Reducing the input data of tactile sensory systems brings a large degree of freedom to real-world implementations from the perspectives of bandwidth and computational complexity. For this, in this letter, we suggest efficient active-cell formations with a high classification accuracy of tactile materials. By revealing that averaged Kullback–Leibler-divergence and common frequency component power to variance ratio are proportional to the classification accuracy, we showed that those methods can be useful in estimating valid active-cell formations.

中文翻译：

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具有各种活跃细胞形态的触觉传感器阵列探索触觉材料的深度神经网络分类

从带宽和计算复杂性的角度来看，减少触觉感觉系统的输入数据为现实世界的实现带来了很大的自由度。为此，在这封信中，我们建议以有效的触觉材料分类精度和高的活性细胞形成。通过揭示平均的Kullback-Leibler散度和公共频率分量的功率与方差之比与分类准确度成正比，我们证明了这些方法可用于估计有效的活动细胞形成。