Fast ICA for Blind Source Separation and its Implementation

Behera, Sasmita Kumari (2009) Fast ICA for Blind Source Separation and its Implementation. MTech thesis.



Independent Component Analysis (ICA) is a statistical signal processing technique having emerging new practical application areas, such as blind signal separation such as mixed voices or images, analysis of several types of data or feature extraction. Fast independent component analysis (Fast ICA ) is one of the most efficient ICA technique. Fast ICA algorithm separates the independent sources from their mixtures by measuring non-gaussian. Fast ICA is a common method to identify aircrafts and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement Fast ICA for real-time signal processing. In this thesis, the Fast ICA algorithm is implemented by hand coding HDL code. In addition, in order to increase the number of precision, the floating point (FP) arithmetic units are also implemented by HDL coding.To verify the algorithm, MATLAB simulations are also performed for both off line signal rocessing and real-time signal processing.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Signal processing,Blind source separation, Fast ICA,VLSI Implementation
Subjects:Engineering and Technology > Electronics and Communication Engineering > VLSI
Engineering and Technology > Electronics and Communication Engineering > Signal Processing
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:1431
Deposited By:Sasmita Kumari Behera
Deposited On:04 Jun 2009 14:34
Last Modified:04 Jun 2009 14:34
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Supervisor(s):Panda , G

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