scholarly journals gpICA: A Novel Nonlinear ICA Algorithm Using Geometric Linearization

2006 ◽  
Vol 2007 (1) ◽  
Author(s):  
Thang Viet Nguyen ◽  
Jagdish Chandra Patra ◽  
Sabu Emmanuel
Keyword(s):  
Ica Algorithm ◽  
Geometric Linearization

An Optimal ICA Algorithm Applied to fMRI DATA

2007 ◽  
Author(s):  
Xian-Chuan Yu ◽  
Jia-Mian Ren ◽  
Nan Zhang ◽  
Guo-Sheng Ding
Keyword(s):  
Fmri Data ◽  
Ica Algorithm

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.

Regression ICA Algorithm for Image Denoising

2008 ◽  
pp. 993-997 ◽  
Author(s):  
Shangming Yang ◽  
Zhang Yi ◽  
Guisong Liu
Keyword(s):  
Image Denoising ◽  
Ica Algorithm

scholarly journals Real Time Blind Source Separation in Reverberant Environments

2021 ◽  
Author(s):  
◽  
Timothy Sherry
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Real World ◽  
Source Separation ◽  
Real World Data ◽  
The Core ◽  
Reverberant Environments ◽  
Ica Algorithm ◽  
Stationary Sources ◽  
Convolutive Blind Source Separation

<p>An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process.</p>

An Extended Online Fast-ICA Algorithm

2006 ◽  
pp. 1109-1114 ◽  
Author(s):  
Gang Wang ◽  
Ni-ni Rao ◽  
Zhi-lin Zhang ◽  
Quanyi Mo ◽  
Pu Wang
Keyword(s):  
Ica Algorithm

Accurate Detection of ECG Signals in ECG Monitoring Systems by Eliminating the Motion Artifacts and Improving the Signal Quality Using SSG Filter with DBE

2019 ◽  
Vol 29 (02) ◽  
pp. 2050024
Author(s):  
Mahesh B. Dembrani ◽  
K. B. Khanchandani ◽  
Anita Zurani
Keyword(s):  
Signal Analysis ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Signal Quality ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Cardiac Disorders ◽  
Digital Processor ◽  
Ica Algorithm

The automatic recognition of QRS complexes in an Electrocardiography (ECG) signal is a critical step in any programmed ECG signal investigation, particularly when the ECG signal taken from the pregnant women additionally contains the signal of the fetus and some motion artifact signals. Separation of ECG signals of mother and fetus and investigation of the cardiac disorders of the mother are demanding tasks, since only one single device is utilized and it gets a blend of different heart beats. In order to resolve such problems we propose a design of new reconfigurable Subtractive Savitzky–Golay (SSG) filter with Digital Processor Back-end (DBE) in this paper. The separation of signals is done using Independent Component Analysis (ICA) algorithm and then the motion artifacts are removed from the extracted mother’s signal. The combinational use of SSG filter and DBE enhances the signal quality and helps in detecting the QRS complex from the ECG signal particularly the R peak accurately. The experimental results of ECG signal analysis show the importance of our proposed method.

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