计符合标准OHCI规范,支持AS5643协议,异步流包数据收发采用链式队列管理,主机与1394B控制器之间通过DMA操作,实现大数据块的并行处理,两路1394B相互独立,显著提升数据传输性能。测试验证结果表明,该模块具备带宽高、可靠性高、CPU资源占用率低等特点,已在某项目获得成功应用,实现了图形图像数据的实时传输,满足系统设计要求,表现出了良好的稳定性和适应性,具有实践应用价值。     

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.     

A residual-based message passing algorithm for constraint satisfaction problems

Message passing algorithms, whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages, provide a powerful toolkit in tackling hard computational tasks in optimization, inference, and learning problems. In the context of constraint satisfaction problems (CSPs), when a control parameter (such as constraint density) is tuned, multiple threshold phenomena emerge, signaling fundamental structural transitions in their solution space. Finding solutions around these transition points is exceedingly challenging for algorithm design, where message passing algorithms suffer from a large message fluctuation far from convergence. Here we introduce a residual-based updating step into message passing algorithms, in which messages with large variation between consecutive steps are given high priority in the updating process. For the specific example of model RB (revised B), a typical prototype of random CSPs with growing domains, we show that our algorithm improves the convergence of message updating and increases the success probability in finding solutions around the satisfiability threshold with a low computational cost. Our approach to message passing algorithms should be of value for exploring their power in developing algorithms to find ground-state solutions and understand the detailed structure of solution space of hard optimization problems.     

运用时间序列预测CET-4考试通过率

运用时间序列分析的原理[1],通过对华中农业大学若干年大学英语四级考试成绩[2]的分析,得到将来的四级考试通过率的预报值,为教学管理部门对计划的制定,管理的安排进行定量的分析提供理论依据.     

Moving beyond message passing. Experiments with a distributed-data model

Summary A message-passing model provides a natural and efficient parallel implementation for many applications in chemical physics on MIMD machines. However, although the distinction between local and non-local memory is at the very heart of writing efficient parallel programs, message passing leaves all responsibility for data management to the applications. This has significant, detrimental implications for both ease of programming and efficient use of shared and distributed resources. Examined here is a simple model which increments message passing with Linda-like tools for the manipulation of distributed-data structures. This is applied to common algorithms in chemical physics.     

加强篮球传球准确性的教学和训练

本文按照篮球运动的规律和教学训练的特点,就传球准确性问题作比较全面、深入的阐述;对球行路线,移动中传球的特点,传球者和防守者之间的相对位置关系,传球的时机、速度、方向、位置、落点以及如何培养学生传球意识等方面问题进行了一定的分析;着重指出学生练习中易犯错误和纠正方法,并提供一些加强传球准确性的教学训练手段。