Provision of differentiated performance in Optical Burst Switching networks based on burst assembly processes

Optical Burst Switching (OBS) has been designed as a practical solution for the high-speed transport network using enabling optical technologies, which can work without optical buffering or pure optical signal processing in the intermediate nodes. As a collection of many packets, optical bursts exhibit different traffic characteristics in the bufferless optical core networks compared to the packet-level traffic in the conventional electronic switching systems. However, many OBS studies often neglect this issue by assuming the inputs to be optical bursts when analyzing the system performance. This paper demonstrates that the loss performance of optical bursts is dramatically different when considering burst assembly process at the edge nodes and the connectivity of the core nodes. We develop some analytical models which can model the loss performance more accurately than the traditional methods. Based on our observations, we propose a scheme to support differentiated loss performances for optical bursts by modifying the burst assembly parameters at the source edge nodes. Our scheme is implemented at the edge nodes and there is no specific requirement to the core nodes. Thus, our design provides good flexibility and scalability in the heterogeneous Internet.     

The persistence probability and the price–price correlation functions in the Korean stock market

We consider the one minute time series of the Korean stock market index (KOSPI). We defined the persisting time as the time interval when the index remains above (or below) an initial index. We observed that the average persistence probability P(t) followed a power-law behavior like P(t)∼t−θ with the persistence exponent θ=0.477(2). The persistence exponent in the Korean stock market deviates slightly from a random process. The Korean stock market has shown a weak anti-persistence. We measured the persistence properties of the stock market by the generalized price–price correlation function Fq(t). The price–price correlation function followed a power law, Fq(t)∼thq, where hq is called the generalized Hurst exponent. The generalized Hurst exponent depends on the order q which means that there are multiscaling properties in the time series of the stock index. We observed the relationship θ+h₂=1 between the error bars where h₂ is the fractal dimension of the time series.     

股票市场混沌吸引子的特征量——基于G-P算法与小数据量算法

针对金融时间序列的特点,论文分析已有混沌特征量算法的基础上,采用特殊的对数线性趋势消除法(简记为LLD)处理数据、引入Rosenstein提出的小数据量算法等计算最大李雅普诺夫指数以及其它混沌系统的特征量,对我国证券市场的混沌动力学结构作出了稳健的分析。结果表明中国股市具有显著的非线性混沌特征,这一结论将为金融理论的研究提供新的方向。     

基于流数据技术的连续查询处理

网络提供了一个无限的、变化的信息源,使得数据库由传统的静态存储变成了动态的存储,数据变成了流数据,查询变成了连续查询。为了有一个完善的系统来处理大量的数据及查询,提出了一个流查询处理方案,同时满足普通用户和高级用户(有计算机语言基础),并从几个重要的指标来阐述了如何提高查询效率,减少了系统瓶颈,并在原型系统中得到了验证。     

The use of data mining and neural networks for forecasting stock market returns

It has been widely accepted by many studies that non-linearity exists in the financial markets and that neural networks can be effectively used to uncover this relationship. Unfortunately, many of these studies fail to consider alternative forecasting techniques, the relevance of input variables, or the performance of the models when using different trading strategies. This paper introduces an information gain technique used in machine learning for data mining to evaluate the predictive relationships of numerous financial and economic variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of several models. The results show that the trading strategies guided by the classification models generate higher risk-adjusted profits than the buy-and-hold strategy, as well as those guided by the level-estimation based forecasts of the neural network and linear regression models.     

A double-threshold GARCH model of stock market and currency shocks on stock returns

International integration of financial markets provides a channel for currency movements to affect stock prices. This paper applies a four-regime double-threshold GARCH (DTGARCH) model of stock market returns to investigate empirically the effects of daily currency movements on five stock market returns, namely in Taiwan, Singapore, South Korea, Japan and the USA. The asymmetric reactions of the mean and volatility stock returns in five markets to stock market and foreign exchange news are investigated using linear and nonlinear models. We discuss a four-regime DTGARCH model, which allows for asymmetry in both the conditional mean and conditional variance simultaneously by using two threshold variables to analyze stock market reactions to different types of information (that is, positive and negative news) that are generated from stock and foreign exchange markets. By applying the four-regime DTGARCH model, this paper finds that the interactions between the information of stock and foreign exchange markets lead to asymmetric reactions of stock returns and their associated variability. The empirical results show that international fund managers who invest in newly emerging stock markets need to evaluate the value and stability of domestic currencies as part of their stock market investment decisions.     

突发OFDM信号调制识别

为了对OFDM信号进行识别,针对高斯性检测法无法识别白噪声和OFDM信号的问题,提出先应用多子带谱熵法进行OFDM信号端点检测,再构造KS检验统计量进行OFDM信号识别的方案。理论分析及仿真结果表明,多子带谱熵法能够有效地进行OFDM信号的端点检测,基于端点检测的KS统计量降低了纯噪声信号带来的虚警,进而提高了对OFDM信号的识别能力,可以有效地进行突发OFDM信号的识别。     

A query index for continuous queries on RFID streaming data

RFID middleware collects and filters RFID streaming data to process applications＇ requests called continuous queries, because they are executed continuously during tag movement. Several approaches to building an index on queries rather than data records, called a query index, have been proposed to evaluate continuous queries over streaming data. EPCglobal proposed an Event Cycle Specification （ECSpec） model, which is a de facto standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments that represent the query conditions. The problem when using any of the existing query indexes on these continuous queries is that it takes a long time to build the index, because it is necessary to insert a large number of segments into the index. To solve this problem, we propose a transform method that converts a group of segments into compressed data. We also propose an efficient query index scheme for the transformed space. Comparing with existing query indexes, the performance of proposed index outperforms the others on various datasets.     

XTREAM: An efficient multi-query evaluation on streaming XML data

Recently, there has been growing interest in streaming XML data. Much of the work on streaming XML data has been focused on efficient filtering. Filtering systems deliver XML documents to interested users. The burden of extracting the XML fragments of interest from XML documents is placed on users. In this paper, we propose XTREAM which evaluates multiple queries in conjunction with the read-once nature of streaming data. In contrast to the previous work, XTREAM supports a wide class of XPath queries including tree shaped expressions, order based predicates, and nested predicates. In addition, to improve the efficiency and scalability of XTREAM, we devise an optimization technique called Query Compaction. Experimental results with real-life and synthetic XML data demonstrate the efficiency and scalability of XTREAM.     

A scalable supervised algorithm for dimensionality reduction on streaming data

Algorithms on streaming data have attracted increasing attention in the past decade. Among them, dimensionality reduction algorithms are greatly interesting due to the desirability of real tasks. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction approaches. However, PCA is not optimal for general classification problems because it is unsupervised and ignores valuable label information for classification. On the other hand, the performance of LDA is degraded when encountering limited available low-dimensional spaces and singularity problem. Recently, Maximum Margin Criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, the original MMC algorithm could not satisfy the streaming data model to handle large-scale high-dimensional data set. Thus an effective, efficient and scalable approach is needed. In this paper, we propose a supervised incremental dimensionality reduction algorithm and its extension to infer adaptive low-dimensional spaces by optimizing the maximum margin criterion. Experimental results on a synthetic dataset and real datasets demonstrate the superior performance of our proposed algorithm on streaming data.     

基于数据管技术的证券数据可视化

数据管技术是一门新的可视化技术,用三维管状图形表示数据集,将数据映射到管状图的内壁,用户可在数据管内移动,并根据颜色来探测数据。介绍了数据管技术的核心部分4种数据转换及其在证券数据中的应用。     

Similar sequence matching supporting variable-length and variable-tolerance continuous queries on time-series data stream

We propose a new similar sequence matching method that efficiently supports variable-length and variable-tolerance continuous query sequences on time-series data stream. Earlier methods do not support variable lengths or variable tolerances adequately for continuous query sequences if there are too many query sequences registered to handle in main memory. To support variable-length query sequences, we use the window construction mechanism that divides long sequences into smaller windows for indexing and searching the sequences. To support variable-tolerance query sequences, we present a new notion of intervaled sequences whose individual entries are an interval of real numbers rather than a real number itself. We also propose a new similar sequence matching method based on these notions, and then, formally prove correctness of the method. In addition, we show that our method has the prematching characteristic, which finds future candidates of similar sequences in advance. Experimental results show that our method outperforms the naive one by 2.6-102.1 times and the existing methods in the literature by 1.4-9.8 times over the entire ranges of parameters tested when the query selectivities are low (<32%), which are practically useful in large database applications.     

突发通信中串行数据采集系统的研究

针对突发通信中高速数据采集系统的需求,采用异步串行通信芯片ST16C2550构建PC机与DSP问实时信息处理系统的高速串行通信模块,能显著提高DSP的通信接口能力.同时上位机采用LabVIEW图形化编程平台,基于循环查询方式的设计思路,实现了上位机通信的快速编程,短时间即可完成系统开发,从而极大的提高了开发效率.经测试和实践证明,该系统在115 200bps速率工作稳定,达到了突发通信应用要求.     

基于递归模式技术的股票数据可视化

可视化技术对于分析和探究大规模的多维数据集变得越来越重要,其中最重要的一种可视化技术是一种面向像素的可视化技术,其基本原理是将数据集中的每个数据值映射成屏幕上的一个像素并对这些像素按一定的规则充分地加以排列,以便将尽可能多的数据对象以人们熟悉的图形图像展现在屏幕上。递归模式技术是面向像素的可视化技术的一种,它基于简单地来回排列,允许用户参与定义结构和设置参数,主要适用于有自然顺序的数据集。在股票数据分析中,利用递归模式技术比较容易描述交易数据库中股票价格的变化情况,并预测股票的走势。     

An indexing scheme for energy-efficient processing of content-based retrieval queries on a wireless data stream

Wireless data broadcasting is a popular data delivery approach in mobile computing environments, where the broadcasting servers usually adopt indexing schemes for mobile clients to energy-efficiently access data on a wireless broadcast stream. However, conventional indexing schemes use primary key attribute values to construct tree structures. Therefore, these schemes do not support content-based retrieval queries such as partial-match queries and range-queries. This paper proposes an indexing method that supports content-based retrieval queries on a wireless data stream. The method uses a tree-structured index, called B2V-Tree, which is composed of bit-vectors that are generated from data records through multi-attribute hashing. Through analysis and experiments, the effectiveness of the proposed method is shown.     

基于相关和小波分析的激光水下目标检测研究

在激光水下目标探测中,由于海水这一特殊的环境,目标反射回来的回波信号中存在大量的散斑噪声,不利于目标检测。该文利用局部相关特性建立起一种信号的广义空间相关模型,再利用三重相关峰值检测原理和通过对信号的二进小波变换,利用小波变换的局部模极大值在不同尺度下的传播特性,对目标进行检测和定位。实验表明这种基于背景相关分析和小波的目标检测定位方法在激光水下目标探测中可有效检测和定位目标。     

基于R/S方法的股票平均循环周期研究

针对中国股票市场的3个重点行业的长期记忆性和平均循环周期问题进行了研究.通过重标极差(R/S)方法来分析他们的长记忆性,在平均循环周期的估计上,使用了线性插值法来确定V统计量曲线的转折点,以及计算最小二乘法的误差来更清楚的确定R/S曲线转折点这两种方法,并将两者进行对照来估计周期,对各行业分析结果表明,得出中国股票3个行业市场都具有分形特征,其变化具有循环性.     

基于电容传感器的电容近炸引信炸高散布研究

基于电容近炸引信的探测场区为准静电场,根据准静电场的分布特征,类比无线电引信的方向函数,建立电容引信的场强分布函数,得到电容近炸引信的等场强探测方向图近似为一个长短半轴相差20％的椭球,验证了电容近炸引信在不同弹目交会条件下炸高散布小的结论.     

The impact of oil price shocks on stock market activities: Asymmetric effect with quantile regression

This paper examines the impact of changes in real oil prices on the real stock returns of G7 countries. In addition to investigating the asymmetric effect of oil price shocks on stock returns, we also examine the effect of the performances of stock markets themselves, which are relevant to firms’ strategies in the future. Although the responses of stock markets to oil price shocks are diverse among G7 countries, we present the inconsistent reflections of stock markets based on their performances. In many cases, quantile regression estimates are quite different from OLS models. These results carry crucial implications for the linkage between oil and stock markets.     

基于数据仓库的证券交易决策支持系统设计与开发

证券交易对海量数据管理和分析存在潜在需求,对证券交易决策支持系统做了分析,针对海量数据管理,提出基于DW、DM、OLAP的系统框架,并设计了网络结构、主题、数据维度、数据库,最后从数据获取、数据的技术分析及多维分析讲述系统的实现,为决策者管理和分析海量证券交易信息提供决策支持.