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[Keyword] independent component analysis (ICA)(10hit)

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  • A Novel Method for Adaptive Beamforming under the Strong Interference Condition

    Zongli RUAN  Hongshu LIAO  Guobing QIAN  

     
    LETTER-Digital Signal Processing

      Pubricized:
    2021/08/02
      Vol:
    E105-A No:2
      Page(s):
    109-113

    In this letter, firstly, a novel adaptive beamformer using independent component analysis (ICA) algorithm is proposed. By this algorithm, the ambiguity of amplitude and phase resulted from blind source separation is removed utilizing the special structure of array manifolds matrix. However, there might exist great calibration error when the powers of interferences are far larger than that of desired signal at many applications such as sonar, radio astronomy, biomedical engineering and earthquake detection. As a result, this will lead to a significant reduction in separation performance. Then, a new method based on the combination of ICA and primary component analysis (PCA) is proposed to recover the desired signal's amplitude under strong interference. Finally, computer simulation is carried out to indicate the effectiveness of our methods. The simulation results show that the proposed methods can obtain higher SNR and more accurate power estimation of desired signal than diagonal loading sample matrix inversion (LSMI) and worst-case performance optimization (WCPO) method.

  • Enhancing Event-Related Potentials Based on Maximum a Posteriori Estimation with a Spatial Correlation Prior

    Hayato MAKI  Tomoki TODA  Sakriani SAKTI  Graham NEUBIG  Satoshi NAKAMURA  

     
    PAPER

      Pubricized:
    2016/04/01
      Vol:
    E99-D No:6
      Page(s):
    1437-1446

    In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

  • Complex Noisy Independent Component Analysis by Negentropy Maximization

    Guobing QIAN  Liping LI  Hongshu LIAO  

     
    LETTER-Noise and Vibration

      Vol:
    E97-A No:12
      Page(s):
    2641-2644

    The maximization of non-Gaussianity is an effective approach to achieve the complex independent component analysis (ICA) problem. However, the traditional complex maximization of non-Gaussianity (CMN) algorithm does not consider the influence of noise. In this letter, a modification of the fixed-point algorithm is proposed for more practical occasions of the complex noisy ICA model. Simulations show that the proposed method demonstrates significantly improved performance over the traditional CMN algorithm in the noisy ICA model when the sample size is sufficient.

  • Performance Evaluation of Band-Limited Baseband Synchronous CDMA Using Orthogonal ICA Sequences

    Ryo TAKAHASHI  Ken UMENO  

     
    PAPER-Nonlinear Problems

      Vol:
    E93-A No:3
      Page(s):
    577-582

    Performance of band-limited baseband synchronous CDMA using orthogonal Independent Component Analysis (ICA) spreading sequences is investigated. The orthogonal ICA sequences have an orthogonality condition in a synchronous CDMA like the Walsh-Hadamard sequences. Furthermore, these have useful correlation properties like the Gold sequences. These sequences are obtained easily by using the ICA which is one of the brain-style signal processing algorithms. In this study, the ICA is used not as a separator for received signal but as a generator of spreading sequences. The performance of the band-limited synchronous CDMA using the orthogonal ICA sequences is compared with the one using the Walsh-Hadamard sequences. For limiting bandwidth, a Root Raised Cosine filter (RRC) is used. We investigate means and variances of correlation outputs after passing the RRC filter and the Bit Error Rates (BERs) of the system in additive white Gaussian noise channel by numerical simulations. It is found that the BER in the band-limited system using the orthogonal ICA sequences is much lower than the one using the Walsh-Hadamard sequences statistically.

  • A Robust and Non-invasive Fetal Electrocardiogram Extraction Algorithm in a Semi-Blind Way

    Yalan YE  Zhi-Lin ZHANG  Jia CHEN  

     
    LETTER-Neural Networks and Bioengineering

      Vol:
    E91-A No:3
      Page(s):
    916-920

    Fetal electrocardiogram (FECG) extraction is of vital importance in biomedical signal processing. A promising approach is blind source extraction (BSE) emerging from the neural network fields, which is generally implemented in a semi-blind way. In this paper, we propose a robust extraction algorithm that can extract the clear FECG as the first extracted signal. The algorithm exploits the fact that the FECG signal's kurtosis value lies in a specific range, while the kurtosis values of other unwanted signals do not belong to this range. Moreover, the algorithm is very robust to outliers and its robustness is theoretically analyzed and is confirmed by simulation. In addition, the algorithm can work well in some adverse situations when the kurtosis values of some source signals are very close to each other. The above reasons mean that the algorithm is an appealing method which obtains an accurate and reliable FECG.

  • Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection

    Xiaowei ZHANG  Nuo ZHANG  Jianming LU  Takashi YAHAGI  

     
    PAPER-Digital Signal Processing

      Vol:
    E90-A No:6
      Page(s):
    1125-1132

    In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

  • Visualization of Brain Activities of Single-Trial and Averaged Multiple-Trials MEG Data

    Yoshio KONNO  Jianting CAO  Takayuki ARAI  Tsunehiro TAKEDA  

     
    PAPER-Neuro, Fuzzy, GA

      Vol:
    E86-A No:9
      Page(s):
    2294-2302

    Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.

  • Blind Separation of Sources Using Density Estimation and Simulated Annealing

    Carlos G. PUNTONET  Ali MANSOUR  

     
    PAPER-Digital Signal Processing

      Vol:
    E84-A No:10
      Page(s):
    2538-2546

    This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

  • Separating Virtual and Real Objects Using Independent Component Analysis

    HERMANTO  Allan Kardec BARROS  Tsuyoshi YAMAMURA  Noboru OHNISHI  

     
    PAPER-Image Processing, Image Pattern Recognition

      Vol:
    E84-D No:9
      Page(s):
    1241-1248

    We often see reflection phenomenon in our life. For example, through window glass, we can see real objects, but reflection causes virtual objects to appear in front of the glass. Thus, it is sometimes difficult to recognize the real objects. Some works have been proposed to separate these real and virtual objects using an optical property called polarization. However, they have a restriction on one assumption: the angle of incidence. In this paper, we overcome this difficulty using independent component analysis (ICA). We show the efficiency of the proposed method, by experimental results.

  • Blind Separation of Sources: Methods, Assumptions and Applications

    Ali MANSOUR  Allan Kardec BARROS  Noboru OHNISHI  

     
    SURVEY PAPER

      Vol:
    E83-A No:8
      Page(s):
    1498-1512

    The blind separation of sources is a recent and important problem in signal processing. Since 1984, it has been studied by many authors whilst many algorithms have been proposed. In this paper, the description of the problem, its assumptions, its currently applications and some algorithms and ideas are discussed.