Accelerating BPC-PaCo through Visually Lossless Techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.     

聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。     

基于变压器最优漏磁信息的绕组变形分类方法

为了判别电力变压器绕组变形状况,提出一种基于最优漏磁信息及多层聚类编码和最优权重译码的ECOC分类器对变压器绕组变形分类的方法,首先利用多层聚类算法构建最优ECOC编码矩阵,然后通过最优权重译码算法对分类器输出进行译码得出变压器绕组具体变形类别,建立变压器绕组二维漏磁场有限元模型,计算得出绕组可能出现的变形形式和绕组区域变形前后的磁感应强度值数据,从而得出磁感应强度测量点;最后利用所建立模型得出的绕组变形数据进行仿真判别。结果表明,所建立的分类器在绕组变形判别时具有较高的准确性,可用于变压器绕组变形类型的检测。     

基于大数据分析的分布式MOLAP技术研究

在计算机系统运行以及研究环节中,会存在大量的规模效应,此类状况难以避免,要想有效解决此类问题,就需要利用分布式的处理方式,开展对文件系统的分析。文章对分布式多维联机分析过程(MOLAP)的数据模型进行了分析,从维编码的算法、映射归约(MapReduce)算法的实现、分析维的遍历算法等方面作深入探讨。     

基于稀疏反演的高效混采数据分离方法

混叠采集技术能极大地提高地震采集效率，但是由于连续激发，混采数据中存在严重的邻炮干扰噪声，而且混采数据规模巨大，这要求后续的信噪分离算法精确、稳定、快速，为此提出一种基于稀疏反演的高效混采数据分离方法。在三维共检波点道集上，首先通过三维快速傅里叶变换，将时空域数据变换到频率-波数-波数域（FKK）；然后采用硬阈值函数提取相干信号，利用相干信号预测混叠噪声；最后以数据残差为驱动，不断收缩阈值，迭代更新模型，实现高效混采数据分离。模拟数据和实际数据应用结果表明，本文方法能够精确、稳定、快速地分离高混叠度的地震数据。     

面向多源数据融合的稀疏表示目标跟踪

传统的基于稀疏表示的目标跟踪方法主要利用目标的灰度特征构建稀疏表示模型。由于灰度特征对光照变化敏感，这会影响目标跟踪在复杂场景下的鲁棒性。基于多源数据融合的目标跟踪可以明显提升目标跟踪鲁棒性，但如何有效融合不同维度，不同类型的多源目标特征成为基于多源数据融合的目标跟踪所要解决的关键问题。提出了一个基于目标状态以及灰度特征的稀疏表示目标跟踪方法。所提出的方法可通过基于核函数表示的稀疏表示模型，在探究目标状态以及灰度特征相关性的基础上，将两种不同维度的特征进行有效融合，提升目标跟踪在复杂场景下的鲁棒性。     

Sub-band coding of hexagonal images

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.     

Two-Phase Bidirectional Dual-Relay Selection Strategy for Wireless Relay Networks

In this article, we introduce a new bi-directional dual-relay selection strategy with its bit error rate (BER) performance analysis. During the first step of the proposed strategy, two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER, based on the suggested algorithm which checks if the selected relays using the max-min criterion are the best ones. In the second step, the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals, leading to a significant improvement in the achievable coding and diversity gain. To further improve the overall system performance, the selected relay-nodes apply also a digital network coding scheme. Furthermore, this paper discusses the analytical approximation of the BER performance of the proposed strategy, where we prove that the analytical results match almost perfectly the simulated ones. Finally, our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.     

CEEMD与KSVD字典训练相结合的去噪方法

本文提出一种完备总体经验模态分解（CEEMD）方法与K奇异值分解（KSVD）学习字典算法相结合的地震信号去噪方法。含噪信号通过CEEMD分解得到一系列不同尺度的固有模态函数（IMF）；按频率由高到低依次排列IMF各分量，并做自相关分析，去除噪声主导的IMF分量；将累加的过渡IMF分量叠加重构并做CEEMD二次分解，通过自相关分析再次去除噪声主导的IMF分量；分别叠加二次CEEMD分解剩余的IMF分量和一次剩余的IMF分量，得到两个新的含噪信号，并利用KSVD过完备字典分别稀疏表示该两个新的含噪信号，即由稀疏系数重构去噪后地震信号，进而重构最终去噪结果。实验结果证明：该算法的去噪效果明显优于F-X去噪、小波阈值去噪和KSVD字典稀疏去噪等传统方法。     

基于K-奇异值分解和层次化分块正交匹配算法的滚动轴承故障诊断

利用层次化分块正交匹配算法（HBW-OOMP）的高稀疏性和运算速度快等优点，提出了一种基于K-奇异值分解（K-SVD）字典和HBW-OOMP算法的故障轴承诊断方法。首先利用K-SVD自学习训练方法得到包含冲击成分的冗余字典，克服了固定结构字典适应性不强的缺点。然后采用基于分块思想的HBW-OOMP算法进行原子的选取和稀疏系数的求解，以重构信号包络谱峭度最大为终止条件，自适应确定分解次数。最后应用所提方法对仿真信号和故障轴承实验信号进行故障特征提取，结果表明该方法能够有效提取强背景噪声下故障特征成分，具有一定的应用前景。