Cell-to-switch level planning in mobile wireless networks for efficient management of radio resources

A major step in effectively managing radio resources in a cellular network is to design an appropriate scheme for assigning cells to a location area (LA), serviced by a switch, and allocate resources for individual switches. However, this assignment is already proven in the literature to be an NP-hard problem [Merchant A, Sengupta B. Assignment of cells to switches in PCS networks. IEEE/ACM Transactions on Networking 3(5) (1995) 521–6] that requires efficient heuristic search techniques for obtaining real-time solutions. This work presents a state-space search technique, which is a variant of best first search heuristic technique. The algorithm called the block depth first search (BDFS), allocates cells to switches during switch level resource planning. Under various simulated performance criteria, we compare the performance of the proposed technique with other similar procedures in the literature. Our results indicate that the BDFS outperforms the meta-heuristic procedures in terms of both efficiency and quality of solutions. Hence, we conclude that our proposed technique can be effectively used for switch level planning leading to an efficient management of scarce radio resource in cellular networks.     

A note on ∞manque in variance covariance components models

Stimualted by C.R. Rao's MINQUE J. Focke and G. Dewess introduced the so called r-and ∞ MINQUE. Although they developed a unique charecterization of ∞-MINQUE, they did not give explicite formulas for its computation. The goal this paper is to close this lack and to etend the concept to more general models.     

一种新的鲁棒自适应模糊控制器的设计及性能分析

针对一类连续时间非线性动态系统，根据李亚普诺夫分析方法设计了一种鲁棒自适应模糊控制器．这种自适应系统在受到有界的外部干扰或存在建模误差时具有鲁棒性．证明了控制算法是全局稳定的，跟踪误差可以收敛到零的一个邻域内     

基于Haar小波变换的无失真图像压缩

提出了一种利用Ｈａａｒ小波进行图像无失真压缩的算法。对线性预测后的图像进行Ｈａａｒ小波分解，将各子带小波系数根据大小分解成两部分，其位置信息分别通过自适应算术编码进行了有效的压缩。试验结果表明，该算法实现简单，达到了很好的压缩效果。     

TMS32010自适应均衡系统的开发与实现

本文使用新一代的高速信号处理片TMS32010设计出LMS算法的自适应均衡(AE)系统。在收敛性能、处理速度、小型化、使用灵活性及成本费用诸方面均优于或等于用模拟电路、专用数字电路及一般16位微机制成的AE系统。文中叙述了TMS32010 AE系统的设计思想,总体设计和硬、软件设计方法,结构框图,设计时需注意的各种问题以及试验結果。通过实验证明,系统是成功的,其性能与理论分析相符。本文所述方法除可用于LMS算法的AE设计外,也可用于其它AE算法的设计,且若适当改变程序及少量输出接口硬件,尚可用于其它数字信号处理,如调制解调、谱估计及声音控制等。     

Exploring quasi Monte Carlo for marginal density approximation

We first review quasi Monte Carlo (QMC) integration for approximating integrals, which we believe is a useful tool often overlooked by statistics researchers. We then present a manually-adaptive extension of QMC for approximating marginal densities when the joint density is known up to a normalization constant. Randomization and a batch-wise approach involving (0,s)-sequences are the cornerstones of our method. By incorporating a variety of graphical diagnostics the method allows the user to adaptively allocate points for joint density function evaluations. Through intelligent allocation of resources to different regions of the marginal space, the method can quickly produce reliable marginal density approximations in moderate dimensions. We demonstrate by examples that adaptive QMC can be a viable alternative to the Metropolis algorithm.     

一种适用于远程医疗的水印算法

研究了脑电图(EEG)的水印算法,提出了一种基于自适应滤波的数字水印算法。实验仿真结果表明水印是不可见的,经过低通滤波、中值滤波、维纳滤波、JPEG压缩、小角度旋转等图像处理操作后仍可检验出水印,比空域算法有更高的鲁棒性。     

基于复杂网络理论的供应链网络研究综述

本文介绍了供应链网络具有复杂网络特征的认识发现过程、供应链网络的复杂网络属性及其具体内涵、基于复杂网络理论的供应链建模技术拓展和重构、基于复杂网络理论的供应链网络行为和现象专题研究、相关实践应用研究,并对未来研究方向提出了建议。     

Phase I trial design for drug combinations with Bayesian model averaging

Various statistical models have been proposed for two‐dimensional dose finding in drug‐combination trials. However, it is often a dilemma to decide which model to use when conducting a particular drug‐combination trial. We make a comprehensive comparison of four dose‐finding methods, and for fairness, we apply the same dose‐finding algorithm under the four model structures. Through extensive simulation studies, we compare the operating characteristics of these methods in various practical scenarios. The results show that different models may lead to different design properties and that no single model performs uniformly better in all scenarios. As a result, we propose using Bayesian model averaging to overcome the arbitrariness of the model specification and enhance the robustness of the design. We assign a discrete probability mass to each model as the prior model probability and then estimate the toxicity probabilities of combined doses in the Bayesian model averaging framework. During the trial, we adaptively allocated each new cohort of patients to the most appropriate dose combination by comparing the posterior estimates of the toxicity probabilities with the prespecified toxicity target. The simulation results demonstrate that the Bayesian model averaging approach is robust under various scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive Warped Kernel Estimators

In this work, we develop a method of adaptive non‐parametric estimation, based on ‘warped’ kernels. The aim is to estimate a real‐valued function s from a sample of random couples (X,Y). We deal with transformed data (Φ(X),Y), with Φ a one‐to‐one function, to build a collection of kernel estimators. The data‐driven bandwidth selection is performed with a method inspired by Goldenshluger and Lepski (Ann. Statist., 39, 2011, 1608). The method permits to handle various problems such as additive and multiplicative regression, conditional density estimation, hazard rate estimation based on randomly right‐censored data, and cumulative distribution function estimation from current‐status data. The interest is threefold. First, the squared‐bias/variance trade‐off is automatically realized. Next, non‐asymptotic risk bounds are derived. Lastly, the estimator is easily computed, thanks to its simple expression: a short simulation study is presented.