摘要
作为计算智能的核心研究内容之一,盲源分离问题结合了信息理论、统计信号处理、人工神经网络,已经成为一个比较重要和热门的研究课题。本文在对盲源分离方法进行深入探讨的基础上进行了应用研究,并提出了改进算法。
本文的主要研究内容包括:
(1)针对盲源分离问题讨论了相关的理论和方法。对自然梯度的ICA学习算法及其性能进行了探讨,研究了采用PCA对信号进行白化预处理的理论与方法;应用自然梯度的ICA学习算法对实际的汽车行驶环境中的混合声音进行了分离。实验结果表明,自然梯度ICA算法应用于实际的盲源分离领域可以取得比较好的效果。
(2)探讨了高效率的线性多层ICA算法,并将其与MaxKurt算法进行了性能比较。线性多层ICA算法作为一种高效率的ICA算法可以解决高维混合信号的盲源分离问题。实验结果表明,线性多层ICA算法比MaxKurt算法具有更高的效率。
(3)利用DS-CDMA传输过程中原码字及原码字前后最近的两个码字信息,对原码字进行估计,提出了基于三码字反馈式DS-CDMA多用户检测的改进算法,并对现有的多路通道的DS-CDMA模型进行了改进。仿真实验结果表明所提出的改进算法与DS-CDMA模型能够很好地匹配,对DS-CDMA多用户检测问题很有效,能够正确地检测用户码字,具有较低的漏检率和很好的实时性。
(4)结合基于三码字反馈式DS-CDMA多用户检测改进算法的思想,构建了基于FastICA-TDS方法的DS-CDMA盲源信号分离系统。通过实验对不同检测器的BER性能进行了比较,结果表明系统具有较低的漏检率,在系统能量控制方面明显好于MMSE和MF。
In recent years, the study of blind source separation methods has become a noticeable problem in the signal processing fields, and related technologies have also been applied to various fields, especially using it to process DS-CDMA multiuser detection. The communication of DS-CDMA has its merits, such as low probability of intercept, strong anti-interference ability and achieving code division multiple access, so it has been widely applied in the anti-interference communication and civilian mobile communication. The most important task of it is the blind detection and separation of DS-CDMA signal in the noise and interference environment. Traditional detection technology carries out spread spectrum code matching respectively to each user's signal absolutely according to the classic direct sequence spread spectrdm theory, so it's less able to interfere with anti-MAI.
In this thesis, applied research has been done based on the depth discussion of the blind source separation method. Two improved algorithms based on the theory of multiuser detection of DS-CDMA have been proposed, and meaningful results have been gained combined the algorithm with blind multiuser detection based on the theory of statistics signal processing. The main related contents and conclusions can be summaried as followings:
(1) Related theory and methods of ICA and PCA have been discussed aimed at BSS problem. Natural gradient ICA learning algorithm and it's properties have been discussed; the theory and method of the whitening processing of the signal using PCA method have been studied; the signal, which is mixed by two ideal sequence signal without noise interference, has been separated by natural gradient ICA learning algorithm, and mixed sound of the actual car traveling conditions has been separated. The experimental results show that the time-domain signal didn't change before and after separation, and the signal amplitude increased by the separation of the natural gradient ICA algorithm, and it was found that the volume of the separated voices became significantly larger by contrasting through audition. Natural gradient ICA algorithm can effectively eliminate the background noise at the same time of preservating of most of the speech signal energy, and the separated spectrum was more similar to the pure language of the speech signal spectrum. The natural gradient ICA algorithm could achieve better effect when it was applied in the actual BSS fields.
(2) Many BSS algorithms have worse effect when they proceed blind source separation to the high-dimentional mixed signal. This thesis has discussed the linear mutilayer ICA algorithm, and analyzed its stability, and also compared the property of MaxKurt algorithm with it. The basic idea of the linear mutilayer ICA algorithm is that local ICA stage and mapping stage were performed in every layer of the algorithm. For local ICA stage, every neighboring mapping stage was separated by MaxKurt algorithm which was in the ICA algorithm. For mapping stage, one dimensional mapping was formed by overall mapping analysis and higher relevance signal. By implementing this two processes cyclarly, linear mutilayer ICA algorithm could almost extract all the dependent components. The experimental results show that, linear mutilayer ICA algorithm has higer efficiency than other standard ICA algorithms.
(3) In order to better predict and improve multiuser detection problem of DS-CDMA, the mathematical model of multiuser DS-CDMA signal and the form of observational data were given, and the model of observational data was given by combining with the spread of multipath and the influence of fading signal to the systems. Based on the DS-CDMA downlink of natural gradient, BMUD can carry out blind estimation directly to all user's symbol sequences by feedback BMUD technology from the received CDMA signals. This technology can be convolution, or even blind source separation signals of non-linear environment. For feedback BMUD their structures can be constrainted by natural gradient algorithm and can apply to the linear convolution of mixed environment by minimizing the mutual information. Forward and backward feedback structures are both suitable for the natural gradient BMUD. Combining of specific update rules of forward and backward feedback structures, algorithm of based TDS feedback DS-CDMA multiuser detection has been proposed, and DS-CDMA model of existing multichannel access has been improved. The basic idea of the improved algorithm is: in the processing DS-CDMA signal of the downlink channel, continuous data firstly sampled in the rate of chip, making each symbol becomes C interval samples. The width of dealt window was two symbols, and these interval discrete data samples R[m] form a 2C-dimensional vector Rm. Since the sampling process and the symbol is asynchronous Rm contains 3 symbols which emerged one after another, and each user is identified according to the unique symbol, and this symbol together with other allocated users' symbols are quasi-orthogonal, and original symbol is estimated by the information of both the original symbol and the nearest two symbols before and after the original symbol during the DS-CDMA transporting. So we can see that DS-CDMA signal system model expressed linear mixed of delay and the source of the convolution with a certain period of time delay. Therefore, by using the maximum entropy principle and the natural gradient algorithm of convolution mixture, we can take feedback system model structure for processing to this semi-blind source separation problem which mixed matrix and symbol sequences are both unknown. In the existing algorithm, learning parameterμwith iterative process-related is the key point of influencing algorithm's convergence, so in the proposed based TDS feedback DS-CDMA multiuser detection, learning parameterμis set to value, and this decreased the complexity of the calculation, and this also could make algorithm faster convergence. The simulation experiment results show that the proposed improved algorithm can match better with the DS-CDMA model, and it is effective to the DS-CDMA multiuser detection problem. It also can detect user symbol exactly with lower undetected rate and better practicability.
(4) Under the channel environment of MAI and high SNR, combined the mind of improving algorithm based TDS feedback DS-CDMA multiuser detection, DS-CDMA blind source separation system was constructed based the method of FastICA-TDS. The basic idea is: each user occupies the same frequency band simultaneously in the CDMA system. Symbol is unique for each user, so user is identified by their symbols. When the public frequency band delivers signals, they should put the user's unique symbols into the data symbols. Different users's signals are mixed together during the transmission. The unique symbol is detected by the signal receiver, each user's delivering signals are identified from the mixed signals. This process is similar to the process that ICA separates the mixed signals. Firstly, we change the traditional DS-CDMA signal model into the basic model of ICA. Secondly, mixed signals are separated by the FastICA algorithm. The final vector derived from algorithm is equal to one row of the orthogonal mixed matrix which is composed of user's chips from the DS-CDMA system, which means the separate a non-Gaussian signal. This signal is one of the user's symbols. If the two adjacent received values is equal or very little difference, the iteration process will be end. In addition, each symbol should be subtracted from the mixed-signal after the symbol being extracted, so it is implementation until all the symbols are separated. Because FastICA algorithm is non-gaussian and can eliminate high-order correlation of the input data, so the proposed DS-CDMA blind source separation system which is based on the FastICA-TDS method has robustness to gassian noise. Also because the signal which is delivered in the transmission channel is genenal from a binary number of a limited alphabet, moreover the definition of the symbol expantion is known, so the blind source separation to DS-CDMA is with priori information, is not blind. Different detector's BER properties are compared by experiment, and the results show that the system has lower undetected rate, and is obviously better than MMSE and MF in the system power controlling. DS-CDMA blind source separation system which is based on FastICA-TDS is certain novelty and ingenuity, and has better referential significance to the researchers of this field.
引文
[1] Shi Z, Zhou Z J A F. A fixed-point algorithm for blind source separation with nonlinear autocorrelation[J]. Journal of Computational and Applied Mathematics. 2008, 223(2): 4.
[2] Sun T, Liu C, Hsiehb S T A S. Blind source separation with dynamic source number using adaptive neural algorithm[J]. Expert Systems with Applications. 2008, 71.
[3] Shwartz S, Schechner Y Y, Zibulevsky A M. Blind separation of convolutive image mixtures[J]. Neurocomputing. 2007, 71(9).
[4] He Q, Du Du S S A R. Separating mixed multi-component signal with an application in mechanical watch movement[J]. Digital Signal Processing. 2008, 18(6): 1.
[5] Pokharel P P, Ozertemb U, Erdogmusb D, et al. Recursive complex BSS via generalized eigendecomposition and application in image rejection for BPSK[J]. Signal Processing. 2007, 88(6): 2.
[6] Dufresne M, Xe J, Vazquezb, et al. Experimental investigation and CFD modelling of flow, sedimentation, and solids separation in a combined sewer detention tank[J]. Computers Fluids. 2008,1(1).
[7] Wang G, Ding Q, Sun Y X A, et al. Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis[J]. Spectrochimica Acta Part A. 2007, 70(3).
[8] Furuya C K, Jr, Oliveira C P M S, et al. 667 Effects of bariatric surgery (FOBI-CAPELLA) in non-alcoholic fatty liver disease: Study after 2 years[J]. Journal of Hepatology. 2006,44(4).
[9] Ollila E, Koivunen H O A V. Complex-valued ICA based on a pair of generalized covariance matrices[J]. Computational Statistics Data Analysis. 2008, 52(7).
[10] Fobi M, Lee H. The Surgical Technique of the Fobi-Pouch Operation for Obesity (The Transected Silastic Vertical Gastric Bypass)[J]. Obesity Surgery. 1998, 8(3): 283-288.
[11] Huckaby D A, Chan T F. On the Convergence of Stewart's QLP Algorithm for Approximating the SVD[J]. Numerical Algorithms. 2003, 32(2): 287-316.
[12] Singh R, Vatsa M, Singh S, et al. Integrating SVM classification with SVD watermarking for?intelligent video?authentication[J]. Telecommunication Systems.
[13] Thomasian A, Li Y, Zhang L. Optimal subspace dimensionality for k-nearest-neighbor queries on clustered and dimensionality reduced datasets with SVD[J].Multimedia Tools and Applications.2008,40(2):241-259.
[14]Yokoi T,Yanagimoto H,Omatu S.Information filtering using SVD and ICA[J].Artificial Life and Robotics.2006,10(2):116-119.
[15]Cardoso W.Teleportation of GHZ-States in QED-Cavities without the Explicit Bell-State Measurement[J].International Journal of Theoretical Physics.2008,47(4):977-982.
[16]Cardoso-Martins C,Frith U.Can individuals with Down syndrome acquire alphabetic literacy skills in the absence of phoneme awareness?[J].Reading and Writing.2001,14(3):361-375.
[17]Cardoso F.On the existence of local solutions of pseudodifferential equations[J].Bulletin of the Brazilian Mathematical Society.1973,4(2):121-137.
[18]Casadio F,Douglas J,Faber K.Noninvasive methods for the investigation of ancient Chinese jades:an integrated analytical approach[J].Analytical and Bioanalytical Chemistry.2007,387(3):791-801.
[19]郭业才,赵俊渭,陈华伟等.基于高阶累积量符号相干累积自适应滤波算法[J].系统仿真学报.2002(10).
[20]金海红,冶继民.盲信号分离中基于四阶累积量的源信号个数估计[J].西安石油大学学报(自然科学版).2007(05).
[21]陆风波,黄知涛,姜文利等.基于差分高阶累积量的pi/4-QPSK和8PSK调制样式识别算法[J].电子对抗.2007(03).
[22]朱婷婷,王英民,林欢欢.高阶累积量盲均衡算法的优化设计[J].电声技术.2007(12).
[23]Jutten C,Chentouf R.A new scheme for incremental learning[J].Neural Processing Letters.1995,2(1):1-4.
[24]Vorobyov S,Cichocki A.Blind noise reduction for multisensory signals using ICA and subspace filtering,with application to EEG analysis[J].Biological Cybernetics.2002,86(4):293-303.
[25]Williams C K I.On a Connection between Kernel PCA and Metric Multidimensional Scaling[J].Machine Learning.2002,46(1):11-19.
[26]Hyv Rinen A,Inki M.Estimating Overcomplete Independent Component Bases for Image Windows[J].Journal of Mathematical Imaging and Vision.2002,17(2): 139-152.
[27] He Q, Du Du S S A R. Separating mixed multi-component signal with an application in mechanical watch movement[J]. Digital Signal Processing. 2008,18(6).
[28] Hosseini S, Deville Y, Saylani A H, et al. Blind separation of linear instantaneous mixtures of non-stationary signals in the frequency domain[J]. Signal Processing. 2008, 44(3).
[29] Calhoun V D B, Liu J, C B A T X. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data[J]. NeuroImage. 2008,20(3).
[30] Al-Ani A, Deriche M. Multi-Channel Subspace Mapping Using an Information Maximization Criterion[J]. Multidimensional Systems and Signal Processing. 2004, 15(2): 117-145.
[31] B Hning D. Maximum likelihood estimation of the logarithmic series distribution[J]. Statistical Papers. 1983,24(1): 121-140.
[32] Andrews B W, Iglesias P A. An information-theoretic characterization of the optimal gradient sensing response of cells[J]. PLOS COMPUTATIONAL BIOLOGY. 2007, 3(8): 1489-1497.
[33] Linsker R. Improved local learning rule for information maximization and related applications[J]. Neural Networks. 2005, 18(3).
[34] Linsker R. Neural network learning of optimal Kalman prediction and control [J]. Neural Networks. 2008, 21(9).
[35] Charter M K, Gull S F. Maximum entropy and its application to the calculation of drug absorption rates[J]. Journal of Pharmacokinetics and Pharmacodynamics. 1987, 15(6): 645-655.
[36] Tolochko I, Faulkner M. Sparse Approximations of the LMMSE Channel Estimation in OFDM with Transmitter Diversity[J]. Wireless Personal Communications. 2006, 38(3): 343-355.
[37] Gibson E, Pearlmutter N J. Distinguishing Serial and Parallel Parsing[J]. Journal of Psycholinguistic Research. 2000, 29(2): 231-240.
[38] Pearlmutter N J. Linear versus Hierarchical Agreement Feature Processing in Comprehension[J]. Journal of Psycholinguistic Research. 2000, 29(1): 89-98.
[39] Abetz V, Stadler R, Leibler L. Order-disorder-and order-order-transitions in AB and ABC block copolymers: description by a simple model[J]. Polymer Bulletin. 1996, 37(1): 135-142.
[40]Bia Kowska E,Leibler K,Kalinowski M K.Einfl(u|¨)sse der L?sungsmittel auf das ESR-Spektrum von Kupfer-acetylacetonat[J].Monatshefle f(u|¨)r Chemie/Chemical Monthly.1976,107(4):865-870.
[41]丁爱玲,谢小军,闻怡.具有高斯核函数的支撑矢量机与径向基函数分类器的比较[J].交通与计算机.2003(03).
[42]Phong T Q,Tao P D,Le An T H.A method for solving d.c.programming problems.Application to fuel mixture nonconvex optimization problem[J].Journal of Global Optimization.1995,6(1):87-105.
[43]Mato G,Parga N.Sequences in neural networks with temporal association[J].Zeitschrift f(u|¨)r Physik B Condensed Matter.1991,84(3):483-486.
[44]Amari S,Yakeuchi A.Mathematical theory on formation of category detecting nerve cells[J].Biological Cybernetics.1978,29(3):127-136.
[45]Lee T W,Girolami M,Bell A J,et al.A unifying information-theoretic framework for independent component analysis[J].COMPUTERS & MATHEMATICS WITH APPLICATIONS.2000,39(11):1-21.
[46]Boogaard H.Maximum likelihood estimations in a nonlinear self-exciting point process model[J].Biological Cybernetics.1986,55(4):219-225.
[47]Dietrich C R,Osbome M R.Estimation of covariance parameters in kriging via restricted maximum likelihood[J].Mathematical Geology.1991,23(1):119-135.
[48]Erdogrnus D,Principe J C.Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information[J].The Journal of VLSI Signal Processing.2004,37(2):305-317.
[49]Yang Z,Zhou G,Zhang Z W A J.New method for signal encryption using blind source separation based on subband decomposition[J].Progress in Natural Science.2008,18(6):1.
[50]Shang L.Denoising natural images based on a modified sparse coding algorithm[J].Applied Mathematics and Computation.2008,205(2).
[51]Song Z G A Z.Online monitoring of nonlinear multiple mode processes based on adaptive local model approach[J].Control Engineering Practice.2008,16(12).
[52]Woolfson M S,Biganb C,Hayes-Gill J A C A.Method to separate sparse components from signal mixtures[J].Digital Signal Processing.2008,18(6).
[53]Ye Y,Zhang Z,Peng J Z A L.A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction[J].Applied Mathematics and Computation.2008, 205(2): 4.
[54] Yi-Nan C, Wei-Qi J, Ling-Xue W, et al. A Gabor subband decomposition ICA and MRF hybrid algorithm for infrared image reconstruction from subpixel shifted sequences[J]. Optics Communications. 2008, 32(1): 4.
[55] Zhang H, Guob C, Feng Z S A E. A new constrained fixed-point algorithm for orderirig independent components [J]. Journal of Computational and Applied Mathematics. 2007, 220(8).
[56] Zhang Y, Qin W, Liub P, et al. An fMRI study of acupuncture using independent component analysis[J]. Neuroscience Letters. 2008,449(1): 2.
[57] Lambert E, Hogan N, Barton S, et al. The Impact of Officer and Agency Characteristics on the Likelihood of Arrest in Domestic Violence Situations among Rural Law Enforcement Officers[J]. Journal of Police and Criminal Psychology. 2007, 22(2): 91-101.
[58] Congedo M, Jutten C X D G. On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics[J]. Clinical Neurophysiology. 2008, 119(12): 2.
[59] Esposito F B, Aragric A, Pesaresid I, et al. Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI[J]. Magnetic Resonance Imaging. 2008,26(7).
[60] Xu H, Chen C H, Shi X. Using the Penalized Mutual Information Criterion in the Multivariate Edgeworth-Expanded Gaussian Mixture Density for Blind Separation of Convolutive Post-Nonlinear Mixtures[J]. Circuits, Systems, and Signal Processing. 2007,26(5): 651-670.
[61] Balasubramaniane S, Panigrahi S, Logueb C M, et al. Independent component analysis-processed electronic nose data for predicting Salmonella typhimurium populations in contaminated beef[J]. Food Control. 2007, 19(3).
[62] Bermejo S. Finite sample effects of the fast ICA algorithm[J]. Neurocomputing. 2006, 71(6): 4.
[63] Briselli E, Garreffa G B, Bianchib L C E, et al. An independent component analysis-based approach on ballistocardiogram artifact removing[J]. Magnetic Resonance Imaging. 2006,24(4): 2.
[64] Brunner C, Naeem M, Leeb R, et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis[J]. Pattern Recognition Letters. 2007,28(8): 2013.
[65] Tavan P, Grubmuller H, Kiihnel H. Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps[J]. Biological Cybernetics. 1990, 64(2): 95-105.
[66] Nakamura H, Yoshida M, Kotanib M, et al. The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part I measuring techniques [J]. Journal of Electromyography and Kinesiology. 2004,14(4).
[67] Taskinen S, Xe S S, Ojab A H. Independent component analysis based on symmetrised scatter matrices[J]. Computational Statistics Data Analysis. 2006, 51(10): 2.
[68] Wang G, Ding Q, Sun Y X A, et al. Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis[J]. Spectrochimica Acta Part A. 2007, 70(3).
[69] Wang G, Sun Y, Ding Q, et al. Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression[J]. Analytica Chimica Acta. 2007, 594(1).
[70] Woolfson M S, Biganb C, Hayes-Gill J A C A. Method to separate sparse components from signal mixtures[J]. Digital Signal Processing. 2008,18(6).
[71] Part H J. Zur theoretischen Beschreibung zylindersymmetrischer modulierter Lichtb?gen[J]. Zeitschrift fur Physik A Hadrons and Nuclei. 1969, 227(2): 99-124.
[72] Part H J, Schmitz G. Zur Theorie der Gasaufheizung in axialsymmetrischen, wandstabilisierten Lichtb?gen[J]. Zeitschrift fur Physik A Hadrons and Nuclei. 1965, 188(1): 1-22.
[73] Part J M, Hamilton G C, Lashomb J H. Responses of two parasitoid wasps to nectar odors as a function of experience[J]. Entomologia Experimentalis et Applicata. 1999, 90(1): 1-8.
[74] Zhao Y, Zhang Z, Wu W. Multicarrier DS-CDMA with adaptive modulation and power allocation[J]. Journal of Electronics (China). 2003,20(3): 177-182.
[75] Zhao Y, Zheng J. Multiuser detection for DS-CDMA systems in non-Gaussian channels[J]. Frontiers of Electrical and Electronic Engineering in China. 2006, 1(2): 160-163.
[76] Jia P, Zhang H, Shi X. Blind Source Separation Based on Nonparametric Density Estimation[J].Circuits,Systems,and Signal Processing.2003,22(1):57-67.
[77]Zijl W,Hendriks M A N,T Hart C M P.Numerical Homogenization of the Rigidity Tensor in Hooke's Law Using the Node-Based Finite Element Method[J].Mathematical Geology.2002,34(3):291-322.
[78]Cances J,Mohammadkhani G,Meghdadi V.Turbo Interference Mitigation in Layered Space-Time MIMO DS-CDMA Uplink with TYCM(Turbo Trellis Coded Modulation)[J].Wireless Personal Communications.2006,37(1):105-121.
[79]张昕,胡波,凌燮亭.盲信号分离在数字无线通信中的一种应用[J].通信学报.2000(02).
[80]陈阳,何振亚.亚、超高斯信号后非线性混合的盲分离[J].应用科学学报.2001(03).
[81]周红,凌燮亭.逆混沌系统加密模型及其保密性能[J].复旦学报(自然科学版).1997(03).
[82]李晓欧,江朝晖,张笑微等.独立分量分析的拟牛顿迭代算法及其在视觉诱发电位特征提取中的应用[J].生物医学工程学杂志.2006(01).
[83]姚鹏,叶学义,庄镇泉等.基于局部频率特征和局部方向特征的虹膜识别算法[J].电子学报.2007(04).
[84]Brookings T,Ortigueb S,Graftonb S,et al.Using ICA and realistic BOLD models to obtain joint EEG/fMRI solutions to the problem of source localization[J].NeuroImage.2008,44(2).
[85]Iannettib A M A G.Across-trial averaging of event-related EEG responses and beyond[J].Magnetic Resonance Imaging.2008,26(7):1.
[86]Mez-Herrero G X N G,Atienzab M,Canterob K E A J.Measuring directional coupling between EEG sources[J].NeuroImage.2008,43(3):2.
[87]Yu S,Chou A K,B.Selection of significant independent components for ECG beat classification[J].Expert Systems with Applications.2007,36(2):3.
[88]Chiu S,Lub C,Wenc D W A C.A histogram based data-reducing algorithm for the fixed-point independent component analysis[J].Pattern Recognition Letters.2007,29(3):5.
[89]陈阳,何振亚.一类新的独立性度量及其在盲信号分离中的应用[J].武汉大学学报(信息科学版).2004(01).
[90]Hsieh P,Tung A P.A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition[J]. Pattern Recognition. 2008, 64(8): 6.
[91] Hu H. ICA-based neighborhood preserving analysis for face recognition[J]. Computer Vision and Image Understanding. 2008, 112(3).
[92] Tian X, Zhang X, Chenb X D A S. Multiway kernel independent component analysis based on feature samples for batch process monitoring[J]. Neurocomputing. 2008, 20(2).
[93] Wu J, Lin M C A Z. Independent component analysis based on marginal density estimation using weighted Parzen windows[J]. Neural Networks. 2008,21(7): 2013.
[94] Xue Y, Yang Y W A J. Independent component analysis based on gradient equation and kernel density estimation[J]. Neurocomputing. 2008, 70.
[95] Kalyakin I, Lezb N G X, Lyytinenc T K X R. Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm[J]. Journal of Neuroscience Methods. 2008,174(2).
[96] Kokkinakis K, Nandi A A K. Generalized gamma density-based score functions for fast and flexible ICA[J]. Signal Processing. 2006, 87(5).
[97] Lee J A B 1, Vrinsc F X D X, Verleysenc A M. Blind source separation based on endpoint estimation with application to the MLSP 2006 data competition[J]. Neurocomputing. 2007, 72(10).
[98] ICA Fluor wins Pemex crude oil dehydration contract[J]. Pump Industry Analyst. 2008, 2008(9).
[99] Tsai D, Lai A S. Defect detection in periodically patterned surfaces using independent component analysis[J]. Pattern Recognition. 2008, 41(9).
[100] Wolf R C, Sambatarob F D, Vasic N, et al. Aberrant connectivity of lateral prefrontal networks in presymptomatic Huntington's disease[J]. Experimental Neurology. 2008, 213(1).
[101] Wu E H C, Yu P L H, Li A W K. A smoothed bootstrap test for independence based on mutual information[J]. Computational Statistics Data Analysis. 2008.
[102] Ollila E B, Koivunenb A V. Complex ICA using generalized uncorrelating transform[J]. Signal Processing. 2008, 89(4).
[103] Amstutz E, Teshima T, Kimura M, et al. PCA-based 3D shape reconstruction of human foot using multiple viewpoint cameras[J]. International Journal of Automation and Computing. 2008, 5(3): 217-225.
[104] Astel A, Astel K, Biziuk M. PCA and multidimensional visualization techniques united to aid in the bioindication of elements from transplanted[J]. Environmental Science and Pollution Research. 2008,15(1): 41-50.
[105] Fyfe C. A Neural Network for PCA and Beyond[J]. Neural Processing Letters. 1997, 6(1): 33-41.
[106] Heron A J, Warren A H. Evaluating VoIP Technology — Technology Push/Market Pull[J]. BT Technology Journal. 2001,19(2): 158-167.
[107] Brezinski C, Redivo-Zaglia M, Rodriguez G, et al. Multi-parameter regularization techniques for ill-conditioned linear systems[J]. Numerische Mathematik. 2003, 94(2): 203-228.
[108] Crabill T B, Evans J P, Gould F J. An example of an ill-conditioned nlp problem[J]. Mathematical Programming. 1971, 1(1): 113-116.
[109] Deuflhard P. A modified Newton method for the solution of ill-conditioned systems of nonlinear equations with application to multiple shooting[J]. Numerische Mathematik. 1974, 22(4): 289-315.
[110] Braun L, G Hringer D, Perschke T, et al. Adaptive real-time image processing exploiting two dimensional reconfigurable architecture[J]. Journal of Real-Time Image Processing.
[111] Cho S, Chi Z, Wang Z, et al. Efficient Learning in Adaptive Processing of Data Structures[J]. Neural Processing Letters. 2003, 17(2): 175-190.
[112] Boudhar M. Dynamic Scheduling on a Single Batch Processing Machine with Split Compatibility Graphs[J]. Journal of Mathematical Modelling and Algorithms. 2003, 2(1): 17-35.
[113] Cheng T C E, Kovalyov M Y. Single machine batch scheduling with sequential job processing[J]. HE Transactions. 2001, 33(5): 413-420.
[114] Yoshitatsu M, Kazunori Y. Linear multilayer independent component analysis using stochastic cradient algorithm[C].ICA2004, LNCS 3195: 303-310.
[115] Funfgeld E. Therapeutic effects in Alzheimer's disease objectified by Dynamic Brain Mapping, the 20-Frequency-Band analysis and the global change of frequencies[J]. Journal of Neural Transmission. 1989, 1(1): 68.
[116] Jiang J, Xu J. An Iterative Method of Point Mapping under Cell Reference for the Global Analysis: Theory and a Multiscale Reference Technique[J]. Nonlinear Dynamics. 1998,15(2): 103-114.
[117] Du Du Q, Koprivab A I. Dependent component analysis for blind restoration of images degraded by turbulent atmosphere[J]. Neurocomputing. 2008,27(4).
[118] Celorrio R, Dominguez V, Sayas F. Periodic Dirac Delta Distributions in the Boundary Element Method[J]. Advances in Computational Mathematics. 2002,17(3): 211-236.
[119] Al-Hussaini E K, Sayed I M, Saad E M. Selection and MRC Diversity for a DS/CDMA Mobile Radio System through Nakagami Fading Channel[J]. Wireless Personal Communications. 2001, 16(2): 115-133.
[120] Ali A A. Optimum Time Diversity and BCH Codes for DS SS-CDMA Cellular Mobile Channels[J]. Wireless Personal Communications. 1999, 9(3): 217-231.
[121] Li T, Vasavada Y M, Woerner B D, et al. A Novel Direct Sequence Spread Spectrum CDMA System with Analog Frequency Modulation[J]. International Journal of Wireless Information Networks. 2000, 7(1): 43-53.
[122] Furukawa H, Konishi T, Oshita Y, et al. Design of Header Recognition Filter for Binary Phase Shift Keying in Header Recognition Unit using Time-Space Conversion[J]. Optical Review. 2004, 11(2): 119-125.
[123] Boche H, M Nich U. Behavior of Shannon' s Sampling Series for Hardy Spaces[J]. Journal of Fourier Analysis and Applications.
[124] Lin T. Blind IB-MSNR Beamforming for DSSS-based Communication Systems[J]. Wireless Personal Communications.
[125] Brown D R, Poor H V, Verdu S, et al. Multiuser Detection for Out-of-Cell Cochannel Interference Mitigation in the IS - 95 Downlink[J]. The Journal of VLSI Signal Processing. 2002, 30(1): 217-233.
[126] Homnan B, Benjapolakul W. Adaptation of CDMA Soft Handoff Thresholds Using Fuzzy Inference System[J]. Wireless Personal Communications. 2003, 26(4): 325-345.
[127] Amaldi E, Belotti P, Capone A, et al. Optimizing base station location and configuration in UMTS networks[J]. Annals of Operations Research. 2006, 146(1): 135-151.
[128] Hong L, Ho K C. Classification of BPSK and QPSK Signals Using an Antenna Array[J]. Circuits, Systems, and Signal Processing. 2005, 24(4): 343-361.
[129] Hocanin A, Sarin S V, Deli H. Medium Access Control for ATM-to-CDMA Interface[J]. Wireless Networks. 2002, 8(4): 337-346.
[130] Miaris G, Siachalou E, Samaras T, et al. On the base stations antenna system design for mobile communications[J]. Electrical Engineering (Archiv fur Elektrotechnik). 2006, 88(2): 157-163.
[131] Lin Z, You X, Wang Y. Low-complexity blind multiuser detection of DS-CDMA signal in dispersive channels[J]. Journal of Electronics (China). 2001, 18(4): 330-336.
[132] Sills J A, Kamen E W. Time-varying matched filters[J]. Circuits, Systems, and Signal Processing. 1996, 15(5): 609-630.
[133] Nihtil T, Kurjenniemi J, Lampinen M, et al. Performance of receive diversity and LMMSE chip equalization in WCDMA HSDPA network[J]. Wireless Personal Communications. 2007,43(2): 261-280.
[134] Al-Hussaini E K, Mourad H M, Newagy F A. A MC-CDMA Mobile Communication System Employing Pre-Rake and Transmit Diversity [J]. Wireless Personal Communications. 2004, 31(3): 131-148.
[135] Chernov N, Lesort C. Least Squares Fitting of Circles[J]. Journal of Mathematical Imaging and Vision. 2005, 23(3): 239-252.
[136] Wang J, Liu Z, Wang Y, et al. Performance of the Zero Forcing precoding MIMO broadcast systems with channel estimation errors [J]. Journal of Electronics (China). 2007, 24(4): 490-495.
[137] Barreto M C M, Barnett V. Best linear unbiased estimators for the simple linear regression model using ranked set sampling[J]. Environmental and Ecological Statistics. 1999,6(2): 119-133.
[138] Buzzi S, Lops M, Tulino A M. MMSE RAKE Reception for Asynchronous DS/CDMA Overlay Systems and Frequency-Selective Fading Channels[J]. Wireless Personal Communications. 2000,13(3): 295-318.
[139] Bravo A M, Monera-Llorca J, Bhargava V K. Implementation of the Decorrelating Receiver for Asynchronous DS-CDMA Systems over Multipath Fading Channels[J]. Wireless Personal Communications. 2000, 15(1): 79-95.
[140] Jumarie G. Generalized Fokker-Planck equation for a class of stochastic dynamical systems driven by additive Gaussian and Poissonian fractional white noises of order[J]. Central European Journal of Physics. 2008, 6(3): 737-753.
[141] Ristaniemi T, Joutsensalo A J. Advanced ICA-based receivers for block fading DS-CDMA channels[J]. Signal Processing. 2001, 82(3).
[142] Chen Q, Sousa E S, Pasupathy S. Performance of a coded multi-carrier DS-CDMA system in multi-path fading channels[J]. Wireless Personal Communications. 1995, 2(1): 167-183.
[143] Buzzi S, Lops M, Tulino AM. MMSE RAKE Reception for Asynchronous DS/CDMA Overlay Systems and Frequency-Selective Fading Channels[J]. Wireless Personal Communications. 2000,13(3): 295-318.
[144] Ayyagari D, Ephremides A. Power Control for Link Quality Protection in Cellular DS-CDMA Networks with Integrated (Packet and Circuit) Services[J]. Wireless Networks. 2002, 8(6): 549-555.
[145] Ghafouri-Shiraz H, Karbassian M, Liu F. Multiple access interference cancellation in Manchester-coded synchronous optical PPM-CDMA network[J]. Optical and Quantum Electronics. 2007, 39(9): 723-734.
[146] Cheng X, Yuan D. A novel iterative scheme for parallel inter-symbol interference channels[J]. Journal of Electronics (China). 2007, 24(1): 50-53.
[147] Saxena J, Rai C, Bansal P. Near - Far Resistant ICA based Detector for DS-CDMA Systems in the Downlink[J]. Wireless Personal Communications. 2007, 43(2): 341-353.
[148] Chiu S, Lu A C. Noise separation of the yarn tension signal on twister using FastICA[J]. Mechanical Systems and Signal Processing. 2005, 19(6): 5.