  Independent component analysis is an unsupervised learning approach for
computing the independent components (ICs) from the multivariate signals or
data matrix. The ICs are evaluated based on the multiplication of the weight
matrix with the multivariate data matrix. This study proposes a novel memristor
crossbar array for the implementation of both ACY ICA and Fast ICA for blind
source separation. The data input was applied in the form of pulse width
modulated voltages to the crossbar array and the weight of the implemented
neural network is stored in the memristor. The output charges from the
memristor columns are used to calculate the weight update, which is executed
through the voltages kept higher than the memristor Set/Reset voltages. In
order to demonstrate its potential application, the proposed memristor crossbar
arrays based fast ICA architecture is employed for image source separation
problem. The experimental results demonstrate that the proposed approach is
very effective to separate image sources, and also the contrast of the images
are improved with an improvement factor in terms of percentage of structural
similarity as 67.27% when compared with the software-based implementation of
conventional ACY ICA and Fast ICA algorithms.
