电信运营支撑系统与企业战略的关系

本文深入分析了电信运营支撑系统和电信运营企业战略两者的内在关系,总结归纳了以电信运营支撑系统为核心的企业信息化在企业战略中的重要地位,最后提出了国内电信运营企业运营支撑系统的总体转型思路.     

NGOSS方法论初探

本文介绍了方法论在OSS/BSS建设中的重要性,说明了抽象方法、商务过程和构件实现的分离、通用性和构件化方法在NGOSS中的应用,以及这些方法对于系统建设和实现的实际意义.这些意义包括:抽象方法的通用性、商务过程和构件实现的分离带来的系统灵活性、通用方法在电子商务方面的必要性以及构件系统在系统演化和系统集成方面的意义等.     

一种适用于微弱信号盲提取的白化方法

独立分量分析(ICA)算法是解决盲信号分离(BSS)问题的最有效方法之一.ICA中,对观测信号预白化处理的作用至关重要.通常采用主分量分析(PCA)来进行预白化处理.实际中,在利用广播、电视等作为照射源的被动雷达系统中,观测信号通常被强噪声和干扰严重污染,这很大程度上降低了BSS方法的性能.然而,传统的BSS方法中没有...     

“Weighting for more”: Enhancing characteristic-function based ICA with asymptotically optimal weighting

The CHaracteristic-function-Enabled Source Separation (CHESS) method for independent component analysis (ICA) is based on approximate joint diagonalization (AJD) of Hessians of the observations' empirical log-characteristic-function, taken at selected off-origin “processing points”. As previously observed in other contexts, the AJD performance can be significantly improved by optimal weighting, using the inverse of the covariance matrix of all of the off-diagonal terms of the target-matrices. Fortunately, this apparently cumbersome weighting scheme takes a convenient form under the assumption that the mixture is already “nearly separated”, e.g., following some initial separation. We derive covariance expressions for the Sample-Hessian matrices, and show that under the near-separation assumption, the weight matrix takes a nearly block-diagonal form, conveniently exploited by the recently proposed WEighted Diagonalization using Gauss itErations (WEDGE) algorithm for weighted AJD. Using our expressions, the weight matrix can be estimated directly from the data—leading to our WeIghTed CHESS (WITCHESS) algorithm. Simulation results demonstrate how WITCHESS can lead to significant performance improvement, not only over unweighted CHESS, but also over other ICA methods.     

基于电信行业IT应用软件的用户体验测评研究

从电信行业用户体验入手,分析电信行业产品及运营支撑系统的用户体验需求,提出并探讨电信行业用户体验测评体系,包括软件易用性测试、用户体验体系结构及测评方法;举例说明用户体验评测方法在电信行业运营支撑系统(BSS)中的实际应用。     

EEMD与EFICA在单通道复合故障诊断中的应用

针对转子不对中和滚动轴承微弱损伤的复合故障诊断问题,提出了一种基于平均经验算法(Ensemble Empirical Mode Decomposition,EEMD)和高效快速独立分量分析(Efficient Variant of FastICA,EFICA)的盲源分离故障诊断方法。利用EEMD算法将单通路复合故障信号分解成多个不同信号特征的本征模函数(Intrinsic Mode Function,IMF),解决了盲源分离中的欠定问题。在此基础上利用EFICA算法对各个不同信号特征的IMF进行故障特征分离。通过仿真实验和转子实验台的实验结果,表明该算法可以有效分离出各个不同的故障特征。     

Across-trial averaging of event-related EEG responses and beyond

Internally and externally triggered sensory, motor and cognitive events elicit a number of transient changes in the ongoing electroencephalogram (EEG): event-related brain potentials (ERPs), event-related synchronization and desynchronization (ERS/ERD), and event-related phase resetting (ERPR). To increase the signal-to-noise ratio of event-related brain responses, most studies rely on across-trial averaging in the time domain, a procedure that is, however, blind to a significant fraction of the elicited cortical activity. Here, we outline the key concepts underlying the limitations of time-domain averaging and consider three alternative methodological approaches that have received increasing interest: time-frequency decomposition of the EEG (using the continuous wavelet transform), blind source separation of the EEG (using Independent Component Analysis) and the analysis of event-related brain responses at the level of single trials. In addition, we provide practical guidelines on the implementation of these methods and on the interpretation of the results they produce.     

基于四阶累积量的稳健的通信信号盲分离算法

该文针对有噪环境中的通信信号盲分离问题,提出了一种在噪声环境下性能良好的盲分离算法,该算法将稳健的白化算法与四阶累积量矩阵的联合对角化相结合。仿真结果表明,该算法分离性能比一般的近似联合对角化(JAD)算法有很大改善,干信比可降低近10dB,而算法的运算量没有太多增加。     

A time-frequency blind separation method for underdetermined speech mixtures

The proposed Blind Source Separation method （BSS）, based on sparse representations, fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources. The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique （DUET） which assumes the number of sources a priori. In the proposed algorithm, the Short-Time Fourier Transform （STFT） is used to obtain the sparse representations, a clustering method called Unsupervised Robust C-Prototypes （URCP） which can accurately identify multiple clusters regardless of the number of them is adopted to replace the histogram-based technique in DUET, and the binary time-frequency masks are constructed to separate the mixtures. Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio （SIR）, and maintains good speech quality in the separation results.