基于步长自适应的独立向量分析卷积盲分离算法

独立向量分析(IVA)是解决频域卷积盲分离排序模糊性最好的方法之一,但存在迭代次数较多、运算时间较长、分离效果易受分离矩阵初值影响的局限性。该文提出一种基于步长自适应的IVA卷积盲分离算法,该算法使用特征矩阵联合近似对角化(JADE)算法对分离矩阵进行初始化,并对步长参数进行了自适应优化。JADE初始化能够使分离矩阵具有合理的初值,避免局部收敛的情况;步长的自适应优化能够显著提升算法的收敛速度。仿真结果表明,该算法进一步提升了分离性能,并显著缩短了运算时间。     

基于独立分量分析的正交空时分组编码盲检测方案

通过在不同天线发射的信号之间引入时域和空域信息,正交空时分组编码(OSTBC)可以获得发射分集增益,而且可以在不牺牲带宽的情况下获得更高的编码增益。但是其译码却需要精确的信道状态信息(CSI),因此,信道信息估计的准确性严重影响系统的性能。基于独立分量分析(ICA)的盲源分离(BSS)技术可以在不进行信道估计的情况下对发射信号实现有效检测。通过利用OSTBC的正交特性,提出了2种基于ICA的盲检测方案,同时,一些基于信道估计的检测算法也被用来进行性能比较。瑞利衰落信道下的仿真结果表明,2种新方案均具有较好的系统适应性和误码率性能。     

3G时代电信业务运营支撑系统建设的变革与发展——构筑SP／CP发展的舞台还是瓶颈？

业务运营支撑系统是电信运营商维持业务正常运转的IT基础设施，对运营商的客户服务水平与市场竞争能力有着重大影响。本文通过分析该系统建设及应用中遇到的实际问题，对该系统的建设策略进行了探讨，并提出利用银行金融系统为SP、CP的信息服务提供实时结算支持的设想。     

A fixed-point algorithm for blind source separation with nonlinear autocorrelation

This paper addresses blind source separation (BSS) problem when source signals have the temporal structure with nonlinear autocorrelation. Using the temporal characteristics of sources, we develop an objective function based on the nonlinear autocorrelation of sources. Maximizing the objective function, we propose a fixed-point source separation algorithm. Furthermore, we give some mathematical properties of the algorithm. Computer simulations for sources with square temporal autocorrelation and the real-world applications in the analysis of the magnetoencephalographic recordings (MEG) illustrate the efficiency of the proposed approach. Thus, the presented BSS algorithm, which is based on the nonlinear measure of temporal autocorrelation, provides a novel statistical property to perform BSS.     

Partially Constrained Blind Source Separation for Localization of Unknown Sources Exploiting Non-homogeneity of the Head Tissues

A new brain source localization technique using electroencephalograms (EEGs) is investigated in this paper. The information which describes the location of certain known sources is used as the constraint within the proposed blind source separation (BSS) algorithm and leads to a solution to the ill-posed inverse problem of source localization. Non-homogeneity of the head tissues, on the other hand, is exploited by introducing a realistic model of the mixing system. This model is used to better identify the location of the unknown sources within the brain from projection of the separated independent components on to the scalp. A separate procedure is employed to highlight the rhythmic EEG sources such as Alpha rhythm as the known sources. The performance of the scheme is shown on real EEG measurements and compared with that of “conventional dipole fitting algorithm”.

一种快速的解盲源分离新算法

 针对盲源分离问题,提出一种基于接收信号不同延时下自相关矩阵组的快速联合对角化算法(FJD).采用乘性迭代机制求解表征联合对角化近似程度的F-范数代价函数.对代价函数的合理近似及巧妙求解,是算法快速有效的核心原因.每步迭代得到的严格对角占优更新矩阵,保证联合对角化器严格可逆,防止收敛到平凡解.算法具有不需要预白化操作,不限定待对角化目标矩阵的正定性,并能处理复值数据等诸多优点,具有极广的适用性.详细的计算复杂度分析说明了算法的高效性及易操作性.仿真结果表明,FJD算法收敛速度快,性能良好,能有效地解决盲源分离问题.     

Performance evaluation of nonnegative matrix factorization algorithms to estimate task-related neuronal activities from fMRI data

Nonnegative matrix factorization (NMF) is a blind source separation (BSS) algorithm which is based on the distinct constraint of nonnegativity of the estimated parameters as well as on the measured data. In this study, according to the potential feasibility of NMF for fMRI data, the four most popular NMF algorithms, corresponding to the following two types of (1) least-squares based update [i.e., alternating least-squares NMF (ALSNMF) and projected gradient descent NMF] and (2) multiplicative update (i.e., NMF based on Euclidean distance and NMF based on divergence cost function), were investigated by using them to estimate task-related neuronal activities. These algorithms were applied firstly to individual data from a single subject and, subsequently, to group data sets from multiple subjects. On the single-subject level, although all four algorithms detected task-related activation from simulated data, the performance of multiplicative update NMFs was significantly deteriorated when evaluated using visuomotor task fMRI data, for which they failed in estimating any task-related neuronal activities. In group-level analysis on both simulated data and real fMRI data, ALSNMF outperformed the other three algorithms. The presented findings may suggest that ALSNMF appears to be the most promising option among the tested NMF algorithms to extract task-related neuronal activities from fMRI data.     

水听器阵共模干扰抵消研究

硬件电路设计不当等原因将会导致水听器前放电路中存在共模干扰,水听器阵共模干扰将会"污染"A/D采样信号,从而对声纳目标探测与跟踪及其他后续处理造成困扰。在实际的声纳系统中,最好是采用硬件预处理的方式避免共模干扰,但对于已经被污染了的采集数据而言,则只能通过干扰抵消等信号处理的方式对被"污染"了的原始数据加以修复或还原。本文对水听器阵共模干扰特性进行了详细分析,并根据其干扰特性,提出了基于错位相减技术和基于独立分量分析的两种干扰抵消方法。湖试数据处理结果验证了本文方法的可行性。     

通信运营企业实施OSS的探讨

电信运营支撑系统 (OSS)的建设在中国才刚刚起步 ,本文在介绍了OSS概念的基础上 ,结合TMF的TOM图 ,对国内电信运营企业建设实施OSS系统的必要性进行了分析 ,对国内企业如何实现OSS提出了建设性的意见。