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。     

一种基于冗余小波变换的DT网格运动估计和运动补偿方法

网格划分和网格顶点的运动估计是基于不规则网格的视频压缩技术的关键。为了进一步提高网格运动估计和运动补偿的效果,在综合比较现有冗余小波变换域运动估计方法和适用规则网格的EMRMC算法的基础上,提出了一种新的基于不规则网格的运动估计和运动补偿算法,即在冗余小波变换域提取特征点和运动潜在区,网格顶点的运动估计采用结合运动潜在区的在时域进行块匹配的运动估计和运动补偿方法,而运动补偿则通过三角形仿射变换完成。同时还给出了冗余小波变换域提取运动潜在区的计算模板。理论分析和实验结果表明,该算法在补偿效果方面较前两种方法得到了改进。     

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。     

QA95—630型钢坯剪切机焊接机身的有限元分析

建立了ＱＡ９５－６３０型钢坯剪切机焊接机身的力学模型，利用有限元分析软件Ｓｕｐｅｒ－ＳＡＰ对机身进行网格划分和有限元分析，输出应力数表、位移数表、等应力线图和等位移线图等结果，以帮助设计人员对机身进行校核、改进或重新设计。     

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.     

Disability and climate change: A critical realist model of climate justice

Existing literature on climate change as an issue of environmental justice documents the heightened vulnerability of people with disabilities to the effects of climate change. Additionally, there are numerous studies showing that access to information is a prerequisite for perceiving risk and taking action. Building on this work, our review seeks to understand how physical disability relates to perceptions of climate-related risk and adaptations to climate-related events. We introduce a critical realist model of climate justice to understand the relationships between the environmental features that disable, risk perception and information seeking, and adaptive capacity and resilience to climate change. In understanding the vulnerability and adaptive capacity of people with disabilities to climate change, this review synthesizes research on one of the U.S.’s largest minority communities with the goals of better understanding how vulnerable populations cope with climate change and integrating them into climate action and policy.     

面向链路状态信息的路由算法LSDSR

提出一种WA mesh网络组网方式,将WALN技术与Ad hoc技术相结合,充分发挥二者的优势。针对WA mesh需要高性能路由算法,提出一种基于链路状态信息的改进路由算法LSDSR。分析表明WA mesh网络不仅具备WLAN网络成本低、吞吐量高、通信可靠的特点,还具备Ad hoc网络的高移动性、高灵活性、高抗毁性的特点。LSDSR路由算法能够减少路由中断的几率,减少网络中洪泛信息的出现,不仅改善了路由发现时间和链路负载,而且改善了路由请求信息和丢包率,提升了网络的效率,对链路的延迟、网络确认控制方面也有更良好的表现。