Publications by authors named "R Vishnupriya"

Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance.

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The title compounds, CHNO, (I), and CHBrNO, (II), differ by the presence of a bromine atom instead of a methyl atom in the position of two benzene rings of compound (II). The two compounds have a structural overlap r.m.

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Recently, deep learning and convolutional neural networks (CNNs) have reported several promising results in the classification of Motor Imagery (MI) using Electroencephalography (EEG). With the gaining popularity of CNN-based BCI, the challenges in deploying it in a real-world mobile and embedded device with limited computational and memory resources need to be explored. Towards this objective, we investigate the impact of the magnitude-based weight pruning technique to reduce the number of parameters of the pre-trained CNN-based classifier while maintaining its performance.

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The germline genome is guarded against invading foreign genetic elements by small RNA-dependent gene-silencing pathways. Components of these pathways localize to, or form distinct aggregates in the vicinity of, germ granules. These components and their dynamics in and out of granules are currently being intensively studied.

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The asymmetric unit of the title compound, CHClNOS, contains two independent mol-ecules ( and ). They differ essentially in the orientation of the 4-meth-oxy-phenyl ring with respect to the pyridine ring of the quinoline moiety; this dihedral angle is 37.01 (18)° in mol-ecule but only 7.

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