Fast handoff with reducing waste rate approach for wireless resources management in UMTS mobile networks

3G Wideband CDMA systems adopt the Orthogonal Variable Spreading Factor code tree as the channelization codes management for achieving high data rate transmission in personal multimedia communications. It assigns a single channelization code for each accepted connection. Nevertheless, it wastes the system capacity when the required rate is not powers of two of the basic rate. One good solution is to assign multiple codes for each accepted connection but it causes two inevitable drawbacks: long handoff delay and new call setup delay due to high complexity of processing with multiple channelization codes, and high cost of using more number of rake combiners. Especially, long handoff delay may result in more call dropping probability and higher Grade of Service, which will degrade significantly the utilization and revenue of the 3G cellular systems. Therefore, we propose herein an adaptive efficient codes determination algorithm based on the Markov Decision Process analysis approach to reduce the waste rate and reassignments significantly while providing fast handoff. Numerical results demonstrate that the proposed approach yields several advantages, including the lowest GOS, the least waste rate, and the least number of reassignments. Meanwhile, the optimal number of rake combiners is also analyzed in this paper. This research was supported in part by the National Science Council of Taiwan, ROC, under contract NSC-93-2213-E-324-018.     

多带抗噪声语音识别算法研究

根据Flether等人的研究,基于感知独立性假设的子带识别方法被用于抗噪声鲁棒语音识别。本文拓展子带方法,采用基于噪声污染假定的多带框架来减少噪声影响。论文不仅从理论上分析了噪声污染假定多带框架在识别性能上的潜在优势,而且提出了多带环境下的鲁棒语音识别算法。研究表明：多带框架不仅回避了独立感知假设要求,而且与子带方法相比,多带方法能更好的减少噪声影响,提高系统识别性能。     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.     

多级网络多处理机系统的性能评估

本文建立了多级网络(δ-网)多处理机系统的马尔柯夫模型,为系统结构参数和任务粒度的选择提供了依据。文中分析了处理机数目、网络结构参数和任务粒度对系统性能的影响,并对模型计算结果和仿真结果作了比较。     

Reliability of structures in high dimensions, part I: algorithms and applications

The present paper is concerned with the estimation of structural reliability when a large number of random variables is present. A sampling technique which uses lines in order to probe the failure domain, is presented. The latter is employed in conjunction with a stepwise procedure which makes use of Markov Chains. The resulting algorithm exhibits accelerated convergence.     

Real-world extensions to scheduling algorithms based on lagrangian relaxation

Recently, efficient scheduling algorithms based on Lagrangian relaxation have been proposed for scheduling parallel machine systems and job shops. In this article, we develop real-world extensions to these scheduling methods. In the first part of the paper, we consider the problem of scheduling single operation jobs on parallel identical machines and extend the methodology to handle multiple classes of jobs, taking into account setup times and setup costs. The proposed methodology uses Lagrangian relaxation and simulated annealing in a hybrid framework. In the second part of the paper, we consider a Lagrangian relaxation based method for scheduling job shops and extend it to obtain a scheduling methodology for a real-world flexible manufacturing system with centralized material handling. This research was supported in part by the Office of Naval Research and the Department of Science and Technology grant N00014-93-1017.     

Calendar maintenance of arbitrarily structured systems

A semi-Markov model is constructed that describes an arbitrarily structured multicomponent technological system and takes into account its calendar maintenance. Using an aggregation algorithm, approximate values of stationary reliability characteristics and an optimal term of maintenance work are determined for the system. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 69–86, March–April 2006.     

Jitter in ATM networks and its impact on peak rate enforcement

Cells arriving to an ATM network experience random delays due to queueing in upstream multiplexing stages, notably in customer premises. This is the phenomenon of jitter and the aim of the present paper is to study its influence on peak rate enforcement. We first introduce some general characterizations of jitter and then, describe two models of jittered flows based on simple queueing systems. We discuss the objectives of peak rate enforcement and study the impact of jitter on the dimensioning of jumping window and leaky bucket mechanisms. A useful synthetic characterization of jitter appears to be a remote quantile of the cell delay distribution expressed in units of the initial inter-cell interval.     

A Fifth Order Flux Implicit WENO Method

The weighted essentially non-oscillatory (WENO) method is an excellent spatial discretization for hyperbolic partial differential equations with discontinuous solutions. However, the time-step restriction associated with explicit methods may pose severe limitations on their use in applications requiring large scale computations. An efficient implicit WENO method is necessary. In this paper, we propose a prototype flux-implicit WENO (iWENO) method. Numerical tests on classical scalar equations show that this is a viable and stable method, which requires appropriate time-stepping methods. Future study will include the examination of such methods as well as extension of iWENO to systems and higher dimensional problems.Sigal Gottlieb - The work of this author supported by NSF grant DMS-0106743.Steven J. Ruuth - The work of this author was partially supported by a grant from NSERC Canada.