SDI接口数字监视设备的设计与实现

介绍了一种对SDV(串行数字视频)信号进行解码的设备设计方案，提出了该系统的总体构架，并对该系统的硬件设计、软件设计进行了全面的论述。设计的产品具有较高的性能价格比，已进入了推广阶段。     

扬州芍药

研究扬州芍药的栽培简史,明确扬州芍药的历史地位,通过对扬州芍药现状的了解,提出积极措施,努力恢复扬州芍药的园艺地位.     

FHCF: A Simple and Efficient Scheduling Scheme for IEEE 802.11e Wireless LAN

The IEEE 802.11e medium access control (MAC) layer protocol is an emerging standard to support quality of service (QoS) in 802.11 wireless networks. Some recent work shows that the 802.11e hybrid coordination function (HCF) can improve significantly the QoS support in 802.11 networks. A simple HCF referenced scheduler has been proposed in the 802.11e which takes into account the QoS requirements of flows and allocates time to stations on the basis of the mean sending rate. As we show in this paper, this HCF referenced scheduling algorithm is only efficient and works well for flows with strict constant bit rate (CBR) characteristics. However, a lot of real-time applications, such as videoconferencing, have some variations in their packet sizes, sending rates or even have variable bit rate (VBR) characteristics. In this paper we propose FHCF, a simple and efficient scheduling algorithm for 802.11e that aims to be fair for both CBR and VBR flows. FHCF uses queue length estimations to tune its time allocation to mobile stations. We present analytical model evaluations and a set of simulations results, and provide performance comparisons with the 802.11e HCF referenced scheduler. Our performance study indicates that FHCF provides good fairness while supporting bandwidth and delay requirements for a large range of network loads. Pierre Ansel received a multidisciplinary in-depth scientific training in different fields such as Pure and Applied Mathematics, Physics, Mechanics, Computer Science and Economics from the Ecole Polytechnique, Palaiseau, France. Then, he joined the Ecole Nationale Superieure des Telecommunications, Paris, France in 2005 where he went further into electronics, databases, computer network security and high speed networks. He received a multidisciplinary master of sciences degree and an additional master of sciences degree in telecommunications in 2005. He did a summer internship in 2003 in INRIA, Sophia Antipolis, France where he worked on the Quality of Service in 802.11 networks at Planete Group, France. Then in 2004, he joined France Telecom R&D, Issy-les-Moulineaux, France to work on Intranet Security issues. He designed a WiFi security supervision architecture based on WiFi Intrusion Detection Sensors. He is currently a French civil servant and belongs to the French Telecommunications Corps. Qiang Ni received the B.Eng., M.Sc. and Ph.D. degrees from Hua Zhong University of Science and Technology (HUST), Wuhan City, China in 1993, 1996 and 1999 respectively. He is currently a faculty member in the Electronic and Computer Engineering Division,School of Engineering and Design, Brunel University, West London, U.K. Between 2004 and 2005 he was a Senior Researcher at the Hamilton Institute, National University of Ireland, Maynooth. From 1999 to 2001, he was a post-doctoral research fellow in the multimedia and wireless communication laboratory, HUST, China. He visited and conducted research at the wireless and networking group of Microsoft Research Asia Lab during the year of 2000. From Sept. 2001 until may 2004, he was a research staff member at the Planète group of INRIA Sophia Antipolis France. Since 2002, he has been active as a voting member at the IEEE 802.11 wireless LAN standard working group. His current research interests include communication protocol design and performance analysis for wireless networks, cross-layer optimizations, vertical handover and mobility management in mobile wireless networks, and adaptive multimedia transmission over hybrid wired/wireless networks. He has authored /co-authored over 40 international journal/conference papers, book chapters, and standard drafts in this field. He is a member of IEEE. E-mail: Qiang.Ni@ieee.org Thierry Turletti received the M.S. (1990) and the Ph.D. (1995) degrees in computer science both from the University of Nice – Sophia Antipolis, France. He has done his PhD studies in the RODEO group at INRIA Sophia Antipolis. During the year 1995–96, he was a postdoctoral fellow in the Telemedia, Networks and Systems group at LCS, MIT. He is currently a research scientist at the Planete group at INRIA Sophia Antipolis. His research interests include multimedia applications, congestion control and wireless networking. Dr. Turletti currently serves on the Editorial Board of Wireless Communications and Mobile Computing.     

Simulation-based sequential analysis of Markov switching stochastic volatility models

We propose a simulation-based algorithm for inference in stochastic volatility models with possible regime switching in which the regime state is governed by a first-order Markov process. Using auxiliary particle filters we developed a strategy to sequentially learn about states and parameters of the model. The methodology is tested against a synthetic time series and validated with a real financial time series: the IBOVESPA stock index (São Paulo Stock Exchange).     

汽车排放控制系统中的氧传感器现状及展望

介绍了浓差型(Nernst型)、极限电流型和半导体电阻型等汽车排放控制系统中的氧传感器原理及结构。在综合该领域国内外研究现状的基础上,对氧传感器的发展进行了展望。     

Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

Synthesis of nonsharp distillation sequences via genetic programming

This paper addresses the application of Genetic Programming (GP) to the synthesis of multicomponent product nonsharp distillation sequences. Combined with the domain knowledge of chemical engineering, some evolutionary factors are improved, and a set of special encoding method and solving strategy is proposed to deal with this kind of problem. The system structural variable is optimized by GP and the continuous variable is optimized by the simulated annealing algorithm simultaneously. Because GP has an automatic searching function, the optimal solution can be found including distillation, splitting, blending and bypassing operations automatically without any superstructures of nonsharp distillation sequences. Three illustrative examples are presented to demonstrate the effective computational strategies.