An FPGA implementation of complex valued independent component analysis for real-time interference canceler

2011 ◽  
Author(s):  
Hidehisa Shiomi ◽  
S. Fujio ◽  
Y. Okamura
Keyword(s):  
Independent Component Analysis ◽  
Real Time ◽  
Component Analysis ◽  
Fpga Implementation ◽  
Independent Component ◽  
Complex Valued

Separation and Reproduction of Mixed Images Using 2-D Complex Wavelet Transform

2010 ◽  
Vol 36 ◽  
pp. 466-475
Author(s):  
Tsutomu Matsuura ◽  
Amirul Faiz ◽  
Kouji Kiryu
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Wavelet Transform ◽  
Time Frequency ◽  
Frequency Information ◽  
Complex Wavelet ◽  
Complex Valued

The differences method between 1-D wavelet transform and 2-D wavelet transform in image processing is discussed. Both proposed method uses the quotient of complex valued time-frequency information of observed signals to detect the number of sources. No less number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. Using the same given Algorithm and parameters for both method, the result on separated images are compared.

scholarly journals Application of Independent Component Analysis With Adaptive Density Model to Complex-Valued fMRI Data

2011 ◽  
Vol 58 (10) ◽  
pp. 2794-2803 ◽  
Author(s):  
Hualiang Li ◽  
N. M. Correa ◽  
P. A. Rodriguez ◽  
V. D. Calhoun ◽  
T. Adali
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Density Model ◽  
Fmri Data ◽  
Complex Valued

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

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