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This paper introduces a novel independent component analysis (ICA) approach to the separation of nonlinear convolutive mixtures.
The simulation results demonstrate the effectiveness of the design procedure to enhance the process monitoring tasks with the less number of sensors for ICA approach.
Results using both synthetic data and real observations suggest that the ICA approach can successfully separate generalized, landscape-level cropping patterns using only available temporal measurements.
Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold.
However, the trade-off between the exploration (i.e. the global search) and the exploitation (i.e. the local search) of the search space is critical to the success of the classical ICA approach.
In this paper, we consider a broadband ICA approach [31, 32] based on the TRINICON framework [33].
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Approaches like ICA are quite often linear, and can thus be quite readily interpreted, and the most widely-used ICA approaches optimize for both independent and sparsity measures with considerable success [ 63].
An improvement in the ICA by implementing an attraction and repulsion concept during the search for better solutions, the AR-ICA approach, is proposed in this paper.
In this Section, we discuss the characteristics of the proposed algorithms, emphasizing the advantages and drawbacks, and their relationships with the solutions based on the cancellation of the QRST complexes and BSS-ICA approach, represented by the STC and ST-BSS methods, respectively.
In this simulation experiment, we observed that when the ICA-P approach was used, ICA on many frequency bins failed to converge; however, when IVA was used, better convergence ability was observed.
For the ICA-based analysis approach, ICA was applied to group level resting fMRI data to define group level RSNs.
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