基于Windows的DSM系统中线程迁移的实现

随着多线程DSM系统的发展,线程迁移已经成为DSM系统中一个非常重要的研究课题。在分析Windows操作系统的存储和控制结构的基础上,讨论了基于Windows操作系统的多线程DSM系统的基本框架,提出了一种对线程进行动态迁移的方法。并且在一个基于Windows操作系统工作站机群的Smonn系统中成功实现,表明了该方法的有效性。     

MLC型NAND闪存中基于MI异构的Polar码优化

为了进一步提高多级单元（multi-level-cell,MLC）闪存的耐久度和可靠性,提出了一种MLC闪存信道中基于互信息量（mutual information,MI）异构的polar码优化方法.该方法利用对数似然比（log-likelihood ratio,LLR）分布在MLC闪存信道和AWGN（additivewhite Gaussian noise）信道中的差异性,以MI重新拟合LLR分布,得到在闪存信道下等效的标准方差,从而进行高密度存储系统中的polar码优化设计.随后,分析了不同的polar码构造法对多级存储单元的纠错性能影响,并与所提的构造方法进行比较.仿真结果表明该文优化方法优于AWGN信道下传统的构造方法,当编程/擦除（program-and-erase,PE）循环为21 000次时,与蒙特卡罗法相比其误码率（bit error rate,BER）性能提升2个数量级,且在BER为2 × 10-5时可增加6 800次的编程/擦除循环.     

Exploring Survey‐Based Inflation Forecasts

This paper first shows that survey‐based expectations (SBE) outperform standard time series models in US quarterly inflation out‐of‐sample prediction and that the term structure of survey‐based inflation forecasts has predictive power over the path of future inflation changes. It then proposes some empirical explanations for the forecasting success of survey‐based inflation expectations. We show that SBE pool a large amount of heterogeneous information on inflation expectations and react more flexibly and accurately to macro conditions both contemporaneously and dynamically. We illustrate the flexibility of SBE forecasts in the context of the 2008 financial crisis. Copyright © 2011 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

税控收款机Flash文件系统的设计与实现

针对税控收款机设计了一种Flash文件系统,通过Flash空间分配实现方式优化、断电保护和空间回收等关键技术,并结合嵌入式数据库设计思想,向用户提供方便可靠的数据导入导出及日常检索功能,从而为税控收款机的推广提供了系统软件支持。     

基于Delphi扫描线技术的快速图像处理

传统的图像处理一般采用像素点赋值的方法,处理速度极慢,对于大型图像几乎无能为力,而Delphi扫描线方法是对图像的每一行进行扫描,获得各像素的内存地址.这种内存操作比常规的像素点赋值效率高很多,从而可以大大提高大型位图图像的处理速度.     

Volterra series based predistortion for broadband RF power amplifiers with memory effects

RF power amplifiers(PAs)are usually considered as memoryless devices in most existing predistortion techniques.However,in broadband communication systems,such as WCDMA,the PA memory effects are significant,and memoryless predistortion cannot linearize the PAs effectively.After analyzing the PA memory effects,a novel predistortion method based on the simplified Volterra series is proposed to linearize broadband RF PAs with memory effects.The indirect learning architecture is adopted to design the predistortion scheme and the recursive least squares algorithm with forgetting factor is applied to identify the parameters of the predistorter.Simulation results show that the proposed predistortion method can compensate the nonlinear distortion and memory effects of broadband RF PAs effectively.     

连续时间环结构神经网络及其设计

在已有文献的基础上,给出了环结构神经网络的连续形式,它是Ｈｏｐｆｉｅｌｄ神经网络和双向联想记忆（ＢＡＭ）神经网络的推广,可以完成多类样本之间的互联想记忆．文中还给出了其综合设计方法．     

人手柔性触觉感知特性

提出了一种新颖的基于弹性梁有效变形长度控制的柔性触觉感知再现方法,并研制了柔性触觉感知装置.通过在该柔性触觉装置上进行实验,对人手的柔性触觉感知特性进行了研究,得出一些结论.刚度越大,分辨率越低,随着刚度的增大,相比较的2个物体间需具有更大的刚度差异才能够分辨.当人们触摸物体时,习惯的次数为2～6次,按压频率主要分布在0.3～1.3 Hz.实验点的刚度越大越难记忆.连续感知的正确率要明显高于不连续感知的正确率.本实验方法简单有效,得出的实验结论不仅可用来改进触觉再现装置的设计,而且为触觉再现技术的研究提供了生理学依据.     

基于神经网络自联想的数字图像水印算法

基于神经网络自联想记忆功能,提出了一种新的数字图像水印算法.首先将水印信息存储到神经网络中;再将其分别嵌入到原始图像小波分解的近似系数分量和细节系数分量中,采用嵌入水印过程的逆过程提取水印;最后利用神经网络对提取出的水印图像进行后处理.实验结果表明所提方法在满足水印的不可感知性的前提下,对多种攻击,如JPEG压缩、加性和乘性噪声干扰、滤波、剪切等具有较强的鲁棒性.