On the Performance of Punctured Turbo Codes

IntroductionTurbo codes, first introduced by Berrou et al.[1],have attracted the attention of many researchers for theirremarkable coding gains close to theoretical limits, yetadmitting a relatively simple iterative decodingtechnique[2]. Since then, abundant of fruitful results on theperformance of turbo codes have been published, in whicha distance spectrum interpretation was presented by [3]and a super trellis structure of turbo codes was derived by[4]. An analytical…     

H.264中树状结构运动估计的连续消除算法

为减少视频编码标准 H.2 6 4中树状结构运动估计的高运算复杂度 ,将连续消除算法 (SEA )应用于树状结构的运动估计中。在性能完全相同的条件下 ,与 H.2 6 4整像素运动矢量全搜索算法相比 ,其块匹配运算量减少到原来的 1%～ 2 0 %。利用不同大小块之间因部分重叠 ,其运动矢量具有更强相关性的特点。提出了一种简单有效地确定运动矢量搜索初值的方法 ,并应用两种快速的目标运动矢量判定方法 ,改进了 SEA算法。在性能损失可以忽略的情况下 ,进一步将基本 SEA运算量降低到全搜索块匹配运算量的 0 .3%～4 %。     

A novel audio watermarking scheme using multiscale wavelet modulation

A novel audio watermarking scheme to embed robust and inaudible watermarks for the purpose of copyright protection is proposed. The key innovation is to add time-frequency redundancy into watermark signals by multiscale wavelet modulation. In order to maximize the watermarking strength within perceptual constraints, the signals synthesized from different scales are masked using a frequency auditory model, respectively, and then intergrated to form the final watermark signal. The detection structure is built using the redundancy in watermark signals, and the performance is further enhanced by modeling the statistical behaviors of wavelet coefficients as generalized Gaussian distribution. The use of original audio signal is not required in watermark detection. The experimental results show that our approach can achieve not only good transparency but also satisfying robustness to common audio manipulations.     

随机过程Wn(x,y)的局部渐近性质

本文讨论了随机过程Wn（x，y）的局部渐近性质，得出y在点y0的邻域取值时，过程弱收敛于Gaussian过程．     

脉冲导函数对UWB信号功率谱的影响分析

根据美国联邦电信委员会（FCC）对超宽带（UWB）信号功率谱分布的规定，论述了作为UWB通信信息载体的脉冲波形对UWB信号功率谱分布的重要性，推导出在脉冲位置调制（PPM）方式下，高斯脉冲及其导函数与UWB信号功率谱中心频率之间的关系．     

暴涨指数的随机理论

回顾了现代宇宙学的历史和现状,从Friedmann方程出发,将标量场的量子涨落作为Gaussian白噪声,建立了反映宇宙暴涨过程的Langevin方程,得到尺度因子的均值、自相关系数和方差;特别是详细计算了暴涨指数,发现用这种方法得到的暴涨指数与经典结果差异甚微.     

基于折扣高斯粒子滤波器的桥梁可靠性动态预测

桥梁健康监测(BHM)系统在长期运营中积累了大量信息,如何利用这些信息动态预测结构可靠性已成为BHM领域的关键科学问题之一.为合理预测桥梁的动态可靠性,应用BHM系统日常监测的极值应力数据,建立带有最优折扣因子的动态线性模型,结合高斯粒子滤波器给出折扣高斯粒子滤波器预测算法,分别对日常监测极值应力的一步向前预测分布参数和状态变量后验分布参数进行修正预测,并基于此,采用一次二阶矩(FOSM)方法预测桥梁的动态可靠性,结合桥梁实测数据对所提方法进行了验证分析,为桥梁预防性养护维修决策提供理论基础.     

基于超螺旋精度提升机制的OFDM网络信道噪声消除算法

为解决当前OFDM网络信道噪声消除算法难以规避信道中的窄带莱斯噪声,且抗信道衰落能力不强,以及信道间普遍存在严重的频率干涉问题,提出了一种基于超螺旋精度提升机制的OFDM网络信道噪声消除算法.首先,根据各个传输子信道具有差异化的频率载波特性,将传输子信道等价为接收背景中的接收点,从而构建了超螺旋接收结构,有效消除不同传输子信道间存在的频率干扰特性,降低信道窄带莱斯噪声对信号预发射过程的衰落影响;随后,基于角度识别方式进行锐-钝识别,并结合拐点思想,采用序列重排技术,将待发射信号进行消波处理,并进行自旋精度提升,从而提高了其抗窄带莱斯噪声的性能.仿真实验表明:与常见的超高频精度自适应机制(UHF adaptive,UHFA)、共线度自旋精度提升机制(the degree of accuracy of collinear spin,DACS)相比,本文算法与理想状态下的OFDM功率谱密度曲线最为接近,具有更高的信号增益水平与更低的信道误码率.     

Semiparametric Preference Learning

Unlike traditional supervised learning problems,preference learning learns from data available in the form of pairwise preference relations between instances.Existing preference learning methods are either parametric or nonparametric in nature.We propose in this paper a semiparametric preference learning model,abbreviated as SPPL,with the aim of combining the strengths of the parametric and nonparametric approaches.SPPL uses multiple Gaussian processes which are linearly coupled to determine the preference relations between instances.SPPL is more powerful than previous models while keeping the computational complexity low （linear in the number of distinct instances）.We devise an efficient algorithm for model learning.Empirical studies have been conducted on two real-world data sets showing that SPPL outperforms related preference learning methods.     

Semantic image annotation based on GMM and random walk model

Automatic image annotation has been an active topic of research in computer vision and patternrecognition for decades.A two stage automatic image annotation method based on Gaussian mixturemodel (GMM) and random walk model (abbreviated as GMM-RW) is presented.To start with,GMM fitted by the rival penalized expectation maximization (RPEM) algorithm is employed to estimatethe posterior probabilities of each annotation keyword.Subsequently, a random walk processover the constructed label similarity graph is implemented to further mine the potential correlations ofthe candidate annotations so as to capture the refining results, which plays a crucial role in semanticbased image retrieval.The contributions exhibited in this work are multifold.First, GMM is exploitedto capture the initial semantic annotations, especially the RPEM algorithm is utilized to train themodel that can determine the number of components in GMM automatically.Second, a label similaritygraph is constructed by a weighted linear combination of label similarity and visual similarity ofimages associated with the corresponding labels, which is able to avoid the phenomena of polysemyand synonym efficiently during the image annotation process.Third, the random walk is implementedover the constructed label graph to further refine the candidate set of annotations generated byGMM.Conducted experiments on the standard Corel5k demonstrate that GMM-RW is significantlymore effective than several state-of-the-arts regarding their effectiveness and efficiency in the task of automatic image annotation.