scholarly journals A New Blind Source Separation Algorithm Framework for Noisy Mixing Model Based on the Energy Concentration Characteristic in Signal Transform Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jiong Li ◽  
Lu Feng
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Energy Concentration ◽  
Transform Domain ◽  
Transform Method ◽  
Analysis Process ◽  
Algorithm Framework ◽  
Concentration Characteristic ◽  
High Level

Blind source separation is a widely used technique to analyze multichannel data. In most real-world applications, noise is inevitable and will affect the quality of signal separation and even make signal separation failure. In this paper, a new signal processing framework is proposed to separate noisy mixing sources. It is composed of two different stages. The first step is to process the mixing signal by a certain signal transform method to make the expected signals have energy concentration characteristics in the transform domain. The second stage is formed by a certain BSS algorithm estimating the demixing matrix in the transform domain. In the energy concentration segment, the SNR can reach a high level so that the demixing matrix can be estimated accurately. The analysis process of the proposed algorithm framework is analyzed by taking the wavelet transform as an example. Numerical experiments demonstrate the efficiency of the proposed algorithm to estimate the mixing matrix in comparison with well-known algorithms.

Application of C Sharp and MATLAB Mixed Programming Based on .Net Assembly in Blind Source Separation

2014 ◽  
Vol 599-601 ◽  
pp. 1407-1410
Author(s):  
Xu Liang ◽  
Ke Ming Wang ◽  
Gui Yu Xin
Keyword(s):  
Numerical Calculation ◽  
Programming Languages ◽  
Blind Source Separation ◽  
Source Separation ◽  
Processing System ◽  
Signal Processing System ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Mixed Programming ◽  
High Level

Comparing with other High-level programming languages, C Sharp (C#) is more efficient in software development. While MATLAB language provides a series of powerful functions of numerical calculation that facilitate adoption of algorithms, which are widely applied in blind source separation (BSS). Combining the advantages of the two languages, this paper presents an implementation of mixed programming and the development of a simplified blind signal processing system. Application results show the system developed by mixed programming is successful.

A Note on Stone's Conjecture of Blind Signal Separation

2005 ◽  
Vol 17 (2) ◽  
pp. 321-330 ◽  
Author(s):  
Shengli Xie ◽  
Zhaoshui He ◽  
Yuli Fu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
The Novel ◽  
Blind Signal ◽  
Simple Simulation ◽  
Novel Approaches

Stone's method is one of the novel approaches to the blind source separation (BSS) problem and is based on Stone's conjecture. However, this conjecture has not been proved. We present a simple simulation to demonstrate that Stone's conjecture is incorrect. We then modify Stone's conjecture and prove this modified conjecture as a theorem, which can be used a basis for BSS algorithms.

scholarly journals A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms

2017 ◽  
Vol 7 (4) ◽  
pp. 1906 ◽  
Author(s):  
Abouzid Houda ◽  
Chakkor Otman
Keyword(s):  
Independent Component Analysis ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real Life ◽  
Signal Separation ◽  
Mixing Process ◽  
The Real ◽  
Input Signals ◽  
Novel Method

Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the  nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation.  But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used.

Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine

2009 ◽  
Vol 419-420 ◽  
pp. 801-804
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Signal Separation ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Vibration Data ◽  
Monitoring And Diagnosis ◽  
Rotating Shafts

The effectiveness of separation and identification of mechanical signals vibrations is crucial to successful fault diagnosis in the condition monitoring and diagnosis of complex machines.Aeroengine vibration signals always include many complicated components, blind source separation (BSS) provides a efficient way to separate the independent component.In order to get the most effective algorithm of vibration signal separation,experiment has been done to acquire plenty of multi-mixed rotor vibration signals,three sets of vibration data generated from aeroengine rotating shafts were separated from the synthetic vibration signal. The results prove that blind source separation is effective and can be applied for vibration signal processing and fault diagnosis of aeroengine.

scholarly journals Blind Source Separation based on Whale Optimization Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03052
Author(s):  
CHU Ding-li ◽  
CHEN Hong ◽  
CHEN Han-yi
Keyword(s):  
Particle Swarm Optimization ◽  
Blind Source Separation ◽  
Optimization Algorithm ◽  
Source Separation ◽  
Whale Optimization Algorithm ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Swarm Optimization ◽  
Blind Signal ◽  
Whale Optimization

Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.

ICA and PCA-Based Cryptology

2014 ◽  
pp. 200-217
Author(s):  
Sattar B. Sadkhan Al Maliky ◽  
Nidaa A. Abbas
Keyword(s):  
Principal Component Analysis ◽  
Blind Source Separation ◽  
Biomedical Engineering ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Mathematical Problems ◽  
Mixing Matrix ◽  
Speech Encryption ◽  
High Level

Blind Source Separation (BSS) represented by Independent Component Analysis (ICA) has been used in many fields such as communications and biomedical engineering. Its application to image and speech encryption, however, has been rare. In this chapter, the authors present ICA and Principal Component Analysis (PCA) as a category of BSS-based method for encrypting images and speech by using Blind Source Separation (BSS) since the security encryption technologies depend on many intractable mathematical problems. Using key signals, they build a suitable BSS underdetermined problem in the encryption and then circumvent this problem with key signals for decoding. The chapter shows that the method based on the BSS can achieve a high level of safety right through building, mixing matrix, and generating key signals.

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