A Research on Complex FastICA Algorithm Based UNB Signal Detection

2011 ◽  
Vol 219-220 ◽  
pp. 1584-1588
Author(s):  
Shi Kai Zhang
Keyword(s):  
Computer Simulation ◽  
Signal Detection ◽  
Cost Function ◽  
Communication Technology ◽  
Modulation Method ◽  
First Principle ◽  
Modulation Format ◽  
High Efficient ◽  
Fastica Algorithm ◽  
Potential Use

Spectrum is limited, in order to develop high efficient modem, Complex FastICA algorithm is studied about its potential use in UNB signal detection. First, principle of Complex FastICA is presented, kurtosis and cost function are analyzed, and realization of Complex FastICA is given; then, M-EBPSK modulation method is supposed as a kind of UNB modulation format, UNB-ICA model for UNB received signal with interference and noise is presented by analyzing the characteristics of M-EBPSK modulation; finally, computer simulation is done by using nosy UNB-ICA model based on Complex FastICA Algorithm, the experimental results show that Complex FastICA Algorithm can suppress the interference and improve detection BER in contrast to the traditional methods, it is hopeful that the method can be used in UNB communication technology.

A Method for Extracting Signals with Specific Normalized Kurtosis Range

2013 ◽  
Vol 756-759 ◽  
pp. 3845-3848
Author(s):  
Yong Jian Zhao ◽  
Mei Xia Qu ◽  
Hai Ning Jiang
Keyword(s):  
A Priori ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Validity And Reliability ◽  
Original Source ◽  
Blind Source Extraction ◽  
Blind Signal ◽  
Extraction Algorithm ◽  
Fastica Algorithm ◽  
Source Extraction

The famous FastICA algorithm has been widely used for blind signal separation. For every process, it only converges to an original source which has the maximum negentropy of the underlying signals. To ensure the first output is the desired signal, we incorporate a priori knowledge as a constraint into the FastICA algorithm to construct a robust blind source extraction algorithm. One can extract the desired signal if its normalized kurtosis is known to lie in a specific range, whereas other unwanted signals do not belong to this range. Experimental results on biomedical signals illustrate the validity and reliability of the proposed method.

Study of Blind Source Separation on Transmission Line EMI

2013 ◽  
Vol 846-847 ◽  
pp. 493-499
Author(s):  
Zi Hua Li ◽  
Chun Yan Xiao ◽  
Shuai Gao
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Electromagnetic Interference ◽  
Electromagnetic Compatibility ◽  
Spectral Characteristics ◽  
Separation Performance ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Interference Signals ◽  
Emc Design

The transmission line is an important part of electrical system. Electromagnetic interference (EMI) signals can be picked up by transmission lines in a way of conduction or radiation, and interfere the sensitive devices located in the power source end and the load end, so it is important and necessary to separate and identify the EMI source signals coupling to the transmission lines in order to guide the electromagnetic compatibility (EMC) design and the further EMI diagnosis and suppression. Fast independent component analysis (FastICA) algorithm is studied and programmed, and its feasibility and separation performance are validated via simulation of BSS of three mixed signals and the average signals to interference ratio (SIR) is approximately 30 dB. The model of crosstalk of transmission lines is built and simulated, the interference signals are separated by the FastICA algorithm, and the average SIR is over 20 dB. Periodicity and spectral characteristics of the separated interference signals are analyzed, and the identification of interference signals is realized.

Application of Frequency-Domain Blind Deconvolution in Mechanical Fault Detection

2011 ◽  
Vol 130-134 ◽  
pp. 2128-2132
Author(s):  
Nan Pan ◽  
Wu Xing ◽  
Yi Lin Chi ◽  
Liu Chang ◽  
Xiao Qin Liu
Keyword(s):  
Fault Detection ◽  
Frequency Domain ◽  
Blind Deconvolution ◽  
Rolling Bearing ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Fastica Algorithm ◽  
Amplitude Correlation ◽  
Signal Processing Methods ◽  
Acceleration Signals

On the basis of summing up the Frequency-Domain Blind Deconvolution (FDBD), a method combine Complex-Domain FastICA algorithm and amplitude correlation was proposed to extract the typical defect signals from mechanical equipment. The application in combined failure rolling bearing acceleration signals demonstrate that, comparing with the existing Time-Domain Blind Signal Processing methods, FDBD has more advantages and better prospects in mechanical fault detection.

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