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As high-resolution fingerprint images are becoming more common, the pores have been found to be one of the promising candidates in improving the performance of automated fingerprint identification systems (AFIS). This paper proposes a deep learning approach towards pore extraction. It exploits the feature learning and classification capability of convolutional neural networks (CNNs) to detect pores...
The resistive cross-point array architecture has been proposed for on-chip implementation of weighted sum and weight update operations in neuro-inspired learning algorithms. However, several limiting factors potentially hamper the learning accuracy, including the nonlinearity and device variations in weight update, and the read noise, limited ON/OFF weight ratio and array parasitics in weighted sum...
The cross-point array architecture with resistive synaptic devices has been proposed for on-chip implementation of weighted sum and weight update in the training process of learning algorithms. However, the non-ideal properties of the synaptic devices available today, such as the nonlinearity in weight update, limited ON/OFF range and device variations, can potentially hamper the learning accuracy...
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