基于PicoBlaze的多软核网络处理结构研究

介绍一种基于多个8位CPU软核PicoBlaze的网络处理结构.该结构利用现场可编程门阵列(FPGA)的并行性和网络协议分层结构的特点,可以实现一些相对复杂的网络处理功能,并且易于扩展和开发.通过在FPGA上完整实现一个远程启动服务器端的设计实例,阐明具体的设计方法.     

Robust stabilisation and L2 -gain analysis for switched systems with actuator saturation under asynchronous switching

Robust stabilisation and L₂-gain analysis for a class of switched systems with actuator saturation are studied in this paper. The switching signal of the controllers lags behind that of the system modes, which leads to the asynchronous switching between the candidate controllers and the subsystems. By combining the piecewise Lyapunov function method with the convex hull technique, sufficient conditions in terms of LMIs for the solvability of the robust stabilisation and weighted L₂-gain problems are presented respectively under the dwell time scheme. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed results.     

On event design in event-triggered feedback systems

This paper studies event design in event-triggered feedback systems. A novel event-triggering scheme is presented to ensure exponential stability of the resulting sampled-data system. The scheme postpones the triggering of events over previously proposed methods and therefore enlarges the intersampling period. The resulting intersampling periods and deadlines are bounded strictly away from zero when the continuous time system is input-to-state stable with respect to measurement errors.     

A method for stochastic multiple criteria decision making based on dominance degrees

This paper proposes a method for solving the stochastic multiple criteria decision making (SMCDM) problem, where consequences of alternatives with respect to criteria are represented by random variables with probability distributions. Firstly, definitions and related analysis of dominance degree of one probability distribution over another are given. Then, by calculating the dominance degrees, the dominance degree matrix of alternative pairwise comparisons with respect to each criterion is built. Further, using PROMETHEE II method, an overall dominance degree matrix of alternative pairwise comparisons is constructed, and a net flow of each alternative is calculated. Based on the obtained net flows, a ranking of alternatives is determined. Finally, numerical examples for the three cases are given to illustrate the use of the proposed method.     

A novel distributed scheduling scheme for OFDMA uplink using channel information and probabilistic transmission

Distributed uplink scheduling in OFDMA systems is considered. In the proposed model, mobile terminals have the responsibility of making their own transmission decisions. The proposed scheme is based on two dimensional reservation in time and frequency. Terminals use channel state information in order to favor transmissions over certain subchannels, and transmission is done in a probabilistic manner. The proposed approach provides more autonomy to mobile devices in making transmission decisions. Furthermore, it allows avoiding collisions during transmission since it leads to collision detection during the resource reservation phase. The proposed approach is compared to other random access methods and shown to be superior in terms of increasing sum-rate, reducing the number of users in outage, and reducing the collision probability in the reservation phase.     

Model reduction of multi-scale chemical Langevin equations

This paper addresses the model reduction problem for a class of stiff chemical Langevin equations that arise as models of biomolecular networks with fast and slow reactions and can be described as continuous Markov processes. Initially, a coordinate transformation is sought that allows the decoupling of fast and slow variables in the model equations. Necessary and sufficient conditions are derived for such a linear transformation to exist, along with an explicit change of variables which achieves the desired decoupling. For the systems for which this step is applicable, the method of adiabatic elimination is applied to determine a representation of the slow dynamics. Theoretical concepts and results are illustrated with simple examples.     

Admission control for a responsive distributed middleware using decision trees to model run-time parameters

The software in modern systems has become too complex to make accurate predictions about their performance under different configurations. Real-time or even responsiveness requirements cannot be met because it is not possible to perform admission control for new or changing tasks if we cannot tell how their execution affects the other tasks already running. Previously, we proposed a resource-allocation middleware that manages the execution of tasks in a complex distributed system with real-time requirements. The middleware behavior can be modeled depending on the configuration of the tasks running, so that the performance of any given configuration can be calculated. This makes it possible to have admission control in such a system, but the model requires knowledge of run-time parameters. We propose the utilization of machine-learning algorithms to obtain the model parameters, and be able to predict the system performance under any configuration, so that we can provide a full admission control mechanism for complex software systems. In this paper, we present such an admission control mechanism, we measure its accuracy in estimating the parameters of the model, and we evaluate its performance to determine its suitability for a real-time or responsive system.     

面向高速工业无线网络的TDMA MAC协议设计与实现

随着无线技术的发展,在工业自动化中引入无线技术已经成为一种潮流,同时TDMA机制由于可以完全避免冲突,因此在大规模高实时要求的面向车间的无线技术研究中受到了广泛关注,但现在面向车间的高速实时无线技术的研究大都局限于软件仿真。通过在商用802.11硬件上设计并实现面向车间级工业应用的高速工业无线TDMAMAC层协议,建立了一个基于TDMA的面向工业应用的高速无线网络原型系统,实验结果表明实验平台完全能够支撑毫秒级时隙调度的TDMA应用。     

蒙古文整词计算机生成理论研究

采用面向对象方法,模拟传统蒙古文整词各种形式构成机理,提出了几种蒙古语整词计算机生成数据模型。文章主要依据整词计算机生成三种模型,探讨了传统蒙古文整词计算机最优化生成理论所涉及的精确度、时间复杂度、空间复杂度三项基本要素以及最优化生成必须考虑的整词复杂特征载荷与一体化合一计算知识表示方法和计算结构,证明了“B - J - T= W”数据模型是传统蒙古文整词计算与生成最优化对象模型。     

Evolving Teams of Predictors with Linear Genetic Programming

This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.