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Deep Neural Network Classification of Tactile Materials Explored by Tactile Sensor Array With Various Active-Cell Formations

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.