Windows 2000终端技术的研究

Windows 2000终端是一项能降低用户“总体拥有成本”的技术，并且能为无法运行Windows 2000的计算机提供访问Windows桌面的能力。本文介绍了Windows 2000终端技术原理和Windows 2000终端网络架构，并论述了建立高效实用Windows 2000终端的方法。     

运用独立元分析的仿射运动估计算法

目前在视频流中的运动估计都是针对平移运动的，对于旋转和扭曲运动效果很差，采用Oirrak等提出的Fourier方法可实现旋转和扭曲运动估计，但当目标轮廓简单或噪声大时效果不佳．提出用独立元分析(ICA)方法对图像目标的扭曲运动进行估计，该方法对噪声具有很好鲁棒性，克服了Fourier方法对简单物体仿射参数估计效果差的局限性；同时，采用快速ICA算法计算量小，在视频处理和通信领域中有很好的应用前景．     

Acoustic signal separation of mechanical devices using ICA with band-pass filters

We propose a method of separating the acoustic signals of motors and the gears of mechanical devices by using independent component analysis (ICA) with band-pass filters. The frequency distribution of a recorded acoustic signal from the operating mechanical device can be divided into three fields, the low-frequency field, which corresponds to the frequency characteristics of the gear, the medium-frequency field, which is mixed with the frequency characteristics of the gear and the motor, and the high-frequency field, which corresponds to the frequency characteristics of the motor. Since only the medium-frequency components are a mixture of the acoustic signals of gears and motors, ICA with band-pass filters is expected to separate the acoustic signals of motors and gears more accurately than conventional ICA. The simulation and experimental results show that the proposed method can separate the acoustic signals of motors and gears of mechanical devices successfully.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Proteomic profile analysis and biomarker discovery from mass spectra using independent component analysis combined with uncorrelated linear discriminant analysis

A strategy based on Independent Component Analysis (ICA) and Uncorrelated linear discriminant analysis (ULDA) was proposed for proteomic profile analysis and potential biomarker discovery from proteomic mass spectra of cancer and control samples. The method mainly includes 3 steps: (1) ICA decomposition for the mass spectra; (2) selection of discriminatory independent components (ICs) using nonparametric Mann-Whitney U-test; and (3) selection of special peaks (m/z locations) as potential biomarkers by executing of ULDA on a mass spectra data set which was reconstructed with the m/z locations that collected from the selected discriminatory ICs. A colorectal cancer data set and an ovarian cancer data set were analyzed with the proposed method. As results, 9 and 10 m/z locations were selected as potential biomarkers for the colorectal and ovarian cancer data set respectively. The classification results of ULDA using the selected potential biomarkers yielded better results than fisher discriminant analysis (FDA) and principal component analysis (PCA), and could distinguish the disease samples from healthy controls on the independent test sets with 100% of sensitivities and specificities for the colorectal cancer dataset and 100% of sensitivity and 96.77% of specificity for the ovarian cancer dataset.     

基于独立分量分析的近红外图像去噪方法的研究与应用

独立分量分析（Independent Component Analysis,ICA）是一种新的盲源信号分离方法,近年来已吸引了信号处理学界和神经网络学界众多学者的研究兴趣.该文提出了基于独立分量分析的近红外图像去噪方法,该方法能有效地处理用黑白CCD传感器监测电力设备所产生的图像,使这种监测方案具有实用价值.文中简单描述了以黑白CCD传感器为核心器件的电力设备在线监测系统,阐述了ICA的基本原理,介绍了基于负熵判据的FastICA算法,给出了所提方法的具体实现步骤及相应的实验处理结果.结果表明利用该方法可以有效的去除监测图像中的噪声,使温度场的显示更清晰,满足了实际的需求.     

基于远程控制的VXI开发管理系统

以开发某大型VXI测试系统为背景,在不增加硬件投入,又能加快开发进度的前提下,提出基于远程控制来实现VXI开发管理系统,文中介绍了两种远程控制实现方法：基于VNC协议和基于Citrix终端服务架构,系统中,开发人员在本地以“透明”方式控制VXI主机,实现分布式调试,根据优先级合理调度开发人员的调试工作,从而有效地提高了VXI系统的利用率,加快了测试系统的开发进度。     

Speech Separation Methodology for Hearing Aid

In the design of hearing aids (HA), the real-time speech-enhancement is done. The digital hearing aids should provide high signal-to-noise ratio, gain improvement and should eliminate feedback. In generic hearing aids the performance towards different frequencies varies and non uniform. Existing noise cancellation and speech separation methods drops the voice magnitude under the noise environment. The performance of the HA for frequency response is non uniform. Existing noise suppression methods reduce the required signal strength also. So, the performance of uniform sub band analysis is poor when hearing aid is concern. In this paper, a speech separation method using Non-negative Matrix Factorization (NMF) algorithm is proposed for wavelet decomposition. The Proposed non-uniform filter-bank was validated by parameters like band power, Signal-to-noise ratio (SNR), Mean Square Error (MSE), Signal to Noise and Distortion Ratio (SINAD), Spurious-free dynamic range (SFDR), error and time. The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union -Telecommunication standard ITU-T P.862.     

Can we learn anything from single-channel unaveraged MEG data?

A method for the decomposition of single-channel unaveraged magnetoencephalographic (MEG) data into statistically independent components is presented. The study of MEG recordings is characterised by a host of difficulties, most of which stem from the inherently noisy recording process by which the data is obtained. MEG time series typically contain a mix of artifactual components from a variety of sources, and the isolation of interesting signals from this noise background poses a difficult problem. In this article, we present a novel approach combining the techniques of independent component analysis (ICA) and dynamical embedding, which can be used to extract and isolate components of interest from single-channel unaveraged MEG data. In our approach, the method of delays is proposed as a means of augmenting the single-channel data, thus, facilitating the application of ICA. Finally, because the single-channel approach yields no information regarding the physiological origins of extracted sources, we discuss a method by which extracted sources may be projected back into the multichannel measurement space, permitting an estimate of the respective spatial distributions to be obtained. The proposed methods are tested on three separate MEG channels and the results are presented and discussed.     

一种新型ICA算法在RFID系统中的应用

针对无线射频识别系统在通信过程中的防碰撞问题,从信号处理的角度,提出一种基于盲源信号并行分离技术的防碰撞处理算法,其中包括信号白化技术,以及一种改进的独立元分析(ICA)信号并行分离方法.功能仿真结果证明,该算法的吞吐率最高达到69％,与吞吐率只有18％的传统Aloha算法相比,该算法能提高防碰撞能力.     

基于峭度的一种RobustICA算法

介绍了由V.Zarzoso and P.Comon提出的一种新的基于峭度的独立成分分析算法RobustICA (Robust Independent Component Analysis),并比较了和FastICA在收敛性和信号质量方面的不同.该算法的主要优点在于可以选取最佳步长,可以选取任何不为零的独立成分,并且解决盲分离信号排序问题,同时提升当信号存在坏点和伪局部极值点时的鲁棒性.仿真实验结果表明了该算法相对于FastICA算法减少了迭代次数和加快了收敛速度,同时在小样本空间下均方误差SMSE也明显优于FastICA算法.