基于电子现金的新型支付协议

本文提出了一种基于电子现金的新型支付协议,该协议通过第三方实时核算客户拥有的电子现金,但不进行实时转账。协议模型较好地克服了现有在线支付的交易瓶颈问题以及离线支付中电子现金容易被重用、伪造或篡改的问题,在交易过程中有效地保证了安全性,并且实现了电子现金的原子性、匿名性、可分割性和可验证性。     

MIMO系统中简单的扩展空时块编码分集技术研究

本文提出一种多输入多输出（MIMO）天线系统中简单的扩展空时块编码（SSTBC）分集技术，采用沃尔什码来区分各天线发送数据子流。采用这种方法，在系统带宽一定时，不降低发送信息速率，同时接收机简单。不同天线的发送信息经过了所有收一发天线对之间的空间子信道，获得了所有路径的部分空间分集增益，仿真结果表明，这种增益的获得不受限于接收分集阶数，并且随发射天线的增加以一定的线性关系增加。     

Probabilistic output admissible set for systems with time-varying uncertainties

This paper considers time-varying uncertain constrained systems, and develops a method for computing a probabilistic output admissible (POA) set. This set consists of the initial states probabilistically assured to satisfy the constraint. The time-invariant counterpart has already been investigated in Hatanaka and Takaba [Computations of probabilistic output admissible set for uncertain constrained systems, Automatica 44 (2) (2008), to appear]. We first define the POA set for time-varying uncertainties with finite dimensional probability space. Then, we show that an algorithm similar to Hatanaka and Takaba [Computations of probabilistic output admissible set for uncertain constrained systems, Automatica 44 (2) (2008), to appear] provides the POA set also in the time-varying case, as long as an upper bound of a what we call future output admissibility (FOA) index is available. We moreover present two methods for computing the upper bound of the FOA index: probabilistic and deterministic methods. A numerical simulation demonstrates the effectiveness of our algorithm.     

单膛石灰竖窑的改造

我厂竖窑容积为250m^3，设计利用系数为0．85t/(m^3．d)。随烧结产量的不断增加，石灰产量已不能满足烧结生产的需要。为了提高石灰产量，进行了扩容改造，容积由原来250m^3增加到298m^3，并针对目前生产实际提出下一步改造设想。     

非线性系统鲁棒吸引的动态补偿方法

考虑非线性不确定系统的鲁棒吸引问题,借助于动态输出反馈,构造了两类控制律。这两类控制律分别可使相应的闭环系统被一任意小的区域鲁棒吸引,或被一预先设置的区域有限吸引。     

面向油气需求的俄罗斯钢管制造业

介绍了俄罗斯制管业的发展概况和国内制管企业格局及产能，同时重点阐述了大直径钢管的发展状况和市场预测，指出俄罗斯钢管业发展趋于稳定，俄罗斯钢管产能已能满足所有地区油气输送部门、机械制造业的需要。     

单通道字幕机实现多频道播出

通过单通道字幕机与视音频矩阵切换器配合使用，实现一台字幕机在多个频道分时播出功能。     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.     

Directional features in online handwriting recognition

The selection of valuable features is crucial in pattern recognition. In this paper we deal with the issue that part of features originate from directional instead of common linear data. Both for directional and linear data a theory for a statistical modeling exists. However, none of these theories gives an integrated solution to problems, where linear and directional variables are to be combined in a single, multivariate probability density function. We describe a general approach for a unified statistical modeling, given the constraint that variances of the circular variables are small. The method is practically evaluated in the context of our online handwriting recognition system frog on hand and the so-called tangent slope angle feature. Recognition results are compared with two alternative modeling approaches. The proposed solution gives significant improvements in recognition accuracy, computational speed and memory requirements.