scholarly journals An Extraction Method of Weak Low-Frequency Magnetic Communication Signals Based on Multisensor

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Chao Huang ◽  
Xin Xu ◽  
Dunge Liu ◽  
Wanhua Zhu ◽  
Xiaojuan Zhang ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Extraction Method ◽  
Field Experiments ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Low Frequency ◽  
Signal Extraction ◽  
Magnetic Noise ◽  
Communication Signals ◽  
Low Snr

It is a technical challenge to effectively remove the influence of magnetic noise from the vicinity of the receiving sensors on low-frequency magnetic communication. The traditional denoising methods are difficult to extract high-quality original signals under the condition of low SNR (the signal-to-noise ratio). In this paper, we analyze the numerical characteristics of the low-frequency magnetic field and propose the algorithms of the fast optimization of blind source separation (FOBSS) and the frequency-domain correlation extraction (FDCE). FOBSS is based on blind source separation (BSS). Signal extraction of low SNR can be implemented through FOBSS and FDCE. This signal extraction method is verified in multiple field experiments which can remove the magnetic noise by about 25 dB or more.

A Novel Blind Watermark Extraction Algorithm Based on Nonsubsampled Contourlet Transform

2014 ◽  
Vol 989-994 ◽  
pp. 1901-1904
Author(s):  
Lei Feng ◽  
Xiao Fei Shi ◽  
Hong Yu Chen ◽  
Yan Hua Li ◽  
Yue Long Zhang
Keyword(s):  
Prior Knowledge ◽  
Blind Source Separation ◽  
Extraction Method ◽  
Low Frequency ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Independent Components ◽  
Extraction Algorithm ◽  
Blind Extraction ◽  
Ica Algorithm

Most existing watermark extraction algorithms were dependent on prior knowledge. This paper proposed a blind extraction method without relying on prior knowledge. According to constructing new observation based on nonsubsampled contourlet transform, which utilizes low frequency and directional components of watermarked image, more independent components are generated. We involve these components into watermarked image and resort this solution to multichannel blind source separation. Estimated watermark is recovered by ICA algorithm. Experiment results indicate that the proposed method can achieve better results in contrast with two existing algorithms.

scholarly journals Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhiwei Zhang ◽  
Hongyuan Gao ◽  
Jingya Ma ◽  
Shihao Wang ◽  
Helin Sun
Keyword(s):  
Objective Function ◽  
Blind Source Separation ◽  
Impulse Noise ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Source Separation ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Stable Performance

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

Convolutive Blind Source Separation Based on Wavelet De-Noising

2013 ◽  
Vol 756-759 ◽  
pp. 3356-3361 ◽  
Author(s):  
Hong Bin Zhang ◽  
Peng Fei Xu
Keyword(s):  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Noise Pollution ◽  
Processing Method ◽  
Separation System ◽  
Communication Signals ◽  
The Time Domain ◽  
Ica Algorithm ◽  
Convolutive Blind Source Separation

The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.

Satellite Multipath Signal Extraction Algorithm Based on Blind Source Separation

2020 ◽  
Vol 57 (19) ◽  
pp. 190701
Author(s):  
冀常鹏 Ji Changpeng ◽  
文倩 Wen Qian ◽  
黄健聪 Huang Jiancong
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Extraction ◽  
Extraction Algorithm ◽  
Multipath Signal

Simulation Research on Rolling Element Bearing Fault Signal Extraction Based on Blind Source Separation

2014 ◽  
Vol 989-994 ◽  
pp. 3738-3742
Author(s):  
Yong Xiang Zhang ◽  
Jie Ping Zhu ◽  
Shuai Zhang
Keyword(s):  
Cost Function ◽  
Blind Source Separation ◽  
Source Separation ◽  
Hessian Matrix ◽  
Rolling Element Bearing ◽  
Signal Extraction ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
The Cost

In order to extract the fault information from rolling element bearing, combined with Kurtosis criteria and Hessian matrix. An improved rolling element bearing fault signal extraction method is proposed. Kurtosis is the cost function. The method is according to the construction principles of blind source separation (BSS), and it uses an analytically derived Hessian matrix in the maximization process of the cost function used. Then the impact signal is extracted successfully. The effectiveness of the method is demonstrated on simulated signal.

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