PAIDD: a hybrid P2P-based architecture for improving data distribution in social networks

Rapid growth in social networks(SNs)presents a unique scalability challenge for SN operators because of the massive amounts of data distribution among large number of concurrent online users.A request from any user may trigger hundreds of server activities to generate a customized page and which has already become a huge burden.Based on the theoretical model and analytical study considering realistic network scenarios,this article proposes a hybrid P2P-based architecture called PAIDD.PAIDD fulfills effective data distribution primarily through P2P connectivity and social graph among users but with the help of central servers.To increase system efficiency,PAIDD performs optimized content prefetching based on social interactions among users.PAIDD chooses interaction as the criteria because user’s interaction graph is measured to be much smaller than the social graph.Our experiments confirm that PAIDD ensures satisfactory user experience without incurring extensive overhead on clients’network.More importantly,PAIDD can effectively achieve one order of magnitude of load reduction at central servers.     

一种简化的网络回声消除算法

针对网络回波抵消器中大量抽头系数需要更新的问题,提出一种基于集员滤波和Mmax局部迭代策略的符号数据归一化LMS算法。仿真与实验结果表明：由于每次迭代将对误差性能贡献最大的输入信号筛选出来作为输入,从而提高集员滤波算法的稳态性能,并加快符号数据归一化LMS算法的收敛速度,同时还能够减少算法的运算量。     

一种改进的NLMS算法及其在回声消除中的应用

针对回波抵消器中大量抽头系数需要更新的问题,提出了一种基于权系数局部迭代和集员滤波的NLMS算法。首先基于权系数瞬时梯度估计的Mmax系数局部迭代方法,在每次迭代中把幅度较大输入元素对应的权系数筛选出来进行更新。其次,为了进一步减少算法的运算量,引入了基于系数稀疏更新理论的集员滤波算法,该算法中只有当参数估计误差大于给定的误差门限时滤波器系数才进行迭代更新,从而有效地减少了滤波器系数的迭代次数。     

基于纹理分析的视差估计算法

针对传统方法难以可靠估计图像中纹理单一像素点视差的问题,提出一种新的基于纹理分析的视差估计算法。与已有方法不同,在以极线约束计算像素点视差时,将极线上纹理单一且近似的像素点合并成直线段,根据连续性和唯一性约束对直线段进行整体匹配,采用直线段的视差得到纹理单一区域的稠密视差图。利用直线段进行整体匹配,提高比较基元包含的信息量,减少扫描范围,从而降低误匹配产生的概率和算法时间复杂度。实验结果表明,该方法能提高纹理单一区域稠密视差图的精度,匹配速度快,具有实用价值。     

基于粗糙集的决策表属性约简方法的研究

求核和属性约简是粗糙集理论研究的一个核心问题。文中主要针对现有的一些决策表属性约简算法存在的不足,尤其是基于信息熵的属性约简算法在较大数据集上效率不高的问题提出改进。主要通过结合粗糙集的相关理论来改进原有的属性约简算法在求核中的约束条件,进而在原有算法的基础上提出了一种改进算法。在求约简属性集时,利用新提出的约简算法,使计算复杂度降低,同时保持了高效的决策准确率。实验结果表明改进后的决策表属性约简方法能够更加快速有效地找到约简集。     

Efficient Acoustic Echo Cancellation With Reduced-Rank Adaptive Filtering Based on Selective Decimation and Adaptive Interpolation

This paper presents a new approach to efficient acoustic echo cancellation (AEC) based on reduced-rank adaptive filtering equipped with selective-decimation and adaptive interpolation. We propose a novel structure of an AEC scheme that jointly optimizes an interpolation filter, a decimation unit, and a reduced-rank filter. With a practical choice of parameters in AEC, the total computational complexity of the proposed reduced-rank scheme with the normalized least mean square (NLMS) algorithm is approximately half of that of the full-rank NLMS algorithm. We discuss the convergence properties of the proposed scheme and present a convergence condition. First, we examine the performance of the proposed scheme in a single-talk situation with an error-minimization criterion adopted in the decimation selection. Second, we investigate the potential of the proposed scheme in a double-talk situation by employing an ideal decimation selection. In addition to mean squared error (MSE) and power spectrum analysis of the echo estimation error, subjective assessments based on absolute category rating are performed, and the results demonstrate that the proposed structure provides significant improvements compared to the full-rank NLMS algorithm.     

一种用于运动估计的十字六边形搜索算法

提出了一种十字六边形搜索算法用于快速运动估计。该算法利用了运动矢量的中心偏置性和相关性，运动估计时通过预测确定搜索起始点，在搜索前期利用十字模板结合提前退出技术优先搜索起始点附近的局部区域，后期则改用六边形模板扩大搜索范围并完成运动估计。实验证明该算法与原始的六边形搜索算法相比平均减少了45%的搜索点数，与一些新的快速搜索算法相比，在搜索精度基本相似的情况下也有效地降低了运动估计的运算复杂度。     

一种新的去相关变步长LMS算法及其应用

在一种变步长算法基础上,从语音信号相关性的角度出发,提出了一种新的去相关变步长LMS算法(DCL—NLMS)。该算法结构简单,收敛速度快,稳态失调小,计算量与NLMS算法相当。仿真结果表明,该算法在处理强相关性信号时,不仅收敛速度明显快于其余算法,而且稳态失调特性也有很大优势。     

A fast and low complexity approach for H.264/AVC intra mode decision

As one of the main features of H.264/AVC, intra prediction coding technique acts as a basis for encoding performance and efficiency. In official reference software Joint Model (JM), it employs the rate distortion optimization (RDO) technique to get to the best encoding performance. By full searching (FS) all of the candidate modes under the rule of RDO, peak-signal-noise-rate (PSNR) decreases to a very low level, but at the same time, the complexity of calculation increases a lot. Many researchers had devoted to searching the fast algorithm which can decrease the complexity, and had designed so many excellent and intelligent fast algorithms. In this paper we introduced a low complexity and fast approach for H.264/AVC intra prediction algorithm. The new approach is based on reducing the number of candidate modes for further RDO calculation, and decreasing the computational complicacy. It can decide the interpolation direction accurately by calculating the directional pixel-value differences (DPD) of target block, and then do statistic with the obtained values to choose the most probable modes. The experimental results demonstrate that the proposed algorithm can achieve more than 70% time saving than JM, but only a tiny degradation of encoding performance is brought in.     

NLMS与RLS算法的仿真比较及其在FECG提取中的应用

该文通过计算机仿真对比研究了归一化最小均方误差（NLMS）和递推最小二乘（RLS）两种自适应滤波算法，并将这两种算法用于胎儿心电图仪的自适应滤波器仿真设计中。该方法通过自适应滤波拾取理想的参考信号，再与腹部混迭信号相减抵消母亲心电图（MECG），从而提取出胎儿心电（FECG）信号。计算机仿真实验结果表明，这两种算法都能通过有效抑制MECG及其它各种干扰以实现FECG的检测。相比之下，RLS算法具有良好的应用性能，除收敛速度快于NLMS以及稳定性强外，还具有更高的起始收敛速率；更小的权失调噪声，更大的抑噪能力，但其计算复杂度高于NLMS算法。     

子空间搜索广义主成分分析

GPCA(Generalized Principal Component Analysis)是近几年提出的一种数据聚类和降维方法,它通过将样本聚类为不同的子空间得到样本的低维表达.GPCA方法已经被应用于图像分割、图像聚类等问题.原有的GPCA算法具有指数计算复杂度,很难应用于高维数据的实际处理.文中针对此问题,提出了基于子空间搜索的SGPCA算法,将聚类问题分解为单个平面的单个垂直向量的搜索问题,对不同子空间分别搜索,从而实现多项式复杂度算法.实验表明,新方法不仅计算复杂度低,而且对噪声的鲁棒性也更强.     

冗余数据约简的研究与设计

Z. Pawlak于1982年提出的Rough集理论有效地分析了不确定、不精确、不一致等各种不完备信息，其优点是无需任何关于数据的初始的或附加的信息，如统计学中的概率分布。该文介绍了Rough集的基本理论在数据约简中的应用。在分析基于信息系统的粗糙集理论的基础上，描述了一种基于核与重要度的约简算法，从降低约简算法计算复杂度角度出发，修改了属性约简算法，计算了算法修改前后的复杂度。实验结果表明，修改后的算法在降低时间复杂度的同时得出了次优属性集的约简。     

改进NSGA算法求解多目标柔性车间作业调度问题

在多目标柔性车间作业调度问题的研究中,求解算法与多目标处理至关重要。因此,基于非支配排序遗传算法提出了改进遗传算法求解该问题,设计了相应的矩阵编码、交叉算子,改进了非劣前沿分级方法,并提出了基于Pareto等级的自适应变异算子以及精英保留策略。实例计算表明,该算法可以利用传统遗传算法全局搜索能力的同时可以防止早熟现象的发生。改进非劣前沿分级方法可以快速得到Pareto最优解集,进一步减小了计算复杂度,而且可以根据种群的多样性改变变异概率,有利于保持种群多样性、发掘潜力个体。     

Efficient vector quantization using genetic algorithm

This paper proposes a new codebook generation algorithm for image data compression using a combined scheme of principal component analysis (PCA) and genetic algorithm (GA). The combined scheme makes full use of the near global optimal searching ability of GA and the computation complexity reduction of PCA to compute the codebook. The experimental results show that our algorithm outperforms the popular LBG algorithm in terms of computational efficiency and image compression performance.     

半动态矩形交查询算法

本文讨论了动态矩形交查询算法.文中介绍了两个半动态矩形查询的新算法，它们分别基于一维数据结构和二维数据结构.一维查询算法的查询时间复杂度是O（logM＋k′），更新时间复杂度是O（logMlogn），空间复杂度是O（nlogM/）.二维查询算法的查询时间复杂度是O（log²M＋k），更新时间复杂度是O（log²Mlogn），空间复杂度是O（nlog²M）.本文分别实现了这两个算法，通过对它们的性能进行比较，发现一维查询算法是一种高效、实用的算法.     

基于投票式属性重要度的快速属性约简算法

作为经典Pawlak粗糙集的扩展,邻域粗糙集能有效处理数值型的数据。但是,因为引入了邻域粒化的概念,所以邻域实数空间下的计算量要比经典离散空间下的计算量大得多。对于邻域粗糙集算法而言,能够有效且快速地找到数据集的属性约简是十分有意义的。为此,针对现有算法中属性重要度定义的不足,首先提出了一种改进的投票式属性重要度,然后进一步提出了一种基于投票式属性重要度的快速属性约简算法。实验证明,与现有算法相比,在保证分类精度的前提下,该算法能更快速地得到属性约简。     

局部迭代NLMS算法研究

针对回波抵消器中大量抽头系数需要更新的问题,分析比较各种局部迭代NLMS算法的性能和计算复杂度。根据权系数局部迭代的简化原理,将滤波器系数分成多个系数子集,通过每次迭代仅更新权系数部分子集的方法,减少算法的计算量。并对不同的子集生成策略进行复杂度和滤波性能分析,分析结果表明,基于权系数瞬时梯度估计的Mmax系数局部迭代方法,以及基于最小化干扰原理的选择性权系数局部迭代算法,其性能与全系数更新算法相当,而计算量与连续局部迭代算法相比仅附加少量比较运算。     

Distributed compressed video sensing based on key frame secondary reconstruction

Distributed compressed video sensing scheme combines advantages of compressive sensing and distributed video coding to get better performance, in the meantime, adapts to the limited-resource wireless multimedia sensor network. However, in the conventional distributed compressed video sensing schemes, self-similarity and high sampling rate of the key frame have not been sufficiently utilized, and the overall computational complexity increases with the development of these schemes. To solve the aforementioned problems, we propose a novel distributed compressed video sensing scheme. A new key frame secondary reconstruction scheme is proposed, which further improves the quality of key frame and decreases computational complexity. The key frame’s initial reconstruction value is deeply exploited to assist the key frame secondary reconstruction. Then, a hypotheses set acquisition algorithm based on motion estimation is proposed to improve the quality of hypotheses set by optimizing the searching window under low complexity. Experimental results demonstrate that the overall performance of the proposed scheme outperforms that of the state-of-the-art methods.     

Dictionary learning for VQ feature extraction in ECG beats classification

Vector quantization(VQ) can perform efficient feature extraction from electrocardiogram (ECG) with the advantages of dimensionality reduction and accuracy increase. However, the existing dictionary learning algorithms for vector quantization are sensitive to dirty data, which compromises the classification accuracy. To tackle the problem, we propose a novel dictionary learning algorithm that employs k-medoids cluster optimized by k-means++ and builds dictionaries by searching and using representative samples, which can avoid the interference of dirty data, and thus boost the classification performance of ECG systems based on vector quantization features. We apply our algorithm to vector quantization feature extraction for ECG beats classification, and compare it with popular features such as sampling point feature, fast Fourier transform feature, discrete wavelet transform feature, and with our previous beats vector quantization feature. The results show that the proposed method yields the highest accuracy and is capable of reducing the computational complexity of ECG beats classification system. The proposed dictionary learning algorithm provides more efficient encoding for ECG beats, and can improve ECG classification systems based on encoded feature.     

一种基于冗余小波变换的快速运动估计算法

由于冗余小波变换克服了离散小波变换的移变性,在冗余小波域进行运动估计可以达到很好的效果,其缺点是算法的时间复杂度过高。在分析视频序列的运动特性和已有的运动估计算法的基础上,提出一种快速的基于冗余小波变换的运动估计算法。在冗余小波域提取潜在运动区域并对图像块的运动剧烈程度进行划分,在此基础上对不同类型的图像块采取不同的搜索策略,从而减少了运动估计中不必要的搜索。实验结果表明,该方法较经典的基于冗余小波变换的运动估计算法在时间复杂度方面具有优势。