Papers by Keyword: Blind Source Separation

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Authors: Gao Ling, Shou Xin Ren
Abstract: A multi-dimensional data processing method, independent component analysis-based principal component regression (ICA-PCR) was developed for simultaneous kinetic determination of Cu (II), Fe (III) and Ni (II). Independent component analysis is a newly developed signal processing technique aiming at solving related blind source separation (BSS) problem. One program, PICAPCR, was designed to perform relative calculations. Experimental results showed the ICA-PCR method to be successful for simultaneous multicomponent kinetic determination even where there was severe overlap of spectra.
Authors: Juhn Horng Chen, Long Jye Sheu, Tzu Yi Tung, Hsien Keng Chen, Horng Shing Chiou, Wei Tai Weng
Abstract: We propose a new perspective on image encryption using chaotic signal and blind source separation. The original image is permuted by the chaotic signal and then mixed with key images. In the receiver, blind source separation technique is used to separate the components of the original image from the ciphertexts. Then chaotic signal is again used to restore the pixels to recover the original image. The experimental results demonstrate that the key space is large enough to resist the brute force attack and the distribution of gray values of the encrypted image has a random-like behavior.
Authors: Shuang Wei, De Fu Jiang, Yang Gao
Abstract: This paper presents a diversity-guided Particle swarm optimization (PSO) algorithm to resolve the Blind source separation (BSS) problem. Because the independent component analysis (ICA) approach, a popular method for the BSS problem, has a shortcoming of premature convergence during the optimization process, the proposed PSO algorithm aims to improve this issue by using the diversity calculation to avoid trapping in the local optima. In the experiment, the performance of the proposed PSO algorithm for the BSS problem has been investigated and the results are compared with the conventional PSO algorithm. It shows that the proposed PSO algorithm outperforms the conventional PSO algorithm.
Authors: Xiao Yan Yang, Xiong Zhou, Yi Ke Tang
Abstract: In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.
Authors: Wei Hua Liu, Yun Zhang, Ying Fu Chen, Lei Wang, Jian Cheng Liu
Abstract: A novel blind source separation (BSS) algorithm for linear mixture signals is proposed. It is shown that the property can be used to separate source signals by finding an un-mixing matrix that maximizes the cost function value of separated signals. Simulation results illustrate the efficiency and the good performance of the algorithm.
Authors: Yu Feng Xue, Yu Jia Wang, Qiu Dong Sun
Abstract: In this paper, a new method is introduced to derive the extended natural gradient, which was proposed by Lewicki and Sejnowski in [1]. However, they made their derivation under many approximations, and the proof is also very complicated. To give a more rigors mathematical proof for this gradient, the Lie group invariance property is introduced which makes the proof much easier and straightforward. In addition, an iterative algorithm through Newton's method is also given to estimate the sources efficiently. The results of the experiments confirm the efficiency of the proposed method.
Authors: Wei Lu, Ming Ma
Abstract: In the modern wars under the condition of informationalization, radar signals are generally characterized by repetitive patterns in time, so it is one important task of Radar Warning Receiver (RWR) to estimate the Pulse Repetition Interval (PRI) of all radar signals by intercepting and identifying the mixed radar signals. Because each transmitted radar signal are arbitrary in general. From the view of statistic, the transmitted radar pulse train from different radars is mutual independent. So the constituted model of RWR and radar transmitters accord with blind source separation which can separate mixed signals into pure signals. A deinterleaving method using blind source separation for estimating the PRI of each radar signals is proposed. The implementation architecture of the proposed method is given. Finally, computer simulations show the proposed method can gain good performance for the estimation of PRI of radar signals.
Authors: Ning Chen, Hong Yi Zhang
Abstract: The blind source separation (BSS) using a two-stage sparse representation approach is discussed in this paper. We presented the algorithm based on linear membership function to estimate the unknown mixing matrix precisely, and then, the optimization algorithm based on integral to get the max value of the function is proposed. Another contribution described in this paper is the discussion of the impact of noise on the estimating the mixing matrix. Given the impact of noise, we set weights to put more emphasis on the more reliable data. Several experiments involving speech signals show the effectiveness and efficiency of this method.
Authors: Hong Yi Li, Meng Ye, Di Zhao
Abstract: The Independent Component Analysis (ICA) is a classical algorithm for exploring statistically independent non-Gaussian signals from multi-dimensional data, which has a wide range of applications in engineering, for instance, the blind source separation. The classical ICA measures the Gaussian characteristic by kurtosis, which has the following two disadvantages. Firstly, the kurtosis relies on the value of samples, and is not robust to outliers. Secondly, the algorithm often falls into local optima. To address these drawbacks, we replace the kurtosis by negative entropy, utilize the simulated annealing algorithm for optimization, and finally propose an improved ICA algorithm. Experimental results demonstrate that the proposed algorithm outperforms the classical ICA in its robustness to outliers and convergent rate.
Authors: Zhu Cheng Li
Abstract: This paper introduces a new algorithm based on non-linear function to adaptively control step-size which is used for updating separation matrix to extract a target speech source accurately in blind source separation (BSS). The use of fixed step-size parameter of the conventional BSS algorithm usually results in a trade-off between convergence speed and steady-state misadjustment. The presented algorithm will eliminate much of this trade-off. It intelligently regulates the step-size according to the time-varying dynamics of other parameters at each iteration. The desirable ability of the new algorithm to improve convergence speed and steady-state misadjustment is demonstrated by MATLAB simulation results.
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