scholarly journals Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1192 ◽  
Author(s):  
Yaqin Xie ◽  
Jiayin Yu ◽  
Xinwu Chen ◽  
Qun Ding ◽  
Erfu Wang
Keyword(s):  
Image Restoration ◽  
Blind Source Separation ◽  
Source Separation ◽  
Signal Recovery ◽  
Separation Performance ◽  
Underdetermined Blind Source Separation ◽  
Elimination Algorithm ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Source Signals

To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.

The Research and Simulation of Blind Source Separation Algorithm

2016 ◽  
Vol 8 (3) ◽  
pp. 1-36
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

scholarly journals Underdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 521 ◽  
Author(s):  
Yuan Xie ◽  
Kan Xie ◽  
Junjie Yang ◽  
Shengli Xie
Keyword(s):  
Blind Source Separation ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Source Separation ◽  
Nonnegative Matrix ◽  
Tensor Decomposition ◽  
Model Parameters ◽  
Separation Performance ◽  
Underdetermined Blind Source Separation ◽  
Source Signals

Underdetermined blind source separation (UBSS) is a hot topic in signal processing, which aims at recovering the source signals from a number of observed mixtures without knowing the mixing system. Recently, expectation-maximization algorithm shows a great potential in the UBSS. However, the final separation results depend strongly on the parameter initialization, leading to poor separation performance. In this paper, we propose an effective algorithm that combines tensor decomposition and nonnegative matrix factorization (NMF). In the proposed algorithm, we first employ tensor decomposition to estimate the mixing matrix, and NMF source model is used to estimate the source spectrogram factors. Then a series of iterations are derived to update the model parameters. At the same time, the spatial images of source signals are estimated with Wiener filters constructed from the learned parameters. Therefore, time-domain sources can be obtained through inverse short-time Fourier transform. Finally, plenty of experimental results demonstrate the effectiveness and advantages of our proposed algorithm over the compared algorithms.

The Research and Simulation of Blind Source Separation Algorithm

2020 ◽  
pp. 494-531
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

scholarly journals Consistent Independent Low-Rank Matrix Analysis for Determined Blind Source Separation

2020 ◽  
Author(s):  
Daichi Kitamura ◽  
Kohei Yatabe
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Matrix Analysis ◽  
Low Rank ◽  
Separation Performance ◽  
Time Frequency ◽  
Rank Matrix ◽  
Permutation Problem ◽  
Low Rank Matrix ◽  
Source Signals

Abstract Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such highly developed source model can effectively solve the permutation problem of the frequency-domain BSS, which should be the reason of the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrogram called consistency, and hence we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related each other via the uncertainty principle. Such co-occurrence among the spectral components can be an assistant for solving the permutation problem, which has been demonstrated by a recent study. Based on these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively studied through the experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA which include some topics have not discussed well in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system.

scholarly journals A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1413 ◽  
Author(s):  
Jiantao Lu ◽  
Wei Cheng ◽  
Yanyang Zi
Keyword(s):  
Blind Source Separation ◽  
Quantitative Estimation ◽  
Source Separation ◽  
Estimation Method ◽  
Least Square ◽  
Underdetermined Blind Source Separation ◽  
Source Contribution ◽  
Time Frequency ◽  
Source Contributions ◽  
Source Signals

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

scholarly journals A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Feng Miao ◽  
Rongzhen Zhao
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Diagnosis Method ◽  
Machine Health Monitoring ◽  
Machine Health

When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

scholarly journals A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1677
Author(s):  
Qingyi Wang ◽  
Yiqiong Zhang ◽  
Shuai Yin ◽  
Yuduo Wang ◽  
Genping Wu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Signal Recovery ◽  
Subspace Projection ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Time Frequency ◽  
Source Signal ◽  
Mixing Matrix

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

A NEW ALGORITHM FOR THE UNDERDETERMINED BLIND SOURCE SEPARATION BASED ON SPARSE COMPONENT ANALYSIS

2009 ◽  
Vol 23 (01) ◽  
pp. 71-85 ◽  
Author(s):  
HAI-LIN LIU ◽  
CHU-JUN YAO ◽  
JIA-XUN HOU
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Underdetermined Blind Source Separation ◽  
Spherical Coordinate ◽  
Sparse Component Analysis ◽  
Hyperplane Clustering ◽  
The Matrix ◽  
Mixing Matrix ◽  
Normal Vectors ◽  
Source Signals

For the purpose of estimating the mixing matrix under the nonstrictly sparse condition, this paper presents the algorithms to approximate the mixing matrix in two different situations in which the source vectors are 1-sparse and (m - 1)-sparse. When the source signals are 1-sparse, we use the generalized spherical coordinate transformation to convert the matrix of observation signals into the new one, which makes the process of estimating column A become the process of finding the center point of these new data. For the situation that source signals are (m - 1)-sparse, we propose a new algorithm for the underdetermined mixtures blind source separation based on hyperplane clustering. The algorithm firstly finds out the linearly independent vectors from the observations, and secondly determines all the normal vectors of hyperplanes by analyzing the number of observations that are in the same hyperplane. Finally, we identify the column vectors of the mixing matrix A by calculating the vectors which are orthogonal to the clustered normal vectors. These two new algorithms for estimating the mixing matrix are more suitable for the general cases as they have lower requirement for the sparsity of the observations.

scholarly journals Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1217
Author(s):  
Jindong Wang ◽  
Xin Chen ◽  
Haiyang Zhao ◽  
Yanyang Li ◽  
Zujian Liu
Keyword(s):  
Blind Source Separation ◽  
Hierarchical Clustering ◽  
Source Separation ◽  
Least Square Method ◽  
Least Square ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Initial Cluster ◽  
Mixing Matrix ◽  
Source Signals

In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

scholarly journals Consistent independent low-rank matrix analysis for determined blind source separation

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Daichi Kitamura ◽  
Kohei Yatabe
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Matrix Analysis ◽  
Low Rank ◽  
Separation Performance ◽  
Time Frequency ◽  
Rank Matrix ◽  
Permutation Problem ◽  
Low Rank Matrix ◽  
Source Signals

AbstractIndependent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such a highly developed source model can solve the permutation problem of the frequency-domain BSS to a large extent, which is the reason for the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrograms, which is called consistency, and hence, we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related to each other via the uncertainty principle. Such co-occurrence among the spectral components can function as an assistant for solving the permutation problem, which has been demonstrated by a recent study. On the basis of these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively evaluated through experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA that include some topics not fully discussed in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system.

Sign in / Sign up

Export Citation Format

Share Document