Independent component analysis (ICA) 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 Pt/Cu:ZnO/Nb:STO 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 (±1.30 V). 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.

中文翻译：

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使用忆阻器交叉开关阵列实现快速ICA的盲图像源分离

独立成分分析（ICA）是一种用于从多元信号或数据矩阵计算独立成分（IC）的无监督学习方法。基于权重矩阵与多元数据矩阵的乘积来评估IC。这项研究提出了一种新颖的Pt / Cu：ZnO / Nb：STO忆阻器交叉开关阵列，用于同时实现ACY ICA和Fast ICA进行盲源分离。数据输入以脉冲宽度调制电压的形式施加到交叉开关阵列，实现的神经网络的权重存储在忆阻器中。忆阻器列的输出电荷用于计算重量更新，该更新通过保持高于忆阻器设置/重置电压（±1.3V）的电压来执行。为了展示其潜在的应用，提出的基于忆阻器交叉开关阵列的快速ICA架构被用于图像源分离问题。实验结果表明，与传统的基于ACY的基于软件的实现相比，该方法对分离图像源非常有效，并且图像的对比度也得到了改善，结构相似性百分比为67.27％。 ICA和快速ICA算法。