多用户NC-OFDM动态频谱分配策略研究*

为提高基于非连续正交频分复用（NC-OFDM）的多用户动态频谱分配的频谱利用率,分析了两种物理层特性——用户干扰和信道衰落对动态频谱分配性能的影响,并在此基础上提出了两种改进的频谱分配策略;改进的策略依次将NC-OFDM系统的物理层特性纳入动态频谱分配策略的设计中。仿真分析验证了改进策略的有效性,其频谱分配的中断概率相比改进前的策略平均降低了约13%,提高了频谱利用率。因此,充分利用物理层特性是动态频谱分配策略提高效率的可行途径。     

多用户存储中自适应动态预取策略

通过分析多用户数据请求规律以及实时分解随机请求序列来获取顺序请求序列。基于对多用户顺序请求进行命令预分解和命中率统计,实现读预取长度自我学习。分析多用户预取率及系统负载与预取失效代价之间的关系,对常规自适应Cache策略进行优化,选择合适预取阈值等参数。与常规自适应预取策略相比,动态调整Cache策略的预取命中率提高了30%。有效解决了多用户访问共享存储系统的预取失效率高问题。     

多用户动态交通分配和信号优化的组合研究

提出了一种饱和路网中考虑多用户行为下的动态交通分配和交通信号优化的组合模型。模型采用广义双层规划来表示。模型的上层是信号控制优化,下层是考虑多用户路径选择条件下的动态交通分配,进行交通网络流的配置。同时,模型中采用具有物理排队的动态网络模型,从而考虑了饱和路网中物理排队对基于路段的网络条件的影响。通过仿真说明了饱和路网中考虑多用户行为下的动态交通分配和交通信号优化的组合模型可以实现交通信号的优化配置和交通网络流的优化,并反映了排队的物理效应。     

Exploring embedded guidance and self-efficacy in educational multi-user virtual environments

In this paper, we present the results of an exploratory study into the relationship between student self-efficacy and guidance use in a Multi-User Virtual Environment (MUVE) science curriculum project. We describe findings from a sample of middle school science students on the combined impact on learning of student self-efficacy in scientific inquiry and use of individualized guidance messages, and on the interplay between levels of self-efficacy and use of an embedded guidance system in an educational MUVE. Results from our study showed that embedded guidance was associated with improved learning outcomes for learners across a spectrum of self-reported efficacy in science. However, we also found that learners with low levels of initial self-efficacy in science viewed fewer guidance messages than their higher efficacy peers, and did not perform as well as their higher efficacy peers regardless of guidance use level. At the same time, outcomes for low self-efficacy students who used the guidance system heavily were raised to the level of high self-efficacy students who did not use the system.     

Are we there yet? Exploring with dynamic visualization

Interactive graphics practitioners have long understood that viewing a virtual object by controlling the viewpoint dynamically is more illuminating than viewing a still image or even a precomputed animation. Dynamic manipulation engages a viewer's kinesthetic sense in addition to his visual sense, adding an immediacy to the exploration experience. Finding the right way to represent data has been an active topic of much thought and discussion since the beginnings of visualization. To explore dynamic visualization's power, the author constructed a tool (Calico), for creating and manipulating bivariate color mappings using several different color models. Using Calico, she conducted two experimental studies of the effects of control over the color mapping on accuracy, confidence, and preference     

Exploring connectivity of the brain's white matter with dynamic queries

Diffusion tensor imaging (DTI) is a magnetic resonance imaging method that can be used to measure local information about the structure of white matter within the human brain. Combining DTI data with the computational methods of MR tractography, neuroscientists can estimate the locations and sizes of nerve bundles (white matter pathways) that course through the human brain. Neuroscientists have used visualization techniques to better understand tractography data, but they often struggle with the abundance and complexity of the pathways. In this paper, we describe a novel set of interaction techniques that make it easier to explore and interpret such pathways. Specifically, our application allows neuroscientists to place and interactively manipulate box or ellipsoid-shaped regions to selectively display pathways that pass through specific anatomical areas. These regions can be used in coordination with a simple and flexible query language which allows for arbitrary combinations of these queries using Boolean logic operators. A representation of the cortical surface is provided for specifying queries of pathways that may be relevant to gray matter structures and for displaying activation information obtained from functional magnetic resonance imaging. By precomputing the pathways and their statistical properties, we obtain the speed necessary for interactive question-and-answer sessions with brain researchers. We survey some questions that researchers have been asking about tractography data and show how our system can be used to answer these questions efficiently.     

Exploring casual point-and-tilt interactions for mobile geo-blogging

People record and share their experiences through text, audio and video. Increasingly they do this blogging from mobile devices. We illustrate a novel, mobile, low interaction cost approach to support the creation of a rich record of journeys made and places encountered: by pointing and tilting a mobile, users indicate their interests in a location. We built three mobile prototypes to explore the approach—the first one combines gestures and visual map feedback; the second provides a simpler visual interface; the third supports eyes-free interaction, allowing the user to simply point-and-tilt, with no visual display required. We describe two field studies undertaken to understand the value of the interaction styles afforded, then continue with a further user study to assess the interaction speed and accuracy between these interaction methods. We present the results of these studies and raise issues relevant to their design and to the wider class of devices and services concerned with mobile spatial information access.     

一种带预处理模块的全局最优多用户检测算法

提出了一种新型的带有预处理模块的全局最优多用户检测算法,该算法可以通过预处理模块直接判断出用户信息,并保证所得的用户信息码与最佳多用户检测得到的结果相同。以STBC系统为例验证了该算法的有效性,仿真结果表明该算法可以精确地判断出用户信息,通过使用该算法可以实现更低的系统误码率,大大提高系统性能。     

Exploring dynamic self-adaptive populations in differential evolution

Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. As a solution, self-adaptation has been found to be highly beneficial in automatically and dynamically adjusting evolutionary parameters such as crossover rates and mutation rates. In this paper, we present a first attempt at self-adapting the population size parameter in addition to self-adapting crossover and mutation rates. Firstly, our main objective is to demonstrate the feasibility of self-adapting the population size parameter in DE. Using De Jong's F1–F5 benchmark test problems, we showed that DE with self-adaptive populations produced highly competitive results compared to a conventional DE algorithm with static populations. In addition to reducing the number of parameters used in DE, the proposed algorithm actually outperformed the conventional DE algorithm for one of the test problems. It was also found that that an absolute encoding methodology for self-adapting population size in DE produced results with greater optimization reliability compared to a relative encoding methodology.     

Exploring overlapping clusters using dynamic re-scaling and sampling

Until recently, the aim of most text-mining work has been to understand major topics and clusters. Minor topics and clusters have been relatively neglected even though they may represent important information on rare events. We present a novel method for exploring overlapping clusters of heterogeneous sizes, which is based on vector space modeling, covariance matrix analysis, random sampling, and dynamic re-weighting of document vectors in massive databases. Our system addresses a combination of difficult issues in database analysis, such as synonymy and polysemy, identification of minor clusters, accommodation of cluster overlap, automatic labeling of clusters based on their document contents, and the user-controlled trade-off between speed of computation and quality of results. We conducted implementation studies with new articles from the Reuters and LA Times TREC data sets and artificially generated data with a known cluster structure to demonstrate the effectiveness of our system. Mei Kobayashi received a Bachelors degree in Chemistry from Princeton and Masters and Ph.D. degrees in Pure and Applied Mathematics from UC Berkeley. She was a student intern in Frick Chemical Laboratory at Princeton, the Biochemical and Math-Physics divisions of Lawrence Berkeley Laboratories, and IBM Research. She has been a Researcher at IBM since 1988 and has been involved in projects ranging from inverse problems, airflow simulation and graphics to speech signal analysis using wavelets. Her most recent work has been on information retrieval, data mining, and unstructured information management. She has served on the Editorial Board of the Bulletin of Japan SIAM and Technical Program Committees of the SIAM Data Mining Conference, SIAM Text Mining Workshops, and Symposiums on Wavelets sponsored by the Japanese Ministry of Education. From 1996 to 1999, she was a Visiting Associate Professor at the Graduate School for Mathematical Sciences of the University of Tokyo. Masaki Aono received Bachelors and Masters in Science degrees in Information Science from the University of Tokyo and a Ph.D. in Computer Science from Rensselaer Polytechnic Institute. He worked for IBM Research, Tokyo Research Laboratory from 1984 to 2003. He is currently a Professor in the Information and Computer Sciences. Department at the Toyohashi University of Technology, where he is teaching object-oriented programming, logic circuit, computer architecture, and knowledge data engineering. His current research interests include text and data mining, information extraction, semantic web, and information visualization. His most recent work on time series data mining from human body bio-signals obtained by microsensors, was been selected to be part of the 21st century Center Of Excellence Program sponsored by Japanese government. He has been a Japanese delegate of the ISO/IEC JTC1 SC24 Standard Committee since 1996.     

Exploring gene causal interactions using an enhanced constraint-based method

DNA microarray provides a powerful basis for analysis of gene expression. Bayesian networks, which are based on directed acyclic graphs (DAGs) and can provide models of causal influence, have been investigated for gene regulatory networks. The difficulty with this technique is that learning the Bayesian network structure is an NP-hard problem, as the number of DAGs is superexponential in the number of genes, and an exhaustive search is intractable. In this paper, we propose an enhanced constraint-based approach for causal structure learning. We integrate with graphical Gaussian modeling and use its independence graph as an input of our constraint-based causal learning method. We also present graphical decomposition techniques to further improve the performance. Our enhanced method makes it feasible to explore causal interactions among genes interactively. We have tested our methodology using two microarray data sets. The results show that the technique is both effective and efficient in exploring causal structures from microarray data.     

Supporting dynamic interactions among Web-based information sources

The ubiquity of the World Wide Web offers an ideal opportunity for the deployment of highly distributed applications. Now that connectivity is no longer an issue, attention has turned to providing a middleware infrastructure that will sustain data sharing among Web-accessible databases. We present a dynamic architecture and system for describing, locating, and accessing data from Web-accessible databases. We propose the use of flexible organizational constructs service links and coalitions to facilitate data organization, discovery, and sharing among Internet-accessible databases. A language is also proposed to support the definition and manipulation of these constructs. The implementation combines Java, CORBA, database API (JDBC), agent, and database technologies to support a scalable and portable architecture interconnecting large networks of heterogeneous and autonomous databases. We report on an experiment to provide uniform access to a Web of healthcare-related databases     

Exploring the theoretical foundations of playfulness in computer interactions

This paper develops an integrated theory of playfulness in computer interactions using multiple constructs. Although previous researchers have suggested that playfulness in computer interactions should be measured as both a state and a trait, we suggest that fully understanding the relevant constructs involves much more than simply a state-trait measurement issue. We synthesize the literature from social psychology, applied psychology, human resource management, management information systems, and marketing and present a comprehensive model of the network of constructs surrounding playfulness in computer interactions. This model serves to clarify both the trait and state influences on playfulness in computer interactions. Trait influences are captured through the Big Five theory of personality and microcomputer playfulness (MCP), while state influences are captured by flow theory. We encourage research into our model of playfulness in computer interactions with directional hypotheses and a suggested research agenda.     

结合波束成形MMSE-THP算法研究*

Tomlinson-Harashima预编码(THP)算法是一种多输入多输出下行链路系统中有效去除用户间干扰(MUI)的预均衡技术,但存在系统容量损失的问题。为此,提出通过在接收端进行波束成形使得MMSE-THP系统和容量最大化,设计了信道状态信息完美和不完美两种情况下结合波束成形的系统方案。理论分析与仿真结果均表明,与传统MMSE-THP算法相比较,该方案在低信噪比时能提高系统和容量,并对高信噪比时系统的误码性能有明显改善。     

Multicriteria,multi-user scheduling in grids with advance reservation

In this paper, we propose a new method for multi-user, multicriteria job scheduling in Grid environments with QoS guarantees concerning time and cost. The main goal of our method is to find a fair schedule of jobs that were submitted by multiple users. To obtain a schedule which is satisfactory for each user we aim at finding a set of advance reservations (ARs) for multiple users at once. This goal is achieved by adequate use of the Ordered Weighted Averaging (OWA) operator and Multiobjective Evolutionary Algorithm (MOEA) with carefully designed problem representation and operators. We also propose a data structure and algorithm used to manage and search for resource availability time slots. Efficiency and usefulness of our approach was demonstrated by computational experiments conducted within a simulation environment.     

基于多用户QoS的中继系统功率分配算法

研究了非再生中继满足不同用户速率需求的公平性功率分配问题。提出了基于用户期望速率比例公平性功率分配算法,由于源端无法预先接收用户期望速率信息,该算法在实际操作中存在一定的不可操作性。进一步提出了预均分公平性功率分配算法,可弥补基于用户期望速率比例公平性功率分配算法的不足。仿真结果表明,两种算法都实现了保证用户服务质量(QoS)的最优功率分配以最小化用户速率与期望速率差值,节约网络资源的优化目标。     

Decentralized adaptive stabilization for interconnected systems with dynamic input-output and nonlinear interactions

This paper considers the problem of robust decentralized adaptive output feedback stabilization for a class of interconnected systems with dynamic input and output interactions and nonlinear interactions by using MT-filters and the backstepping design method. It is shown that the closed-loop decentralized system based on MT-filters is globally uniformly bounded, all the signals except for the parameter estimates can be regulated to zero asymptotically, and the L₂ and L_∞ norms of the system outputs are also be bounded by functions of design parameters. The scheme is demonstrated by a simulation example.     

Exploring STIP-based models for recognizing human interactions in TV videos

Human motion recognition – action (HAR) or interaction (HIR) – in real video data is identified as a very challenging task. In the last few years models of increasing complexity have been proposed in order to improve the performance in the task. However, it still remains unclear whether it is the features or the models what deserves the increase in complexity. In this paper an evaluation of such problem is carried out in the HIR task. For that purpose, we compare the results obtained in our experiments – by using STIP-based features and BOW models as basis and combined with a standard classifier – with some of the more effective and recent approaches that use alternative representation models. We perform a comprehensive experimental study on two state-of-the-art databases in HIR: TV Human interactions and UT-interactions. We compare the results of our experiments with recent results published on these datasets. In addition, we run cross-data experiments on Hollywood-2 dataset in order to study the capability of generalization of the trained models through different datasets. The most relevant result is that the model combining STIP + BOW is competitive in the HIR task in comparison with the most complex ones. It is also shown that the vocabulary learning subtask can be improved by using compression algorithms on large enough initial set of features. In contrast to other categorization tasks the context does not help, the results show that dense sampling of STIP is the best choice, but only when it is used inside the region of interest of the interaction.     

一种改进型非线性多用户检测器

本文通过分析传统匹配滤波检测器的性能缺陷;并在传统检测器的基础上,依据极大似然和最小方差准则,对传统检测器估计错误的信息比特进行适当修正的思想上,提出了一种改进型非线性多用户检测器.理论分析和仿真表明这种检测器在误码率和复杂度性能方面有了极大提高.